# Patent

## Title
Compositions, methods and kits for diagnosis of lung cancer

## Abstract

Methods are provided for identifying biomarker proteins that exhibit differential expression in subjects with a first lung condition versus healthy subjects or subjects with a second lung condition. Also provided are compositions comprising these biomarker proteins and methods of using these biomarker proteins or panels thereof to diagnose, classify, and monitor various lung conditions. The methods and compositions provided herein may be used to diagnose or classify a subject as having lung cancer or a non-cancerous condition, and to distinguish between different types of cancer (e.g., malignant versus benign, SCLC versus NSCLC).

## Background
RELATED APPLICATIONS
This application is a continuation-in-part of U.S. application Ser. No. 13/724,823, filed Dec. 21, 2012, which claims priority to, and the benefit of, U.S. Application No. 61/578,712, filed Dec. 21, 2011, U.S. Application No. 61/589,920, filed Jan. 24, 2012, U.S. Application No. 61/676,859, filed Jul. 27, 2012 and U.S. Application No. 61/725,153, filed Nov. 12, 2012, the contents of each of which are incorporated herein by reference in their entireties.


INCORPORATION-BY-REFERENCE OF SEQUENCE LISTING
The contents of the text file named “IDIA-005_X02US_Sequence Listing_ST25.txt”, which was created on Feb. 27, 2015 and is 14 KB in size, are hereby incorporated by reference in their entireties.
BACKGROUND
Lung conditions and particularly lung cancer present significant diagnostic challenges. In many asymptomatic patients, radiological screens such as computed tomography (CT) scanning are a first step in the diagnostic paradigm. Pulmonary nodules (PNs) or indeterminate nodules are located in the lung and are often discovered during screening of both high risk patients or incidentally. The number of PNs identified is expected to rise due to increased numbers of patients with access to health care, the rapid adoption of screening techniques and an aging population. It is estimated that over 3 million PNs are identified annually in the US. Although the majority of PNs are benign, some are malignant leading to additional interventions. For patients considered low risk for malignant nodules, current medical practice dictates scans every three to six months for at least two years to monitor for lung cancer. The time period between identification of a PN and diagnosis is a time of medical surveillance or “watchful waiting” and may induce stress on the patient and lead to significant risk and expense due to repeated imaging studies. If a biopsy is performed on a patient who is found to have a benign nodule, the costs and potential for harm to the patient increase unnecessarily. Major surgery is indicated in order to excise a specimen for tissue biopsy and diagnosis. All of these procedures are associated with risk to the patient including: illness, injury and death as well as high economic costs.
Frequently, PNs cannot be biopsied to determine if they are benign or malignant due to their size and/or location in the lung. However, PNs are connected to the circulatory system, and so if malignant, protein markers of cancer can enter the blood and provide a signal for determining if a PN is malignant or not.
Diagnostic methods that can replace or complement current diagnostic methods for patients presenting with PNs are needed to improve diagnostics, reduce costs and minimize invasive procedures and complications to patients. The present invention provides novel compositions, methods and kits for identifying protein markers to identify, diagnose, classify and monitor lung conditions, and particularly lung cancer. The present invention uses a blood-based multiplexed assay to distinguish benign pulmonary nodules from malignant pulmonary nodules to classify patients with or without lung cancer. The present invention may be used in patients who present with symptoms of lung cancer, but do not have pulmonary nodules.
SUMMARY
The present invention provides a method of determining the likelihood that a lung condition in a subject is cancer by measuring an abundance of a panel of proteins in a sample obtained from the subject; calculating a probability of cancer score based on the protein measurements and ruling out cancer for the subject if the score is lower than a pre-determined score. When cancer is ruled out, the subject does not receive a treatment protocol. Treatment protocols include for example pulmonary function test (PFT), pulmonary imaging, a biopsy, a surgery, a chemotherapy, a radiotherapy, or any combination thereof. In some embodiments, the imaging is an x-ray, a chest computed tomography (CT) scan, or a positron emission tomography (PET) scan.
The present invention further provides a method of ruling in the likelihood of cancer for a subject by measuring an abundance of panel of proteins in a sample obtained from the subject, calculating a probability of cancer score based on the protein measurements and ruling in the likelihood of cancer for the subject if the score is higher than a pre-determined score.
In another aspect, the invention further provides a method of determining the likelihood of the presence of a lung condition in a subject by measuring an abundance of panel of proteins in a sample obtained from the subject, calculating a probability of cancer score based on the protein measurements and concluding the presence of said lung condition if the score is equal or greater than a pre-determined score. The lung condition is lung cancer such as for example, non-small cell lung cancer (NSCLC). The subject is at risk of developing lung cancer.
In some embodiments, the panel includes at least 3 proteins selected from ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1 and CD14. Optionally, the panel further includes at least one protein selected from BGH3, COIA1, TETN, GRP78, PRDX, FIBA and GSLG1.
In some embodiments, the panel includes at least 4 proteins selected from ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1 and CD14.
In a preferred embodiment, the panel comprises LRP1, COIA1, ALDOA, and LG3BP.
In another preferred embodiment, the panel comprises LRP1, COIA1, ALDOA, LG3BP, BGH3, PRDX1, TETN, and ISLR.
In yet another preferred embodiment, the panel comprises LRP1, COIA1, ALDOA, LG3BP, BGH3, PRDX1, TETN, ISLR, TSP1, GRP78, FRIL, FIBA and GSLG1.
The subject has or is suspected of having a pulmonary nodule. The pulmonary nodule has a diameter of less than or equal to 3 cm. In one embodiment, the pulmonary nodule has a diameter of about 0.8 cm to 2.0 cm.
The score is calculated from a logistic regression model applied to the protein measurements. For example, the score is determined as Ps=1/[1+exp(−α−Σi=1Nβi*{hacek over (I)}i,s)], where {hacek over (I)}i,s is logarithmically transformed and normalized intensity of transition i in said sample (s), βi is the corresponding logistic regression coefficient, α was a panel-specific constant, and N was the total number of transitions in said panel.
In various embodiments, the method of the present invention further comprises normalizing the protein measurements. For example, the protein measurements are normalized by one or more proteins selected from PEDF, MASP1, GELS, LUM, C163A and PTPRJ.
The biological sample includes, such as for example tissue, blood, plasma, serum, whole blood, urine, saliva, genital secretion, cerebrospinal fluid, sweat and excreta.
In one aspect, the determining the likelihood of cancer is determined by the sensitivity, specificity, negative predictive value or positive predictive value associated with the score. The score determined has a negative predictive value (NPV) at least about 80%.
The measuring step is performed by selected reaction monitoring mass spectrometry, using a compound that specifically binds the protein being detected or a peptide transition. In one embodiment, the compound that specifically binds to the protein being measured is an antibody or an aptamer.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a line graph showing area under the curve for a receiving operating curve for 15 protein LC-SRM-MS panels.
FIG. 2 shows six line graphs each showing area under the curve for a receiving operating curve for 15 protein LC-SRM-MS panels for different patient populations and for subjects with large and small PN
FIG. 3 is a graph showing variability among three studies used to evaluate 15 protein panels.
FIG. 4 is a line graph showing area under the curve for a receiving operating curve for a 15 protein LC-SRM-MS panel.
FIG. 5 shows three line graphs each showing area under the curve for a receiving operating curve for a 15 protein LC-SRM-MS panel for a different patient population.
FIG. 6 shows the results of a query of blood proteins used to identify lung cancer using the “Ingenuity”® program.
FIG. 7 is a bar diagram showing Pearson correlations for peptides from the same peptide, from the same protein and from different proteins.
FIG. 8 is a graph showing performance of the classifier on the training samples, validation samples and all samples combined.
FIG. 9 is a graph showing clinical and molecular factors.
FIG. 10 is a schematic showing the molecular network containing the 13 classifier proteins (green), 5 transcription factors (blue) and the three networks (orange lines) of lung cancer, response to oxidative stress and lung inflammation.
FIG. 11 is a graph depicting interpretation of classifier score in terms of risk.
FIG. 12 is a graph showing performance of the classifier on the discovery samples (n=143) and validation samples (n=104). Negative predictive value (NPV) and specificity (SPC) are presented in terms of classifier score. A cancer prevalence of 20% was assumed.
FIG. 13 is a graph showing multivariate analysis of clinical (smoking, nodule size) and molecular (classifier score) factors as they relate to cancer and benign samples (n=247) in the discovery and validation studies. Smoking is measured by pack-years on the vertical. Nodule size is represented by circle diameter. A reference value of 0.43 is presented to illustrate the discrimination between low numbers of cancer samples less than the reference value as compared to the high number of cancer samples above the reference value.
FIG. 14 is a graph showing the 13 classifier proteins (green), 4 transcription regulators (blue) and the three networks (orange lines) of lung cancer, oxidative stress response and lung inflammation. All references are human UniProt identifiers.
FIG. 15 is a graph showing scattering plot of nodule size vs. classifier score of all 247 patients, demonstrating the lack of correlation between the two variables.
FIG. 16 is a diagram showing the Pearson correlations for peptides from the same peptide (blue), from the same protein (green) and from different proteins (red).



DETAILED DESCRIPTION
The disclosed invention derives from the surprising discovery, that in patients presenting with pulmonary nodule(s), protein markers in the blood exist that specifically identify and classify lung cancer. Accordingly the invention provides unique advantages to the patient associated with early detection of lung cancer in a patient, including increased life span, decreased morbidity and mortality, decreased exposure to radiation during screening and repeat screenings and a minimally invasive diagnostic model. Importantly, the methods of the invention allow for a patient to avoid invasive procedures.
The routine clinical use of chest computed tomography (CT) scans identifies millions of pulmonary nodules annually, of which only a small minority are malignant but contribute to the dismal 15% five-year survival rate for patients diagnosed with non-small cell lung cancer (NSCLC). The early diagnosis of lung cancer in patients with pulmonary nodules is a top priority, as decision-making based on clinical presentation, in conjunction with current non-invasive diagnostic options such as chest CT and positron emission tomography (PET) scans, and other invasive alternatives, has not altered the clinical outcomes of patients with Stage I NSCLC. The subgroup of pulmonary nodules between 8 mm and 20 mm in size is increasingly recognized as being “intermediate” relative to the lower rate of malignancies below 8 mm and the higher rate of malignancies above 20 mm [9]. Invasive sampling of the lung nodule by biopsy using transthoracic needle aspiration or bronchoscopy may provide a cytopathologic diagnosis of NSCLC, but are also associated with both false-negative and non-diagnostic results. In summary, a key unmet clinical need for the management of pulmonary nodules is a non-invasive diagnostic test that discriminates between malignant and benign processes in patients with indeterminate pulmonary nodules (IPNs), especially between 8 mm and 20 mm in size.
The clinical decision to be more or less aggressive in treatment is based on risk factors, primarily nodule size, smoking history and age [9] in addition to imaging. As these are not conclusive, there is a great need for a molecular-based blood test that would be both non-invasive and provide complementary information to risk factors and imaging.
Accordingly, these and related embodiments will find uses in screening methods for lung conditions, and particularly lung cancer diagnostics. More importantly, the invention finds use in determining the clinical management of a patient. That is, the method of invention is useful in ruling in or ruling out a particular treatment protocol for an individual subject.
Cancer biology requires a molecular strategy to address the unmet medical need for an assessment of lung cancer risk. The field of diagnostic medicine has evolved with technology and assays that provide sensitive mechanisms for detection of changes in proteins. The methods described herein use a LC-SRM-MS technology for measuring the concentration of blood plasma proteins that are collectively changed in patients with a malignant PN. This protein signature is indicative of lung cancer. LC-SRM-MS is one method that provides for both quantification and identification of circulating proteins in plasma. Changes in protein expression levels, such as but not limited to signaling factors, growth factors, cleaved surface proteins and secreted proteins, can be detected using such a sensitive technology to assay cancer. Presented herein is a blood-based classification test to determine the likelihood that a patient presenting with a pulmonary nodule has a nodule that is benign or malignant. The present invention presents a classification algorithm that predicts the relative likelihood of the PN being benign or malignant.
More broadly, it is demonstrated that there are many variations on this invention that are also diagnostic tests for the likelihood that a PN is benign or malignant. These are variations on the panel of proteins, protein standards, measurement methodology and/or classification algorithm.
As disclosed herein, archival plasma samples from subjects presenting with PNs were analyzed for differential protein expression by mass spectrometry and the results were used to identify biomarker proteins and panels of biomarker proteins that are differentially expressed in conjunction with various lung conditions (cancer vs. non-cancer).
In one aspect of the invention, one hundred and sixty three panels were discovered that allow for the classification of PN as being benign or malignant. These panels include those listed on Table 1. In some embodiments the panel according to the invention includes measuring 1, 2, 3, 4, 5 or more proteins selected from ISLR, ALDOA, KIT, GRP78, AIFM1, CD14, COIA1, IBP3, TSP1, BGH3, TETN, FRI, LG3BP, GGH, PRDX1 or LRP1. In other embodiments the panel includes any panel or protein exemplified on Table 1. For, example the panel includes ALDOA, GRP78, CD14, COIA1, IBP3, FRIL, LG3BP, and LRP1











TABLE 1









Number
pAUC
Proteins
















Identifier
Proteins
Factor
ISLR
ALDOA
KIT
GRP78
AIFM1
CD14
COIA1





1
9
4.562
0
1
0
1
0
1
1


2
8
4.488
0
1
0
1
0
1
1


3
11
4.451
1
1
0
1
0
0
1


4
11
4.357
1
1
0
1
0
0
1


5
11
4.331
1
1
0
0
0
1
1


6
13
4.324
1
1
0
0
0
1
1


7
10
4.205
1
1
0
1
0
0
1


8
11
4.193
1
1
0
0
0
0
1


9
12
4.189
1
1
0
1
0
0
1


10
12
4.182
1
0
0
0
0
1
1


11
12
4.169
1
1
0
1
0
0
1


12
8
4.107
1
1
0
1
0
1
1


13
13
4.027
0
1
1
1
0
1
1


14
10
3.994
0
1
1
1
0
1
1


15
11
3.979
1
1
1
1
0
1
1


16
10
3.932
1
1
0
1
0
1
1


17
11
3.926
1
1
0
0
0
1
1


18
12
3.913
1
0
1
1
0
0
1


19
12
3.872
0
1
1
1
0
1
1


20
12
3.864
1
1
1
0
0
1
1


21
14
3.853
1
1
0
1
0
1
1


22
9
3.849
1
1
0
1
0
0
1


23
12
3.846
1
1
1
1
0
0
1


24
10
3.829
0
1
1
1
0
1
0


25
10
3.829
0
1
1
1
0
1
1


26
12
3.826
1
0
0
0
1
0
1


27
7
3.804
1
1
0
1
0
1
1


28
10
3.802
0
1
0
1
0
1
1


29
10
3.787
0
1
0
1
0
1
0


30
9
3.779
1
1
0
1
0
1
1


31
11
3.774
0
1
0
1
0
1
1


32
8
3.759
1
1
0
0
0
0
1


33
13
3.758
1
1
0
0
0
1
1


34
11
3.757
1
1
0
1
0
0
0


35
12
3.754
0
1
1
1
0
1
1


36
10
3.750
1
1
0
1
0
1
1


37
11
3.747
0
1
1
1
0
1
1


38
12
3.744
1
0
1
1
0
0
1


39
11
3.742
1
1
0
1
0
1
1


40
9
3.740
1
1
0
1
0
1
1


41
12
3.740
1
1
1
1
0
1
1


42
12
3.739
1
1
0
1
0
1
1


43
9
3.734
1
1
0
0
0
0
1


44
12
3.730
1
1
0
1
0
0
1


45
11
3.725
0
1
1
1
0
1
1


46
12
3.717
0
1
0
0
1
1
1


47
9
3.713
0
1
0
1
0
1
1


48
9
3.713
1
1
1
1
0
1
1


49
10
3.709
0
1
0
1
0
1
1


50
11
3.709
1
1
0
1
0
1
1


51
11
3.701
0
1
1
1
1
1
1


52
12
3.685
1
1
0
1
0
1
1


53
10
3.680
0
0
0
1
0
1
0


54
11
3.676
1
1
1
1
0
0
1


55
9
3.668
0
1
0
1
0
1
1


56
9
3.659
0
0
0
1
0
1
0


57
14
3.657
1
1
0
1
1
1
1


58
10
3.655
1
1
0
1
0
0
1


59
11
3.643
0
1
1
1
0
1
1


60
9
3.643
0
1
0
1
0
1
0


61
8
3.640
1
1
0
1
0
1
0


62
12
3.640
1
1
1
1
0
1
1


63
10
3.638
1
1
0
1
0
0
1


64
12
3.633
1
0
0
1
1
0
1


65
10
3.632
1
1
0
1
0
1
1


66
11
3.627
1
1
0
1
0
1
0


67
10
3.627
1
1
0
0
0
1
0


68
10
3.623
1
1
1
0
0
0
1


69
11
3.619
1
0
0
1
0
1
1


70
6
3.617
1
1
0
1
0
0
1


71
12
3.617
1
0
0
1
0
1
1


72
11
3.613
1
1
0
1
0
1
0


73
11
3.608
1
1
0
1
0
1
0


74
13
3.608
1
1
1
1
0
1
1


75
11
3.605
0
1
1
1
0
1
1


76
11
3.602
0
1
1
1
0
1
1


77
10
3.600
1
1
0
1
0
0
0


78
11
3.596
1
1
0
1
0
0
1


79
10
3.592
1
1
0
1
0
1
0


80
11
3.587
1
0
1
0
0
0
1


81
13
3.584
1
1
0
1
1
1
1


82
8
3.584
0
1
0
1
0
1
0


83
11
3.581
1
1
1
1
0
1
0


84
13
3.578
1
1
0
1
0
1
0


85
9
3.573
1
1
1
0
0
1
1


86
9
3.572
1
1
0
1
0
0
1


87
13
3.571
1
1
1
1
0
1
0


88
10
3.569
1
1
0
1
0
0
1


89
9
3.569
0
1
0
1
0
1
0


90
8
3.559
0
1
0
1
0
1
0


91
10
3.558
0
1
0
1
0
1
0


92
12
3.554
1
1
0
1
0
1
1


93
11
3.552
0
1
0
1
0
1
0


94
12
3.549
0
1
0
1
0
1
0


95
8
3.547
1
1
1
0
0
1
1


96
12
3.545
1
1
1
1
0
1
1


97
8
3.542
1
1
1
0
0
0
0


98
11
3.536
1
1
1
1
0
0
1


99
14
3.530
1
1
1
1
0
1
1


100
9
3.527
1
1
0
1
0
1
1


101
10
3.522
0
1
1
0
1
1
1


102
12
3.509
1
1
0
1
0
1
1


103
5
3.505
0
1
0
0
0
1
0


104
11
3.500
1
1
0
0
1
0
1


105
11
3.497
1
1
1
1
0
0
1


106
9
3.491
1
1
0
0
0
1
0


107
7
3.489
0
1
1
0
0
1
0


108
13
3.486
1
1
1
1
0
1
1


109
11
3.483
1
1
1
1
0
0
1


110
10
3.477
1
1
1
1
0
1
1


111
10
3.473
1
1
0
0
0
1
1


112
15
3.468
1
1
0
1
1
1
1


113
10
3.467
0
1
0
0
1
1
0


114
12
3.467
1
1
0
0
1
1
1


115
13
3.467
1
1
0
1
1
0
1


116
10
3.467
0
1
0
1
0
1
0


117
8
3.465
1
1
0
1
0
0
1


118
10
3.464
0
1
0
1
1
1
1


119
15
3.464
1
1
0
1
1
1
1


120
11
3.462
1
1
0
1
0
1
1


121
9
3.460
1
1
0
0
0
1
0


122
13
3.453
1
1
0
1
0
1
1


123
12
3.449
1
1
1
0
0
1
0


124
10
3.448
1
1
0
1
0
1
0


125
10
3.445
0
1
1
1
0
1
0


126
6
3.441
0
1
0
0
0
1
0


127
11
3.440
1
1
0
1
0
1
0


128
12
3.440
1
1
0
1
1
0
0


129
11
3.439
1
1
0
1
0
1
0


130
10
3.426
0
1
0
0
1
1
0


131
11
3.423
1
1
0
0
0
0
1


132
10
3.420
1
1
0
0
0
1
0


133
10
3.419
1
1
1
1
0
1
0


134
11
3.417
1
1
0
1
1
0
1


135
12
3.414
0
1
0
1
1
1
1


136
10
3.413
0
1
1
1
0
1
0


137
11
3.400
0
1
0
0
1
1
0


138
12
3.398
1
1
0
1
0
1
0


139
13
3.396
1
1
0
1
0
1
0


140
9
3.386
1
1
0
0
0
1
0


141
9
3.373
1
1
0
1
0
1
0


142
12
3.363
1
1
0
0
1
0
1


143
8
3.362
0
1
0
1
0
1
0


144
10
3.360
1
1
0
1
0
1
1


145
9
3.359
1
1
1
0
0
1
0


146
7
3.349
0
1
0
0
0
0
0


147
7
3.348
1
1
0
0
0
1
1


148
9
3.340
1
0
0
0
0
1
0


149
9
3.335
1
1
0
1
0
1
0


150
11
3.333
0
1
1
1
0
1
0


151
9
3.333
0
0
0
1
0
1
0


152
10
3.328
1
1
0
1
0
1
0


153
7
3.315
0
1
0
1
0
1
0


154
11
3.311
1
1
0
1
1
1
1


155
11
3.293
1
1
0
1
0
1
0


156
8
3.292
1
1
0
1
0
0
0


157
9
3.289
0
1
0
1
0
1
0


158
7
3.229
0
1
0
0
0
1
0


159
7
3.229
1
1
0
0
0
1
0


160
7
3.203
1
1
0
1
0
0
0


161
12
3.161
1
1
1
0
1
1
0


162
9
3.138
1
1
0
0
1
0
1


163
13
3.078
1
1
0
0
1
0
1












Proteins


















Identifier
IBP3
TSP1
BGH3
TETN
FRIL
LG3BP
GGH
PRDX1
LRP1







 1
1
0
0
0
1
1
0
0
1



 2
1
0
0
0
1
1
0
0
1



 3
1
1
1
1
1
0
0
1
1



 4
1
1
0
0
1
1
1
1
1



 5
0
1
1
1
1
0
1
1
1



 6
1
1
1
1
1
1
1
1
1



 7
0
1
1
1
1
0
0
1
1



 8
0
1
1
1
1
0
1
1
1



 9
1
1
1
1
1
0
0
1
1



 10
1
1
1
1
1
1
0
1
1



 11
1
1
0
0
1
1
1
1
1



 12
0
0
0
0
1
1
0
0
1



 13
1
1
0
0
1
1
1
1
1



 14
1
0
0
0
1
1
0
0
1



 15
0
0
0
0
1
1
1
0
1



 16
0
0
0
1
1
1
0
0
1



 17
1
1
1
1
1
0
0
1
1



 18
1
1
0
0
1
1
1
1
1



 19
1
0
0
0
1
1
1
1
1



 20
0
1
1
1
1
1
0
1
1



 21
1
1
1
1
1
1
0
1
1



 22
0
1
1
1
1
0
0
0
1



 23
1
1
0
0
1
1
1
1
1



 24
1
0
0
0
1
1
1
1
1



 25
1
0
0
0
1
1
1
0
1



 26
1
1
1
1
1
0
1
1
1



 27
0
0
0
0
0
1
0
0
1



 28
1
0
0
0
1
1
1
1
1



 29
1
1
0
0
1
1
1
1
1



 30
0
0
0
0
1
1
0
0
1



 31
1
0
0
0
1
1
1
1
1



 32
0
0
1
1
1
0
0
1
1



 33
1
1
1
1
1
1
0
1
1



 34
1
1
1
1
1
1
0
1
1



 35
1
1
0
0
1
1
1
1
1



 36
1
0
0
0
1
1
0
1
1



 37
1
1
0
0
1
1
1
1
0



 38
1
1
1
1
1
0
0
1
1



 39
1
1
0
1
1
1
0
0
1



 40
1
0
0
0
1
1
0
0
1



 41
1
0
0
1
1
1
0
0
1



 42
1
1
0
0
1
1
1
1
1



 43
0
1
1
1
1
0
0
1
1



 44
1
1
1
1
1
1
0
1
1



 45
1
0
0
1
1
1
0
0
1



 46
1
1
1
1
1
1
1
1
0



 47
1
0
0
0
1
1
0
1
1



 48
0
0
0
0
1
1
0
0
1



 49
1
0
0
0
1
1
1
0
1



 50
0
1
1
1
1
1
0
0
1



 51
1
0
0
0
1
1
0
0
1



 52
1
1
1
1
1
1
0
0
1



 53
1
1
1
1
1
1
0
1
1



 54
0
1
1
1
1
0
0
1
1



 55
1
0
0
0
1
1
1
0
1



 56
1
1
0
0
1
1
1
1
0



 57
1
1
1
1
1
0
0
1
1



 58
0
1
0
0
1
1
1
0
1



 59
1
0
0
0
1
1
1
1
1



 60
1
0
1
0
1
1
0
0
1



 61
1
0
0
0
1
1
0
0
1



 62
0
0
0
1
1
1
0
1
1



 63
0
1
1
1
1
1
0
0
1



 64
1
1
1
1
1
0
0
1
1



 65
1
0
0
0
1
1
0
0
1



 66
1
1
1
1
1
1
0
0
1



 67
1
1
1
1
1
1
0
0
1



 68
0
1
1
1
1
1
0
0
1



 69
1
1
1
0
1
1
0
0
1



 70
0
0
0
0
0
1
0
0
1



 71
1
1
1
1
1
0
0
1
1



 72
1
1
0
0
1
1
1
1
1



 73
1
1
1
0
1
1
0
1
1



 74
1
1
0
0
1
1
0
1
1



 75
1
0
0
0
1
1
0
1
1



 76
1
0
0
0
1
1
1
0
1



 77
1
1
1
1
1
1
0
1
0



 78
1
1
1
1
1
0
1
0
1



 79
1
1
0
0
1
1
0
1
1



 80
1
1
1
1
0
1
0
1
1



 81
1
1
1
1
1
1
0
0
1



 82
1
1
0
0
1
1
0
1
0



 83
1
1
0
0
1
1
1
1
0



 84
1
1
1
1
1
1
0
1
1



 85
1
0
0
0
1
1
0
0
0



 86
0
1
0
0
1
1
0
0
1



 87
1
1
0
0
1
1
1
1
1



 88
1
1
0
1
1
0
0
1
1



 89
1
1
0
0
1
1
0
1
1



 90
1
0
0
0
1
1
0
0
1



 91
1
0
0
1
1
1
1
1
1



 92
0
1
1
1
1
0
1
1
1



 93
1
1
0
0
1
1
1
1
1



 94
1
1
1
1
1
1
1
1
1



 95
1
1
0
0
0
1
0
0
0



 96
1
0
0
0
1
1
1
0
1



 97
1
1
0
1
0
1
0
0
0



 98
1
0
0
0
1
1
1
1
1



 99
1
1
0
1
1
1
1
1
0



100
0
1
0
0
1
1
0
0
1



101
1
1
0
0
1
1
0
1
0



102
0
0
1
1
1
1
0
1
1



103
1
1
0
0
0
1
0
0
0



104
1
1
1
1
1
0
1
1
0



105
1
1
0
0
1
1
0
0
1



106
1
1
0
0
0
1
1
1
0



107
1
1
0
0
0
1
0
1
0



108
1
0
0
1
1
1
0
1
1



109
1
0
0
0
1
1
1
0
1



110
1
0
0
0
1
1
0
0
1



111
0
0
1
1
1
1
0
0
1



112
1
1
1
1
1
0
1
1
1



113
1
1
1
1
1
1
0
1
0



114
1
1
1
1
0
1
0
1
1



115
1
1
1
1
1
0
0
1
1



116
1
1
0
0
1
1
1
0
1



117
0
1
0
0
1
1
0
0
1



118
1
0
0
0
1
1
0
0
1



119
1
1
1
1
1
1
1
1
0



120
0
0
0
1
1
1
0
1
1



121
1
1
1
1
0
1
0
1
0



122
1
1
1
1
1
1
1
1
0



123
1
1
0
1
1
1
1
1
0



124
1
1
0
0
1
1
1
1
0



125
1
1
0
0
1
1
0
1
1



126
1
1
0
0
0
1
0
0
0



127
1
1
0
0
1
1
1
0
1



128
1
1
1
1
1
0
0
1
1



129
1
0
0
0
1
1
1
1
1



130
1
1
1
1
0
1
0
1
0



131
1
1
1
1
1
1
1
1
0



132
1
1
0
1
1
1
1
1
0



133
1
0
0
0
1
1
0
0
1



134
0
0
1
1
1
0
0
1
1



135
1
1
0
1
1
1
0
0
1



136
1
1
0
0
1
1
0
1
0



137
1
1
1
1
1
1
0
1
0



138
1
0
1
1
1
1
1
1
1



139
1
1
1
1
1
1
1
1
1



140
1
1
0
0
1
1
1
1
0



141
1
0
0
0
1
1
0
0
1



142
1
1
1
1
1
1
1
1
0



143
1
0
0
0
1
1
0
1
1



144
0
0
0
1
1
1
0
1
0



145
1
1
0
0
1
1
0
0
0



146
1
1
1
1
0
1
0
0
0



147
1
1
0
0
0
1
0
0
0



148
1
1
1
1
0
1
0
1
0



149
1
1
0
0
1
1
0
0
1



150
1
1
0
0
1
1
0
1
1



151
1
1
1
0
1
1
0
0
1



152
1
0
0
0
1
1
1
0
1



153
1
0
0
0
1
1
0
0
1



154
0
0
0
1
1
1
1
0
0



155
1
0
1
0
1
1
0
1
1



156
1
1
0
0
1
1
0
0
1



157
1
1
0
0
1
1
0
1
0



158
1
1
0
0
1
1
0
0
0



159
1
1
0
0
0
1
0
1
0



160
1
0
0
0
1
1
0
0
1



161
1
1
1
1
1
1
0
1
0



162
0
0
1
1
1
1
0
0
0



163
1
1
1
1
1
1
1
1
0







1 = in the panel;



0 = not in the panel.






The one hundred best random panels of proteins out of the million generated are shown in Table 2.

















TABLE 2






Protein 1
Protein 2
Protein 3
Protein 4
Protein 5
Protein 6
Protein 7
Protein 8
Protein 9
Protein 10

























1
IBP3
TSP1
CO6A3
PDIA3
SEM3G
SAA
6PGD
EF1A1
PRDX1
TERA


2
EPHB6
CNTN1
CLUS
IBP3
BGH3
6PGD
FRIL
LRP1
TBB3
ERO1A


3
PPIB
LG3BP
MDHC
DSG2
BST1
CD14
DESP
PRDX1
CDCP1
MMP9


4
TPIS
COIA1
IBP3
GGH
ISLR
MMP2
AIFM1
DSG2
1433T
CBPB2


5
TPIS
IBP3
CH10
SEM3G
6PGD
FRIL
ICAM3
TERA
FINC
ERO1A


6
BGH3
ICAM1
MMP12
6PGD
CD14
EF1A1
HYOU1
PLXC1
PROF1
ERO1A


7
KIT
LG3BP
TPIS
IBP3
LDHB
GGH
TCPA
ISLR
CBPB2
EF1A1


8
LG3BP
IBP3
LDHB
TSP1
CRP
ZA2G
CD14
LRP1
PLIN2
ERO1A


9
COIA1
TSP1
ISLR
TFR1
CBPB2
FRIL
LRP1
UGPA
PTPA
ERO1A


10
CO6A3
SEM3G
APOE
FRIL
ICAM3
PRDX1
EF2
HS90B
NCF4
PTPA


11
PPIB
LG3BP
COIA1
APOA1
DSG2
APOE
CD14
PLXC1
NCF4
GSLG1


12
SODM
EPHB6
C163A
COIA1
LDHB
TETN
1433T
CD14
PTPA
ERO1A


13
SODM
KPYM
IBP3
TSP1
BGH3
SEM3G
6PGD
CD14
RAP2B
EREG


14
EPHB6
ALDOA
MMP7
COIA1
TIMP1
GRP78
MMP12
CBPB2
G3P
PTPA


15
KIT
TSP1
SCF
TIMP1
OSTP
PDIA3
GRP78
TNF12
PRDX1
PTPA


16
IBP2
LG3BP
GELS
HPT
FIBA
GGH
ICAM1
BST1
HYOU1
GSLG1


17
KIT
CD44
CH10
PEDF
ICAM1
6PGD
S10A1
ERO1A
GSTP1
MMP9


18
LG3BP
C163A
GGH
ERBB3
TETN
BGH3
ENOA
GDIR2
LRP1
ERO1A


19
SODM
KPYM
BGH3
FOLH1
6PGD
DESP
LRP1
TBA1B
ERO1A
GSTP1


20
CNTN1
TETN
ICAM1
K1C19
ZA2G
6PGD
EF2
RAN
ERO1A
GSTP1


21
GELS
ENPL
OSTP
PEDF
ICAM1
BST1
TNF12
GDIR2
LRP1
ERO1A


22
KIT
LDHA
IBP3
PEDF
DSG2
FOLH1
CD14
LRP1
UGPA
ERO1A


23
KIT
TSP1
ISLR
BGH3
COF1
PTPRJ
6PGD
LRP1
S10A6
MPRI


24
LG3BP
C163A
GGH
DSG2
ICAM1
6PGD
GDIR2
HYOU1
EREG
ERO1A


25
IBP2
C163A
ENPL
FIBA
BGH3
CERU
6PGD
LRP1
PRDX1
MMP9


26
LG3BP
C163A
TENX
PDIA3
SEM3G
BST1
VTNC
FRIL
PRDX1
ERO1A


27
ALDOA
COIA1
TETN
1433T
CBPB2
CD14
G3P
CD59
ERO1A
MMP9


28
IBP3
TENX
CRP
TETN
MMP2
SEM3G
VTNC
CD14
PROF1
ERO1A


29
SODM
EPHB6
TPIS
TENX
ERBB3
SCF
TETN
FRIL
LRP1
ERO1A


30
LG3BP
IBP3
POSTN
DSG2
MDHM
1433Z
CD14
EF1A1
PLXC1
ERO1A


31
IBP2
LG3BP
COIA1
CNTN1
IBP3
POSTN
TETN
BGH3
6PGD
ERO1A


32
PVR
TSP1
GGH
CYTB
AIFM1
ICAM1
MDHM
1433Z
6PGD
FRIL


33
LYOX
GELS
COIA1
IBP3
AIFM1
ICAM1
FRIL
PRDX1
RAP2B
NCF4


34
KIT
AMPN
TETN
TNF12
6PGD
FRIL
LRP1
EF2
ERO1A
MMP9


35
LG3BP
GELS
COIA1
CLUS
CALU
AIFM1
1433T
CD14
UGPA
S10A1


36
ALDOA
IBP3
TSP1
TETN
SEM3G
ICAM1
EF1A1
G3P
RAP2B
NCF4


37
ALDOA
COIA1
CH10
TETN
PTPRJ
SEM3G
1433T
6PGD
FRIL
ERO1A


38
LG3BP
COIA1
PLSL
FIBA
TENX
POSTN
CD14
LRP1
NCF4
ERO1A


39
LUM
IBP3
CH10
AIFM1
MDHM
6PGD
PLXC1
EF2
CD59
GSTP1


40
SODM
LG3BP
LUM
LDHA
MDHC
GGH
ICAM1
LRP1
TBA1B
ERO1A


41
LG3BP
CD44
IBP3
CALU
CERU
1433T
CD14
CLIC1
NCF4
ERO1A


42
LG3BP
TPIS
COIA1
HPT
FIBA
AIFM1
1433Z
6PGD
CD14
EF2


43
ALDOA
CD44
MMP2
CD14
FRIL
PRDX1
RAN
NCF4
MPRI
PTPA


44
COIA1
CLUS
OSTP
ICAM1
1433T
PLXC1
PTGIS
RAP2B
PTPA
GSTP1


45
KIT
LYOX
IBP3
GRP78
FOLH1
MASP1
CD14
LRP1
ERO1A
GSTP1


46
LG3BP
GGH
CRP
SCF
ICAM1
ZA2G
1433T
RAN
NCF4
ERO1A


47
LG3BP
C163A
BGH3
MMP2
GRP78
LRP1
RAN
ITA5
HS90B
PTPA


48
ALDOA
CLUS
TENX
ICAM1
K1C19
MASP1
6PGD
CBPB2
PRDX1
PTPA


49
IBP3
PDIA3
PEDF
FOLH1
ICAM1
NRP1
6PGD
UGPA
RAN
ERO1A


50
ENPL
FIBA
ISLR
SAA
6PGD
PRDX1
EF2
PLIN2
HS90B
GSLG1


51
LG3BP
COIA1
CO6A3
GGH
ERBB3
FOLH1
ICAM1
RAN
CDCP1
ERO1A


52
GELS
ENPL
A1AG1
SCF
COF1
ICAM1
6PGD
RAP2B
EF2
HS90B


53
SODM
IBP2
COIA1
CLUS
IBP3
ENPL
PLSL
TNF12
6PGD
ERO1A


54
KIT
MMP7
COIA1
TSP1
CO6A3
GGH
PDIA3
ICAM1
LRP1
GSLG1


55
ALDOA
COIA1
TSP1
CH10
NRP1
CD14
DESP
LRP1
CLIC1
ERO1A


56
C163A
GELS
CALU
A1AG1
AIFM1
DSG2
ICAM1
6PGD
RAP2B
NCF4


57
PPIB
LG3BP
IBP3
TSP1
PLSL
GRP78
FOLH1
6PGD
HYOU1
RAP2B


58
KIT
LG3BP
LUM
GELS
OSTP
ICAM1
CD14
EF1A1
NCF4
MMP9


59
KIT
PPIB
LG3BP
GELS
FOLH1
ICAM1
MASP1
GDIR2
ITA5
NCF4


60
IBP3
ENPL
ERBB3
BGH3
VTNC
6PGD
EF1A1
TBA1B
S10A6
HS90B


61
LG3BP
CLUS
IBP3
SCF
TCPA
ISLR
GRP78
6PGD
ERO1A
GSTP1


62
LG3BP
LEG1
GELS
GGH
TETN
ENOA
ICAM1
MASP1
FRIL
NCF4


63
LG3BP
CD44
TETN
BGH3
G3P
LRP1
PRDX1
CDCP1
PTPA
MMP9


64
CALU
ENPL
ICAM1
VTNC
FRIL
LRP1
PROF1
TBB3
GSLG1
ERO1A


65
PPIB
PLSL
TENX
A1AG1
COF1
6PGD
FRIL
LRP1
CLIC1
ERO1A


66
IBP2
IBP3
CERU
ENOA
6PGD
CD14
LRP1
PDGFB
ERO1A
GSTP1


67
COIA1
1433T
CD14
DESP
GDIR2
PLXC1
PROF1
RAP2B
RAN
ERO1A


68
LYOX
OSTP
TETN
SEM3G
ICAM1
ZA2G
FRIL
EREG
RAN
ERO1A


69
LG3BP
IBP3
TSP1
PEDF
FOLH1
MDHM
TNF12
NRP1
S10A6
RAP2B


70
KIT
ALDOA
LG3BP
COIA1
TSP1
A1AG1
BGH3
SEM3G
FOLH1
RAN


71
ALDOA
OSTP
BST1
CD14
G3P
PRDX1
PTGIS
FINC
PTPA
MMP9


72
EPHB6
TETN
PEDF
ICAM1
APOE
PROF1
UGPA
NCF4
GSLG1
PTPA


73
LG3BP
COIA1
ENPL
MMP2
1433T
EF1A1
LRP1
HS90B
GSLG1
ERO1A


74
KIT
IBP3
CYTB
MMP2
1433Z
6PGD
CLIC1
EF2
NCF4
PTPA


75
SODM
LYOX
IBP3
TETN
SEM3G
CD14
PRDX1
PTPA
ERO1A
GSTP1


76
SODM
KPYM
COIA1
MDHC
TCPA
CD14
FRIL
LRP1
EF2
ERO1A


77
PPIB
LG3BP
FIBA
GRP78
AIFM1
ICAM1
6PGD
NCF4
GSLG1
PTPA


78
LG3BP
C163A
PVR
MDHC
TETN
SEM3G
AIFM1
6PGD
EREG
ERO1A


79
GELS
ISLR
BGH3
DSG2
ICAM1
SAA
HYOU1
ICAM3
PTGIS
RAP2B


80
KPYM
TPIS
IBP3
TIMP1
GRP78
ICAM1
LRP1
TERA
ERO1A
MMP9


81
IBP3
HPT
TSP1
GRP78
SAA
MMP12
1433Z
6PGD
CD14
S10A6


82
TENX
A1AG1
ENOA
AIFM1
6PGD
CD14
FRIL
LRP1
RAP2B
CD59


83
ALDOA
KPYM
ISLR
TETN
BGH3
VTNC
LRP1
ITA5
PTPA
MMP9


84
SODM
TENX
ISLR
TETN
VTNC
6PGD
LRP1
EF2
ERO1A
MMP9


85
LG3BP
C163A
COIA1
FOLH1
CD14
LRP1
TBA1B
GSLG1
ERO1A
GSTP1


86
SODM
PVR
COIA1
ISLR
PDIA3
APOE
CD14
FRIL
LRP1
CDCP1


87
ALDOA
PEDF
ICAM1
6PGD
CD14
FINC
RAN
NCF4
GSLG1
PTPA


88
LG3BP
KPYM
GELS
COIA1
IBP3
CD14
EF1A1
PLIN2
HS90B
ERO1A


89
LG3BP
PVR
CLUS
TETN
COF1
SEM3G
DESP
EF2
HS90B
ERO1A


90
LG3BP
COIA1
FIBA
TETN
TFR1
ICAM1
MDHM
CD14
PLXC1
ERO1A


91
PPIB
LG3BP
GELS
CLUS
TENX
ICAM1
SAA
NCF4
PTPA
ERO1A


92
COIA1
TSP1
ISLR
BGH3
SAA
6PGD
LRP1
PROF1
EREG
ERO1A


93
CALU
FIBA
OSTP
ISLR
PDIA3
SEM3G
K1C19
6PGD
HYOU1
RAP2B


94
FIBA
CH10
GRP78
SEM3G
AIFM1
ICAM1
MDHM
FRIL
UGPA
GSTP1


95
COIA1
IBP3
PDIA3
ICAM1
K1C19
CD14
EF1A1
FRIL
PTGIS
PDGFB


96
LG3BP
C163A
COIA1
LDHA
1433T
1433Z
FRIL
LRP1
ERO1A
MMP9


97
LG3BP
GELS
COIA1
GRP78
SEM3G
FRIL
PLXC1
PROF1
S10A1
ERO1A


98
LG3BP
COIA1
ENPL
GRP78
AIFM1
ICAM1
1433Z
CD14
LRP1
ERO1A


99
COIA1
PLSL
NRP1
1433T
CD14
FRIL
LRP1
RAP2B
PDGFB
ERO1A


100
IBP2
COIA1
TETN
DSG2
FOLH1
1433T
CD14
FRIL
LRP1
ERO1A









Preferred panels for ruling in treatment for a subject include the panels listed on Table 3 and 4. In various other embodiments, the panels according to the invention include measuring at least 2, 3, 4, 5, 6, 7, or more of the proteins listed on Tables 2 and 3.












TABLE 3







Average (19)
Rule-out (20)
Rule-in (16)









ERO1A
ERO1A
ERO1A



6PGD
6PGD
6PGD



FRIL
FRIL
FRIL



GSTP1
GSTP1
GSTP1



COIA1
COIA1
COIA1



GGH
GGH
GGH



PRDX1
PRDX1
PRDX1



LRP1
CD14
SEM3G



ICAM1
LRP1
GRP78



CD14
LG3BP
TETN



LG3BP
PTPA
AIFM1



PTPA
ICAM1
TSP1



TETN
TSP1
MPRI



GRP78
IBP3
TNF12



AIFM1
FOLH1
MMP9



SEM3G
SODM
OSTP



BGH3
FIBA



PDIA3
GSLG1



FINC
RAP2B




C163A





















TABLE 4







Average (13)
Rule-out (13)
Rule-in (9)









LRP1
LRP1 (
LRP1



BGH3
COIA1
COIA1



COIA1
TETN
TETN



TETN
TSP1
TSP1



TSP1
ALDOA
ALDOA



PRDX1
GRP78
GRP78



PROF1
FRIL
FRIL



GRP78
LG3BP
APOE



FRIL
BGH3
TBB3



LG3BP
ISLR



CD14
PRDX1



GGH
FIBA



AIFM1
GSLG1










A preferred normalizer panel is listed in Table 5.








TABLE 5





Normalizer (6)

















PEDF



MASP1



GELS



LUM



C163A



PTPRJ










The term “pulmonary nodules” (PNs) refers to lung lesions that can be visualized by radiographic techniques. A pulmonary nodule is any nodules less than or equal to three centimeters in diameter. In one example a pulmonary nodule has a diameter of about 0.8 cm to 2 cm.
The term “masses” or “pulmonary masses” refers to lung nodules that are greater than three centimeters maximal diameter.
The term “blood biopsy” refers to a diagnostic study of the blood to determine whether a patient presenting with a nodule has a condition that may be classified as either benign or malignant.
The term “acceptance criteria” refers to the set of criteria to which an assay, test, diagnostic or product should conform to be considered acceptable for its intended use. As used herein, acceptance criteria are a list of tests, references to analytical procedures, and appropriate measures, which are defined for an assay or product that will be used in a diagnostic. For example, the acceptance criteria for the classifier refers to a set of predetermined ranges of coefficients.
The term “average maximal AUC” refers to the methodology of calculating performance. For the present invention, in the process of defining the set of proteins that should be in a panel by forward or backwards selection proteins are removed or added one at a time. A plot can be generated with performance (AUC or partial AUC score on the Y axis and proteins on the X axis) the point which maximizes performance indicates the number and set of proteins the gives the best result.
The term “partial AUC factor or pAUC factor” is greater than expected by random prediction. At sensitivity=0.90 the pAUC factor is the trapezoidal area under the ROC curve from 0.9 to 1.0 Specificity/(0.1*0.1/2).
The term “incremental information” refers to information that may be used with other diagnostic information to enhance diagnostic accuracy. Incremental information is independent of clinical factors such as including nodule size, age, or gender.
The term “score” or “scoring” refers to the refers to calculating a probability likelihood for a sample. For the present invention, values closer to 1.0 are used to represent the likelihood that a sample is cancer, values closer to 0.0 represent the likelihood that a sample is benign.
The term “robust” refers to a test or procedure that is not seriously disturbed by violations of the assumptions on which it is based. For the present invention, a robust test is a test wherein the proteins or transitions of the mass spectrometry chromatograms have been manually reviewed and are “generally” free of interfering signals
The term “coefficients” refers to the weight assigned to each protein used to in the logistic regression equation to score a sample.
In certain embodiments of the invention, it is contemplated that in terms of the logistic regression model of MC CV, the model coefficient and the coefficient of variation (CV) of each protein's model coefficient may increase or decrease, dependent upon the method (or model) of measurement of the protein classifier. For each of the listed proteins in the panels, there is about, at least, at least about, or at most about a 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, or 10-, -fold or any range derivable therein for each of the coefficient and CV. Alternatively, it is contemplated that quantitative embodiments of the invention may be discussed in terms of as about, at least, at least about, or at most about 10, 20, 30, 40, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% or more, or any range derivable therein.
The term “best team players” refers to the proteins that rank the best in the random panel selection algorithm, i.e., perform well on panels. When combined into a classifier these proteins can segregate cancer from benign samples. “Best team player” proteins is synonymous with “cooperative proteins”. The term “cooperative proteins” refers proteins that appear more frequently on high performing panels of proteins than expected by chance. This gives rise to a protein's cooperative score which measures how (in)frequently it appears on high performing panels. For example, a protein with a cooperative score of 1.5 appears on high performing panels 1.5× more than would be expected by chance alone.
The term “classifying” as used herein with regard to a lung condition refers to the act of compiling and analyzing expression data for using statistical techniques to provide a classification to aid in diagnosis of a lung condition, particularly lung cancer.
The term “classifier” as used herein refers to an algorithm that discriminates between disease states with a predetermined level of statistical significance. A two-class classifier is an algorithm that uses data points from measurements from a sample and classifies the data into one of two groups. In certain embodiments, the data used in the classifier is the relative expression of proteins in a biological sample. Protein expression levels in a subject can be compared to levels in patients previously diagnosed as disease free or with a specified condition.
The “classifier” maximizes the probability of distinguishing a randomly selected cancer sample from a randomly selected benign sample, i.e., the AUC of ROC curve.
In addition to the classifier's constituent proteins with differential expression, it may also include proteins with minimal or no biologic variation to enable assessment of variability, or the lack thereof, within or between clinical specimens; these proteins may be termed endogenous proteins and serve as internal controls for the other classifier proteins.
The term “normalization” or “normalizer” as used herein refers to the expression of a differential value in terms of a standard value to adjust for effects which arise from technical variation due to sample handling, sample preparation and mass spectrometry measurement rather than biological variation of protein concentration in a sample. For example, when measuring the expression of a differentially expressed protein, the absolute value for the expression of the protein can be expressed in terms of an absolute value for the expression of a standard protein that is substantially constant in expression. This prevents the technical variation of sample preparation and mass spectrometry measurement from impeding the measurement of protein concentration levels in the sample.
The term “condition” as used herein refers generally to a disease, event, or change in health status.
The term “treatment protocol” as used herein including further diagnostic testing typically performed to determine whether a pulmonary nodule is benign or malignant. Treatment protocols include diagnostic tests typically used to diagnose pulmonary nodules or masses such as for example, CT scan, positron emission tomography (PET) scan, bronchoscopy or tissue biopsy. Treatment protocol as used herein is also meant to include therapeutic treatments typically used to treat malignant pulmonary nodules and/or lung cancer such as for example, chemotherapy, radiation or surgery.
The terms “diagnosis” and “diagnostics” also encompass the terms “prognosis” and “prognostics”, respectively, as well as the applications of such procedures over two or more time points to monitor the diagnosis and/or prognosis over time, and statistical modeling based thereupon. Furthermore the term diagnosis includes: a. prediction (determining if a patient will likely develop a hyperproliferative disease) b. prognosis (predicting whether a patient will likely have a better or worse outcome at a pre-selected time in the future) c. therapy selection d. therapeutic drug monitoring e. relapse monitoring.
In some embodiments, for example, classification of a biological sample as being derived from a subject with a lung condition may refer to the results and related reports generated by a laboratory, while diagnosis may refer to the act of a medical professional in using the classification to identify or verify the lung condition.
The term “providing” as used herein with regard to a biological sample refers to directly or indirectly obtaining the biological sample from a subject. For example, “providing” may refer to the act of directly obtaining the biological sample from a subject (e.g., by a blood draw, tissue biopsy, lavage and the like). Likewise, “providing” may refer to the act of indirectly obtaining the biological sample. For example, providing may refer to the act of a laboratory receiving the sample from the party that directly obtained the sample, or to the act of obtaining the sample from an archive.
As used herein, “lung cancer” preferably refers to cancers of the lung, but may include any disease or other disorder of the respiratory system of a human or other mammal. Respiratory neoplastic disorders include, for example small cell carcinoma or small cell lung cancer (SCLC), non-small cell carcinoma or non-small cell lung cancer (NSCLC), squamous cell carcinoma, adenocarcinoma, broncho-alveolar carcinoma, mixed pulmonary carcinoma, malignant pleural mesothelioma, undifferentiated large cell carcinoma, giant cell carcinoma, synchronous tumors, large cell neuroendocrine carcinoma, adenosquamous carcinoma, undifferentiated carcinoma; and small cell carcinoma, including oat cell cancer, mixed small cell/large cell carcinoma, and combined small cell carcinoma; as well as adenoid cystic carcinoma, hamartomas, mucoepidermoid tumors, typical carcinoid lung tumors, atypical carcinoid lung tumors, peripheral carcinoid lung tumors, central carcinoid lung tumors, pleural mesotheliomas, and undifferentiated pulmonary carcinoma and cancers that originate outside the lungs such as secondary cancers that have metastasized to the lungs from other parts of the body. Lung cancers may be of any stage or grade. Preferably the term may be used to refer collectively to any dysplasia, hyperplasia, neoplasia, or metastasis in which the protein biomarkers expressed above normal levels as may be determined, for example, by comparison to adjacent healthy tissue.
Examples of non-cancerous lung condition include chronic obstructive pulmonary disease (COPD), benign tumors or masses of cells (e.g., hamartoma, fibroma, neurofibroma), granuloma, sarcoidosis, and infections caused by bacterial (e.g., tuberculosis) or fungal (e.g. histoplasmosis) pathogens. In certain embodiments, a lung condition may be associated with the appearance of radiographic PNs.
As used herein, “lung tissue”, and “lung cancer” refer to tissue or cancer, respectively, of the lungs themselves, as well as the tissue adjacent to and/or within the strata underlying the lungs and supporting structures such as the pleura, intercostal muscles, ribs, and other elements of the respiratory system. The respiratory system itself is taken in this context as representing nasal cavity, sinuses, pharynx, larynx, trachea, bronchi, lungs, lung lobes, aveoli, aveolar ducts, aveolar sacs, aveolar capillaries, bronchioles, respiratory bronchioles, visceral pleura, parietal pleura, pleural cavity, diaphragm, epiglottis, adenoids, tonsils, mouth and tongue, and the like. The tissue or cancer may be from a mammal and is preferably from a human, although monkeys, apes, cats, dogs, cows, horses and rabbits are within the scope of the present invention. The term “lung condition” as used herein refers to a disease, event, or change in health status relating to the lung, including for example lung cancer and various non-cancerous conditions.
“Accuracy” refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), or as a likelihood, odds ratio, among other measures.
The term “biological sample” as used herein refers to any sample of biological origin potentially containing one or more biomarker proteins. Examples of biological samples include tissue, organs, or bodily fluids such as whole blood, plasma, serum, tissue, lavage or any other specimen used for detection of disease.
The term “subject” as used herein refers to a mammal, preferably a human.
The term “biomarker protein” as used herein refers to a polypeptide in a biological sample from a subject with a lung condition versus a biological sample from a control subject. A biomarker protein includes not only the polypeptide itself, but also minor variations thereof, including for example one or more amino acid substitutions or modifications such as glycosylation or phosphorylation.
The term “biomarker protein panel” as used herein refers to a plurality of biomarker proteins. In certain embodiments, the expression levels of the proteins in the panels can be correlated with the existence of a lung condition in a subject. In certain embodiments, biomarker protein panels comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 60, 70, 80, 90 or 100 proteins. In certain embodiments, the biomarker proteins panels comprise from 100-125 proteins, 125-150 proteins, 150-200 proteins or more.
“Treating” or “treatment” as used herein with regard to a condition may refer to preventing the condition, slowing the onset or rate of development of the condition, reducing the risk of developing the condition, preventing or delaying the development of symptoms associated with the condition, reducing or ending symptoms associated with the condition, generating a complete or partial regression of the condition, or some combination thereof.
The term “ruling out” as used herein is meant that the subject is selected not to receive a treatment protocol.
The term “ruling-in” as used herein is meant that the subject is selected to receive a treatment protocol.
Biomarker levels may change due to treatment of the disease. The changes in biomarker levels may be measured by the present invention. Changes in biomarker levels may be used to monitor the progression of disease or therapy.
“Altered”, “changed” or “significantly different” refer to a detectable change or difference from a reasonably comparable state, profile, measurement, or the like. One skilled in the art should be able to determine a reasonable measurable change. Such changes may be all or none. They may be incremental and need not be linear. They may be by orders of magnitude. A change may be an increase or decrease by 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or more, or any value in between 0% and 100%. Alternatively the change may be 1-fold, 1.5-fold 2-fold, 3-fold, 4-fold, 5-fold or more, or any values in between 1-fold and five-fold. The change may be statistically significant with a p value of 0.1, 0.05, 0.001, or 0.0001.
Using the methods of the current invention, a clinical assessment of a patient is first performed. If there exists is a higher likelihood for cancer, the clinician may rule in the disease which will require the pursuit of diagnostic testing options yielding data which increase and/or substantiate the likelihood of the diagnosis. “Rule in” of a disease requires a test with a high specificity.
“FN” is false negative, which for a disease state test means classifying a disease subject incorrectly as non-disease or normal.
“FP” is false positive, which for a disease state test means classifying a normal subject incorrectly as having disease.
The term “rule in” refers to a diagnostic test with high specificity that coupled with a clinical assessment indicates a higher likelihood for cancer. If the clinical assessment is a lower likelihood for cancer, the clinician may adopt a stance to rule out the disease, which will require diagnostic tests which yield data that decrease the likelihood of the diagnosis. “Rule out” requires a test with a high sensitivity.
The term “rule out” refers to a diagnostic test with high sensitivity that coupled with a clinical assessment indicates a lower likelihood for cancer.
The term “sensitivity of a test” refers to the probability that a patient with the disease will have a positive test result. This is derived from the number of patients with the disease who have a positive test result (true positive) divided by the total number of patients with the disease, including those with true positive results and those patients with the disease who have a negative result, i.e. false negative.
The term “specificity of a test” refers to the probability that a patient without the disease will have a negative test result. This is derived from the number of patients without the disease who have a negative test result (true negative) divided by all patients without the disease, including those with a true negative result and those patients without the disease who have a positive test result, e.g. false positive. While the sensitivity, specificity, true or false positive rate, and true or false negative rate of a test provide an indication of a test's performance, e.g. relative to other tests, to make a clinical decision for an individual patient based on the test's result, the clinician requires performance parameters of the test with respect to a given population.
The term “positive predictive value” (PPV) refers to the probability that a positive result correctly identifies a patient who has the disease, which is the number of true positives divided by the sum of true positives and false positives.
The term “negative predictive value” or “NPV” is calculated by TN/(TN+FN) or the true negative fraction of all negative test results. It also is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.
The term “disease prevalence” refers to the number of all new and old cases of a disease or occurrences of an event during a particular period. Prevalence is expressed as a ratio in which the number of events is the numerator and the population at risk is the denominator.
The term disease incidence refers to a measure of the risk of developing some new condition within a specified period of time; the number of new cases during some time period, it is better expressed as a proportion or a rate with a denominator.
Lung cancer risk according to the “National Lung Screening Trial” is classified by age and smoking history. High risk—age ≧55 and ≧30 pack-years smoking history; Moderate risk—age ≧50 and ≧20 pack-years smoking history; Low risk—<age 50 or <20 pack-years smoking history.
The term “negative predictive value” (NPV) refers to the probability that a negative test correctly identifies a patient without the disease, which is the number of true negatives divided by the sum of true negatives and false negatives. A positive result from a test with a sufficient PPV can be used to rule in the disease for a patient, while a negative result from a test with a sufficient NPV can be used to rule out the disease, if the disease prevalence for the given population, of which the patient can be considered a part, is known.
The clinician must decide on using a diagnostic test based on its intrinsic performance parameters, including sensitivity and specificity, and on its extrinsic performance parameters, such as positive predictive value and negative predictive value, which depend upon the disease's prevalence in a given population.
Additional parameters which may influence clinical assessment of disease likelihood include the prior frequency and closeness of a patient to a known agent, e.g. exposure risk, that directly or indirectly is associated with disease causation, e.g. second hand smoke, radiation, etc., and also the radiographic appearance or characterization of the pulmonary nodule exclusive of size. A nodule's description may include solid, semi-solid or ground glass which characterizes it based on the spectrum of relative gray scale density employed by the CT scan technology.
“Mass spectrometry” refers to a method comprising employing an ionization source to generate gas phase ions from an analyte presented on a sample presenting surface of a probe and detecting the gas phase ions with a mass spectrometer.
The technology liquid chromatography selected reaction monitoring mass spectrometry (LC-SRM-MS) was used to assay the expression levels of a cohort of 388 proteins in the blood to identify differences for individual proteins which may correlate with the absence or presence of the disease. The individual proteins have not only been implicated in lung cancer biology, but are also likely to be present in plasma based on their expression as membrane-anchored or secreted proteins. An analysis of epithelial and endothelial membranes of resected lung cancer tissues (including the subtypes of adenocarcinoma, squamous, and large cell) identified 217 tissue proteins. A review of the scientific literature with search terms relevant to lung cancer biology identified 319 proteins. There was an overlap of 148 proteins between proteins identified by cancer tissue analysis or literature review, yielding a total of 388 unique proteins as candidates. The majority of candidate proteins included in the multiplex LC-SRM-MS assay were discovered following proteomics analysis of secretory vesicle contents from fresh NSCLC resections and from adjacent non-malignant tissue. The secretory proteins reproducibly upregulated in the tumor tissue were identified and prioritized for inclusion in the LC-SRM-MS assay using extensive bioinformatic and literature annotation. An additional set of proteins that were present in relevant literature was also added to the assay. In total, 388 proteins associated with lung cancer were prioritized for SRM assay development. Of these, 371 candidate protein biomarkers were ultimately included in the assay. These are listed in Table 6, below.













TABLE 6










Subcellular
Evidence for


UniProt
Protein
Gene
Sources of
Biomarkers
Location
Presence in


Protein
Name
Symbol
Tissue Biomarkers
in Literature
(UniProt)
Blood







1433B_HUMAN
14-3-3
YWHAB
Secreted,
LungCancers
Cytoplasm.
Literature,



protein

EPI

Melanosome.
Detection



beta/alpha



Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


1433E_HUMAN
14-3-3
YWHAE
ENDO
LungCancers,
Cytoplasm
Literature,



protein


Benign-
(By similarity).
Detection



epsilon


Nodules
Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


1433S_HUMAN
14-3-3
SFN
Secreted,
LungCancers
Cytoplasm.
UniProt, Literature,



protein

EPI

Nucleus (By
Detection



sigma



similarity).







Secreted.







Note = May







be secreted







by a non-







classical







secretory







pathway.


1433T_HUMAN
14-3-3
YWHAQ
EPI
LungCancers,
Cytoplasm.
Detection



protein


Benign-
Note = In



theta


Nodules
neurons,







axonally







transported







to the nerve







terminals.


1433Z_HUMAN
14-3-3
YWHAZ
EPI
LungCancers,
Cytoplasm.
Detection



protein


Benign-
Melanosome.



zeta/delta


Nodules
Note = Located







to stage I







to stage IV







melanosomes.


6PGD_HUMAN
6-
PGD
EPI, ENDO

Cytoplasm
Detection



phosphogluconate



(By similarity).



dehydrogenase,



decarboxylating


A1AG1_HUMAN
Alpha-1-
ORM1
EPI
Symptoms
Secreted.
UniProt, Literature,



acid glycoprotein 1




Detection,








Prediction


ABCD1_HUMAN
ATP-
ABCD1
ENDO

Peroxisome
Detection,



binding



membrane;
Prediction



cassette



Multi-pass



sub-



membrane



family D



protein.



member 1


ADA12_HUMAN
Disintegrin
ADAM12

LungCancers,
Isoform 1:
UniProt, Detection,



and


Benign-
Cell membrane;
Prediction



metallo-


Nodules,
Single-



proteinase


Symptoms
pass



domain-



type I membrane



containing



protein.



protein 12



|Isoform







2: Secreted.







|Isoform







3: Secreted







(Potential).







|Isoform







4: Secreted







(Potential).


ADML_HUMAN
ADM
ADM

LungCancers,
Secreted.
UniProt, Literature,






Benign-

Detection,






Nodules,

Prediction






Symptoms


AGR2_HUMAN
Anterior
AGR2
EPI
LungCancers
Secreted.
UniProt, Prediction



gradient



Endoplasmic



protein 2



reticulum



homolog



(By







similarity).


AIFM1_HUMAN
Apoptosis-
AIFM1
EPI, ENDO
LungCancers
Mitochondrion
Detection,



inducing



inter-
Prediction



factor 1,



membrane



mitochondrial



space. Nucleus.







Note = Translocated







to the







nucleus upon







induction







of apoptosis.


ALDOA_HUMAN
Fructose-
ALDOA
Secreted,
LungCancers,

Literature,



bisphosphate

EPI
Symptoms

Detection



aldolase A


AMPN_HUMAN
Aminopeptidase N
ANPEP
EPI, ENDO
LungCancers,
Cell membrane;
UniProt, Detection






Benign-
Single-






Nodules,
pass






Symptoms
type II







membrane







protein. Cytoplasm,







cytosol (Potential).







Note = A







soluble form







has also







been detected.


ANGP1_HUMAN
Angiopoietin-1
ANGPT1

LungCancers,
Secreted.
UniProt, Literature,






Benign-

Prediction






Nodules


ANGP2_HUMAN
Angiopoietin-2
ANGPT2

LungCancers,
Secreted.
UniProt, Literature,






Benign-

Prediction






Nodules


APOA1_HUMAN
Apolipo-
APOA1

LungCancers,
Secreted.
UniProt, Literature,



protein A-I


Benign-

Detection,






Nodules,

Prediction






Symptoms


APOE_HUMAN
Apolipo-
APOE
EPI, ENDO
LungCancers,
Secreted.
UniProt, Literature,



protein E


Benign-

Detection,






Nodules,

Prediction






Symptoms


ASM3B_HUMAN
Acid
SMPDL3B
EPI, ENDO

Secreted (By
UniProt, Prediction



sphingo-



similarity).



myelinase-



like



phosphodiesterase



3b


AT2A2_HUMAN
Sarcoplas-
ATP2A2
EPI, ENDO
LungCancers,
Endoplasmic
Detection



plasmic/


Benign-
reticulum



endoplasmic


Nodules
membrane;



reticulum



Multi-



calcium



pass



ATPase 2



membrane







protein. Sarcoplasmic







reticulum







membrane;







Multi-pass







membrane







protein.


ATS1_HUMAN
A disintegrin
ADAMTS1

LungCancers,
Secreted,
UniProt, Literature,



and


Benign-
extracellular
Prediction



metallo-


Nodules,
space, extra-



proteinase


Symptoms
cellular matrix



with



(By similarity).



thrombospondin



motifs 1


ATS12_HUMAN
A disintegrin
ADAMTS12

LungCancers
Secreted,
UniProt, Detection,



and



extracellular
Prediction



metallo-



space, extra-



proteinase



cellular matrix



with



(By similarity).



thrombospondin



motifs 12


ATS19_HUMAN
A disintegrin
ADAMTS19

LungCancers
Secreted,
UniProt, Prediction



and



extracellular



metallo-



space, extra-



proteinase



cellular matrix



with



(By similarity).



thrombospondin



motifs 19


BAGE1_HUMAN
B melanoma
BAGE

LungCancers
Secreted
UniProt, Prediction



antigen 1



(Potential).


BAGE2_HUMAN
B melanoma
BAGE2

LungCancers
Secreted
UniProt, Prediction



antigen 2



(Potential).


BAGE3_HUMAN
B melanoma
BAGE3

LungCancers
Secreted
UniProt, Prediction



antigen 3



(Potential).


BAGE4_HUMAN
B melanoma
BAGE4

LungCancers
Secreted
UniProt, Prediction



antigen 4



(Potential).


BAGE5_HUMAN
B melanoma
BAGE5

LungCancers
Secreted
UniProt, Prediction



antigen 5



(Potential).


BASP1_HUMAN
Brain acid
BASP1
Secreted,

Cell membrane;
Detection



soluble

EPI

Lipid-



protein 1



anchor.







Cell projection,







growth







cone.







Note = Associated







with







the membranes







of







growth







cones that







form the tips







of elongating







axons.


BAX_HUMAN
Apoptosis
BAX
EPI
LungCancers,
Isoform Alpha:
UniProt, Literature,



regulator


Benign-
Mitochondrion
Prediction



BAX


Nodules
membrane;







Single-pass







membrane







protein. Cytoplasm.







Note = Colocalizes







with







14-3-3 proteins







in the







cytoplasm.







Under stress







conditions,







redistributes







to the mitochondrion







membrane







through the







release from







JNK-







phosphorylated







14-3-3







proteins.







|Isoform







Beta: Cytoplasm.







|Isoform







Gamma:







Cytoplasm.







|Isoform







Delta:







Cytoplasm







(Potential).


BDNF_HUMAN
Brain-
BDNF

Benign-
Secreted.
UniProt, Literature,



derived


Nodules,

Prediction



neurotrophic


Symptoms



factor


BGH3_HUMAN
Transforming
TGFBI

LungCancers,
Secreted,
UniProt, Detection



growth


Benign-
extracellular



factor-


Nodules
space, extra-



beta-



cellular matrix.



induced



Note = May



protein igh3



be associated







both with







microfibrils







and with the







cell surface.


BMP2_HUMAN
Bone
BMP2

LungCancers,
Secreted.
UniProt, Literature



morphogenetic


Benign-



protein 2


Nodules,






Symptoms


BST1_HUMAN
ADP-
BST1
EPI
Symptoms
Cell membrane;
Detection,



ribosyl



Lipid-
Prediction



cyclase 2



anchor,







GPI-anchor.


C163A_HUMAN
Scavenger
CD163
EPI
Symptoms
Soluble
UniProt, Detection



receptor



CD163: Secreted.



cysteine-



|Cell



rich type 1



membrane;



protein



Single-pass



M130



type I membrane







protein.







Note = Isoform







1 and







isoform 2







show a lower







surface







expression







when expressed







in







cells.


C4BPA_HUMAN
C4b-
C4BPA

LungCancers,
Secreted.
UniProt, Detection,



binding


Symptoms

Prediction



protein



alpha



chain


CAH9_HUMAN
Carbonic
CA9

LungCancers,
Nucleus.
UniProt



anhydrase 9


Benign-
Nucleus,






Nodules,
nucleolus.






Symptoms
Cell membrane;







Single-







pass







type I membrane







protein.







Cell







projection,







microvillus







membrane;







Single-pass







type I membrane







protein.







Note = Found







on the surface







micro-







villi and in







the nucleus,







particularly







in nucleolus.


CALR_HUMAN
Calreticulin
CALR
EPI
Symptoms
Endoplasmic
UniProt, Literature,







reticulum
Detection,







lumen.
Prediction







Cytoplasm,







cytosol. Secreted,







extracellular







space, extra-







cellular matrix.







Cell







surface.







Note = Also







found in cell







surface (T







cells), cytosol







and extracellular







matrix. Associated







with the







lytic granules







in the







cytolytic T-







lymphocytes.


CALU_HUMAN
Calumenin
CALU
EPI
Symptoms
Endoplasmic
UniProt, Detection,







reticulum
Prediction







lumen.







Secreted.







Melanosome.







Sarcoplasmic







reticulum







lumen (By







similarity).







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


CALX_HUMAN
Calnexin
CANX
Secreted,
Benign-
Endoplasmic
UniProt, Literature,





EPI, ENDO
Nodules
reticulum
Detection







membrane;







Single-







pass







type I membrane







protein.







Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


CAP7_HUMAN
Azurocidin
AZU1
EPI
Symptoms
Cytoplasmic
Prediction







granule.







Note = Cytoplasmic







granules







of neutrophils.


CATB_HUMAN
Cathepsin B
CTSB
Secreted
LungCancers
Lysosome.
Literature,







Melanosome.
Detection,







Note = Identified
Prediction







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


CATG_HUMAN
Cathepsin G
CTSG
Secreted,
Benign-
Cell surface.
Detection,





ENDO
Nodules

Prediction


CBPB2_HUMAN
Carboxy-
CPB2

LungCancers,
Secreted.
UniProt, Detection,



peptidase


Benign-

Prediction



B2


Nodules,






Symptoms


CCL22_HUMAN
C-C motif
CCL22

LungCancers,
Secreted.
UniProt, Prediction



chemokine


Benign-



22


Nodules


CD14_HUMAN
Monocyte
CD14
EPI
LungCancers,
Cell membrane;
Literature,



differentiation


Benign-
Lipid-
Detection,



antigen


Nodules,
anchor,
Prediction



CD14


Symptoms
GPI-anchor.


CD24_HUMAN
Signal
CD24

LungCancers,
Cell membrane;
Literature



transducer


Benign-
Lipid-



CD24


Nodules
anchor,







GPI-anchor.


CD2A2_HUMAN
Cyclin-
CDKN2A

LungCancers,
Cytoplasm.
Literature,



dependent


Benign-
Nucleus.
Prediction



kinase


Nodules
|Nucleus,



inhibitor



nucleolus



2A, isoform 4



(By similarity).


CD38_HUMAN
ADP-
CD38
EPI, ENDO
Symptoms
Membrane;
UniProt, Literature



ribosyl



Single-pass



cyclase 1



type II







membrane







protein.


CD40L_HUMAN
CD40
CD40LG

LungCancers,
Cell membrane;
UniProt, Literature



ligand


Benign-
Single-






Nodules,
pass






Symptoms
type II







membrane







protein.







|CD40







ligand, soluble







form:







Secreted.


CD44_HUMAN
CD44
CD44
EPI
LungCancers,
Membrane;
UniProt, Literature,



antigen


Benign-
Single-pass
Detection,






Nodules,
type I membrane
Prediction






Symptoms
protein.


CD59_HUMAN
CD59
CD59

LungCancers,
Cell membrane;
UniProt, Literature,



glycoprotein


Benign-
Lipid-
Detection,






Nodules,
anchor,
Prediction






Symptoms
GPI-anchor.







Secreted.







Note = Soluble







form







found in a







number of







tissues.


CD97_HUMAN
CD97
CD97
EPI, ENDO
Symptoms
Cell membrane;
UniProt



antigen



Multi-







pass







membrane







protein.







|CD97







antigen sub-







unit alpha:







Secreted,







extracellular







space.


CDCP1_HUMAN
CUB domain-
CDCP1

LungCancers
Isoform 1:
UniProt, Prediction



containing



Cell membrane;



protein 1



Single-







pass







membrane







protein (Potential).







Note = Shedding







may also







lead to a







soluble peptide.







|Isoform







3: Secreted.


CDK4_HUMAN
Cell division
CDK4

LungCancers,

Literature



protein


Symptoms



kinase 4


CEAM5_HUMAN
Carcinoembryonic
CEACAM5
EPI
LungCancers,
Cell membrane;
Literature,



antigen


Benign-
Lipid-
Prediction



related


Nodules,
anchor,



cell adhesion


Symptoms
GPI-anchor.



molecule 5


CEAM8_HUMAN
Carcinoembryonic
CEACAM8
EPI
LungCancers
Cell membrane;
Detection,



antigen-



Lipid-
Prediction



related



anchor,



cell adhesion



GPI-anchor.



molecule 8


CERU_HUMAN
Ceruloplasmin
CP
EPI
LungCancers,
Secreted.
UniProt, Literature,






Symptoms

Detection,








Prediction


CH10_HUMAN
10 kDa
HSPE1
ENDO
LungCancers
Mitochondrion
Literature,



heat shock



matrix.
Detection,



protein,




Prediction



mitochondrial


CH60_HUMAN
60 kDa
HSPD1
Secreted,
LungCancers,
Mitochondrion
Literature,



heat shock

EPI, ENDO
Symptoms
matrix.
Detection



protein,



mitochondrial


CKAP4_HUMAN
Cytoskeleton-
CKAP4
EPI, ENDO
LungCancers
Endoplasmic
UniProt



associated



reticulum-



protein 4



Golgi







intermediate







compartment







membrane;







Single-







pass







membrane







protein (Potential).


CL041_HUMAN
Uncharacterized
C12orf41
ENDO


Prediction



protein



C12orf41


CLCA1_HUMAN
Calcium-
CLCA1

LungCancers,
Secreted,
UniProt, Prediction



activated


Benign-
extracellular



chloride


Nodules
space. Cell



channel



membrane;



regulator 1



Peripheral







membrane







protein; Extracellular







side.







Note = Protein







that remains







attached







to the







plasma







membrane







appeared to







be predominantly







localized







to microvilli.


CLIC1_HUMAN
Chloride
CLIC1
EPI

Nucleus.
UniProt, Literature,



intracellular



Nucleus
Detection



channel



membrane;



protein 1



Single-pass







membrane







protein







(Probable).







Cytoplasm.







Cell membrane;







Single-







pass







membrane







protein







(Probable).







Note = Mostly







in the nucleus







including







in the







nuclear







membrane.







Small







amount in







the cytoplasm







and







the plasma







membrane.







Exists both







as soluble







cytoplasmic







protein and







as membrane







protein







with







probably a







single







transmembrane







domain.


CLUS_HUMAN
Clusterin
CLU
EPI, ENDO
LungCancers,
Secreted.
UniProt, Literature,






Benign-

Detection,






Nodules,

Prediction






Symptoms


CMGA_HUMAN
Chromogranin-A
CHGA

LungCancers,
Secreted.
UniProt, Literature,






Benign-
Note = Neuro
Detection,






Nodules
endocrine
Prediction







and endocrine







secretory







granules.


CNTN1_HUMAN
Contactin-1
CNTN1

LungCancers
Isoform 1:
Detection,







Cell membrane;
Prediction







Lipid-







anchor,







GPI-anchor;







Extracellular







side.|Isoform







2: Cell







membrane;







Lipid-







anchor, GPI-







anchor; Extracellular







side.


CO4A1_HUMAN
Collagen
COL4A1

LungCancers
Secreted,
UniProt, Detection,



alpha-



extracellular
Prediction



1(IV)



space, extra-



chain



cellular matrix,







basement







membrane.


CO5A2_HUMAN
Collagen
COL5A2

LungCancers
Secreted,
UniProt, Detection,



alpha-



extracellular
Prediction



2(V) chain



space, extra-







cellular matrix







(By similarity).


CO6A3_HUMAN
Collagen
COL6A3
Secreted
Symptoms
Secreted,
UniProt, Detection,



alpha-



extracellular
Prediction



3(VI)



space, extra-



chain



cellular matrix







(By similarity).


COCA1_HUMAN
Collagen
COL12A1
ENDO
LungCancers,
Secreted,
UniProt, Prediction



alpha-


Symptoms
extracellular



1(XII)



space, extra-



chain



cellular matrix







(By similarity).


COF1_HUMAN
Cofilin-1
CFL1
Secreted,
LungCancers,
Nucleus
Detection,





EPI
Benign-
matrix. Cytoplasm,
Prediction






Nodules
cytoskeleton.







Note = Almost







completely







in nucleus in







cells exposed







to







heat shock







or 10% di-







methyl sulfoxide.


COIA1_HUMAN
Collagen
COL18A1

LungCancers,
Secreted,
UniProt, Literature,



alpha-


Benign-
extracellular
Detection,



1(XVIII)


Nodules
space, extra-
Prediction



chain



cellular matrix







(By similarity).


COX5A_HUMAN
Cytochrome c
COX5A
Secreted,

Mitochondrion
Prediction



oxidase

ENDO

inner



subunit



membrane.



5A, mitochondrial


CRP_HUMAN
C-reactive
CRP

LungCancers,
Secreted.
UniProt, Literature,



protein


Benign-

Detection,






Nodules,

Prediction






Symptoms


CS051_HUMAN
UPF0470
C19orf51
ENDO


Prediction



protein



C19orf51


CSF1_HUMAN
Macrophage
CSF1

LungCancers,
Cell membrane;
UniProt, Literature,



colony-


Benign-
Single-
Detection



stimulating


Nodules
pass



factor 1



membrane







protein (By







similarity).







|Processed







macrophage







colony-







stimulating







factor 1:







Secreted,







extracellular







space (By







similarity).


CSF2_HUMAN
Granulocyte-
CSF2

LungCancers,
Secreted.
UniProt, Literature,



macrophage


Benign-

Prediction



colony-


Nodules



stimulating



factor


CT085_HUMAN
Uncharacterized
C20orf85

LungCancers,

Prediction



protein


Benign-



C20orf85


Nodules


CTGF_HUMAN
Connective
CTGF

LungCancers,
Secreted,
UniProt, Literature,



tissue


Benign-
extracellular
Detection,



growth


Nodules
space, extra-
Prediction



factor



cellular matrix







(By similarity).







Secreted







(By







similarity).


CYR61_HUMAN
Protein
CYR61

LungCancers,
Secreted.
UniProt, Prediction



CYR61


Benign-






Nodules


CYTA_HUMAN
Cystatin-A
CSTA

LungCancers
Cytoplasm.
Literature,








Detection


CYTB_HUMAN
Cystatin-B
CSTB
Secreted

Cytoplasm.
Literature,







Nucleus.
Detection


DDX17_HUMAN
Probable
DDX17
ENDO
LungCancers,
Nucleus.
Detection,



ATP-


Benign-

Prediction



dependent


Nodules



RNA helicase



DDX17


DEFB1_HUMAN
Beta-
DEFB1

LungCancers,
Secreted.
UniProt, Prediction



defensin 1


Benign-






Nodules


DESP_HUMAN
Desmoplakin
DSP
EPI, ENDO
LungCancers
Cell junction,
Detection







desmosome.







Cytoplasm,







cytoskeleton.







Note = Inner







most portion







of the desmosomal







plaque.


DFB4A_HUMAN
Beta-
DEFB4A

LungCancers,
Secreted.
UniProt



defensin


Benign-



4A


Nodules


DHI1L_HUMAN
Hydroxysteroid
HSD11B1L

LungCancers
Secreted
UniProt, Prediction



11-



(Potential).



beta-



dehydro-



genase 1-



like protein


DMBT1_HUMAN
Deleted in
DMBT1

LungCancers,
Secreted (By
UniProt, Detection,



malignant


Benign-
similarity).
Prediction



brain tumors 1


Nodules
Note = Some



protein



isoforms







may be







membrane-







bound. Localized







to







the lumenal







aspect of







crypt cells in







the small







intestine. In







the colon,







seen in the







lumenal







aspect of







surface epithelial







cells.







Formed in







the ducts of







von Ebner







gland, and







released into







the fluid







bathing the







taste buds







contained in







the taste







papillae (By







similarity).


DMKN_HUMAN
Dermokine
DMKN

LungCancers
Secreted.
UniProt, Detection,








Prediction


DPP4_HUMAN
Dipeptidyl
DPP4
EPI
LungCancers,
Dipeptidyl
UniProt, Detection



peptidase 4


Benign-
peptidase 4






Nodules,
soluble






Symptoms
form: Secreted.







|Cell







membrane;







Single-pass







type II







membrane







protein.


DSG2_HUMAN
Desmoglein-2
DSG2
ENDO
Symptoms
Cell membrane;
UniProt, Detection







Single-







pass







type I membrane







protein.







Cell







junction,







desmosome.


DX39A_HUMAN
ATP-
DDX39A
EPI

Nucleus (By
Prediction



dependent



similarity).



RNA helicase



DDX39A


DX39B_HUMAN
Spliceosome
DDX39B
EPI

Nucleus.
Prediction



RNA helicase



Nucleus



DDX39B



speckle.


DYRK2_HUMAN
Dual specificity
DYRK2
ENDO
LungCancers
Cytoplasm.
Literature



tyrosine-



Nucleus.



phosphorylation-



Note = Translocates



regulated



into



kinase 2



the nucleus







following







DNA damage.


EDN2_HUMAN
Endothelin-2
EDN2

LungCancers
Secreted.
UniProt, Prediction


EF1A1_HUMAN
Elongation
EEF1A1
Secreted,
LungCancers,
Cytoplasm.
Detection



factor

EPI
Benign-



1-alpha 1


Nodules


EF1D_HUMAN
Elongation
EEF1D
Secreted,
LungCancers

Prediction



factor

EPI



1-delta


EF2_HUMAN
Elongation
EEF2
Secreted,

Cytoplasm.
Literature,



factor 2

EPI


Detection


EGF_HUMAN
Pro-
EGF

LungCancers,
Membrane;
UniProt, Literature



epidermal


Benign-
Single-pass



growth


Nodules,
type I membrane



factor


Symptoms
protein.


EGFL6_HUMAN
Epidermal
EGFL6

LungCancers
Secreted,
UniProt, Detection,



growth



extracellular
Prediction



factor-like



space, extra-



protein 6



cellular matrix,







basement







membrane







(By







similarity).


ENOA_HUMAN
Alpha-
ENO1
Secreted,
LungCancers,
Cytoplasm.
Literature,



enolase

EPI, ENDO
Benign-
Cell membrane.
Detection,






Nodules,
Cytoplasm,
Prediction






Symptoms
myofibril,







sarcomere,







M-







band.







Note = Can







translocate







to the plasma







membrane







in







either the







homodimeric







(alpha/







alpha)







or heterodimeric







(alpha/







gamma)







form. ENO1







is localized







to the M-







band.|Isoform







MBP-1:







Nucleus.


ENOG_HUMAN
Gamma-
ENO2
EPI
LungCancers,
Cytoplasm
Literature,



enolase


Symptoms
(By similarity).
Detection,







Cell
Prediction







membrane







(By similarity).







Note = Can







translocate







to the plasma







membrane







in







either the







homodimeric







(alpha/







alpha)







or heterodimeric







(alpha/







gamma)







form (By







similarity).


ENOX2_HUMAN
Ecto-
ENOX2

LungCancers
Cell membrane.
UniProt, Detection



NOX di-



Secreted,



sulfide-



extracellular



thiol exchanger 2



space.







Note = Extracellular







and







plasma







membrane-







associated.


ENPL_HUMAN
Endo-
HSP90B1
Secreted,
LungCancers,
Endoplasmic
Literature,



plasmin

EPI, ENDO
Benign-
reticulum
Detection,






Nodules,
lumen.
Prediction






Symptoms
Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


EPHB6_HUMAN
Ephrin
EPHB6

LungCancers
Membrane;
UniProt, Literature



type-B



Single-pass



receptor 6



type I membrane







protein.







|Isoform







3: Secreted







(Probable).


EPOR_HUMAN
Erythro-
EPOR

LungCancers,
Cell membrane;
UniProt, Literature,



poietin


Benign-
Single-
Detection



receptor


Nodules,
pass






Symptoms
type I membrane







protein.







|Isoform







EPOR-S:







Secreted.







Note = Secreted







and located







to the







cell surface.


ERBB3_HUMAN
Receptor
ERBB3

LungCancers,
Isoform 1:
UniProt, Literature,



tyrosine-


Benign-
Cell membrane;
Prediction



protein


Nodules
Single-



kinase



pass



erbB-3



type I membrane







protein.







|Isoform







2: Secreted.


EREG_HUMAN
Proepiregulin
EREG

LungCancers
Epiregulin:
UniProt







Secreted,







extracellular







space.|Proepiregulin:







Cell membrane;







Single-







pass







type I membrane







protein.


ERO1A_HUMAN
ERO1-
ERO1L
Secreted,
Symptoms
Endoplasmic
Prediction



like protein

EPI, ENDO

reticulum



alpha



membrane;







Peripheral







membrane







protein;







Lumenal







side.







Note = The







association







with ERP44







is essential







for its retention







in the







endoplasmic







reticulum.


ESM1_HUMAN
Endothelial
ESM1

LungCancers,
Secreted.
UniProt, Prediction



cell-


Benign-



specific


Nodules



molecule 1


EZRI_HUMAN
Ezrin
EZR
Secreted
LungCancers,
Apical cell
Literature,






Benign-
membrane;
Detection,






Nodules
Peripheral
Prediction







membrane







protein; Cytoplasmic







side. Cell







projection.







Cell projection,







micro-







villus membrane;







Peripheral







membrane







protein; Cytoplasmic







side. Cell







projection,







ruffle membrane;







Peripheral







membrane







protein; Cytoplasmic







side. Cytoplasm,







cell







cortex. Cytoplasm,







cytoskeleton.







Note = Localization







to the







apical membrane







of







parietal cells







depends on







the interaction







with







MPP5. Localizes







to







cell extensions







and







peripheral







processes of







astrocytes







(By similarity).







Micro-







villar peripheral







membrane







protein (cytoplasmic







side).


F10A1_HUMAN
Hsc70-
ST13
EPI

Cytoplasm
Detection,



interacting



(By similarity).
Prediction



protein



|Cytoplasm







(Probable).


FAM3C_HUMAN
Protein
FAM3C
EPI, ENDO

Secreted
UniProt, Detection



FAM3C



(Potential).


FAS_HUMAN
Fatty acid
FASN
EPI
LungCancers,
Cytoplasm.
Literature,



synthase


Benign-
Melanosome.
Detection






Nodules,
Note = Identified






Symptoms
by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


FCGR1_HUMAN
High affinity
FCGR1A
EPI
LungCancers,
Cell membrane;
UniProt



immuno-


Benign-
Single-



globulin


Nodules,
pass



gamma Fc


Symptoms
type I membrane



receptor I



protein.







Note = Stabilized







at the







cell membrane







through interaction







with







FCER1G.


FGF10_HUMAN
Fibroblast
FGF10

LungCancers
Secreted
UniProt, Prediction



growth



(Potential).



factor 10


FGF2_HUMAN
Heparin-
FGF2

LungCancers,

Literature



binding


Benign-



growth


Nodules,



factor 2


Symptoms


FGF7_HUMAN
Keratinocyte
FGF7

LungCancers,
Secreted.
UniProt, Literature,



growth


Benign-

Prediction



factor


Nodules


FGF9_HUMAN
Glia-
FGF9

LungCancers
Secreted.
UniProt, Literature,



activating




Prediction



factor


FGFR2_HUMAN
Fibroblast
FGFR2

LungCancers,
Cell membrane;
UniProt, Literature,



growth


Benign-
Single-
Prediction



factor


Nodules
pass



receptor 2



type I membrane







protein.







|Isoform







14: Secreted.







|Isoform







19: Secreted.


FGFR3_HUMAN
Fibroblast
FGFR3

LungCancers
Membrane;
UniProt, Literature,



growth



Single-pass
Prediction



factor



type I membrane



receptor 3



protein.


FGL2_HUMAN
Fibroleukin
FGL2

Benign-
Secreted.
UniProt, Detection,






Nodules,

Prediction






Symptoms


FHIT_HUMAN
Bis(5′-
FHIT

LungCancers,
Cytoplasm.
Literature



adenosyl)-


Benign-



triphosphatase


Nodules,






Symptoms


FIBA_HUMAN
Fibrinogen
FGA

LungCancers,
Secreted.
UniProt, Literature,



alpha


Benign-

Detection,



chain


Nodules,

Prediction






Symptoms


FINC_HUMAN
Fibronectin
FN1
Secreted,
LungCancers,
Secreted,
UniProt, Literature,





EPI, ENDO
Benign-
extracellular
Detection,






Nodules,
space, extra-
Prediction






Symptoms
cellular matrix.


FKB11_HUMAN
Peptidyl-
FKBP11
EPI, ENDO

Membrane;
UniProt, Prediction



prolyl cis-



Single-pass



trans isomerase



membrane



FKBP11



protein (Potential).


FOLH1_HUMAN
Glutamate
FOLH1
ENDO
LungCancers,
Cell membrane;
UniProt, Literature



carboxy-


Symptoms
Single-



peptidase 2



pass







type II







membrane







protein.







|Isoform







PSMA′:







Cytoplasm.


FOLR1_HUMAN
Folate
FOLR1

LungCancers
Cell membrane;
UniProt



receptor



Lipid-



alpha



anchor,







GPI-anchor.







Secreted







(Probable).


FOXA2_HUMAN
Hepatocyte
FOXA2

LungCancers
Nucleus.
Detection,



nuclear




Prediction



factor



3-beta


FP100_HUMAN
Fanconi
C17orf70
ENDO
Symptoms
Nucleus.
Prediction



anemia-



associated



protein of



100 kDa


FRIH_HUMAN
Ferritin
FTH1
EPI
LungCancers,

Literature,



heavy


Benign-

Detection,



chain


Nodules

Prediction


FRIL_HUMAN
Ferritin
FTL
Secreted,
Benign-

Literature,



light chain

EPI, ENDO
Nodules,

Detection






Symptoms


G3P_HUMAN
Glyceraldehyde-
GAPDH
Secreted,
LungCancers,
Cytoplasm.
Detection



3-

EPI, ENDO
Benign-
Cytoplasm,



phosphate


Nodules,
perinuclear



dehydrogenase


Symptoms
region.







Membrane.







Note = Postnuclear







and







Perinuclear







regions.


G6PD_HUMAN
Glucose-
G6PD
Secreted,
LungCancers,

Literature,



6-

EPI
Symptoms

Detection



phosphate



1-



dehydrogenase


G6PI_HUMAN
Glucose-
GPI
Secreted,
Symptoms
Cytoplasm.
UniProt, Literature,



6-

EPI

Secreted.
Detection



phosphate



isomerase


GA2L1_HUMAN
GAS2-
GAS2L1
ENDO

Cytoplasm,
Prediction



like protein 1



cytoskeleton







(Probable).


GALT2_HUMAN
Polypeptide
GALNT2
EPI, ENDO

Golgi apparatus,
UniProt, Detection



N-



Golgi



acetylgalactosaminyl-



stack membrane;



transferase 2



Single-







pass







type II







membrane







protein. Secreted.







Note = Resides







preferentially







in the







trans and







medial parts







of the Golgi







stack. A







secreted







form also







exists.


GAS6_HUMAN
Growth
GAS6

LungCancers
Secreted.
UniProt, Detection,



arrest-




Prediction



specific



protein 6


GDIR2_HUMAN
Rho GDP-
ARHGDIB
EPI

Cytoplasm.
Detection



dissociation



inhibitor 2


GELS_HUMAN
Gelsolin
GSN

LungCancers,
Isoform 2:
UniProt, Literature,






Benign-
Cytoplasm,
Detection,






Nodules
cytoskeleton.
Prediction







|Isoform







1: Secreted.


GGH_HUMAN
Gamma-
GGH

LungCancers
Secreted,
UniProt, Detection,



glutamyl



extracellular
Prediction



hydrolase



space. Lysosome.







Melanosome.







Note = While







its intracellular







location







is primarily







the







lysosome,







most of the







enzyme activity







is secreted.







Identified







by







mass spectrometry







in







melanosome







fractions







from stage I







to stage IV.


GPC3_HUMAN
Glypican-3
GPC3

LungCancers,
Cell membrane;
UniProt, Literature,






Symptoms
Lipid-
Prediction







anchor,







GPI-anchor;







Extracellular







side (By







similarity).







|Secreted







glypican-3:







Secreted,







extracellular







space (By







similarity).


GRAN_HUMAN
Grancalcin
GCA
EPI

Cytoplasm.
Prediction







Cytoplasmic







granule







membrane;







Peripheral







membrane







protein; Cytoplasmic







side.







Note = Primarily







cytosolic







in the







absence of







calcium or







magnesium







ions. Relocates







to







granules and







other membranes







in







response to







elevated







calcium and







magnesium







levels.


GREB1_HUMAN
Protein
GREB1
ENDO

Membrane;
UniProt, Prediction



GREB1



Single-pass







membrane







protein (Potential).


GREM1_HUMAN
Gremlin-1
GREM1

LungCancers,
Secreted
UniProt, Prediction






Benign-
(Probable).






Nodules


GRP_HUMAN
Gastrin-
GRP

LungCancers,
Secreted.
UniProt, Prediction



releasing


Symptoms



peptide


GRP78_HUMAN
78 kDa
HSPA5
Secreted,
LungCancers,
Endoplasmic
Detection,



glucose-

EPI, ENDO
Benign-
reticulum
Prediction



regulated


Nodules
lumen.



protein



Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


GSLG1_HUMAN
Golgi
GLG1
EPI, ENDO
Benign-
Golgi apparatus
UniProt



apparatus


Nodules
membrane;



protein 1



Single-







pass







type I membrane







protein.


GSTP1_HUMAN
Glutathione
GSTP1
Secreted
LungCancers,

Literature,



S-


Benign-

Detection,



transferase P


Nodules,

Prediction






Symptoms


GTR1_HUMAN
Solute
SLC2A1
EPI, ENDO
LungCancers,
Cell membrane;
Literature



carrier


Benign-
Multi-



family 2,


Nodules,
pass



facilitated


Symptoms
membrane



glucose



protein (By



transporter



similarity).



member 1



Melanosome.







Note = Localizes







primarily







at the cell







surface (By







similarity).







Identified by







mass spectrometry







in







melanosome







fractions







from stage I







to stage IV.


GTR3_HUMAN
Solute
SLC2A3
EPI

Membrane;
Detection



carrier



Multi-pass



family 2,



membrane



facilitated



protein.



glucose



transporter



member 3


H2A1_HUMAN
Histone
HIST1H2AG
Secreted

Nucleus.
Detection,



H2A type 1




Prediction


H2A1B_HUMAN
Histone
HIST1H2AB
Secreted

Nucleus.
Detection,



H2A type




Prediction



1-B/E


H2A1C_HUMAN
Histone
HIST1H2AC
Secreted

Nucleus.
Literature,



H2A type




Detection,



1-C




Prediction


H2A1D_HUMAN
Histone
HIST1H2AD
Secreted

Nucleus.
Detection,



H2A type




Prediction



1-D


HG2A_HUMAN
HLA class
CD74

LungCancers,
Membrane;
UniProt, Literature



II histo-


Benign-
Single-pass



compatibility


Nodules,
type II



antigen


Symptoms
membrane



gamma



protein (Potential).



chain


HGF_HUMAN
Hepatocyte
HGF

LungCancers,

Literature,



growth


Benign-

Prediction



factor


Nodules,






Symptoms


HMGA1_HUMAN
High mobility
HMGA1

LungCancers,
Nucleus.
Literature



group


Benign-



protein


Nodules,



HMG-


Symptoms



I/HMG-Y


HPRT_HUMAN
Hypoxanthine-
HPRT1
EPI

Cytoplasm.
Detection,



guanine




Prediction



phosphoribosyltransferase


HPSE_HUMAN
Heparanase
HPSE

LungCancers,
Lysosome
UniProt, Prediction






Benign-
membrane;






Nodules,
Peripheral






Symptoms
membrane







protein. Secreted.







Note = Secreted,







internalised







and







transferred







to late endosomes/







lysosomes







as a







proheparanase.







In lysosomes,







it







is processed







into the active







form,







the heparanase.







The







uptake or







internalisation







of proheparanase







is mediated







by HSPGs.







Heparin







appears to







be a competitor







and retain







proheparanase







in







the extracellular







medium.


HPT_HUMAN
Haptoglobin
HP

LungCancers,
Secreted.
UniProt, Literature,






Benign-

Detection,






Nodules,

Prediction






Symptoms


HS90A_HUMAN
Heat
HSP90AA1
Secreted,
LungCancers,
Cytoplasm.
Literature,



shock

EPI
Symptoms
Melanosome.
Detection



protein



Note = Identified



HSP 90-



by mass



alpha



spectrometry







in melanosome







fractions







from stage I







to stage IV.


HS90B_HUMAN
Heat
HSP90AB1
Secreted,
LungCancers
Cytoplasm.
Literature,



shock

EPI

Melanosome.
Detection



protein



Note = Identified



HSP 90-



by mass



beta



spectrometry







in melanosome







fractions







from stage I







to stage IV.


HSPB1_HUMAN
Heat
HSPB1
Secreted,
LungCancers,
Cytoplasm.
Literature,



shock

EPI
Benign-
Nucleus.
Detection,



protein


Nodules
Cytoplasm,
Prediction



beta-1



cytoskeleton,







spindle.







Note = Cytoplasmic







in







interphase







cells. Colo-







calizes with







mitotic







spindles in







mitotic cells.







Translocates







to the nucleus







during







heat shock.


HTRA1_HUMAN
Serine
HTRA1

LungCancers
Secreted.
UniProt, Prediction



protease



HTRA1


HXK1_HUMAN
Hexokinase-1
HK1
ENDO
Symptoms
Mitochondrion
Literature,







outer
Detection







membrane.







Note = Its







hydrophobic







N-terminal







sequence







may be in-







volved in







membrane







binding.


HYAL2_HUMAN
Hyaluronidase-2
HYAL2

LungCancers
Cell membrane;
Prediction







Lipid-







anchor,







GPI-anchor.


HYOU1_HUMAN
Hypoxia
HYOU1
EPI, ENDO
Symptoms
Endoplasmic
Detection



up-



reticulum



regulated



lumen.



protein 1


IBP2_HUMAN
Insulin-
IGFBP2

LungCancers
Secreted.
UniProt, Literature,



like




Detection,



growth




Prediction



factor-



binding



protein 2


IBP3_HUMAN
Insulin-
IGFBP3

LungCancers,
Secreted.
UniProt, Literature,



like


Benign-

Detection,



growth


Nodules,

Prediction



factor-


Symptoms



binding



protein 3


ICAM1_HUMAN
Intercellular
ICAM1

LungCancers,
Membrane;
UniProt, Literature,



adhesion


Benign-
Single-pass
Detection



molecule 1


Nodules,
type I membrane






Symptoms
protein.


ICAM3_HUMAN
Intercellular
ICAM3
EPI, ENDO
LungCancers,
Membrane;
UniProt, Detection



adhesion


Benign-
Single-pass



molecule 3


Nodules,
type I membrane






Symptoms
protein.


IDHP_HUMAN
Isocitrate
IDH2
Secreted,

Mitochondrion.
Prediction



dehydro-

ENDO



genase



[NADP],



mitochondrial


IF4A1_HUMAN
Eukaryotic
EIF4A1
Secreted,


Detection,



initiation

EPI, ENDO


Prediction



factor



4A-I


IGF1_HUMAN
Insulin-
IGF1

LungCancers,
Secreted.
UniProt, Literature,



like


Benign-
|Secreted.
Detection,



growth


Nodules,

Prediction



factor I


Symptoms


IKIP_HUMAN
Inhibitor
IKIP
ENDO
Symptoms
Endoplasmic
UniProt, Prediction



of nuclear



reticulum



factor



membrane;



kappa-B



Single-



kinase-



pass



interacting



membrane



protein



protein.







Note = Isoform







4 deletion







of the hydrophobic,







or transmembrane







region between







AA







45-63 results







in uniform







distribution







troughout







the cell,







suggesting







that this







region is







responsible







for endoplasmic







reticulum







localization.


IL18_HUMAN
Interleukin-
IL18

LungCancers,
Secreted.
UniProt, Literature,



18


Benign-

Prediction






Nodules,






Symptoms


IL19_HUMAN
Interleukin-
IL19

LungCancers
Secreted.
UniProt, Detection,



19




Prediction


IL22_HUMAN
Interleukin-
IL22

LungCancers,
Secreted.
UniProt, Prediction



22


Benign-






Nodules


IL32_HUMAN
Interleukin-
IL32

LungCancers,
Secreted.
UniProt, Prediction



32


Benign-






Nodules


IL7_HUMAN
Interleukin-7
IL7

LungCancers,
Secreted.
UniProt, Literature,






Benign-

Prediction






Nodules


IL8_HUMAN
Interleukin-8
IL8

LungCancers,
Secreted.
UniProt, Literature






Benign-






Nodules,






Symptoms


ILEU_HUMAN
Leukocyte
SERPINB1
Secreted,

Cytoplasm
Detection,



elastase

EPI

(By similarity).
Prediction



inhibitor


ILK_HUMAN
Integrin-
ILK
Secreted
LungCancers,
Cell junction,
Literature,



linked


Benign-
focal
Detection



protein


Nodules,
adhesion.



kinase


Symptoms
Cell membrane;







Peripheral







membrane







protein; Cytoplasmic







side.


INHBA_HUMAN
Inhibin
INHBA

LungCancers,
Secreted.
UniProt, Literature,



beta A


Benign-

Prediction



chain


Nodules


ISLR_HUMAN
Immuno-
ISLR

LungCancers
Secreted
UniProt, Detection,



globulin



(Potential).
Prediction



super-



family



containing



leucine-



rich repeat



protein


ITA5_HUMAN
Integrin
ITGA5
EPI
LungCancers,
Membrane;
UniProt, Literature,



alpha-5


Benign-
Single-pass
Detection






Nodules,
type I membrane






Symptoms
protein.


ITAM_HUMAN
Integrin
ITGAM
EPI, ENDO
LungCancers,
Membrane;
UniProt, Literature



alpha-M


Benign-
Single-pass






Nodules,
type I membrane






Symptoms
protein.


K0090_HUMAN
Uncharacterized
KIAA0090
EPI
Symptoms
Membrane;
UniProt, Prediction



protein



Single-pass



KI-



type I membrane



AA0090



protein







(Potential).


K1C18_HUMAN
Keratin,
KRT18
Secreted
LungCancers,
Cytoplasm,
Literature,



type I


Benign-
perinuclear
Detection,



cytoskeletal


Nodules
region.
Prediction



18


K1C19_HUMAN
Keratin,
KRT19

LungCancers,

Literature,



type I


Benign-

Detection,



cytoskeletal


Nodules

Prediction



19


K2C8_HUMAN
Keratin,
KRT8
EPI
LungCancers
Cytoplasm.
Literature,



type II




Detection



cytoskeletal 8


KIT_HUMAN
Mast/stem
KIT

LungCancers
Membrane;
UniProt, Literature,



cell



Single-pass
Detection



growth



type I membrane



factor



protein.



receptor


KITH_HUMAN
Thymidine
TK1

LungCancers
Cytoplasm.
Literature,



kinase,




Prediction



cytosolic


KLK11_HUMAN
Kallikrein-
KLK11

LungCancers
Secreted.
UniProt, Literature,



11




Prediction


KLK13_HUMAN
Kallikrein-
KLK13

LungCancers
Secreted
UniProt, Literature,



13



(Probable).
Detection,








Prediction


KLK14_HUMAN
Kallikrein-
KLK14

LungCancers,
Secreted,
UniProt, Literature,



14


Symptoms
extracellular
Prediction







space.


KLK6_HUMAN
Kallikrein-6
KLK6

LungCancers,
Secreted.
UniProt, Literature,






Benign-
Nucleus,
Detection,






Nodules,
nucleolus.
Prediction






Symptoms
Cytoplasm.







Mitochondrion.







Microsome.







Note = In







brain, detected







in the







nucleus of







glial cells







and in the







nucleus and







cytoplasm of







neurons.







Detected in







the mitochondrial







and microsomal







fractions







of







HEK-293







cells and







released into







the cytoplasm







following







cell







stress.


KNG1_HUMAN
Kininogen-1
KNG1

LungCancers,
Secreted,
UniProt, Detection,






Benign-
extracellular
Prediction






Nodules,
space.






Symptoms


KPYM_HUMAN
Pyruvate
PKM2
Secreted,
LungCancers,
Cytoplasm.
Literature,



kinase

EPI
Symptoms
Nucleus.
Detection



isozymes



Note = Translocates



M1/M2



to the







nucleus in







response to







different







apoptotic







stimuli. Nuclear







trans-







location is







sufficient to







induce cell







death that is







caspase independent,







isoform-







specific and







independent







of its enzymatic







activity.


KRT35_HUMAN
Keratin,
KRT35
ENDO


Detection,



type I




Prediction



cuticular



Ha5


LAMB2_HUMAN
Laminin
LAMB2
ENDO
LungCancers,
Secreted,
UniProt, Detection,



subunit


Symptoms
extracellular
Prediction



beta-2



space, extra-







cellular matrix,







basement







membrane.







Note = S-







laminin is







concentrated







in the synaptic







cleft of







the neuro-







muscular







junction.


LDHA_HUMAN
L-lactate
LDHA
Secreted,
LungCancers
Cytoplasm.
Literature,



dehydro-

EPI, ENDO


Detection,



genase A




Prediction



chain


LDHB_HUMAN
L-lactate
LDHB
EPI
LungCancers
Cytoplasm.
Detection,



dehydro-




Prediction



genase B



chain


LEG1_HUMAN
Galectin-1
LGALS1
Secreted
LungCancers
Secreted,
UniProt, Detection







extracellular







space, extra-







cellular matrix.


LEG3_HUMAN
Galectin-3
LGALS3

LungCancers,
Nucleus.
Literature,






Benign-
Note = Cytoplasmic
Detection,






Nodules
in
Prediction







adenomas







and carcinomas.







May







be secreted







by a non-







classical







secretory







pathway and







associate







with the cell







surface.


LEG9_HUMAN
Galectin-9
LGALS9
ENDO
Symptoms
Cytoplasm
UniProt







(By similarity).







Secreted







(By similarity).







Note = May







also be secreted







by a







non-







classical







secretory







pathway (By







similarity).


LG3BP_HUMAN
Galectin-
LGALS3BP
Secreted
LungCancers,
Secreted.
UniProt, Literature,



3-binding


Benign-
Secreted,
Detection,



protein


Nodules,
extracellular
Prediction






Symptoms
space, extra-







cellular matrix.


LPLC3_HUMAN
Long palate,
C20orf185

LungCancers
Secreted (By
UniProt, Prediction



lung



similarity).



and nasal



Cytoplasm.



epithelium



Note = According



carcinoma-



to Pub-



associated



Pub-



protein 3



Med: 12837268







it is cytoplasmic.


LPLC4_HUMAN
Long palate,
C20orf186

LungCancers
Secreted (By
UniProt, Prediction



lung



similarity).



and nasal



Cytoplasm.



epithelium



carcinoma-



associated



protein 4


LPPRC_HUMAN
Leucine-
LRPPRC
Secreted,
LungCancers,
Mitochondrion.
Prediction



rich PPR

ENDO
Symptoms
Nucleus,



motif-



nucleoplasm.



containing



Nucleus



protein,



inner membrane.



mitochondrial



Nucleus







outer







membrane.







Note = Seems







to be pre-







dominantly







mitochondrial.


LRP1_HUMAN
Prolow-
LRP1
EPI
LungCancers,
Low-density
UniProt, Detection



density


Symptoms
lipoprotein



lipoprotein



receptor-



receptor-



related protein



related



1 85 kDa



protein 1



subunit:







Cell membrane;







Single-







pass







type I membrane







protein.







Membrane,







coated







pit.|Low-







density lipo-







protein receptor-







related protein







1 515 kDa







subunit:







Cell membrane;







Peripheral







membrane







protein; Extracellular







side. Membrane,







coated







pit.|Low-







density lipo-







protein receptor-







related protein







1 intra-







cellular domain:







Cytoplasm.







Nucleus.







Note = After







cleavage, the







intracellular







domain







(LRPICD) is







detected







both in the







cytoplasm







and in the







nucleus.


LUM_HUMAN
Lumican
LUM
Secreted,
LungCancers,
Secreted,
UniProt, Detection,





EPI
Benign-
extracellular
Prediction






Nodules,
space, extra-






Symptoms
cellular matrix







(By similarity).


LY6K_HUMAN
Lymphocyte
LY6K

LungCancers,
Secreted.
UniProt, Prediction



antigen


Symptoms
Cytoplasm.



6K



Cell membrane;







Lipid-







anchor,







GPI-anchor







(Potential).


LYAM2_HUMAN
E-selectin
SELE

LungCancers,
Membrane;
UniProt, Literature,






Benign-
Single-pass
Detection






Nodules,
type I membrane






Symptoms
protein.


LYAM3_HUMAN
P-selectin
SELP

LungCancers,
Membrane;
UniProt, Literature,






Benign-
Single-pass
Detection






Nodules,
type I membrane






Symptoms
protein.


LYOX_HUMAN
Protein-
LOX

LungCancers,
Secreted,
UniProt, Detection,



lysine 6-


Benign-
extracellular
Prediction



oxidase


Nodules
space.


LYPD3_HUMAN
Ly6/PLAUR
LYPD3

LungCancers
Cell membrane;
Detection,



domain-



Lipid-
Prediction



containing



anchor,



protein 3



GPI-anchor.


MAGA4_HUMAN
Melanoma-
MAGEA4

LungCancers

Literature,



associated




Prediction



antigen 4


MASP1_HUMAN
Mannan-
MASP1

LungCancers,
Secreted.
UniProt, Detection,



binding


Symptoms

Prediction



lectin serine



protease 1


MDHC_HUMAN
Malate
MDH1
Secreted

Cytoplasm.
Literature,



dehydro-




Detection,



genase,




Prediction



cytoplasmic


MDHM_HUMAN
Malate
MDH2
ENDO
LungCancers
Mitochondrion
Detection,



dehydro-



matrix.
Prediction



genase,



mitochondrial


MIF_HUMAN
Macrophage
MIF
Secreted
LungCancers,
Secreted.
UniProt, Literature,



migration


Benign-
Cytoplasm.
Prediction



inhibitory


Nodules,
Note = Does



factor


Symptoms
not have a







cleavable







signal sequence







and







is secreted







via a specialized,







non-classical







pathway.







Secreted by







macrophages







upon







stimulation







by bacterial







lipopolysaccharide







(LPS), or by







M. tuberculosis







antigens.


MLH1_HUMAN
DNA
MLH1
ENDO
LungCancers,
Nucleus.
Literature



mismatch


Benign-



repair


Nodules,



protein


Symptoms



Mlh1


MMP1_HUMAN
Interstitial
MMP1

LungCancers,
Secreted,
UniProt, Literature,



collagenase


Benign-
extracellular
Prediction






Nodules,
space, extra-






Symptoms
cellular matrix







(Probable).


MMP11_HUMAN
Stromelysin-3
MMP11

LungCancers,
Secreted,
UniProt, Literature,






Symptoms
extracellular
Prediction







space, extra-







cellular matrix







(Probable).


MMP12_HUMAN
Macrophage
MMP12

LungCancers,
Secreted,
UniProt, Literature,



metalloelastase


Benign-
extracellular
Prediction






Nodules,
space, extra-






Symptoms
cellular matrix







(Probable).


MMP14_HUMAN
Matrix
MMP14
ENDO
LungCancers,
Membrane;
UniProt, Literature,



metallo-


Benign-
Single-pass
Detection



proteinase-


Nodules,
type I membrane



14


Symptoms
protein







(Potential).







Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


MMP2_HUMAN
72 kDa
MMP2

LungCancers,
Secreted,
UniProt, Literature,



type IV


Benign-
extracellular
Detection,



collagenase


Nodules,
space, extra-
Prediction






Symptoms
cellular matrix







(Probable).


MMP26_HUMAN
Matrix
MMP26

LungCancers
Secreted,
UniProt, Prediction



metallo-



extracellular



proteinase-



space, extra-



26



cellular matrix.


MMP7_HUMAN
Matrilysin
MMP7

LungCancers,
Secreted,
UniProt, Literature,






Benign-
extracellular
Prediction






Nodules,
space, extra-






Symptoms
cellular matrix







(Probable).


MMP9_HUMAN
Matrix
MMP9

LungCancers,
Secreted,
UniProt, Literature,



metallo-


Benign-
extracellular
Detection,



proteinase-9


Nodules,
space, extra-
Prediction






Symptoms
cellular matrix







(Probable).


MOGS_HUMAN
Mannosyl-
MOGS
ENDO

Endoplasmic
UniProt, Prediction



oligosaccharide



reticulum



glucosidase



membrane;







Single-







pass







type II







membrane







protein.


MPRI_HUMAN
Cation-
IGF2R
EPI, ENDO
LungCancers,
Lysosome
UniProt, Literature,



independent


Symptoms
membrane;
Detection



mannose-



Single-pass



6-



type I membrane



phosphate



protein.



receptor


MRP3_HUMAN
Canalicular
ABCC3
EPI
LungCancers
Membrane;
Literature,



multi-



Multi-pass
Detection



specific



membrane



organic



protein.



anion



transporter 2


MUC1_HUMAN
Mucin-1
MUC1
EPI
LungCancers,
Apical cell
UniProt, Literature,






Benign-
membrane;
Prediction






Nodules,
Single-pass






Symptoms
type I membrane







protein.







Note = Exclusively







located







in the







apical domain







of the







plasma







membrane







of highly







polarized







epithelial







cells. After







endocytosis,







internalized







and recycled







to the cell







membrane.







Located to







microvilli







and to the







tips of long







filopodial







protusitusisions.







|Isoform







5: Secreted.







|Isoform







7: Secreted.







|Isoform







9: Secreted.







|Mucin-1







subunit beta:







Cell membrane.







Cytoplasm.







Nucleus.







Note = On







EGF and







PDGFRB







stimulation,







transported







to the nucleus







through







interaction







with







CTNNB1, a







process







which is







stimulated







by phosphorylation.







On HRG







stimulation,







colocalizes







with







JUP/gamma-







catenin at







the nucleus.


MUC16_HUMAN
Mucin-16
MUC16

LungCancers
Cell membrane;
UniProt, Detection







Single-







pass







type I membrane







protein.







Secreted,







extracellular







space.







Note = May







be liberated







into the extracellular







space following







the







phosphorylation







of the







intracellular







C-terminus







which induces







the







proteolytic







cleavage and







liberation of







the extracellular







domain.


MUC4_HUMAN
Mucin-4
MUC4

LungCancers,
Membrane;
UniProt






Benign-
Single-pass






Nodules
membrane







protein (Potential).







Secreted.







Note = Isoforms







lacking







the Cys-rich







region,







EGF-like







domains and







transmembrane







region







are secreted.







Secretion







occurs by







splicing or







proteolytic







processcessing.







|Mucin-4







beta chain:







Cell membrane;







Single-







pass







membrane







protein.







|Mucin-







4 alpha







chain:







creted.







|Isoform







3: Cell







membrane;







Single-pass







membrane







protein.







|Isoform







15: Secreted.


MUC5B_HUMAN
Mucin-5B
MUC5B

LungCancers,
Secreted.
UniProt, Detection,






Benign-

Prediction






Nodules


MUCL1_HUMAN
Mucin-
MUCL1

LungCancers
Secreted
UniProt, Prediction



like protein 1



(Probable).







Membrane







(Probable).


NAMPT_HUMAN
Nicotinamide
NAMPT
EPI
LungCancers,
Cytoplasm
Literature,



phosphoribosyltransferase


Benign-
(By similarity).
Detection






Nodules,






Symptoms


NAPSA_HUMAN
Napsin-A
NAPSA
Secreted
LungCancers

Prediction


NCF4_HUMAN
Neutrophil
NCF4
ENDO

Cytoplasm.
Prediction



cytosol



factor 4


NDKA_HUMAN
Nucleoside
NME1
Secreted
LungCancers,
Cytoplasm.
Literature,



di-


Benign-
Nucleus.
Detection



phosphate


Nodules,
Note = Cell-



kinase A


Symptoms
cycle dependent







nuclear







localization







which







can be induced







by







interaction







with Epstein-







barr







viral proteins







or by







degradation







of the SET







complex by







GzmA.


NDKB_HUMAN
Nucleoside
NME2
Secreted,
Benign-
Cytoplasm.
Literature,



di-

EPI
Nodules
Nucleus.
Detection



phosphate



Note = Isoform



kinase B



2 is mainly







cytoplasmic







and







isoform 1







and isoform







2 are excluded







from







the nucleolus.


NDUS1_HUMAN
NADH-
NDUFS1
Secreted,
Symptoms
Mitochondrion
Prediction



ubiquinone

ENDO

inner



oxidoreductase



membrane.



75 kDa



subunit,



mitochondrial


NEBL_HUMAN
Nebulette
NEBL
ENDO


Prediction


NEK4_HUMAN
Serine/
NEK4
ENDO
LungCancers
Nucleus
Prediction



threonine



(Probable).



protein



kinase



Nek4


NET1_HUMAN
Netrin-1
NTN1

LungCancers,
Secreted,
UniProt, Literature,






Benign-
extracellular
Prediction






Nodules
space, extra-







cellular matrix







(By similarity).


NEU2_HUMAN
Vasopressin
AVP

LungCancers,
Secreted.
UniProt, Prediction



neurophysin


Symptoms



2-



copeptin


NGAL_HUMAN
Neutrophil
LCN2
EPI
LungCancers,
Secreted.
UniProt, Detection,



gelatinase-


Benign-

Prediction



associated


Nodules,



lipocalin


Symptoms


NGLY1_HUMAN
Peptide-
NGLY1
ENDO

Cytoplasm.
Detection,



N(4)-(N-




Prediction



acetyl-



beta-



glucosamiminyl)asparagine



amidase


NHRF1_HUMAN
Na(+)/H(+)
SLC9A3R1
EPI
Benign-
Endomembrane
Detection



exchange


Nodules
system;



regulatory



Peripheral



cofactor



membrane



NHE-RF1



protein.







Cell







projection,







filopodium.







Cell projection,







ruffle.







Cell projection,







microvillus.







Note = Colocalizes







with







actin in microvilli-







rich







apical regions







of the







syncytio-







trophoblast.







Found in







microvilli,







ruffling







membrane







and filopodia







of HeLa







cells. Present







in lipid







rafts of T-







cells.


NIBAN_HUMAN
Protein
FAM129A
EPI

Cytoplasm.
Literature,



Niban




Detection


NMU_HUMAN
Neuromedin-U
NMU

LungCancers
Secreted.
UniProt, Prediction


NRP1_HUMAN
Neuropilin-1
NRP1

LungCancers,
Cell membrane;
UniProt, Literature,






Benign-
Single-
Detection,






Nodules,
pass
Prediction






Symptoms
type I membrane







protein.







|Isoform







2: Secreted.


ODAM_HUMAN
Odontogenic
ODAM

LungCancers
Secreted (By
UniProt, Prediction



ameloblast-



similarity).



associated



protein


OSTP_HUMAN
Osteopontin
SPP1

LungCancers,
Secreted.
UniProt, Literature,






Benign-

Detection,






Nodules,

Prediction






Symptoms


OVOS2_HUMAN
Ovostatin
OVOS2
ENDO

Secreted (By
UniProt, Prediction



homolog 2



similarity).


P5CS_HUMAN
Delta-1-
ALDH18A1
ENDO

Mitochondrion
Prediction



pyrroline-



inner



5-



membrane.



carboxylate



synthase


PA2GX_HUMAN
Group 10
PLA2G10

Symptoms
Secreted.
UniProt



secretory



phospholipase



A2


PAPP1_HUMAN
Pappalysin-1
PAPPA

LungCancers,
Secreted.
UniProt, Literature,






Benign-

Prediction






Nodules,






Symptoms


PBIP1_HUMAN
Pre-B-cell
PBXIP1
EPI

Cytoplasm,
Prediction



leukemia



cytoskeleton.



transcription



Nucleus.



factor-



Note = Shuttles



interacting



between



protein 1



the nucleus







and the cytosol.







Mainly







localized







in the cytoplasm,







associated







with







microtubules.







Detected







in







small







amounts in







the nucleus.


PCBP1_HUMAN
Poly(rC)-
PCBP1
EPI, ENDO

Nucleus.
Detection,



binding



Cytoplasm.
Prediction



protein 1



Note = Loosely







bound in







the nucleus.







May shuttle







between the







nucleus and







the cytoplasm.


PCBP2_HUMAN
Poly(rC)-
PCBP2
EPI

Nucleus.
Detection,



binding



Cytoplasm.
Prediction



protein 2



Note = Loosely







bound in







the nucleus.







May shuttle







between the







nucleus and







the cytoplasm.


PCD15_HUMAN
Protocadherin-
PCDH15
ENDO

Cell membrane;
UniProt, Detection



15



Single-







pass







type I membrane







protein







(By







similarity).







|Isoform







3: Secreted.


PCNA_HUMAN
Proliferating
PCNA
EPI
LungCancers,
Nucleus.
Literature,



cell


Benign-

Prediction



nuclear


Nodules,



antigen


Symptoms


PCYOX_HUMAN
Prenylcysteine
PCYOX1
Secreted
LungCancers,
Lysosome.
Detection,



oxidase 1


Symptoms

Prediction


PDGFA_HUMAN
Platelet-
PDGFA

LungCancers
Secreted.
UniProt, Literature,



derived




Prediction



growth



factor



subunit A


PDGFB_HUMAN
Platelet-
PDGFB

LungCancers,
Secreted.
UniProt, Literature,



derived


Benign-

Detection,



growth


Nodules,

Prediction



factor


Symptoms



subunit B


PDGFD_HUMAN
Platelet-
PDGFD

LungCancers
Secreted.
UniProt, Prediction



derived



growth



factor D


PDIA3_HUMAN
Protein
PDIA3
ENDO
LungCancers
Endoplasmic
Detection,



disulfide-



reticulum
Prediction



isomerase



lumen



A3



(By similarity).







Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


PDIA4_HUMAN
Protein
PDIA4
Secreted,

Endoplasmic
Detection,



disulfide-

EPI, ENDO

reticulum
Prediction



isomerase



lumen.



A4



Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


PDIA6_HUMAN
Protein
PDIA6
Secreted,

Endoplasmic
Detection,



disulfide-

EPI, ENDO

reticulum
Prediction



isomerase



lumen



A6



(By similarity).







Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


PECA1_HUMAN
Platelet
PECAM1

LungCancers,
Membrane;
UniProt, Literature,



endothelial


Benign-
Single-pass
Detection



cell


Nodules,
type I membrane



adhesion


Symptoms
protein.



molecule


PEDF_HUMAN
Pigment
SERPINF1

LungCancers,
Secreted.
UniProt, Literature,



epithelium-


Symptoms
Melanosome.
Detection,



derived



Note = Enriched
Prediction



factor



in stage I







melanosomes.


PERM_HUMAN
Myeloperoxidase
MPO
Secreted,
LungCancers,
Lysosome.
Literature,





EPI, ENDO
Benign-

Detection,






Nodules,

Prediction






Symptoms


PERP1_HUMAN
Plasma
PACAP
EPI, ENDO

Secreted
UniProt, Detection,



cell-



(Potential).
Prediction



induced



Cytoplasm.



resident



Note = In



endoplasmic



(Pub-



reticulum



Med: 11350957)



protein



diffuse







granular







localization







in the cytoplasm







surrounding







the







nucleus.


PGAM1_HUMAN
Phospho-
PGAM1
Secreted,
LungCancers,

Detection



glycerate

EPI
Symptoms



mutase 1


PLAC1_HUMAN
Placenta-
PLAC1

LungCancers
Secreted
UniProt, Prediction



specific



(Probable).



protein 1


PLACL_HUMAN
Placenta-
PLAC1L

LungCancers
Secreted
UniProt, Prediction



specific 1-



(Potential).



like protein


PLIN2_HUMAN
Perilipin-2
ADFP
ENDO
LungCancers
Membrane;
Prediction







Peripheral







membrane







protein.


PLIN3_HUMAN
Perilipin-3
M6PRBP1
EPI

Cytoplasm.
Detection,







Endosome
Prediction







membrane;







Peripheral







membrane







protein; Cytoplasmic







side (Potential).







Lipid







droplet (Potential).







Note = Membrane







associated







on endosomes.







Detected in







the envelope







and the core







of lipid bodies







and in







lipid sails.


PLOD1_HUMAN
Procollagen-
PLOD1
EPI, ENDO

Rough endoplasmic
Prediction



lysine,2-



reticulum



oxoglutarate



membrane;



5-



Peripheral



dioxygenase 1



membrane







protein;







Lumenal







side.


PLOD2_HUMAN
Procollagen-
PLOD2
ENDO
Benign-
Rough endoplasmic
Prediction



lysine,2-


Nodules,
reticulum



oxoglutarate


Symptoms
membrane;



5-



Peripheral



dioxygenase 2



membrane







protein;







Lumenal







side.


PLSL_HUMAN
Plastin-2
LCP1
Secreted,
LungCancers
Cytoplasm,
Detection,





EPI

cytoskeleton.
Prediction







Cell







junction.







Cell projection.







Cell







projection,







ruffle membrane;







Peripheral







membrane







protein; Cytoplasmic







side (By







similarity).







Note = Relocalizes







to the







immunological







synapse







between







peripheral







blood T







lymphocytes







and anti-







body-







presenting







cells in response







to







costimulation







through







TCR/CD3







and CD2 or







CD28. Associated







with the







actin cytoskeleton







at







membrane







ruffles (By







similarity).







Relocalizes







to actin-rich







cell projections







upon







serine phosphorylation.


PLUNC_HUMAN
Protein
PLUNC

LungCancers,
Secreted (By
UniProt, Prediction



Plunc


Benign-
similarity).






Nodules
Note = Found







in the nasal







mucus (By







similarity).







Apical side







of airway







epithelial







cells. Detected







in







nasal mucus







(By similarity).


PLXB3_HUMAN
Plexin-B3
PLXNB3
ENDO

Membrane;
UniProt, Detection,







Single-pass
Prediction







type I membrane







protein.


PLXC1_HUMAN
Plexin-C1
PLXNC1
EPI

Membrane;
UniProt, Detection







Single-pass







type I membrane







protein







(Potential).


POSTN_HUMAN
Periostin
POSTN
Secreted,
LungCancers,
Secreted,
UniProt, Literature,





ENDO
Benign-
extracellular
Detection,






Nodules,
space, extra-
Prediction






Symptoms
cellular matrix.


PPAL_HUMAN
Lysosomal
ACP2
EPI
Symptoms
Lysosome
UniProt, Prediction



acid



membrane;



phosphatase



Single-pass







membrane







protein;







Lumenal







side. Lysosome







lumen.







Note = The







soluble form







arises by







proteolytic







processing







of the membrane-







bound







form.


PPBT_HUMAN
Alkaline
ALPL
EPI
LungCancers,
Cell membrane;
Literature,



phosphatase,


Benign-
Lipid-
Detection,



tissue-


Nodules,
anchor,
Prediction



nonspecific


Symptoms
GPI-anchor.



isozyme


PPIB_HUMAN
Peptidyl-
PPIB
Secreted,

Endoplasmic
Detection,



prolyl cis-

EPI, ENDO

reticulum
Prediction



trans isomerase B



lumen.







Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


PRDX1_HUMAN
Peroxiredoxin-1
PRDX1
EPI
LungCancers
Cytoplasm.
Detection,







Melanosome.
Prediction







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage IV.


PRDX4_HUMAN
Peroxiredoxin-4
PRDX4
Secreted,

Cytoplasm.
Literature,





EPI, ENDO


Detection,








Prediction


PROF1_HUMAN
Profilin-1
PFN1
Secreted,
LungCancers
Cytoplasm,
Detection





EPI

cytoskeleton.


PRP31_HUMAN
U4/U6
PRPF31
ENDO

Nucleus
Prediction



small nuclear



speckle.



ribo-



Nucleus,



nucleo-



Cajal body.



protein



Note = Predominantly



Prp31



found in







speckles and







in Cajal







bodies.


PRS6A_HUMAN
26S protease
PSMC3
EPI
Benign-
Cytoplasm
Detection



regulatory


Nodules
(Potential).



subunit



Nucleus



6A



(Potential).


PSCA_HUMAN
Prostate
PSCA

LungCancers
Cell membrane;
Literature,



stem cell



Lipid-
Prediction



antigen



anchor,







GPI-anchor.


PTGIS_HUMAN
Prostacyclin
PTGIS
EPI
LungCancers,
Endoplasmic
UniProt, Detection,



synthase


Benign-
reticulum
Prediction






Nodules
membrane;







Single-







pass







membrane







protein.


PTPA_HUMAN
Serine/
PPP2R4
ENDO
Symptoms

Detection,



threonine-




Prediction



protein



phosphatase



2A



activator


PTPRC_HUMAN
Receptor-
PTPRC
Secreted,
LungCancers
Membrane;
UniProt, Detection,



type tyrosine-

EPI, ENDO

Single-pass
Prediction



protein



type I membrane



phosphatase C



protein.


PTPRJ_HUMAN
Receptor-
PTPRJ
EPI
LungCancers,
Membrane;
UniProt, Detection,



type tyrosine-


Symptoms
Single-pass
Prediction



protein



type I membrane



phosphatase



protein.



eta


PVR_HUMAN
Poliovirus
PVR

Symptoms
Isoform Alpha:
UniProt, Detection,



receptor



Cell
Prediction







membrane;







Single-pass







type I membrane







protein.







|Isoform







Delta: Cell







membrane;







Single-pass







type I membrane







protein.







|Isoform







Beta: Secretcreted.







|Isoform







Gamma:







Secreted.


RAB32_HUMAN
Ras-
RAB32
EPI

Mitochondrion.
Prediction



related



protein



Rab-32


RAGE_HUMAN
Advanced
AGER
Secreted
LungCancers,
Isoform 1:
UniProt, Literature



glycosylation


Benign-
Cell membrane;



end


Nodules
Single-



product-



pass



specific



type I membrane



receptor



protein.







|Isoform







2: Secreted.


RAN_HUMAN
GTP-
RAN
Secreted,
LungCancers,
Nucleus.
Detection,



binding

EPI
Benign-
Cytoplasm.
Prediction



nuclear


Nodules
Melanosome.



protein



Note = Becomes



Ran



dispersed







throughout







the cytoplasm







during







mitosis.







Identified by







mass spectrometry







in







melanosome







fractions







from stage I







to stage IV.


RAP2B_HUMAN
Ras-
RAP2B
EPI

Cell membrane;
Prediction



related



Lipid-



protein



anchor;



Rap-2b



Cytoplasmicside







(Potential).


RAP2C_HUMAN
Ras-
RAP2C
EPI

Cell membrane;
Prediction



related



Lipid-



protein



anchor;



Rap-2c



Cytoplasmic







side (Potential).


RCN3_HUMAN
Reticulocalbin-3
RCN3
EPI
Symptoms
Endoplasmic
Prediction







reticulum







lumen







(Potential).


RL24_HUMAN
60S ribosomal
RPL24
EPI


Prediction



protein



L24


S10A1_HUMAN
Protein
S100A1

Symptoms
Cytoplasm.
Literature,



S100-A1




Prediction


S10A6_HUMAN
Protein
S100A6
Secreted
LungCancers
Nucleus
Literature,



S100-A6



envelope.
Detection,







Cytoplasm.
Prediction


S10A7_HUMAN
Protein
S100A7

LungCancers
Cytoplasm.
UniProt, Literature,



S100-A7



Secreted.
Detection,







Note = Secreted
Prediction







by a non-







classical







secretory







pathway.


SAA_HUMAN
Serum
SAA1

Symptoms
Secreted.
UniProt, Literature,



amyloid A




Detection,



protein




Prediction


SCF_HUMAN
Kit ligand
KITLG

LungCancers,
Isoform 1:
UniProt, Literature






Symptoms
Cell membrane;







Single-







pass







type I membrane







protein







(By







similarity).







Secreted (By







similarity).







Note = Also







exists as a







secreted







soluble form







(isoform 1







only) (By







similarity).







|Isoform







2: Cell







membrane;







Single-pass







type I membrane







protein







(By







similarity).







Cytoplasm,







cytoskeleton







(By similarity).


SDC1_HUMAN
Syndecan-1
SDC1

LungCancers,
Membrane;
UniProt, Literature,






Benign-
Single-pass
Detection






Nodules,
type I membrane






Symptoms
protein.


SEM3G_HUMAN
Semaphorin-
SEMA3G

LungCancers
Secreted (By
UniProt, Prediction



3G



similarity).


SEPR_HUMAN
Seprase
FAP
ENDO
Symptoms
Cell membrane;
UniProt, Literature,







Single-
Detection







pass







type II







membrane







protein. Cell







projection,







lamellipodium







membrane;







Single-







pass







type II







membrane







protein. Cell







projection,







invadopodium







membrane;







Single-







pass







type II







membrane







protein.







Note = Found







in cell surface







lamel-







lipodia, in-







vadopodia







and on shed







vesicles.


SERPH_HUMAN
Serpin H1
SERPINH1
Secreted,
LungCancers,
Endoplasmic
Detection,





EPI, ENDO
Benign-
reticulum
Prediction






Nodules
lumen.


SFPA2_HUMAN
Pulmonary
SFTPA2
Secreted
LungCancers,
Secreted,
UniProt, Prediction



surfactant-


Benign-
extracellular



associated


Nodules
space, extra-



protein A2



cellular matrix.







Secreted,







extracellular







space,







surface film.


SFTA1_HUMAN
Pulmonary
SFTPA1
Secreted
LungCancers,
Secreted,
UniProt, Prediction



surfactant-


Benign-
extracellular



associated


Nodules,
space, extra-



protein A1


Symptoms
cellular matrix.







Secreted,







extracellular







space,







surface film.


SG3A2_HUMAN
Secreto-
SCGB3A2

LungCancers,
Secreted.
UniProt, Prediction



globin


Benign-



family 3A


Nodules



member 2


SGPL1_HUMAN
Sphingosine-
SGPL1
ENDO

Endoplasmic
UniProt, Prediction



1-



reticulum



phosphate



membrane;



lyase 1



Single-







pass







type III







membrane







protein.


SIAL_HUMAN
Bone sialoprotein 2
IBSP

LungCancers
Secreted.
UniProt, Literature,








Prediction


SLPI_HUMAN
Antileukoproteinase
SLPI

LungCancers,
Secreted.
UniProt, Literature,






Benign-

Detection,






Nodules

Prediction


SMD3_HUMAN
Small
SNRPD3
Secreted
Benign-
Nucleus.
Prediction



nuclear


Nodules



ribonucleoprotein



Sm D3


SMS_HUMAN
Somato-
SST

LungCancers
Secreted.
UniProt, Literature,



statin




Prediction


SODM_HUMAN
Superoxide
SOD2
Secreted
LungCancers,
Mitochondrion
Literature,



dismutase


Benign-
matrix.
Detection,



[Mn],


Nodules,

Prediction



mitochondrial


Symptoms


SORL_HUMAN
Sortilin-
SORL1
EPI
LungCancers,
Membrane;
UniProt, Detection



related


Symptoms
Single-pass



receptor



type I membrane







protein







(Potential).


SPB3_HUMAN
Serpin B3
SERPINB3

LungCancers,
Cytoplasm.
Literature,






Benign-
Note = Seems
Detection






Nodules
to also be







secreted in







plasma by







cancerous







cells but at a







low level.


SPB5_HUMAN
Serpin B5
SERPINB5

LungCancers
Secreted,
UniProt, Detection







extracellular







space.


SPON2_HUMAN
Spondin-2
SPON2

LungCancers,
Secreted,
UniProt, Prediction






Benign-
extracellular






Nodules
space, extra-







cellular matrix







(By similarity).


SPRC_HUMAN
SPARC
SPARC

LungCancers,
Secreted,
UniProt, Literature,






Benign-
extracellular
Detection,






Nodules,
space, extra-
Prediction






Symptoms
cellular matrix,







basement







membrane.







Note = In or







around the







basement







membrane.


SRC_HUMAN
Proto-
SRC
ENDO
LungCancers,

Literature



oncogene


Benign-



tyrosine-


Nodules,



protein


Symptoms



kinase Src


SSRD_HUMAN
Trans-
SSR4
Secreted,

Endoplasmic
UniProt, Prediction



locon-

ENDO

reticulum



associated



membrane;



protein



Single-



subunit



pass



delta



type I membrane







protein.


STAT1_HUMAN
Signal
STAT1
EPI
LungCancers,
Cytoplasm.
Detection



transducer


Benign-
Nucleus.



and activator


Nodules
Note = Translocated



of



into



transcription



the nucleus



1-



in response



alpha/beta



to IFN-







gamma-







induced tyrosine







phosphorylation







and dimerization.


STAT3_HUMAN
Signal
STAT3
ENDO
LungCancers,
Cytoplasm.
Prediction



transducer


Benign-
Nucleus.



and activator


Nodules,
Note = Shuttles



of


Symptoms
between



transcription 3



the nucleus







and the cytoplasm.







Constitutive







nuclear







presence is







independent







of tyrosine







phosphorylation.


STC1_HUMAN
Stannio-
STC1

LungCancers,
Secreted.
UniProt, Prediction



calcin-1


Symptoms


STT3A_HUMAN
Dolichyl-
STT3A
EPI
Symptoms
Endoplasmic
Literature



diphosphooligo-



reticulum



saccharide--



membrane;



protein



Multi-



glycosyl-



pass



transferase



membrane



subunit



protein.



STT3A


TAGL_HUMAN
Transgelin
TAGLN
EPI
LungCancers
Cytoplasm
Literature,







(Probable).
Prediction


TARA_HUMAN
TRIO and
TRIOBP
ENDO

Nucleus.
Detection,



F-actin-



Cytoplasm,
Prediction



binding



cytoskeleton.



protein



Note = Localized







to F-







actin in a







periodic







pattern.


TBA1B_HUMAN
Tubulin
TUBA1B
EPI
LungCancers

Detection



alpha-1B



chain


TBB2A_HUMAN
Tubulin
TUBB2A
EPI
LungCancers,

Detection,



beta-2A


Benign-

Prediction



chain


Nodules


TBB3_HUMAN
Tubulin
TUBB3
EPI
LungCancers,

Detection



beta-3


Benign-



chain


Nodules


TBB5_HUMAN
Tubulin
TUBB
EPI
LungCancers,

Detection



beta chain


Benign-






Nodules


TCPA_HUMAN
T-
TCP1
EPI

Cytoplasm.
Prediction



complex



protein 1



subunit



alpha


TCPD_HUMAN
T-
CCT4
EPI

Cytoplasm.
Detection,



complex



Melanosome.
Prediction



protein 1



Note = Identified



subunit



by mass



delta



spectrometry







in melanosome







fractions







from stage I







to stage IV.


TCPQ_HUMAN
T-
CCT8
Secreted,

Cytoplasm.
Prediction



complex

EPI



protein 1



subunit



theta


TCPZ_HUMAN
T-
CCT6A
Secreted,

Cytoplasm.
Detection



complex

EPI



protein 1



subunit



zeta


TDRD3_HUMAN
Tudor
TDRD3
ENDO

Cytoplasm.
Prediction



domain-



Nucleus.



containing



Note = Predominantly



protein 3



cytoplasmic.







Associated







with actively







translating







polyribosomes







and







with mRNA







stress granules.


TENA_HUMAN
Tenascin
TNC
ENDO
LungCancers,
Secreted,
UniProt, Literature,






Benign-
extracellular
Detection






Nodules,
space, extra-






Symptoms
cellular matrix.


TENX_HUMAN
Tenascin-X
TNXB
ENDO
LungCancers,
Secreted,
UniProt, Detection,






Symptoms
extracellular
Prediction







space, extra-







cellular matrix.


TERA_HUMAN
Transitional
VCP
EPI
LungCancers,
Cytoplasm,
Detection



endo-


Benign-
cytosol. Nucleus.



plasmic


Nodules
Note = Present



reticulum



in the neuronal



ATPase



hyaline







inclusion







bodies







specifically







found in







motor neurons







from







amyotrophic







lateral sclerosis







patients.







Present







in the







Lewy bodies







specifically







found in







neurons







from Parkinson







disease







patients.


TETN_HUMAN
Tetranectin
CLEC3B

LungCancers
Secreted.
UniProt, Literature,








Detection,








Prediction


TF_HUMAN
Tissue
F3

LungCancers,
Membrane;
UniProt, Literature



factor


Benign-
Single-pass






Nodules,
type I membrane






Symptoms
protein.


TFR1_HUMAN
Transferrin
TFRC
Secreted,
LungCancers,
Cell membrane;
UniProt, Literature,



receptor

EPI, ENDO
Benign-
Single-
Detection



protein 1


Nodules,
pass






Symptoms
type II







membrane







protein.







Melanosome.







Note = Identified







by mass







spectrometry







in melanosome







fractions







from stage I







to stage







IV.|Transferrin







receptor







protein 1,







serum form:







Secreted.


TGFA_HUMAN
Protransforming
TGFA

LungCancers,
Transforming
UniProt, Literature



growth


Benign-
growth



factor


Nodules
factor alpha:



alpha



Secreted,







extracellular







space.|Protransforming







growth factor







alpha:







Cell membrane;







Single-







pass







type I membrane







protein.


THAS_HUMAN
Thromboxane-A
TBXAS1
EPI, ENDO
LungCancers,
Membrane;
Prediction



synthase


Benign-
Multi-pass






Nodules,
membrane






Symptoms
protein.


THY1_HUMAN
Thy-1
THY1
EPI
Symptoms
Cell membrane;
Detection,



membrane



Lipid-
Prediction



glycoprotein



anchor,







GPI-anchor







(By similarity).


TIMP1_HUMAN
Metallo-
TIMP1

LungCancers,
Secreted.
UniProt, Literature,



proteinase


Benign-

Detection,



inhibitor 1


Nodules,

Prediction






Symptoms


TIMP3_HUMAN
Metallo-
TIMP3

LungCancers,
Secreted,
UniProt, Literature,



proteinase


Benign-
extracellular
Prediction



inhibitor 3


Nodules
space, extra-







cellular matrix.


TLL1_HUMAN
Tolloid-
TLL1
ENDO

Secreted
UniProt, Prediction



like protein 1



(Probable).


TNF12_HUMAN
Tumor
TNFSF12

LungCancers,
Cell membrane;
UniProt



necrosis


Benign-
Single-



factor


Nodules
pass



ligand



type II



super-



membrane



family



protein.



member



|Tumor



12



necrosis







factor ligand







superfamily







member 12,







secreted







form: Secreted.


TNR6_HUMAN
Tumor
FAS

LungCancers,
Isoform 1:
UniProt, Literature,



necrosis


Benign-
Cell membrane;
Prediction



factor


Nodules,
Single-



receptor


Symptoms
pass



super-



type I membrane



family



protein.



member 6



|Isoform







2: Secreted.







|Isoform







3: Secreted.







|Isoform







4: Secreted.







|Isoform







5: Secreted.







|Isoform







6: Secreted.


TPIS_HUMAN
Tri-
TPI1
Secreted,
Symptoms

Literature,



osephosphate

EPI


Detection,



isomerase




Prediction


TRFL_HUMAN
Lacto-
LTF
Secreted,
LungCancers,
Secreted.
UniProt, Literature,



transferrin

EPI, ENDO
Benign-

Detection,






Nodules,

Prediction






Symptoms


TSP1_HUMAN
Thrombospondin-1
THBS1

LungCancers,

Literature,






Benign-

Detection,






Nodules,

Prediction






Symptoms


TTHY_HUMAN
Transthyretin
TTR

LungCancers,
Secreted.
UniProt, Literature,






Benign-
Cytoplasm.
Detection,






Nodules

Prediction


TYPH_HUMAN
Thymidine
TYMP
EPI
LungCancers,

Literature,



phosphorylase


Benign-

Detection,






Nodules,

Prediction






Symptoms


UGGG1_HUMAN
UDP-
UGGT1
Secreted,

Endoplasmic
Detection,



glucose:glyco

ENDO

reticulum
Prediction



protein



lumen.



glucosyl-



Endoplasmic



transferase 1



reticulum-







Golgi







intermediate







compartment.


UGGG2_HUMAN
UDP-
UGGT2
ENDO

Endoplasmic
Prediction



glucose:glyco



reticulum



protein



lumen.



glucosyl-



Endoplasmic



transferase 2



reticulum-







Golgi







intermediate







compartment.


UGPA_HUMAN
UTP--
UGP2
EPI
Symptoms
Cytoplasm.
Detection



glucose-1-



phosphate



uridyl-



dyl-



yltransferase


UPAR_HUMAN
Urokinase
PLAUR

LungCancers,
Isoform 1:
UniProt, Literature,



plasminogen


Benign-
Cell membrane;
Prediction



activator


Nodules,
Lipid-



surface


Symptoms
anchor,



receptor



GPI-anchor.







|Isoform







2: Secreted







(Probable).


UTER_HUMAN
Utero-
SCGB1A1

LungCancers,
Secreted.
UniProt, Literature,



globin


Benign-

Detection,






Nodules,

Prediction






Symptoms


VA0D1_HUMAN
V-type
ATP6V0D1
EPI


Prediction



proton



ATPase



subunit d1


VAV3_HUMAN
Guanine
VAV3
ENDO


Prediction



nucleotide



exchange



factor



VAV3


VEGFA_HUMAN
Vascular
VEGFA

LungCancers,
Secreted.
UniProt, Literature,



endothelial


Benign-
Note = VEGF
Prediction



growth


Nodules,
121 is acidic



factor A


Symptoms
and freely







secreted.







VEGF165 is







more basic,







has heparin-







binding







properties







and, although a







signicant







proportion







remains cell-







associated,







most is







freely secreted.







VEGF189 is







very basic, it







is cell-







associated







after secretion







and is







bound avidly







by heparin







and the







extracellular







matrix, although







it







may be released







as a







soluble form







by heparin,







heparinase







or plasmin.


VEGFC_HUMAN
Vascular
VEGFC

LungCancers,
Secreted.
UniProt, Literature,



endothelial


Benign-

Prediction



growth


Nodules



factor C


VEGFD_HUMAN
Vascular
FIGF

LungCancers
Secreted.
UniProt, Literature,



endothelial




Prediction



growth



factor D


VGFR1_HUMAN
Vascular
FLT1

LungCancers,
Isoform
UniProt, Literature,



endothelial


Benign-
Flt1: Cell
Detection,



growth


Nodules,
membrane;
Prediction



factor


Symptoms
Single-pass



receptor 1



type I membrane







protein.







|Isoform







sFlt1: Secreted.


VTNC_HUMAN
Vitronectin
VTN
ENDO
Symptoms
Secreted,
UniProt, Literature,







extracellular
Detection,







space.
Prediction


VWC2_HUMAN
Brorin
VWC2

LungCancers
Secreted,
UniProt, Prediction







extracellular







space, extra-







cellular matrix,







basement







membrane







(By







similarity).


WNT3A_HUMAN
Protein
WNT3A

LungCancers,
Secreted,
UniProt, Prediction



Wnt-3a


Symptoms
extracellular







space, extra-







cellular matrix.


WT1_HUMAN
Wilms
WT1

LungCancers,
Nucleus.
Literature,



tumor


Benign-
Cytoplasm
Prediction



protein


Nodules,
(By similarity).






Symptoms
Note = Shuttles







between







nucleus and







cytoplasm







(By similarity).







|Isoform







1: Nucleus







speckle.







|Isoform







4: Nucleus,







nucleoplasm.


ZA2G_HUMAN
Zinc-
AZGP1

LungCancers,
Secreted.
UniProt, Literature,



alpha-2-


Symptoms

Detection,



glycoprotein




Prediction


ZG16B_HUMAN
Zymogen
ZG16B

LungCancers
Secreted
UniProt, Prediction



granule



(Potential).



protein 16



homolog B









190 of these candidate protein biomarkers were shown to be measured reproducibly in blood. A moderately powered multisite and unbiased study of 242 blood samples from patients with PN was designed to determine whether a statistically significant subpanel of proteins could be identified to distinguish benign and malignant nodules of sizes under 2 cm. The three sites contributing samples and clinical data to this study were the University of Laval, University of Pennsylvania and New York University.
In an embodiment of the invention, a panel of 15 proteins effectively distinguished between samples derived from patients with benign and malignant nodules less than 2 cm diameter.
Bioinformatic and biostatistical analyses were used first to identify individual proteins with statistically significant differential expression, and then using these proteins to derive one or more combinations of proteins or panels of proteins, which collectively demonstrated superior discriminatory performance compared to any individual protein. Bioinformatic and biostatistical methods are used to derive coefficients (C) for each individual protein in the panel that reflects its relative expression level, i.e. increased or decreased, and its weight or importance with respect to the panel's net discriminatory ability, relative to the other proteins. The quantitative discriminatory ability of the panel can be expressed as a mathematical algorithm with a term for each of its constituent proteins being the product of its coefficient and the protein's plasma expression level (P) (as measured by LC-SRM-MS), e.g. C×P, with an algorithm consisting of n proteins described as: C1×P1+C2×P2+C3×P3+ . . . +Cn×Pn. An algorithm that discriminates between disease states with a predetermined level of statistical significance may be refers to a “disease classifier”. In addition to the classifier's constituent proteins with differential expression, it may also include proteins with minimal or no biologic variation to enable assessment of variability, or the lack thereof, within or between clinical specimens; these proteins may be termed typical native proteins and serve as internal controls for the other classifier proteins.
In certain embodiments, expression levels are measured by MS. MS analyzes the mass spectrum produced by an ion after its production by the vaporization of its parent protein and its separation from other ions based on its mass-to-charge ratio. The most common modes of acquiring MS data are 1) full scan acquisition resulting in the typical total ion current plot (TIC), 2) selected ion monitoring (SIM), and 3) selected reaction monitoring (SRM).
In certain embodiments of the methods provided herein, biomarker protein expression levels are measured by LC-SRM-MS. LC-SRM-MS is a highly selective method of tandem mass spectrometry which has the potential to effectively filter out all molecules and contaminants except the desired analyte(s). This is particularly beneficial if the analysis sample is a complex mixture which may comprise several isobaric species within a defined analytical window. LC-SRM-MS methods may utilize a triple quadrupole mass spectrometer which, as is known in the art, includes three quadrupole rod sets. A first stage of mass selection is performed in the first quadrupole rod set, and the selectively transmitted ions are fragmented in the second quadrupole rod set. The resultant transition (product) ions are conveyed to the third quadrupole rod set, which performs a second stage of mass selection. The product ions transmitted through the third quadrupole rod set are measured by a detector, which generates a signal representative of the numbers of selectively transmitted product ions. The RF and DC potentials applied to the first and third quadruples are tuned to select (respectively) precursor and product ions that have m/z values lying within narrow specified ranges. By specifying the appropriate transitions (m/z values of precursor and product ions), a peptide corresponding to a targeted protein may be measured with high degrees of sensitivity and selectivity. Signal-to-noise ratio is superior to conventional tandem mass spectrometry (MS/MS) experiments, which select one mass window in the first quadrupole and then measure all generated transitions in the ion detector. LC-SRM-MS.
In certain embodiments, an SRM-MS assay for use in diagnosing or monitoring lung cancer as disclosed herein may utilize one or more peptides and/or peptide transitions derived from the proteins set forth in Table 6. In certain embodiments, the assay may utilize peptides and/or peptide transitions from 100 or more, 150 or more, 200 or more, 250 or more, 300 or more, 345 or more, or 371 or more biomarker proteins. In certain embodiments, two or more peptides may be utilized per biomarker proteins, and in certain of these embodiments three or more of four or more peptides may be utilized. Similarly, in certain embodiments two or more transitions may be utilized per peptide, and in certain of these embodiments three or more; four or more; or five or more transitions may be utilized per peptide. In one embodiment, an LC-SRM-MS assay for use in diagnosing lung cancer may measure the intensity of five transitions that correspond to selected peptides associated with each biomarker protein. The achievable limit of quantification (LOQ) may be estimated for each peptide according to the observed signal intensities during this analysis. For examples, for sets of target proteins associated with lung cancer see Table 12.
The expression level of a biomarker protein can be measured using any suitable method known in the art, including but not limited to mass spectrometry (MS), reverse transcriptase-polymerase chain reaction (RT-PCR), microarray, serial analysis of gene expression (SAGE), gene expression analysis by massively parallel signature sequencing (MPSS), immunoassays (e.g., ELISA), immunohistochemistry (IHC), transcriptomics, and proteomics.
To evaluate the diagnostic performance of a particular set of peptide transitions, a ROC curve is generated for each significant transition.
An “ROC curve” as used herein refers to a plot of the true positive rate (sensitivity) against the false positive rate (specificity) for a binary classifier system as its discrimination threshold is varied. A ROC curve can be represented equivalently by plotting the fraction of true positives out of the positives (TPR=true positive rate) versus the fraction of false positives out of the negatives (FPR=false positive rate). Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. FIGS. 7 and 9 provide a graphical representation of the functional relationship between the distribution of biomarker or biomarker panel sensitivity and specificity values in a cohort of diseased subjects and in a cohort of non-diseased subjects.
AUC represents the area under the ROC curve. The AUC is an overall indication of the diagnostic accuracy of 1) a biomarker or a panel of biomarkers and 2) a ROC curve. AUC is determined by the “trapezoidal rule.” For a given curve, the data points are connected by straight line segments, perpendiculars are erected from the abscissa to each data point, and the sum of the areas of the triangles and trapezoids so constructed is computed. In certain embodiments of the methods provided herein, a biomarker protein has an AUC in the range of about 0.75 to 1.0. In certain of these embodiments, the AUC is in the range of about 0.8 to 0.8, 0.9 to 0.95, or 0.95 to 1.0.
The methods provided herein are minimally invasive and pose little or no risk of adverse effects. As such, they may be used to diagnose, monitor and provide clinical management of subjects who do not exhibit any symptoms of a lung condition and subjects classified as low risk for developing a lung condition. For example, the methods disclosed herein may be used to diagnose lung cancer in a subject who does not present with a PN and/or has not presented with a PN in the past, but who nonetheless deemed at risk of developing a PN and/or a lung condition. Similarly, the methods disclosed herein may be used as a strictly precautionary measure to diagnose healthy subjects who are classified as low risk for developing a lung condition.
The present invention provides a method of determining the likelihood that a lung condition in a subject is cancer by measuring an abundance of a panel of proteins in a sample obtained from the subject; calculating a probability of cancer score based on the protein measurements and ruling out cancer for the subject if the score) is lower than a pre-determined score, wherein when cancer is ruled out the subject does not receive a treatment protocol. Treatment protocols include for example pulmonary function test (PFT), pulmonary imaging, a biopsy, a surgery, a chemotherapy, a radiotherapy, or any combination thereof. In some embodiments, the imaging is an x-ray, a chest computed tomography (CT) scan, or a positron emission tomography (PET) scan.
The present invention further provides a method of ruling in the likelihood of cancer for a subject by measuring an abundance of panel of proteins in a sample obtained from the subject, calculating a probability of cancer score based on the protein measurements and ruling in the likelihood of cancer for the subject if the score in step is higher than a pre-determined score
In another aspect the invention further provides a method of determining the likelihood of the presence of a lung condition in a subject by measuring an abundance of panel of proteins in a sample obtained from the subject, calculating a probability of cancer score based on the protein measurements and concluding the presence of said lung condition if the score is equal or greater than a pre-determined score. The lung condition is lung cancer such as for example, non-small cell lung cancer (NSCLC). The subject at risk of developing lung cancer
The panel includes at least 4 proteins selected from ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1 and CD14. Optionally, the panel further includes at least one protein selected from BGH3, COIA1, TETN, GRP78, PRDX, FIBA and GSLG1.
Alternatively, the panel includes at least 3 proteins selected from ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1 and CD14. In some embodiments, the panel comprises at least 1, 2, 3, or 4 proteins selected from LRP1, COIA1, ALDOA, and LG3BP. In some embodiments, the panel comprises at least 1, 2, 3, 4, 5, 6, 7, or 8 proteins selected from LRP1, COIA1, ALDOA, LG3BP, BGH3, PRDX1, TETN, and ISLR. In some embodiments, the panel comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 proteins selected from LRP1, COIA1, ALDOA, LG3BP, BGH3, PRDX1, TETN, ISLR, TSP1, GRP78, FRIL, FIBA, GSLG1.
Optionally, the panel includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, or 36 proteins selected from TSP1, COIA1, ISLR, TETN, FRIL, GRP78, ALDOA, BGH3, LG3BP, LRP1, FIBA, PRDX1, GSLG1, KIT, CD14, EF1A1, TENX, AIFM1, GGH, IBP3, ENPL, ERO1A, 6PGD, ICAM1, PTPA, NCF4, SEM3G, 1433T, RAP2B, MMP9, FOLH1, GSTP1, EF2, RAN, SODM, and DSG2.
Optionally, the panel includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 proteins selected from FRIL, TSP1, LRP1, PRDX1, TETN, TBB3, COIA1, GGH, A1AG1, AIFM1, AMPN, CRP, GSLG1, IBP3, KIT, NRP1, 6PGD, CH10, CLIC1, COF1, CSF1, CYTB, DMKN, DSG2, EREG, ERO1A, FOLH1, ILEU, K1C19, LYOX, MMPI, NCF4, PDIA3, PTGIS, PTPA, RAN, SCF, SEM3G, TBA1B, TCPA, TERA, TIMP1, TNF12, and UGPA.
The subject has or is suspected of having a pulmonary nodule. The pulmonary nodule has a diameter of less than or equal to 3 cm. In one embodiment, the pulmonary nodule has a diameter of about 0.8 cm to 2.0 cm. The subject may have stage IA lung cancer (i.e., the tumor is smaller than 3 cm).
The score is calculated from a logistic regression model applied to the protein measurements. For example, the score is determined as Ps=1/[1+exp(−α−Σi=1Nβi{hacek over (I)}i,s)], where {hacek over (I)}i,s is logarithmically transformed and normalized intensity of transition i in said sample (s), βi is the corresponding logistic regression coefficient, α was a panel-specific constant, and N was the total number of transitions in said panel.
In various embodiments, the method of the present invention further comprises normalizing the protein measurements. For example, the protein measurements are normalized by one or more proteins selected from PEDF, MASP1, GELS, LUM, C163A and PTPRJ.
The biological sample such as for example tissue, blood, plasma, serum, whole blood, urine, saliva, genital secretion, cerebrospinal fluid, sweat and excreta.
In one aspect, the determining the likelihood of cancer is determined by the sensitivity, specificity, negative predictive value or positive predictive value associated with the score. The score determined has a negative predictive value (NPV) is at least about 60%, at least 70% or at least 80%.
The measuring step is performed by selected reaction monitoring mass spectrometry, using a compound that specifically binds the protein being detected or a peptide transition. In one embodiment, the compound that specifically binds to the protein being measured is an antibody or an aptamer.
In specific embodiments, the diagnostic methods disclosed herein are used to rule out a treatment protocol for a subject, measuring the abundance of a panel of proteins in a sample obtained from the subject, calculating a probability of cancer score based on the protein measurements and ruling out the treatment protocol for the subject if the score determined in the sample is lower than a pre-determined score. In some embodiments the panel contains at least 3 proteins selected ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1 and CD14.
Optionally, the panel further comprises one or more proteins selected from ERO1A, 6PGD, GSTP1, GGH, PRDX1, CD14, PTPA, ICAM1, FOLH1, SODM, FIBA, GSLG1, RAP2B, or C163A or one or more proteins selected from LRP1, COIA1, TSP1, ALDOA, GRP78, FRIL, LG3BP, BGH3, ISLR, PRDX1, FIBA, or GSLG. In preferred embodiments, the panel contains at least TSP1, LG3BP, LRP1, ALDOA, and COIA1. In more a preferred embodiment, the panel contains at least TSP1, LRP1, ALDOA and COIA1.
In specific embodiments, the diagnostic methods disclosed herein are used to rule in a treatment protocol for a subject by measuring the abundance of a panel of proteins in a sample obtained from the subject, calculating a probability of cancer score based on the protein measurements and ruling in the treatment protocol for the subject if the score determined in the sample is greater than a pre-determined score. In some embodiments the panel contains at least 3 proteins selected ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR or TSP1 or ALDOA, FRIL, LG3BP, IBP3, LRP1, ISLR, TSP1, COIA1, GRP78, TETN, PRDX1 and CD14. Optionally, the panel further comprises one or more proteins selected from ERO1A, 6PGD, GSTP1, COIA1, GGH, PRDX1, SEM3G, GRP78, TETN, AIFM1, MPRI, TNF12, MMP9 or OSTP or COIA1, TETN, GRP78, APOE or TBB3.
In certain embodiments, the diagnostic methods disclosed herein can be used in combination with other clinical assessment methods, including for example various radiographic and/or invasive methods. Similarly, in certain embodiments, the diagnostic methods disclosed herein can be used to identify candidates for other clinical assessment methods, or to assess the likelihood that a subject will benefit from other clinical assessment methods.
The high abundance of certain proteins in a biological sample such as plasma or serum can hinder the ability to assay a protein of interest, particularly where the protein of interest is expressed at relatively low concentrations. Several methods are available to circumvent this issue, including enrichment, separation, and depletion. Enrichment uses an affinity agent to extract proteins from the sample by class, e.g., removal of glycosylated proteins by glycocapture. Separation uses methods such as gel electrophoresis or isoelectric focusing to divide the sample into multiple fractions that largely do not overlap in protein content. Depletion typically uses affinity columns to remove the most abundant proteins in blood, such as albumin, by utilizing advanced technologies such as IgY14/Supermix (SigmaSt. Louis, Mo.) that enable the removal of the majority of the most abundant proteins.
In certain embodiments of the methods provided herein, a biological sample may be subjected to enrichment, separation, and/or depletion prior to assaying biomarker or putative biomarker protein expression levels. In certain of these embodiments, blood proteins may be initially processed by a glycocapture method, which enriches for glycosylated proteins, allowing quantification assays to detect proteins in the high pg/ml to low ng/ml concentration range. Exemplary methods of glycocapture are well known in the art (see, e.g., U.S. Pat. No. 7,183,188; U.S. Patent Appl. Publ. No. 2007/0099251; U.S. Patent Appl. Publ. No. 2007/0202539; U.S. Patent Appl. Publ. No. 2007/0269895; and U.S. Patent Appl. Publ. No. 2010/0279382). In other embodiments, blood proteins may be initially processed by a protein depletion method, which allows for detection of commonly obscured biomarkers in samples by removing abundant proteins. In one such embodiment, the protein depletion method is a Supermix (Sigma) depletion method.
In certain embodiments, a biomarker protein panel comprises two to 100 biomarker proteins. In certain of these embodiments, the panel comprises 2 to 5, 6 to 10, 11 to 15, 16 to 20, 21-25, 5 to 25, 26 to 30, 31 to 40, 41 to 50, 25 to 50, 51 to 75, 76 to 100, biomarker proteins. In certain embodiments, a biomarker protein panel comprises one or more subpanels of biomarker proteins that each comprise at least two biomarker proteins. For example, biomarker protein panel may comprise a first subpanel made up of biomarker proteins that are overexpressed in a particular lung condition and a second subpanel made up of biomarker proteins that are underexpressed in a particular lung condition.
In certain embodiments of the methods, compositions, and kits provided herein, a biomarker protein may be a protein that exhibits differential expression in conjunction with lung cancer. For example, in certain embodiments a biomarker protein may be one of the proteins associated with lung cancer set forth in Table 6.
In other embodiments, the diagnosis methods disclosed herein may be used to distinguish between two different lung conditions. For example, the methods may be used to classify a lung condition as malignant lung cancer versus benign lung cancer, NSCLC versus SCLC, or lung cancer versus non-cancer condition (e.g., inflammatory condition).
In certain embodiments, kits are provided for diagnosing a lung condition in a subject. These kits are used to detect expression levels of one or more biomarker proteins. Optionally, a kit may comprise instructions for use in the form of a label or a separate insert. The kits can contain reagents that specifically bind to proteins in the panels described, herein. These reagents can include antibodies. The kits can also contain reagents that specifically bind to mRNA expressing proteins in the panels described, herein. These reagents can include nucleotide probes. The kits can also include reagents for the detection of reagents that specifically bind to the proteins in the panels described herein. These reagents can include fluorophores.
The following examples are provided to better illustrate the claimed invention and are not to be interpreted as limiting the scope of the invention. To the extent that specific materials are mentioned, it is merely for purposes of illustration and is not intended to limit the invention. One skilled in the art may develop equivalent means or reactants without the exercise of inventive capacity and without departing from the scope of the invention
EXAMPLES
Example 1
Identification of Lung Cancer Biomarker Proteins
A retrospective, case-control study design was used to identify biomarker proteins and panels thereof for diagnosing various lung diseases in pre-defined control and experimental groups. The first goal of these studies was to demonstrate statistically significant differential expression for individual proteins between control and experimental groups. The second goal is to identify a panel of proteins which all individually demonstrate statistically significant differential expression between control and experimental groups. This panel of proteins can then be used collectively to distinguish between dichotomous disease states.
Specific study comparisons may include 1) cancer vs. non-cancer, 2) small cell lung cancer versus non-small cell lung cancer (NSCLC), 3) cancer vs. inflammatory disease state (e.g., infectious granuloma), or 4) different nodule size, e.g., <10 mm versus ≧10 mm (alternatively using 10, 15 or 20 mm cut-offs depending upon sample distributions).
Data for each subject consisted of the following:
Archived plasma samples from subjects previously enrolled in Institute Review Board (IRB)-approved studies was used to identify biomarker proteins and biomarker panels for distinguishing lung malignancies from non-malignancies. Plasma samples were originally obtained by routine phlebotomy, aliquotted, and stored at −80° C. or lower. Sample preparation, assignment of subject identification codes, initial subject record entry, and specimen storage were performed as per IRB study protocols. Sample eligibility is based on clinical parameters, including the subject, PN, and clinical staging parameters. Parameters for inclusion and exclusion are set forth in Table 7.







TABLE 7





Inclusion Criteria
















Sample
Sample eligibility will be based on clinical parameters,


Inclusion
including the following subject, nodule and clinical


Criteria
staging parameters:



Subject









age ≧40



any smoking status, e.g. current, former, or never



co-morbid conditions, e.g. COPD



prior malignancy - only skin carcinomas - squamous



or basal cell









Nodule









radiology









size ≧4 mm and ≦30 mm



solid, semi-solid or non-solid



any spiculation or ground glass opacity









pathology









malignant - e.g. adenocarcinoma, squamous,



or large cell



benign - inflammatory (e.g. granulomatous,



infectious) or non-inflammatory (e.g. hamartoma)



confirmed by biopsy, surgery or stability of lung



nodule for 2 years or more.









Clinical stage









Primary tumor: ≦T1 (e.g. 1A, 1B)



Regional lymph nodes: N0 or N1 only



Distant metastasis: M0 only








Sample
Subject








Exclusion
prior malignancy within 5 years of lung nodule diagnosis








Criteria
Nodule









size data unavailable



for cancer or benign nodule, no pathology or follow-up



CT data available









Clinical stage









Primary tumor: ≧T2



Regional lymph nodes: ≧N2



Distant metastasis: ≧M1










The assignment of a sample to a control or experimental group, and its further stratification or matching to other samples within and between these groups, is dependent on various clinical data about the subject. This data includes, for example, demographic information such as age, gender, and clinical history (e.g., smoking status), co-morbid conditions, PN characterization, and pathologic interpretation of resected lesions and tissues (Table 8).










TABLE 8









1.
Enrollment Data



a.
Demographics - age, birth date, gender, ethnicity



b.
Measurements - Height (cm) and weight (kg)



c.
Smoking history - never, former, or current with pack-year estima-




tion



d.
Medical history - details of co-morbid conditions, e.g. chronic




obstructive pulmonary disease (COPD), inflammatory or autoimmune




diseases, endocrine (diabetes), and cardiovascular



e.
Medication history - current medications, dosages and indications



f.
Radiographic data and nodule characteristics










1)
nodule size in millimeters (width × height × length)



2)
location, e.g. right or left and upper, lower or middle



3)
quality, e.g. solid, semi-solid, ground glass, calcified, etc.










2.
Diagnostic Evaluation Data



a.
Primary diagnosis and associated reports (clinical history, physical




exam, and laboratory tests report)



b.
Pulmonary Function Tests (PFTs), if available



c.
Follow-up CT scans - subsequent nodule evaluations by chest CT



d.
PET scan



e.
Clinical Staging



f.
Biopsy procedures










1)
FNA or TTNA



2)
bronchoscopy with transbronchial or needle biopsy



3)
surgical diagnostic procedures, e.g. VATS and/or thoracotomy










3.
Radiology Report(s)



4.
Pathology Report(s)



5.
Blood Sample Collection Information



6.
Reporting of Adverse Events



a.
AEs resulting from center's SOC, e.g. procedural morbidity.








Subject
demographics - e.g. age, gender, ethnicity



smoking status - e.g. never-, former- (“ex-”) or current- smoker; pack-years



clinical history - e.g. co-morbid conditions, e.g. COPD, infection


Nodule
size - e.g. planar (width × height × length) and volume dimensions



appearance - e.g. calcifications, ground glass appearance, eccentricity


Pathology
primary lung vs. systemic disorder



malignancy status - malignant vs. benign (vs. indeterminate)



histopathology - e.g. small cell lung cancer (SCLC) vs. non-small cell lung



cancer (NSCLC - adenocarcinoma, squamous carcinoma, large cell



carcinoma); other types, e.g. hematologic, carcinoid, etc.



immunologically quiescent, e.g. hamartoma, vs. inflammatory, e.g.



granulomatous and/or infectious, e.g. fungal









The study design and analytical plan prioritizes the control:experimental group pairings set forth in Table 9. Additional clinical and molecular insights may be gained by selective inclusion of phenotypes, e.g. effect of smoking, in the assignment of experimental and control groups. Demographic information available in the clinical database will enable further refinements in sample selection via the stratification or matching of samples in the case-control analyses with respect to clinical parameters, e.g., age and nodule size.







TABLE 9







Assignment of Experimental and Control Groups


to Achieve Proteomic Analysis Objectives












Experimental



Analysis
Objective
Group
Control Group





1
Differentiate
A. Cancer
Any non-



cancer from benign
nodule
malignant



lung nodule

(benign)





phenotype with





nodule ≧4 mm in





diameter


2
Differentiate
A. Cancer
Non-malignant



cancer from non-
nodule
(non-benign) lung



malignant

disorder, e.g.



(inflammatory,

granulomatous



infectious)

(fungal) disease,



lung nodule

with nodule









LC-SRM-MS is performed to identify and quantify various plasma proteins in the plasma samples. Prior to LC-SRM-MS analysis, each sample is depleted using IgY14/Supermix (Sigma) and then trypsin-digested. Samples from each control or experimental group are batched randomly and processed together on a QTrap 5500 instrument (AB SCIEX, Foster City, Calif.) for unbiased comparisons. Each sample analysis takes approximately 30 minutes. Peak areas for two transitions (native and heavy label) are collected and reported for all peptides and proteins. The data output for each protein analyzed by LC-SRM-MS typically yields four measurements consisting of two transition measurements from each of two peptides from the same protein. These measurements enable an inference of the relative abundance of the target protein, which will be used as its expression level in the bioinformatics and statistical analyses.
Identification of biomarker proteins having differential expression levels between the control and experimental groups yields one or more novel proteomic profiles. For example, biomarker proteins are identified with expression levels that differ in subjects with PNs who are diagnosed with NSCLC versus those without an NSCLC diagnosis, or in subjects with PNs who are diagnosed with NSCLC versus an inflammatory disorder. Panels of biomarker proteins are also identified which can collectively discriminate between dichotomous disease states.
Analyses may be (a priori) powered appropriately to control type 1 and type 2 errors at 0.05 and to detect inter-cohort differences of 25% per analyte. The diagnostic power of individual proteins is generally assessed to distinguish between two cohorts, assuming a one-sided paired non-parametric test is used. This provides a lower bound on the sample size required to demonstrate differential expression between experimental and control groups. Multiple testing effects apply for the identification of panels of proteins for assessing diagnostic efficacy, which requires larger sample sizes.
The sequence of steps for determining statistical significance for differential expression of an individual protein includes the following: 1) assessing and correlating the calibrated values of transitions of a single protein (a quality control measure); 2) comparing paired analysis of groups to control for other influences using the Mann-Whitney U-test (rank sum) to determine statistical significance; and 3) determining its significance based on a pre-defined significance threshold. Transitions within a protein that are not correlated across samples (e.g., Pearson correlation <0.5) will be deemed unreliable and excluded from the analysis.
Comparison of calibrated samples between two cohorts, e.g., cancer and non-cancer, requires pairing or matching using a variety of clinical parameters such as nodule size, age and gender. Such pairing controls for the potential influence of these other parameters on the actual comparison goal, e.g. cancer and non-cancer. A non-parametric test such as the Mann-Whitney U-test (rank sum) will then be applied to measure the statistical difference between the groups. The resulting p value can be adjusted using multiple testing corrections such as the false discovery rate. Permutation tests can be used for further significance assessments.
Significance will be determined by the satisfaction of a pre-defined threshold, such as 0.05, to filter out assays, with the potential use of higher threshold values for additional filtering. An additional significance criterion is that two of three replicate assays must individually be significant in order for the assay, e.g., single protein, to be significant.
Panels of proteins that individually demonstrate statistically significant differential expression as defined above and which can collectively be used to distinguish dichotomous disease states are identified using statistical methods described herein. This requires developing multivariate classifiers and assessing sensitivity, specificity, and ROC AUC for panels. In addition, protein panels with optimal discriminatory performance, e.g., ROC AUC, are identified and may be sufficient for clinical use in discriminating disease states.
The sequence of steps for determining the statistical significance of the discriminatory ability of a panel of proteins includes 1) developing multivariate classifiers for protein panels, and 2) identifying a protein panel with optimal discriminatory performance, e.g. ROC AUC, for a set of disease states.
A multivariate classifier (e.g., majority rule) will be developed for protein panels, including single protein assays deemed to be significant. The sensitivity and specificity of each classifier will be determined and used to generate a receiver operating characteristics (ROC) curve and its AUC to assess a given panel's discriminatory performance for a specific comparison, e.g. cancer versus non-cancer.
Protocol
1. Review clinical data from a set of subjects presenting with lung disease.
2. Provide plasma samples from the subjects wherein the samples are either benign, cancerous, COPD or another lung disease.
3. Group the plasma samples that are benign or cancerous by PNs that are separated by size of the nodule.
4. Target a pool of 371 putative lung cancer biomarker proteins consisting of at least two peptides per protein and at least two LC-SRM-MS transitions per peptide. Measuring the LC-SRM-MS transitions in each specimen along with 5 synthetic internal standards consisting of 10 transitions to compare peptide transitions from the plasma to the synthetic internal standards by LC-SRM-MS mass spectroscopy.
5. Quantitate the intensity of each transition.
6. Normalize the quantitated transitions to internal standards to obtain a normalized intensity.
7. Review the measured peptide transitions for correlations from the same peptide, rejecting discordant transitions.
8. Generate an ROC for each transition by comparing cancerous with benign samples. (ROC compare specificity (true positive) to (1-sensitivity) false positive).
9. Define the AUC for each transition. (An AUC of 0.5 is a random classifier; 1.0 is a perfect classifier).
10. Determine an AUC cut-off point to determine transitions that are statistically significant.
11. Define the transitions that exceed the AUC cutoff point.
12. Combine all pairings of significant transitions.
13. Define a new AUC for each transition pair by means of logistical regression.
14. Repeat pairing combinations into triples, quad, etc.; defining a new AUC based upon the logistical regression of combined transitions until a panel of biomarker transitions with combined desired performance (sensitivity & specificity) have been achieved.
15. The panel of biomarker transitions is verified against previously unused set of plasma panels.
Example 2
Diagnosis/Classification of Lung Disease Using Biomarker Proteins
Plasma samples will be obtained from one or more subjects presenting with PNs to evaluate whether the subjects have a lung condition. The plasma samples will be depleted using IgY14/Supermix (Sigma) and optionally subjected to one or more rounds of enrichment and/or separation, and then trypsinized. The expression level of one or more biomarker proteins previously identified as differentially expressed in subjects with the lung condition will be measured using an LC-SRM-MS assay. The LC-SRM-MS assay will utilize two to five peptide transitions for each biomarker protein. For example, the assay may utilize one or more of the peptide transitions generated from any of the proteins listed in Table 6. Subjects will be classified as having the lung condition if one or more of the biomarker proteins exhibit expression levels that differ significantly from the pre-determined control expression level for that protein.
Example 3
Blood-Based Diagnostic Test to Determine the Likelihood that a Pulmonary Nodule (PN) is Benign or Malignant
A panel of 15 proteins was created where the concentration of these 15 proteins relative to the concentration of 6 protein standards is indicative of likelihood of cancer. The relative concentration of these 15 proteins to the 6 protein standards was measured using a mass spectrometry methodology. A classification algorithm is used to combine these relative concentrations into a relative likelihood of the PN being benign or malignant. Further it has been demonstrated that there are many variations on these panels that are also diagnostic tests for the likelihood that a PN is benign or malignant. Variations on the panel of proteins, protein standards, measurement methodology and/or classification algorithm are described herein.
Study Design
A Single Reaction Monitoring (SRM) mass spectrometry (MS) assay was developed consisting of 1550 transitions from 345 lung cancer associated proteins. The SRM-MS assay and methodology is described above. The goal of this study was to develop a blood-based diagnostic for classifying PNs under 2 cm in size as benign or malignant. The study design appears in Table 10.







TABLE 10







Study Design










Small (<2 cm)
Large (>2 cm)














Laval
UPenn
NYU
Laval
UPenn
NYU
















Benign
14
29
29
13
21
15


Malignant
14
29
29
13
21
15


Batches
1
2
2
1
2
1










72 vs. 72 (94% power)
49 vs. 49 (74% power)









The study consisted of 242 plasma samples from three sites (Laval, UPenn and NYU). The number of benign and malignant samples from each site are indicated in Table 10. The study consisted of 144 plasma samples from patients with PNs of size 2 cm or less and of 98 samples from patients with PNs of size larger than 2 cm. This resulted in an estimated power of 94% for discovering proteins with blood concentrations of 1.5 fold or more between benign and malignant cancer samples of size 2 cm or less. Power is 74% for PNs of size larger than 2 cm.
This study was a retrospective multisite study that was intended to derive protein biomarkers of lung cancer that are robust to site-to-site variation. The study included samples larger than 2 cm to ensure that proteins not detectable due to the limit of detection of the measurement technology (LC-SRM-MS) for tumors of size 2 cm or less could still be detected in tumors of size 2 cm or larger.
Samples from each site and in each size class (above and below 2 cm) were matched on nodule size, age and gender.
Sample Analysis
Each sample was analyzed using the LC-SRM-MS measurement methodology as follows:
1. Samples were depleted of high abundance proteins using the IGy14 and Supermix depletion columns from Sigma-Aldrich.
2. Samples were digested using trypsin into tryptic peptides.
3. Samples were analyzed by LC-SRM-MS using a 30 minute gradient on a Waters nanoacuity LC system followed by SRM-MS analysis of the 1550 transitions on a AB-Sciex 5500 triple quad device.
4. Raw transition ion counts were obtained and recorded for each of the 1550 transitions.
It is important to note that matched samples were processed at each step either in parallel (steps 2 and 4) or back-to-back serially (steps 1 and 3). This minimizes analytical variation. Finally, steps 1 and 2 of the sample analysis are performed in batches of samples according to day of processing. There were five batches of ‘small’ samples and four batches of ‘large’ samples as denoted in Table 10.
Protein Shortlist
A shortlist of 68 proteins reproducibly diagnostic across sites was derived as follows. Note that each protein can be measured by multiple transitions.
Step 1: Normalization
Six proteins were identified that had a transition detected in all samples of the study and with low coefficient of variation. For each protein the transition with highest median intensity across samples was selected as the representative transition for the protein. These proteins and transitions are found in Table 11.







TABLE 11







Normalizing Factors









Protein (Uniprot ID)
Peptide (Amino Acid Sequence)
Transition (m/z)





CD44_HUMAN
YGFIEGHVVIPR (SEQ ID NO: 1)
272.2


 


TENX_HUMAN
YEVTVVSVR (SEQ ID NO: 2)
759.5


 


CLUS_HUMAN
ASSIIDELFQDR (SEQ ID NO: 3)
565.3


 


IBP3_HUMAN
FLNVLSPR (SEQ ID NO: 4)
685.4


 


GELS_HUMAN
TASDFITK (SEQ ID NO: 5)
710.4


 


MASP1_HUMAN
TGVITSPDFPNPYPK (SEQ ID NO: 6)
258.10









We refer to the transitions in Table 11 as normalizing factors (NFs). Each of the 1550 transitions were normalized by each of the six normalizing factors where the new intensity of a transition t in a sample s by NF f, denoted New(s,t,f), is calculated as follows:

New(s,t,f)=Raw(s,t)*Median(f)/Raw(s,f)

where Raw(s,t) is the original intensity of transition t in sample s; Median(f) is the median intensity of the NF f across all samples; and Raw(s,f) is the original intensity of the NF f in sample s.
For each protein and normalized transition, the AUC of each batch was calculated. The NF that minimized the coefficient of variation across the 9 batches was selected as the NF for that protein and for all transitions of that protein. Consequently, every protein (and all of its transitions) are now normalized by a single NF.
Step 2: Reproducible Diagnostic Proteins
For each normalized transition its AUC for each of the nine batches in the study is calculated as follows. If the transition is detected in fewer than half of the cancer samples and in fewer than half of the benign samples then the batch AUC is ‘ND’. Otherwise, the batch AUC is calculated comparing the benign and cancer samples in the batch.
The batch AUC values are transformed into percentile AUC scores for each transition. That is, if a normalized transition is in the 82nd percentile of AUC scores for all transitions then it is assigned percentile AUC 0.82 for that batch.
Reproducible transitions are those satisfying at least one of the following criteria:
1. In at least four of the five small batches the percentile AUC is 75% or more (or 25% and less).
2. In at least three of the five small batches the percentile AUC is 80% or more (or 20% and less) AND the remaining percentile AUCs in the small batches are above 50% (below 50%).
3. In all five small batches the percentile AUC is above 50% (below 50%).
4. In at least three of the four large batches the percentile AUC is 85% or more (or 15% and less).
5. In at least three of the four large batches the percentile AUC is 80% or more (or 20% and less) AND the remaining percentile AUCs in the large batches are above 50% (below 50%).
6. In all four large batches the percentile AUC is above 50% (below 50%).
These criteria result in a list of 67 proteins with at least one transition satisfying one or more of the criteria. These proteins appear in Table 12.











TABLE 12







Percentage





Occurrence
Occurrence



Across131
Across 131

Uniprot


Protein (Uniprot)
Panels
Panels
Protein Names
Accession No.



















G3P_HUMAN
113
86%
Glyceraldehyde-3-phosphate
P04406





dehydrogenase; Short name = GAPDH;





Alternative name(s):





Peptidyl-cysteine S-nitrosylase GAPDH


FRIL_HUMAN
107
82%
Recommended name:
P02792





Ferritin light chain





Short name = Ferritin L subunit


HYOU1_HUMAN
69
53%
Recommended name:
Q9Y4L1





Hypoxia up-regulated protein 1





Alternative name(s):





150 kDa oxygen-regulated protein





Short name = ORP-150





170 kDa glucose-regulated protein





Short name = GRP-170


ALDOA_HUMAN
66
50%
Recommended name:
P04075





Fructose-bisphosphate aldolase A





EC = 4.1.2.13





Alternative name(s):





Lung cancer antigen NY-LU-1





Muscle-type aldolase


HXK1_HUMAN
65
50%
Recommended name:
P19367





Hexokinase-1





EC = 2.7.1.1





Alternative name(s):





Brain form hexokinase





Hexokinase type I





Short name = HK I


APOE_HUMAN
63
48%
Recommended name:
P02649





Apolipoprotein E





Short name = Apo-E


TSP1_HUMAN
63
48%
Recommended name:
P07996





Thrombospondin-1


FINC_HUMAN
62
47%
Recommended name:
P02751





Fibronectin





Short name = FN





Alternative name(s):





Cold-insoluble globulin





Short name = CIG





Cleaved into the following 4 chains:





1. Anastellin





2. Ugl-Y1





3. Ugl-Y2





4. Ugl-Y3


LRP1_HUMAN
58
44%
Recommended name:





Prolow-density lipoprotein receptor-





related protein 1





Short name = LRP-1





Alternative name(s):





Alpha-2-macroglobulin receptor





Short name = A2MR





Apolipoprotein E receptor





Short name = APOER





CD_antigen = CD91





Cleaved into the following 3 chains:





1. Low-density lipoprotein receptor-related





protein 1 85 kDa subunit





Short name = LRP-85





2.Low-density lipoprotein receptor-related





protein 1 515 kDa subunit





Short name = LRP-515





3. Low-density lipoprotein receptor-related





protein 1 intracellular domain





Short name = LRPICD


6PGD_HUMAN
50
38%
Recommended name:
P52209





6-phosphogluconate dehydrogenase,





decarboxylating


S10A6_HUMAN
47
36%
Recommended name:
P06703





Protein S100-A6





Alternative name(s):





Calcyclin





Growth factor-inducible protein 2A9





MLN 4





Prolactin receptor-associated protein





Short name = PRA





S100 calcium-binding protein A6


CALU_HUMAN
45
34%
Recommended name:
O43852





Calumenin





Alternative name(s):





Crocalbin





IEF SSP 9302


PRDX1_HUMAN
45
34%
Recommended name:
Q06830





Peroxiredoxin-1





EC = 1.11.1.15





Alternative name(s):





Natural killer cell-enhancing factor A





Short name = NKEF-A





Proliferation-associated gene protein





Short name = PAG





Thioredoxin peroxidase 2





Thioredoxin-dependent peroxidereductase 2


RAN_HUMAN
45
34%
Recommended name:
P62826





GTP-binding nuclear protein Ran





Alternative name(s):





Androgen receptor-associated protein 24





GTPase Ran





Ras-like protein TC4





Ras-related nuclear protein


CD14_HUMAN
43
33%
Recommended name:
P08571





Monocyte differentiation antigen CD14





Alternative name(s):





Myeloid cell-specific leucine-rich





glycoprotein





CD_antigen = CD14





Cleaved into the following 2 chains:





1. Monocyte differentiation antigen CD14,





urinary form





2. Monocyte differentiation antigen CD14,





membrane-bound form


AMPN_HUMAN
41
31%
Recommended name:
P15144





Aminopeptidase N





Short name = AP-N





Short name = hAPN





EC = 3.4.11.2





Alternative name(s):





Alanyl aminopeptidase





Aminopeptidase M





Short name = AP-M





Microsomal aminopeptidase





Myeloid plasma membrane glycoprotein





CD13





gp150





CD_antigen = CD13


GSLG1_HUMAN
36
27%
Recommended name:
Q92896





Golgi apparatus protein 1





Alternative name(s):





CFR-1





Cysteine-rich fibroblast growth factor





receptor





E-selectin ligand 1





Short name = ESL-1





Golgi sialoglycoprotein MG-160


1433Z_HUMAN
32
24%
Recommended name:
P63104





14-3-3 protein zeta/delta





Alternative name(s):





Protein kinase C inhibitor protein 1





Short name = KCIP-1


IBP3_HUMAN
31
24%
Recommended name:
P17936





Insulin-like growth factor-binding protein 3





Short name = IBP-3





Short name = IGF-binding protein 3





Short name = IGFBP-3


ILK_HUMAN
31
24%
Recommended name:
Q13418





Integrin-linked protein kinase





EC = 2.7.11.1





Alternative name(s):





59 kDa serine/threonine-protein kinase





ILK-1





ILK-2





p59ILK


LDHB_HUMAN
30
23%
Recommended name:
P07195





L-lactate dehydrogenase B chain





Short name = LDH-B





EC = 1.1.1.27





Alternative name(s):





LDH heart subunit





Short name = LDH-H





Renal carcinoma antigen NY-REN-46


MPRI_HUMAN
29
22%
Recommended name:
P11717





Cation-independent mannose-6-phosphate





receptor





Short name = CI Man-6-P receptor





Short name = CI-MPR





Short name = M6PR





Alternative name(s):





300 kDa mannose 6-phosphate receptor





Short name = MPR 300





Insulin-like growth factor 2 receptor





Insulin-like growth factor II receptor





Short name = IGF-II receptor





M6P/IGF2 receptor





Short name = M6P/IGF2R





CD_antigen = CD222


PROF1_HUMAN
29
22%
Recommended name:
P07737





Profilin-1





Alternative name(s):





Profilin I


PEDF_HUMAN
28
21%
Recommended name:
P36955





Pigment epithelium-derived factor





Short name = PEDF





Alternative name(s):





Cell proliferation-inducing gene 35 protein





EPC-1





Serpin F1


CLIC1_HUMAN
26
20%
Recommended name:
O00299





Chloride intracellular channel protein 1





Alternative name(s):





Chloride channel ABP





Nuclear chloride ion channel 27





Short name = NCC27





Regulatory nuclear chloride ion channel





protein





Short name = hRNCC


GRP78_HUMAN
25
19%
Recommended name:
P11021





78 kDa glucose-regulated protein





Short name = GRP-78





Alternative name(s):





Endoplasmic reticulum lumenal Ca(2+)-





binding protein grp78





Heat shock 70 kDa protein 5





Immunoglobulin heavy chain-binding





protein





Short name = BiP


CEAM8_HUMAN
24
18%
Recommended name:
P31997





Carcinoembryonic antigen-related cell





adhesion molecule 8





Alternative name(s):





CD67 antigen





Carcinoembryonic antigen CGM6





Non-specific cross-reacting antigen





NCA-95





CD_antigen = CD66b


VTNC_HUMAN
24
18%
Recommended name:
P04004





Vitronectin





Alternative name(s):





S-protein





Serum-spreading factor





V75





Cleaved into the following 3 chains:





1. Vitronectin V65 subunit





2. Vitronectin V10 subunit





3. Somatomedin-B


CERU_HUMAN
22
17%
Recommended name:
P00450





Ceruloplasmin





EC = 1.16.3.1





Alternative name(s):





Ferroxidase


DSG2_HUMAN
22
17%
Recommended name:
Q14126





Desmoglein-2





Alternative name(s):





Cadherin family member 5





HDGC


KIT HUMAN
22
17%
Recommended name:
P10721





Mast/stem cell growth factor receptor Kit





Short name = SCFR





EC = 2.7.10.1





Alternative name(s):





Piebald trait protein





Short name = PBT





Proto-oncogene c-Kit





Tyrosine-protein kinase Kit





p145 c-kit





v-kit Hardy-Zuckerman 4 feline sarcoma





viral oncogene homolog





CD_antigen = CD117


TBB3_HUMAN
22
17%
Recommended name:
Q13509





Tubulin beta-3 chain





Alternative name(s):





Tubulin beta-4 chain





Tubulin beta-III


CH10_HUMAN
21
16%
Recommended name:
P61604





10 kDa heat shock protein, mitochondrial





Short name = Hsp10





Alternative name(s):





10 kDa chaperonin





Chaperonin 10





Short name = CPN10





Early-pregnancy factor





Short name = EPF


ISLR_HUMAN
21
16%
Immunoglobulin superfamily containing
O14498





leucine-rich repeat protein


MASP1_HUMAN
21
16%
Recommended name:
P48740





Mannan-binding lectin serine protease 1





EC = 3.4.21.—





Alternative name(s):





Complement factor MASP-3





Complement-activating component of Ra-





reactive factor





Mannose-binding lectin-associated serine





protease 1





Short name = MASP-1





Mannose-binding protein-associated serine





protease





Ra-reactive factor serine protease p100





Short name = RaRF





Serine protease 5





Cleaved into the following 2 chains:





1. Mannan-binding lectin serine protease 1





heavy chain





2. Mannan-binding lectin serine protease 1





light chain


ICAM3_HUMAN
20
15%
Recommended name:
P32942





Intercellular adhesion molecule 3





Short name = ICAM-3





Alternative name(s):





CDw50





ICAM-R





CD_antigen = CD50


PTPRJ_HUMAN
20
15%
Recommended name:
Q12913





Receptor-type tyrosine-protein





phosphatase eta





Short name = Protein-tyrosine phosphatase





eta





Short name = R-PTP-eta





EC = 3.1.3.48





Alternative name(s):





Density-enhanced phosphatase 1





Short name = DEP-1





HPTP eta





Protein-tyrosine phosphatase receptor





type J





Short name = R-PTP-J





CD_antigen = CD148


A1AG1_HUMAN
19
15%
Recommended name:
P02763





Alpha-1-acid glycoprotein 1





Short name = AGP 1





Alternative name(s):





Orosomucoid-1





Short name = OMD 1


CD59_HUMAN
18
14%
Recommended name:
P13987





CD59 glycoprotein





Alternative name(s):





1F5 antigen





20 kDa homologous restriction factor





Short name = HRF-20





Short name = HRF20





MAC-inhibitory protein





Short name = MAC-IP





MEM43 antigen





Membrane attack complex inhibition





factor





Short name = MACIF





Membrane inhibitor of reactive lysis





Short name = MIRL





Protectin





CD_antigen = CD59


MDHM_HUMAN
18
14%
commended name:
P40926





Malate dehydrogenase, mitochondrial


PVR_HUMAN
18
14%
Recommended name:
P15151





Poliovirus receptor





Alternative name(s):





Nectin-like protein 5





Short name = NECL-5





CD_antigen = CD155


SEM3G_HUMAN
18
14%
Recommended name:
Q9NS98





Semaphorin-3G





Alternative name(s):





Semaphorin sem2


CO6A3_HUMAN
17
13%
Collagen alpha-3(VI) chain
P12111


MMP9_HUMAN
17
13%
Recommended name:
P14780





Matrix metalloproteinase-9





Short name = MMP-9





EC = 3.4.24.35





Alternative name(s):





92 kDa gelatinase





92 kDa type IV collagenase





Gelatinase B





Short name = GELB





Cleaved into the following 2 chains:





1. 67 kDa matrix metalloproteinase-9





2. 82 kDa matrix metalloproteinase-9


TETN_HUMAN
17
13%
Recommended name:
P05452





Tetranectin





Short name = TN





Alternative name(s):





C-type lectin domain family 3 member B





Plasminogen kringle 4-binding protein


TNF12_HUMAN
17
13%
Recommended name:
O43508





Tumor necrosis factor ligand superfamily





member 12





Alternative name(s):





APO3 ligand





TNF-related weak inducer of apoptosis





Short name = TWEAK





Cleaved into the following 2 chains:





1. Tumor necrosis factor ligand superfamily





member 12, membrane form





2. Tumor necrosis factor ligand superfamily





member 12, secreted form


BST1_HUMAN
16
12%
Recommended name:
Q10588





ADP-ribosyl cyclase 2





EC = 3.2.2.5





Alternative name(s):





Bone marrow stromal antigen 1





Short name = BST-1





Cyclic ADP-ribose hydrolase 2





Short name = cADPr hydrolase 2





CD_antigen = CD157


COIA1_HUMAN
16
12%
Recommended name:
P39060





Collagen alpha-1(XVIII) chain





Cleaved into the following chain:





1. Endostatin


CRP_HUMAN
16
12%
Recommended name:
P02741





C-reactive protein





Cleaved into the following chain:





1. C-reactive protein(1-205)


PLSL_HUMAN
16
12%
Recommended name:
P13796





Plastin-2





Alternative name(s):





L-plastin





LC64P





Lymphocyte cytosolic protein 1





Short name = LCP-1


BGH3_HUMAN
15
11%
Recommended name:
Q15582





Transforming growth factor-beta-induced





protein ig-h3





Short name = Beta ig-h3





Alternative name(s):





Kerato-epithelin





RGD-containing collagen-associated





protein





Short name = RGD-CAP


CD44_HUMAN
15
11%
Recommended name:
P16070





CD44 antigen





Alternative name(s):





CDw44





Epican





Extracellular matrix receptor III





Short name = ECMR-III





GP90 lymphocyte homing/adhesion





receptor





HUTCH-I





Heparan sulfate proteoglycan





Hermes antigen





Hyaluronate receptor





Phagocytic glycoprotein 1





Short name = PGP-1





Phagocytic glycoprotein I





Short name = PGP-I





CD_antigen = CD44


ENOA_HUMAN
15
11%
Recommended name:
P06733





Alpha-enolase





EC = 4.2.1.11





Alternative name(s):





2-phospho-D-glycerate hydrolyase





C-myc promoter-binding protein





Enolase 1





MBP-1





MPB-1





Non-neural enolase





Short name = NNE





Phosphopyruvate hydratase





Plasminogen-binding protein


LUM_HUMAN
15
11%


SCF_HUMAN
15
11%
Recommended name:
P21583





Kit ligand





Alternative name(s):





Mast cell growth factor





Short name = MGF





Stem cell factor





Short name = SCF





c-Kit ligand





Cleaved into the following chain:





1. Soluble KIT ligand





Short name = sKITLG


UGPA_HUMAN
15
11%
Recommended name:
Q16851





UTP--glucose-1-phosphate





uridylyltransferase





EC = 2.7.7.9





Alternative name(s):





UDP-glucose pyrophosphorylase





Short name = UDPGP





Short name = UGPase


ENPL_HUMAN
14
11%
Recommended name:
P14625





Endoplasmin





Alternative name(s):





94 kDa glucose-regulated protein





Short name = GRP-94





Heat shock protein 90 kDa beta member 1





Tumor rejection antigen 1





gp96 homolog


GDIR2_HUMAN
14
11%
Recommended name:
P52566





Rho GDP-dissociation inhibitor 2





Short name = Rho GDI 2





Alternative name(s):





Ly-GDI





Rho-GDI beta


GELS_HUMAN
14
11%
Recommended name:
P06396





Gelsolin





Alternative name(s):





AGEL





Actin-depolymerizing factor





Short name = ADF





Brevin


SODM_HUMAN
14
11%
Recommended name:
P04179





Superoxide dismutase [Mn], mitochondrial


TPIS_HUMAN
14
11%
Recommended name:
P60174





Triosephosphate isomerase





Short name = TIM





EC = 5.3.1.1





Alternative name(s):





Triose-phosphate isomerase


TENA_HUMAN
13
10%
Recommended name:
P24821





Tenascin





Short name = TN





Alternative name(s):





Cytotactin





GMEM





GP 150-225





Glioma-associated-extracellular matrix





antigen





Hexabrachion





JI





Myotendinous antigen





Neuronectin





Tenascin-C





Short name = TN-C


ZA2G_HUMAN
13
10%
Recommended name:
P25311





Zinc-alpha-2-glycoprotein





Short name = Zn-alpha-2-GP





Short name = Zn-alpha-2-glycoprotein


LEG1_HUMAN
11
 8%
Recommended name:
P09382





Galectin-1





Short name = Gal-1





Alternative name(s):





14 kDa laminin-binding protein





Short name = HLBP14





14 kDa lectin





Beta-galactoside-binding lectin L-14-I





Galaptin





HBL





HPL





Lactose-binding lectin 1





Lectin galactoside-binding soluble 1





Putative MAPK-activating protein PM12





S-Lac lectin 1


FOLH1_HUMAN
9
 7%
Recommended name:
Q04609





Glutamate carboxypeptidase 2





EC = 3.4.17.21





Alternative name(s):





Cell growth-inhibiting gene 27 protein





Folate hydrolase 1





Folylpoly-gamma-glutamate





carboxypeptidase





Short name = FGCP





Glutamate carboxypeptidase II





Short name = GCPII





Membrane glutamate carboxypeptidase





Short name = mGCP





N-acetylated-alpha-linked acidic





dipeptidase I





Short name = NAALADase I





Prostate-specific membrane antigen





Short name = PSM





Short name = PSMA





Pteroylpoly-gamma-glutamate





carboxypeptidase


PLXC1_HUMAN
9
 7%


PTGIS_HUMAN
9
 7%
Recommended name:
Q16647





Prostacyclin synthase





EC = 5.3.99.4





Alternative name(s):





Prostaglandin I2 synthase









Step 3: Significance and Occurrence
To find high performing panels, 10,000 trials were performed where on each trial the combined AUC of a random panel of 15 proteins selected from Table 12 was estimated. To calculate the combined AUC of each panel of 15 proteins, the highest intensity normalized transition was utilized. Logistic regression was used to calculate the AUC of the panel of 15 across all small samples. 131 panels of 15 proteins had combined AUC above 0.80, as shown in FIG. 1. (The significance by study separated into small (<2.0 cm) and large (>2.0 cm) PN are shown in FIG. 2). The resilience of the panels persisted despite site based variation in the samples as shown in FIG. 3. The panels are listed in Table 13.














TABLE 13







AUC
P1
P2
P3
P4
P5
P6
P7





0.8282
CD59
CALU
LDHB
ALDOA
DSG2
MDHM
TENA


0.8255
CD59
TSP1
KIT
ISLR
ALDOA
DSG2
1433Z


0.8194
S10A6
ALDOA
PVR
TSP1
CD44
CH10
PEDF


0.8189
ALDOA
LEG1
CALU
LDHB
TETN
FOLH1
MASP1


0.8187
PVR
CD59
CRP
ALDOA
GRP78
DSG2
6PGD


0.8171
AMPN
IBP3
CALU
CD44
BGH3
GRP78
1433Z


0.8171
CALU
CH10
ALDOA
BST1
MDHM
VTNC
APOE


0.8165
LDHB
CO6A3
CD44
A1AG1
GRP78
DSG2
MDHM


0.8163
TPIS
CD59
S10A6
CALU
ENPL
CH10
ALDOA


0.8163
LEG1
AMPN
S10A6
CALU
ISLR
ENOA
VTNC


0.8161
AMPN
S10A6
TSP1
MPRI
VTNC
LUM
6PGD


0.8159
ALDOA
AMPN
TSP1
BGH3
GRP78
PTPRJ
MASP1


0.8159
ALDOA
CO6A3
MPRI
SEM3G
CERU
LUM
APOE


0.8159
AMPN
CALU
ISLR
SODM
CERU
LUM
6PGD


0.8159
CALU
PEDF
CRP
GRP78
VTNC
1433Z
CD14


0.8157
TPIS
LEG1
S10A6
LDHB
TSP1
ENPL
MDHM


0.8155
CALU
CRP
ALDOA
SODM
SEM3G
1433Z
FRIL


0.8153
CALU
MPRI
ALDOA
PEDF
DSG2
CERU
APOE


0.814
LEG1
COIA1
AMPN
S10A6
TSP1
MPRI
PEDF


0.8138
TSP1
KIT
CERU
6PGD
APOE
CD14
FRIL


0.8132
S10A6
COIA1
AMPN
TSP1
PEDF
ISLR
PTPRJ


0.8128
TPIS
LEG1
AMPN
S10A6
IBP3
CALU
DSG2


0.8128
TPIS
AMPN
TSP1
PEDF
A1AG1
MPRI
ALDOA


0.8124
ALDOA
CALU
LDHB
PLSL
PEDF
MASP1
6PGD


0.8124
AMPN
S10A6
TSP1
ENOA
GRP78
6PGD
APOE


0.812
IBP3
TSP1
CRP
A1AG1
SCF
ALDOA
PEDF


0.8106
COIA1
CALU
CD44
BGH3
ALDOA
TETN
BST1


0.8106
TSP1
PLSL
CRP
ALDOA
GRP78
MDHM
APOE


0.8099
CD59
CALU
ENPL
CD44
ALDOA
TENA
6PGD


0.8097
AMPN
S10A6
IBP3
A1AG1
MPRI
ALDOA
GRP78


0.8093
ALDOA
S10A6
TSP1
ENPL
PEDF
A1AG1
GRP78


0.8093
PVR
IBP3
LDHB
SCF
TNF12
LUM
1433Z


0.8093
CALU
LDHB
CO6A3
PEDF
CH10
BGH3
PTPRJ


0.8087
ALDOA
AMPN
ENPL
KIT
MPRI
GRP78
LUM


0.8087
CD59
S10A6
IBP3
TSP1
ENPL
SODM
MDHM


0.8083
ALDOA
AMPN
S10A6
IBP3
PLSL
CRP
SCF


0.8081
PVR
IBP3
TSP1
CRP
ALDOA
SODM
MDHM


0.8081
S10A6
LDHB
ENPL
PLSL
CH10
CERU
FRIL


0.8081
IBP3
LDHB
PEDF
MPRI
SEM3G
VTNC
APOE


0.8079
ALDOA
AMPN
CALU
PLSL
PEDF
CH10
MASP1


0.8077
S10A6
IBP3
LDHB
MDHM
ZA2G
FRIL
G3P


0.8077
CD59
S10A6
LDHB
TSP1
CD44
ISLR
CERU


0.8077
AMPN
CALU
LDHB
TSP1
PLSL
CD44
ALDOA


0.8075
TPIS
AMPN
S10A6
TSP1
CH10
COIA1
CERU


0.8073
CALU
PEDF
MPRI
ISLR
BGH3
ENOA
CERU


0.8071
TPIS
CALU
CO6A3
KIT
DSG2
MASP1
6PGD


0.8071
LEG1
COIA1
TSP1
CD44
MPRI
ALDOA
FOLH1


0.8065
AMPN
S10A6
CALU
CO6A3
TSP1
PLSL
KIT


0.8063
S10A6
TSP1
A1AG1
BGH3
ZA2G
1433Z
FRIL


0.8063
CALU
KIT
ENOA
6PGD
APOE
CD14
G3P


0.8061
AMPN
MPRI
GRP78
DSG2
TENA
APOE
CD14


0.8059
TPIS
IBP3
TSP1
PEDF
TNF12
1433Z
6PGD


0.8059
CALU
LDHB
PLSL
CRP
PEDF
SEM3G
MDHM


0.8058
ALDOA
TSP1
PLSL
CD44
KIT
CRP
ISLR


0.8058
TPIS
TSP1
MPRI
ISLR
ALDOA
PEDF
GRP78


0.8054
ALDOA
S10A6
CALU
CRP
A1AG1
VTNC
TENA


0.8054
TPIS
CO6A3
TSP1
MPRI
DSG2
TNF12
FRIL


0.8054
CALU
LDHB
DSG2
1433Z
CD14
FRIL
G3P


0.805
CALU
MPRI
ENOA
FOLH1
LUM
ZA2G
APOE


0.8048
PVR
S10A6
IBP3
PEDF
ALDOA
BST1
MDHM


0.8048
AMPN
CALU
CH10
DSG2
TNF12
CERU
6PGD


0.8046
ALDOA
LDHB
TSP1
KIT
ISLR
DSG2
MASP1


0.8046
ALDOA
COIA1
CD59
IBP3
PTPRJ
SEM3G
CERU


0.8046
PVR
CD59
S10A6
PLSL
PEDF
CH10
SCF


0.8046
COIA1
IBP3
MASP1
DSG2
TENA
ZA2G
1433Z


0.8042
BGH3
CD59
CALU
LDHB
CO6A3
SODM
TENA


0.8042
IBP3
TSP1
ENPL
CH10
CD14
FRIL
G3P


0.8042
IBP3
TSP1
KIT
ZA2G
6PGD
APOE
CD14


0.804
TPIS
BGH3
S10A6
LDHB
CO6A3
CH10
PEDF


0.804
CALU
LDHB
BGH3
TETN
FOLH1
TNF12
VTNC


0.8038
TPIS
PVR
COIA1
CALU
SCF
MPRI
ALDOA


0.8036
S10A6
TPIS
COIA1
CD59
CO6A3
TSP1
MPRI


0.8036
LEG1
CD59
AMPN
CALU
CH10
GRP78
SEM3G


0.8036
AMPN
S10A6
TSP1
ENPL
PEDF
SODM
FOLH1


0.8036
S10A6
CALU
MASP1
A1AG1
MPRI
ALDOA
VTNC


0.8036
IBP3
CALU
PLSL
CD44
KIT
CERU
6PGD


0.8036
TSP1
PLSL
FOLH1
COIA1
TNF12
VTNC
6PGD


0.8034
ALDOA
BGH3
CD59
TSP1
KIT
CH10
SODM


0.8034
S10A6
CALU
LDHB
TSP1
GRP78
1433Z
6PGD


0.8032
S10A6
CALU
TSP1
KIT
CH10
PEDF
GRP78


0.8032
TSP1
MASP1
CRP
ALDOA
GRP78
TETN
TNF12


0.803
AMPN
TSP1
KIT
MPRI
SEM3G
TETN
DSG2


0.803
CALU
CO6A3
PLSL
A1AG1
ALDOA
GRP78
6PGD


0.8028
COIA1
CD59
AMPN
TSP1
KIT
ISLR
ALDOA


0.8024
S10A6
CD44
SCF
MPRI
ISLR
ALDOA
APOE


0.8024
S10A6
TSP1
ALDOA
SODM
ENOA
BST1
FRIL


0.8024
IBP3
TSP1
SCF
ALDOA
SODM
DSG2
VTNC


0.802
ALDOA
TSP1
PLSL
CD44
CH10
A1AG1
ENOA


0.802
LEG1
CALU
LDHB
TSP1
CH10
ALDOA
MDHM


0.802
CD59
IBP3
TSP1
A1AG1
MPRI
PTPRJ
6PGD


0.802
IBP3
TSP1
CRP
BST1
TNF12
VTNC
1433Z


0.8018
LEG1
S10A6
IBP3
CALU
TSP1
MASP1
A1AG1


0.8018
COIA1
CD59
AMPN
CALU
MASP1
BST1
VTNC


0.8018
AMPN
ALDOA
SODM
GRP78
MDHM
VTNC
6PGD


0.8018
LDHB
CO6A3
ALDOA
SEM3G
DSG2
6PGD
APOE


0.8016
S10A6
LDHB
SCF
MPRI
ALDOA
PEDF
ENOA


0.8016
LDHB
CO6A3
TSP1
1433Z
APOE
CD14
FRIL


0.8014
ALDOA
PEDF
MPRI
ISLR
FOLH1
TNF12
MASP1


0.8014
COIA1
PEDF
CRP
A1AG1
ENOA
CERU
FRIL


0.8014
CD59
IBP3
TSP1
KIT
MASP1
ENOA
TNF12


0.8014
LDHB
KIT
SCF
BGH3
SEM3G
VTNC
1433Z


0.8013
PVR
AMPN
LDHB
CD44
DSG2
TETN
MDHM


0.8013
S10A6
LDHB
TSP1
ISLR
LUM
G3P
HYOU1


0.8013
CALU
A1AG1
MPRI
ALDOA
PEDF
DSG2
VTNC


0.8013
TSP1
ENPL
KIT
SODM
SEM3G
DSG2
TETN


0.8013
TSP1
PLSL
ISLR
ALDOA
ENOA
MDHM
APOE


0.8011
ALDOA
AMPN
CO6A3
SEM3G
APOE
CD14
FRIL


0.8011
TPIS
BGH3
AMPN
S10A6
CALU
LDHB
KIT


0.8011
COIA1
IBP3
TSP1
A1AG1
TETN
DSG2
6PGD


0.8011
AMPN
S10A6
IBP3
CALU
KIT
SCF
ALDOA


0.8011
IBP3
A1AG1
PEDF
SEM3G
MDHM
TNF12
VTNC


0.8009
ALDOA
BGH3
AMPN
LDHB
TSP1
PLSL
MPRI


0.8009
LEG1
COIA1
IBP3
CH10
MASP1
SCF
ALDOA


0.8009
AMPN
ENPL
ALDOA
TETN
FOLH1
BST1
ZA2G


0.8009
CALU
CO6A3
ENPL
ALDOA
GRP78
PTPRJ
VTNC


0.8009
TSP1
CH10
PTPRJ
TETN
TNF12
VTNC
TENA


0.8007
CD59
S10A6
IBP3
CO6A3
TSP1
KIT
ISLR


0.8007
AMPN
TSP1
KIT
SCF
TETN
ZA2G
1433Z


0.8007
S10A6
IBP3
TSP1
CD44
PEDF
A1AG1
PTPRJ


0.8007
CALU
CO6A3
TSP1
CH10
SCF
BGH3
ALDOA


0.8007
ENPL
CD44
MASP1
GRP78
1433Z
CD14
FRIL


0.8005
TPIS
LEG1
LDHB
TSP1
MASP1
A1AG1
MPRI


0.8005
PEDF
CRP
ISLR
ALDOA
GRP78
PTPRJ
ZA2G


0.8003
ALDOA
S10A6
CALU
CRP
BGH3
TETN
6PGD


0.8003
AMPN
TSP1
A1AG1
MPRI
ISLR
ALDOA
MASP1


0.8003
CO6A3
TSP1
SCF
MPRI
ISLR
FOLH1
1433Z


0.8001
S10A6
IBP3
TSP1
KIT
TETN
COIA1
CERU


0.8001
S10A6
CALU
CH10
ISLR
ALDOA
SODM
PTPRJ


0.8001
IBP3
TSP1
ENPL
CH10
CRP
ISLR
ALDOA


0.8001
IBP3
TSP1
PTPRJ
ALDOA
BST1
LUM
1433Z


0.8001
LDHB
TSP1
MPRI
GRP78
SEM3G
LUM
ZA2G


















AUC
P8
P9
P10
P11
P12
P13
P14
P15





0.8282
6PGD
APOE
FRIL
G3P
HYOU1
LRP1
RAN
HXK1


0.8255
CD14
FRIL
HYOU1
LRP1
PROF1
TBB3
FINC
CEAM8


0.8194
APOE
FRIL
G3P
HYOU1
LRP1
TBB3
CLIC1
RAN


0.8189
1433Z
APOE
G3P
HYOU1
PRDX1
PROF1
ILK
HXK1


0.8187
CD14
FRIL
G3P
PRDX1
ILK
FINC
GSLG1
HXK1


0.8171
6PGD
CD14
FRIL
G3P
LRP1
TBB3
FINC
RAN


0.8171
CD14
FRIL
G3P
ICAM3
PRDX1
PROF1
PVR
HXK1


0.8165
VTNC
1433Z
FRIL
G3P
S10A6
FINC
GSLG1
HXK1


0.8163
DSG2
6PGD
FRIL
G3P
HYOU1
ICAM3
PRDX1
FINC


0.8163
6PGD
APOE
G3P
LRP1
UGPA
RAN
CEAM8
HXK1


0.8161
APOE
CD14
FRIL
G3P
LRP1
PROF1
RAN
CEAM8


0.8159
CERU
6PGD
FRIL
G3P
HYOU1
LRP1
PRDX1
CEAM8


0.8159
CD14
FRIL
G3P
LRP1
TBB3
FINC
GSLG1
HXK1


0.8159
APOE
CD14
FRIL
G3P
PRDX1
CLIC1
ILK
HXK1


0.8159
FRIL
G3P
TBB3
ILK
GELS
FINC
RAN
GSLG1


0.8157
6PGD
APOE
FRIL
G3P
HYOU1
CLIC1
ILK
HXK1


0.8155
G3P
HYOU1
LRP1
PRDX1
PROF1
FINC
RAN
GSLG1


0.8153
G3P
HYOU1
PLXC1
PRDX1
ILK
CEAM8
HXK1
BST1


0.814
GRP78
CERU
FRIL
G3P
PLXC1
PRDX1
ILK
HXK1


0.8138
G3P
HYOU1
PLXC1
RAN
CEAM8
HXK1
BST1
MMP9


0.8132
CERU
6PGD
CD14
FRIL
HYOU1
FINC
GSLG1
BST1


0.8128
PTPRJ
BST1
6PGD
G3P
HYOU1
ILK
FINC
HXK1


0.8128
VTNC
1433Z
APOE
FRIL
G3P
LRP1
PTGIS
RAN


0.8124
APOE
CD14
FRIL
G3P
GDIR2
FINC
GSLG1
HXK1


0.8124
FRIL
GDIR2
LRP1
CLIC1
FINC
GSLG1
HXK1
BST1


0.812
DSG2
1433Z
APOE
FRIL
LRP1
PRDX1
PROF1
FINC


0.8106
LUM
1433Z
6PGD
FRIL
G3P
HYOU1
PRDX1
CLIC1


0.8106
FRIL
G3P
PRDX1
UGPA
ILK
CEAM8
GSLG1
HXK1


0.8099
FRIL
G3P
HYOU1
PRDX1
PROF1
FINC
GSLG1
HXK1


0.8097
FRIL
G3P
HYOU1
LRP1
PTGIS
ILK
FINC
MMP9


0.8093
APOE
CD14
FRIL
G3P
LRP1
PLXC1
CLIC1
GSLG1


0.8093
FRIL
G3P
GDIR2
PRDX1
UGPA
CLIC1
FINC
HXK1


0.8093
ALDOA
SEM3G
MASP1
G3P
HYOU1
FINC
CEAM8
HXK1


0.8087
1433Z
6PGD
CD14
FRIL
HYOU1
TBB3
CLIC1
FINC


0.8087
6PGD
FRIL
G3P
HYOU1
LRP1
FINC
CEAM8
HXK1


0.8083
MPRI
GRP78
CERU
CD14
FRIL
LRP1
FINC
CEAM8


0.8081
TNF12
TENA
FRIL
G3P
HYOU1
PROF1
RAN
HXK1


0.8081
G3P
HYOU1
ICAM3
PLXC1
CLIC1
ILK
FINC
GSLG1


0.8081
CD14
FRIL
G3P
HYOU1
S10A6
CEAM8
GSLG1
HXK1


0.8079
TNF12
LUM
6PGD
APOE
FRIL
HYOU1
RAN
HXK1


0.8077
HYOU1
LRP1
PTGIS
CLIC1
FINC
RAN
GSLG1
MMP9


0.8077
1433Z
FRIL
G3P
HYOU1
LRP1
ILK
GSLG1
HXK1


0.8077
TETN
APOE
CD14
FRIL
G3P
LRP1
PRDX1
GSLG1


0.8075
ZA2G
6PGD
FRIL
G3P
LRP1
UGPA
ILK
HXK1


0.8073
1433Z
6PGD
FRIL
G3P
HYOU1
LRP1
PRDX1
FINC


0.8071
APOE
CD14
FRIL
G3P
LRP1
AMPN
RAN
HXK1


0.8071
TNF12
APOE
FRIL
HYOU1
LRP1
PTGIS
CLIC1
AMPN


0.8065
MASP1
ALDOA
APOE
FRIL
G3P
TBB3
RAN
HXK1


0.8063
G3P
LRP1
PROF1
TBB3
UGPA
CLIC1
AMPN
RAN


0.8063
ICAM3
LRP1
PLXC1
PROF1
FINC
RAN
HXK1
MMP9


0.8061
FRIL
G3P
LRP1
PLXC1
PROF1
PVR
FINC
CEAM8


0.8059
APOE
CD14
FRIL
G3P
LRP1
TBB3
RAN
GSLG1


0.8059
APOE
G3P
HYOU1
PRDX1
TBB3
ILK
RAN
HXK1


0.8058
TNF12
APOE
CD14
FRIL
G3P
HYOU1
RAN
HXK1


0.8058
SEM3G
FRIL
G3P
HYOU1
PROF1
GELS
PVR
RAN


0.8054
ZA2G
6PGD
FRIL
G3P
HYOU1
ILK
GSLG1
HXK1


0.8054
G3P
HYOU1
ICAM3
PLXC1
TBB3
GELS
RAN
BST1


0.8054
HYOU1
PLXC1
PRDX1
PROF1
FINC
CEAM8
GSLG1
MMP9


0.805
CD14
G3P
HYOU1
ICAM3
PRDX1
UGPA
ILK
HXK1


0.8048
VTNC
CD14
FRIL
G3P
HYOU1
PTGIS
FINC
RAN


0.8048
APOE
FRIL
G3P
LRP1
PRDX1
UGPA
RAN
CEAM8


0.8046
1433Z
FRIL
G3P
GDIR2
HYOU1
RAN
GSLG1
HXK1


0.8046
CD14
FRIL
G3P
LRP1
PRDX1
FINC
GSLG1
MMP9


0.8046
BST1
FRIL
G3P
CLIC1
ILK
AMPN
FINC
HXK1


0.8046
APOE
CD14
FRIL
G3P
ICAM3
AMPN
FINC
HXK1


0.8042
APOE
G3P
HYOU1
S10A6
ILK
FINC
RAN
HXK1


0.8042
HYOU1
ICAM3
LRP1
PRDX1
PROF1
GELS
FINC
GSLG1


0.8042
FRIL
GDIR2
HYOU1
LRP1
PRDX1
PROF1
CLIC1
HXK1


0.804
TENA
FRIL
G3P
HYOU1
LRP1
PRDX1
ILK
GSLG1


0.804
FRIL
G3P
GDIR2
PRDX1
CLIC1
GELS
FINC
HXK1


0.8038
ENOA
MASP1
APOE
FRIL
G3P
PRDX1
FINC
HXK1


0.8036
ALDOA
ENOA
6PGD
FRIL
G3P
GDIR2
LRP1
PRDX1


0.8036
TETN
APOE
G3P
HYOU1
ICAM3
RAN
CEAM8
HXK1


0.8036
6PGD
APOE
FRIL
G3P
HYOU1
LRP1
HXK1
MMP9


0.8036
TENA
FRIL
G3P
PROF1
PTGIS
FINC
CEAM8
HXK1


0.8036
CD14
FRIL
G3P
HYOU1
PRDX1
FINC
CEAM8
HXK1


0.8036
FRIL
G3P
LRP1
PRDX1
PROF1
GELS
FINC
RAN


0.8034
VTNC
TENA
6PGD
G3P
HYOU1
LRP1
TBB3
ILK


0.8034
G3P
HYOU1
ICAM3
PROF1
ILK
GELS
AMPN
FINC


0.8032
SEM3G
MASP1
6PGD
CD14
FRIL
G3P
HYOU1
ILK


0.8032
1433Z
APOE
CD14
G3P
HYOU1
PVR
RAN
HXK1


0.803
1433Z
APOE
FRIL
G3P
TBB3
UGPA
PVR
RAN


0.803
APOE
CD14
FRIL
G3P
HYOU1
ICAM3
PRDX1
RAN


0.8028
MDHM
CERU
LUM
ZA2G
APOE
FRIL
LRP1
MMP9


0.8024
FRIL
G3P
HYOU1
PRDX1
GELS
FINC
CEAM8
HXK1


0.8024
HYOU1
LRP1
PROF1
CLIC1
GELS
FINC
CEAM8
GSLG1


0.8024
1433Z
APOE
FRIL
G3P
LRP1
PRDX1
UGPA
PTPRJ


0.802
TETN
TENA
APOE
FRIL
G3P
TBB3
AMPN
GSLG1


0.802
APOE
FRIL
G3P
HYOU1
ILK
PVR
GSLG1
PTPRJ


0.802
APOE
FRIL
G3P
LRP1
ILK
RAN
CEAM8
MMP9


0.802
FRIL
G3P
GDIR2
HYOU1
LRP1
PRDX1
TBB3
FINC


0.8018
SCF
ALDOA
SEM3G
VTNC
FRIL
G3P
LRP1
CLIC1


0.8018
CERU
6PGD
APOE
CD14
FRIL
HYOU1
PROF1
GSLG1


0.8018
FRIL
G3P
HYOU1
LRP1
PTGIS
GELS
FINC
RAN


0.8018
FRIL
G3P
HYOU1
ICAM3
PROF1
FINC
PTPRJ
HXK1


0.8016
SEM3G
APOE
FRIL
G3P
HYOU1
PRDX1
CLIC1
GSLG1


0.8016
G3P
HYOU1
PROF1
UGPA
CLIC1
RAN
CEAM8
PTPRJ


0.8014
CERU
6PGD
FRIL
G3P
HYOU1
PRDX1
FINC
HXK1


0.8014
G3P
GDIR2
LRP1
S10A6
GELS
FINC
GSLG1
HXK1


0.8014
CD14
FRIL
G3P
PRDX1
UGPA
FINC
PTPRJ
HXK1


0.8014
FRIL
G3P
HYOU1
LRP1
PRDX1
PROF1
FINC
HXK1


0.8013
FRIL
G3P
LRP1
PRDX1
ILK
FINC
HXK1
MMP9


0.8013
ICAM3
LRP1
PROF1
UGPA
ILK
FINC
PTPRJ
HXK1


0.8013
ZA2G
6PGD
FRIL
G3P
CLIC1
S10A6
ILK
PVR


0.8013
LUM
APOE
FRIL
G3P
HYOU1
CLIC1
RAN
HXK1


0.8013
G3P
GDIR2
LRP1
PTGIS
FINC
RAN
HXK1
MMP9


0.8011
G3P
GDIR2
HYOU1
ICAM3
PRDX1
FINC
HXK1
MMP9


0.8011
TENA
6PGD
APOE
G3P
LRP1
PROF1
GELS
MMP9


0.8011
FRIL
GDIR2
HYOU1
LRP1
CLIC1
S10A6
PVR
GSLG1


0.8011
APOE
G3P
ICAM3
LRP1
GELS
FINC
RAN
CEAM8


0.8011
1433Z
G3P
HYOU1
PRDX1
FINC
GSLG1
PTPRJ
HXK1


0.8009
ISLR
APOE
FRIL
LRP1
PVR
FINC
RAN
PTPRJ


0.8009
TNF12
CERU
APOE
CD14
FRIL
TBB3
ILK
FINC


0.8009
6PGD
CD14
FRIL
CLIC1
S10A6
ILK
FINC
MMP9


0.8009
APOE
CD14
G3P
TBB3
CLIC1
GELS
RAN
HXK1


0.8009
1433Z
6PGD
FRIL
G3P
HYOU1
RAN
HXK1
MMP9


0.8007
GRP78
MDHM
CD14
FRIL
G3P
HYOU1
GSLG1
HXK1


0.8007
6PGD
APOE
G3P
GDIR2
LRP1
PRDX1
TBB3
RAN


0.8007
SODM
CERU
APOE
FRIL
ICAM3
LRP1
UGPA
GSLG1


0.8007
ENOA
TETN
LUM
APOE
FRIL
G3P
RAN
HXK1


0.8007
G3P
GDIR2
ICAM3
LRP1
PRDX1
PROF1
FINC
HXK1


0.8005
ALDOA
ENOA
FRIL
G3P
LRP1
UGPA
ILK
FINC


0.8005
6PGD
G3P
HYOU1
PRDX1
TBB3
FINC
RAN
CEAM8


0.8003
CD14
FRIL
G3P
CLIC1
FINC
GSLG1
HXK1
MMP9


0.8003
LUM
6PGD
APOE
FRIL
ICAM3
TBB3
GSLG1
BST1


0.8003
APOE
G3P
HYOU1
ICAM3
PRDX1
UGPA
RAN
HXK1


0.8001
6PGD
CD14
FRIL
G3P
PROF1
FINC
HXK1
MMP9


0.8001
MDHM
VTNC
FRIL
G3P
CLIC1
ILK
AMPN
HXK1


0.8001
SODM
1433Z
G3P
HYOU1
LRP1
PRDX1
PROF1
CEAM8


0.8001
APOE
G3P
HYOU1
LRP1
PTGIS
TBB3
PVR
RAN


0.8001
FRIL
G3P
ICAM3
PROF1
TBB3
FINC
RAN
GSLG1









To calculate the combined AUC of each panel of 15 proteins, the highest intensity normalized transition was utilized. Logistic regression was used to calculate the AUC of the panel of 15 across all small samples. 5 panels of 15 proteins had combined AUC above 0.80.
Finally, the frequency of each of the 67 proteins on the 131 panels listed in Table 13 is presented in Table 12 both as raw counts (column 2) and percentage (column 3). It is an important observation that the panel size of 15 was pre-selected to prove that there are diagnostic proteins and panels. Furthermore, there are numerous such panels. Smaller panels selected from the list of 67 proteins can also be formed and can be generated using the same methods here.
Example 4
A Diagnostic Panel of 15 Proteins for Determining the Probability that a Blood Sample from a Patient with a PN of Size 2 cm or Less is Benign or Malignant
In Table 14 a logistic regression classifier trained on all small samples is presented.












TABLE 14







Transition

Normalized
Logistic




column

By column
Regression


Protein
Transition
SEQ ID NO:
Normalized By
SEQ ID NO:
Coefficient




















ALDOA_HUMAN
ALQASALK_401.25_617.40
7
YGFIEGHVVIPR_462.92_272.20
1
−1.96079


BGH3_HUMAN
LTLLAPLNSVFK_658.40_804.50
8
YEVTVVSVR_526.29_759.50
2
2.21074


CLIC1_HUMAN
LAALNPESNTAGLDIFAK_922.99_256.20
9
ASSIIDELFQDR_465.24_565.30
3
0.88028


CO6A3_HUMAN
VAVVQYSDR_518.77_767.40
10
ASSIIDELFQDR_465.24_565.30
3
−1.52046


COIA1_HUMAN
AVGLAGTFR_446.26_721.40
11
YGFIEGHVVIPR_462.92_272.20
1
−0.76786


FINC_HUMAN
VPGTSTSATLTGLTR_487.94_446.30
12
FLNVLSPR_473.28_685.40
4
0.98842


G3P_HUMAN
GALQNIIPASTGAAK_706.40_815.50
13
TASDFITK_441.73_710.40
5
0.58843


ISLR_HUMAN
ALPGTPVASSQPR_640.85_841.50
14
FLNVLSPR_473.28_685.40
4
1.02005


LRP1_HUMAN
TVLWPNGLSLDIPAGR_855.00_400.20
15
YEVTVVSVR_526.29_759.50
2
−2.14383


PRDX1_HUMAN
QITVNDLPVGR_606.30_428.30
16
YGFIEGHVVIPR_462.92_272.20
1
−1.38044


PROF1_HUMAN
STGGAPTFNVTVTK_690.40_503.80
17
TASDFITK_441.73_710.40
5
−1.78666


PVR_HUMAN
SVDIWLR_444.75_702.40
18
TASDFITK_441.73_710.40
5
2.26338


TBB3_HUMAN
ISVYYNEASSHK_466.60_458.20
19
FLNVLSPR_473.28_685.40
4
−0.46786


TETN_HUMAN
LDTLAQEVALLK_657.39_330.20
20
TASDFITK_441.73_710.40
5
−1.99972


TPIS_HUMAN
VVFEQTK_425.74_652.30
21
YGFIEGHVVIPR_462.92_272.20
1
2.65334


Constant (C0)




21.9997









The classifier has the structure



  
    Probability
    =
    
      
        exp
        ⁡
        
          (
          W
          )
        
      
      
        1
        +
        
          exp
          ⁡
          
            (
            W
            )
          
        
      
    
  




  
    W
    =
    
      
        C
        0
      
      +
      
        
          ∑
          
            i
            =
            1
          
          15
        
        ⁢
        
          
            C
            i
          
          *
          
            P
            i
          
        
      
    
  



Where C0 and Ci are logistic regression coefficients, Pi are logarithmically transformed normalized transition intensities. Samples are predicted as cancer if Probability ≧0.5 or as benign otherwise. In Table 14 the coefficients Ci appear in the sixth column, C0 in the last row, and the normalized transitions for each protein are defined by column 2 (protein transition) and column 4 (the normalizing factor).
The performance of this classifier, presented as a ROC plot, appears in FIG. 4. Overall AUC is 0.81. The performance can also be assessed by applying the classifier to each study site individually which yields the three ROC plots appearing in FIG. 5. The resulting AUCs are 0.79, 0.88 and 0.78 for Laval, NYU and UPenn, respectively.
Example 5
The Program “Ingenuity”® was Used to Query the Blood Proteins that are Used to Identify Lung Cancer in Patients with Nodules that were Identified Using the Methods of the Present Invention
Using a subset of 35 proteins (Table 15) from the 67 proteins identified as a diagnostic panel (Table 13), a backward systems analysis was performed. Two networks were queried that are identified as cancer networks with the identified 35 proteins. The results show that the networks that have the highest percentage of “hits” when the proteins are queried that are found in the blood of patients down to the level of the nucleus are initiated by transcription factors that are regulated by either cigarette smoke or lung cancer among others. See also Table 16 and FIG. 6.
These results are further evidence that the proteins that were identified using the methods of the invention as diagnostic for lung cancer are prognostic and relevant.











TABLE 15





No.
Protein
Protein Name
Gene Symbol
Gene Name



















1
6PGD_HUMAN
6-phosphogluconate
PGD
phosphogluconate dehydrogenase




dehydrogenase, decar-






boxylating




2
AIFM1_HUMAN
Apoptosis-inducing
AIFM1
apoptosis-inducing factor, mito-




factor 1, mitochondrial

chondrion-associated, 1


3
ALDOA_HUMAN
Fructose-bisphosphate
ALDOA
aldolase A, fructose-bisphosphate




aldolase A




4
BGH3_HUMAN
Transforming growth
TGFBI
transforming growth factor, beta-




factor-beta-induced

induced, 68 kDa




protein ig-h3




5
C163A_HUMAN
Scavenger receptor
CD163
CD163 molecule




cysteine-rich type 1






protein M130




6
CD14_HUMAN
Monocyte differentia-
CD14
CD14 molecule




tion antigen CD14




7
COIA1_HUMAN
Collagen alpha-
COL18A1
collagen, type XVIII, alpha 1




1(XVIII) chain




8
ERO1A_HUMAN
ERO1-like protein
ERO1L
ERO1-like (S. cerevisiae)




alpha




9
FIBA_HUMAN
Fibrinogen alpha chain
FGA
fibrinogen alpha chain


10
FINC_HUMAN
Fibronectin
FN1
fibronectin 1


11
FOLH1_HUMAN
Glutamate carboxy-
FOLH1
folate hydrolase (prostate-specific




peptidase 2

membrane antigen) 1


12
FRIL_HUMAN
Ferritin light chain
FTL
ferritin, light polypeptide


13
GELS_HUMAN
Gelsolin
GSN
gelsolin (amyloidosis, Finnish






type)


14
GGH_HUMAN
Gamma-glutamyl
GGH
gamma-glutamyl hydrolase (con-




hydrolase

jugase, folylpolygammaglutamyl






hydrolase)


15
GRP78_HUMAN
78 kDa glucose-
HSPA5
heat shock 70 kDa protein 5 (glu-




regulated protein

cose-regulated protein, 78 kDa)


16
GSLG1_HUMAN
Golgi apparatus protein
GLG1
golgi apparatus protein 1




1




17
GSTP1_HUMAN
Glutathione S-
GSTP1
glutathione S-transferase pi 1




transferase P




18
IBP3_HUMAN
Insulin-like growth
IGFBP3
insulin-like growth factor binding




factor-binding protein 3

protein 3


19
ICAM1_HUMAN
Intercellular adhesion
ICAM1
intercellular adhesion molecule 1




molecule 1




20
ISLR_HUMAN
Immunoglobulin super-
ISLR
immunoglobulin superfamily




family containing leu-

containing leucine-rich repeat




cine-rich repeat protein




21
LG3BP_HUMAN
Galectin-3-binding
LGALS3BP
lectin, galactoside-binding,




protein

soluble, 3 binding protein


22
LRP1_HUMAN
Prolow-density lipo-
LRP1
low density lipoprotein-related




protein receptor-related

protein 1 (alpha-2-macroglobulin




protein 1

receptor)


23
LUM_HUMAN
Lumican
LUM
lumican


24
MASP1_HUMAN
Mannan-binding lectin
MASP1
mannan-binding lectin serine pep-




serine protease 1

tidase 1 (C4/C2 activating com-






ponent of Ra-reactive factor)


25
PDIA3_HUMAN
Protein disulfide-
PDIA3
protein disulfide isomerase family




isomerase A3

A, member 3


26
PEDF_HUMAN
Pigment epithelium-
SERPINF1
serpin peptidase inhibitor, clade F




derived factor

(alpha-2 antiplasmin, pigment






epithelium derived factor), mem-






ber 1


27
PRDX1_HUMAN
Peroxiredoxin-1
PRDX1
peroxiredoxin 1


28
PROF1_HUMAN
Profilin-1
PFN1
profilin 1


29
PTPA_HUMAN
Serine/threonine-
PPP2R4
protein phosphatase 2A activator,




protein phosphatase 2A

regulatory subunit 4




activator




30
PTPRJ_HUMAN
Receptor-type tyrosine-
PTPRJ
protein tyrosine phosphatase,




protein phosphatase eta

receptor type, J


31
RAP2B_HUMAN
Ras-related protein
RAP2B
RAP2B, member of RAS onco-




Rap-2b

gene family


32
SEM3G_HUMAN
Semaphorin-3G
SEMA3G
sema domain, immunoglobulin






domain (Ig), short basic domain,






secreted, (semaphorin) 3G


33
SODM_HUMAN
Superoxide dismutase
SOD2
superoxide dismutase 2, mito-




[Mn], mitochondrial

chondrial


34
TETN_HUMAN
Tetranectin
CLEC3B
C-type lectin domain family 3,






member B


35
TSP1_HUMAN
Thrombospondin-1
THBS1
thrombospondin 1



















TABLE 16





Gene

Lung Cancer PubMed



Name
Protein
Associations
Sample Publications







NFE2L2
nuclear
92
Cigarette Smoking Blocks the Protective


(NRF2)
factor
transcription
Expression of Nrf2/ARE Pathway . . .



(erythroid-
factor
Molecular mechanisms for the regulation



derived 2)-
protecting
of Nrf2-mediated cell proliferation in non-



like 2
cell from
small-cell lung cancers . . .




oxidative stress



EGR1
early
38
Cigarette smoke-induced Egr-1 upregulates



growth
transcription
proinflammatory cytokines in pulmonary



response
factor
epithelial cells . . .




involved
EGR-1 regulates Ho-1 expression induced




oxidative stress
by cigarette smoke . . .





Chronic hypoxia induces Egr-1 via activa-





tion of ERK1/2 and contributes to pulmo-





nary vascular remodeling.





Early growth response-1 induces and en-





hances vascular endothelial growth factor-





A expression in lung cancer cells . . .









Example 6
Cooperative Proteins for Diagnosing Pulmonary Nodules
To achieve unbiased discovery of cooperative proteins, selected reaction monitoring (SRM) mass spectrometry (Addona, Abbatiello et al. 2009) was utilized. SRM is a form of mass spectrometry that monitors predetermined and highly specific mass products of particularly informative (proteotypic) peptides of selected proteins. These peptides are recognized as specific transitions in mass spectra. SRM possesses the following required features that other technologies, notably antibody-based technologies, do not possess:

    
    
        Highly multiplexed SRM assays can be rapidly and cost-effectively developed for tens or hundreds of proteins.
        The assays developed are for proteins of one's choice and are not restricted to a catalogue of pre-existing assays. Furthermore, the assays can be developed for specific regions of a protein, such as the extracellular portion of a transmembrane protein on the cell surface of a tumor cell, or for a specific isoform.
        SRM technology can be used from discovery to clinical testing. Peptide ionization, the foundation of mass spectrometry, is remarkably reproducible. Using a single technology platform avoids the common problem of translating an assay from one technology platform to another.

SRM has been used for clinical testing of small molecule analytes for many years, and recently in the development of biologically relevant assays [10].

    
    


Labeled and unlabeled SRM peptides are commercially available, together with an open-source library and data repository of mass spectra for design and conduct of SRM analyses. Exceptional public resources exist to accelerate assay development including the PeptideAtlas [11] and the Plasma Proteome Project [12, 13], the SRM Atlas and PASSEL, the PeptideAtlas SRM Experimental Library (www.systemsbiology.org/passel).
Two SRM strategies that enhance technical performance were introduced. First, large scale SRM assay development introduces the possibility of monitoring false signals. Using an extension of expression correlation techniques [14], the rate of false signal monitoring was reduced to below 3%. This is comparable and complementary to the approach used by mProphet (Reiter, Rinner et al. 2011).
Second, a panel of endogenous proteins was used for normalization. However, whereas these proteins are typically selected as “housekeeping” proteins (Lange, Picotti et al. 2008), proteins that were strong normalizers for the technology platform were identified. That is, proteins that monitored the effects of technical variation so that it could be controlled effectively. This resulted, for example, in the reduction of technical variation due to sample depletion of high abundance proteins from 23.8% to 9.0%. The benefits of endogenous signal normalization has been previously discussed (Price, Trent et al. 2007).
The final component of the strategy was to carefully design the discovery and validation studies using emerging best practices. Specifically, the cases (malignant nodules) and controls (benign nodules) were pairwise matched on age, nodule size, gender and participating clinical site. This ensures that the candidate markers discovered are not markers of age or variations in sample collection from site to site. The studies were well-powered, included multiple sites, a new site participated in the validation study, and importantly, were designed to address the intended use of the test. The careful selection and matching of samples resulted in an exceptionally valuable feature of the classifier. The classifier generates a score that is independent of nodule size and smoking status. As these are currently used risk factors for clinical management of IPNs, the classifier is a complementary molecular tool for use in the diagnosis of IPNs.
Selection of Biomarker Candidates for Assay Development
To identify lung cancer biomarkers in blood that originate from lung tumor cells, resected lung tumors and distal normal tissue of the same lobe were obtained. Plasma membranes were isolated from both endothelial and epithelial cells and analyzed by tandem mass spectrometry to identify cell surface proteins over expressed on tumor cells. Similarly, Golgi apparatus were isolated to identify over-secreted proteins from tumor cells. Proteins with evidence of being present in blood or secreted were prioritized resulting in a set of 217 proteins. See Example 7: Materials and Methods for details.
To ensure other viable lung cancer biomarkers were not overlooked, a literature search was performed and manually curated for lung cancer markers. As above, proteins with evidence of being present in blood or secreted were prioritized. This resulted in a set of 319 proteins. See Example 7: Materials and Methods for details.
The tissue (217) and literature (319) candidates overlapped by 148 proteins resulting in a final candidate list of 388 protein candidates. See Example 7: Materials and Methods.
Development of SRM Assays
SRM assays for the 388 proteins were developed using standard synthetic peptide techniques (See Example 7: Materials and Methods). Of the 388 candidates, SRM assays were successfully developed for 371 candidates. The 371 SRM assays were applied to benign and lung cancer plasma samples to evaluate detection rate in blood. 190 (51% success rate) of the SRM assays were detected. This success rate compares favorably to similar attempts to develop large scale SRM assays for detection of cancer markers in plasma. Recently 182 SRM assays for general cancer markers were developed from 1172 candidates (16% success rate) [15]. Despite focusing only on lung cancer markers, the 3-fold increase in efficiency is likely due to sourcing candidates from cancer tissues with prior evidence of presence in blood. Those proteins of the 371 that were previously detected by mass spectrometry in blood had a 64% success rate of detection in blood whereas those without had a 35% success rate. Of the 190 proteins detected in blood, 114 were derived from the tissue-sourced candidates and 167 derived from the literature-sourced candidates (91 protein overlap). See Example 7: Materials and Methods and Table 6.
Typically, SRM assays are manually curated to ensure assays are monitoring the intended peptide. However, this becomes unfeasible for large scale SRM assays such as this 371 protein assay. More recently, computational tools such as mProphet (Reiter, Rinner et al. 2011) enable automated qualification of SRM assays. A complementary strategy to mProphet was introduced that does not require customization for each dataset set. It utilizes correlation techniques (Kearney, Butler et al. 2008) to confirm the identity of protein transitions with high confidence. In FIG. 7 a histogram of the Pearson correlations between every pair of transitions in the assay is presented. The correlation between a pair of transitions is obtained from their expression profiles over all 143 samples in the discovery study detailed below. As expected, transitions from the same peptide are highly correlated. Similarly, transitions from different peptide fragments of the same protein are also highly correlated. In contrast, transitions from different proteins are not highly correlated and enables a statistical analysis of the quality of a protein's SRM assay. For example, if the correlation of transitions from two peptides from the same protein is above 0.5 then there is less than a 3% probability that the assay is false. See Example 7: Materials and Methods.
Classifier Discovery
A summary of the 143 samples used for classifier discovery appears in Table 17. Samples were obtained from three sites to avoid overfitting to a single site. Participating sites were Laval (Institut Universitaire de Cardiologie et de Pneumologie de Quebec), NYU (New York University) and UPenn (University of Pennsylvania). Samples were also selected to be representative of the intended use population in terms of nodule size (diameter), age and smoking status.
Benign and cancer samples were paired by matching on age, gender, site and nodule size (benign and cancer samples were required to have a nodule identified radiologically). The benign and cancer samples display a bias in smoking (pack years), however, the majority of benign and cancer samples were current or past smokers. In comparing malignant and benign samples, the intent was to find proteins that were markers of lung cancer; not markers of age, nodule size or differences in site sample collection. Note that cancer samples were pathologically confirmed and benign samples were either pathologically confirmed or radiologically confirmed (no tumor growth demonstrated over two years of CT scan surveillance).







TABLE 17







Clinical data summaries and demographic analysis for discovery and validation sets.










Discovery
Validation














Cancer
Benign
P value
Cancer
Benign
P value

















Sample

72
71

52
52



(total)









Sample
Laval
14
14
1.00†
13
12
0.89†


(Center)
NYU
29
28

6
9




UPenn
29
29

14
13




Vanderbilt
0
0

19
18



Sample
Male
29
28
1.00†
25
27
0.85†


(Gender)
Female
43
43

27
25



Sample
Never
5
19
0.006†
3
15
0.006†


(Smoking
Past
60
44

38
29



History)
Current
6
6

11
7




No data
1
2

0
1



Age
Median
65 (59-72)
64 (52-71)
0.46‡
63 (60-73)
62 (56-67)
0.03‡



(quartile









range)








Nodule
Median
13 (10-16)
13 (10-18)
0.69‡
16 (13-20)
15 (12-22)
0.68‡


Size (mm)
(quartile









range)








Pack-year§
Median
37 (20-52)
20 (0-40) 
0.001‡
40 (19-50)
27 (0-50) 
0.09‡



(quartile









range)





†Based on Fisher's exact test.


‡Based on Mann-Whitney test.


§No data (cancer, benign): Discovery (4, 6), Validation (2, 3)






The processing of samples was conducted in batches. Each batch contained a set of randomly selected cancer-benign pairs and three plasma standards, included for calibration and quality control purposes.
All plasma samples were immunodepleted, trypsin digested and analyzed by reverse phase HPLC-SRM-MS. Protein transitions were normalized using an endogenous protein panel. The normalization procedure was designed to reduce overall variability, but in particular, the variability introduced by the depletion step. Overall technical variability was reduced from 32.3% to 25.1% and technical variability due to depletion was reduced from 23.8% to 9.0%. Details of the sample analysis and normalization procedure are available in Example 7: Materials and Methods.
To assess panels of proteins, they were fit to a logistic regression model. Logistic regression was chosen to avoid the overfitting that can occur with non-linear models, especially when the number of variables measured (transitions) is similar or larger than the number of samples in the study. The performance of a panel was measured by partial area under the curve (AUC) with sensitivity fixed at 90% (McClish 1989). Partial AUC correlates to high NPV performance while maximizing ROR.
To derive the 13 protein classifier, four criteria were used:

    
    
        The protein must have transitions that are reliably detected above noise across samples in the study.
        The protein must be highly cooperative.
        The protein must have transitions that are robust (high signal to noise, no interference, etc.)
        The protein's coefficient within the logistic regression model must have low variability during cross validation, that is, it must be stable.

Details of how each of these criteria were applied appear in Example 7: Materials and Methods.

    
    


Finally, the 13 protein classifier was trained to a logistic regression model by Monte Carlo cross validation (MCCV) with a hold out rate of 20% and 20,000 iterations. The thirteen proteins for the rule-out classifier are listed in Table 18 along with their highest intensity transition and model coefficient.







TABLE 18







The 13 protein classifier.












SEQ ID



Protein
Transition
NO
Coefficient













Constant(α)


36.16


LRP1_HUMAN
TVLWPNGLSLDIPAGR_855.00_400.20
15
−1.59


BGH3_HUMAN
LTLLAPLNSVFK_658.40_804.50
 8
1.73


COIA1_HUMAN
AVGLAGTFR_446.26_721.40
11
−1.56


TETN_HUMAN
LDTLAQEVALLK_657.39_330.20
20
−1.79


TSP1_HUMAN
GFLLLASLR_495.31_559.40
22
0.53


ALDOA_HUMAN
ALQASALK_401.25_617.40
 7
−0.80


GRP78_HUMAN
TWNDPSVQQDIK_715.85_260.20
23
1.41


ISLR_HUMAN
ALPGTPVASSQPR_640.85_841.50
14
1.40


FRIL_HUMAN
LGGPEAGLGEYLFER_804.40_913.40
24
0.39


LG3BP_HUMAN
VEIFYR_413.73_598.30
25
−0.58


PRDX1_HUMAN
QITVNDLPVGR_606.30_428.30
16
−0.34


FIBA_HUMAN
NSLFEYQK_514.76_714.30
26
0.31


GSLG1_HUMAN
IIIQESALDYR_660.86_338.20
27
−0.70









Validation of the Rule-Out Classifier
52 cancer and 52 benign samples (see Table 17) were used to validate the performance of the 13 protein classifier. All samples were independent of the discovery samples, in addition, over 36% of the validation samples were sourced from a new fourth site (Vanderbilt University). Samples were selected to be consistent with intended use and matched in terms of gender, clinical site and nodule size. We note a slight age bias, which is due to 5 benign samples from young patients. Anticipating a NPV of 90%, the 95% confidence interval is +/−5%.
At this point we refer to the 13 protein classifier trained on 143 samples the Discovery classifier. However, once validation is completed, to find the optimal coefficients for the classifier, it was retrained on all 247 samples (discovery and validation sets) as this is most predictive of future performance. We refer to this classifier as the Final classifier. The coefficients of the Final classifier appear in Table 21.
The performance of the Discovery and Final classifiers is summarized in FIG. 8. Reported are the NPV and ROR for the Discovery classifier when applied to the discovery set, the validation set. The NPV and ROR for the Final classifier are reported for all samples and also for all samples restricted to nodule size 8 mm to 20 mm (191 samples).
NPV and ROR are each reported as a fraction from 0 to 1. Similarly, the classifier produces a score between 0 and 1, which is the probability of cancer predicted by the classifier.
The discovery and validation curves for NPV and ROR are similar with the discovery curves superior as expected. This demonstrates the reproducibility of performance on an independent set of samples. A Discovery classifier rule out threshold of 0.40 achieves NPV of 96% and 90%, whereas ROR is 33% and 23%, for the discovery samples and the validation samples, respectively. Final classifier rule threshold of 0.60 achieves NPV of 91% and 90%, whereas ROR is 45% and 43%, for all samples and all samples restricted to be 8 mm-20 mm, respectively.
Applications of the Classifier
FIG. 9 presents the application of the final classifier to all 247 samples from the discovery and validation sets. The intent of FIG. 9 is to contrast the clinical risk factors of smoking (measured in pack years) and nodule size (proportional to the size of each circle) to the classifier score assigned to each sample.
First, note the density of cancer samples with high classifier scores. The classifier has been designed to detect a cancer signature in blood with high sensitivity. As a consequence, to the left of the rule out threshold (0.60) there are very few (<10%) cancer samples, assuming cancer prevalence of 25% [16, 17].
Third is the observation that nodule size does not appear to increase with the classifier score. Both large and small nodules are spread across the classifier score spectrum. Similarly, although there are a few very heavy smokers with very high classifier scores, increased smoking does not seem to increase with classifier score. To quantify this observation the correlation between the classifier score and nodule size, smoking and age were calculated and appear in Table 19. In all cases there is no significant relationship between the classifier score and the risk factors. The one exception is a weak correlation between benign classifier scores and benign ages. However, this correlation is so weak that the classifier score increases by only 0.04 every 10 years.







TABLE 19







Correlation between classifier scores and clinical risk factors.











Age
Nodule Size
Smoking















Benign
0.25
−0.06
0.11



Cancer
0.01
−0.01
0.06









This lack of correlation has clinical utility. It implies that the classifier provides molecular information about the disease status of an IPN that is incremental upon risk factors such as nodule size and smoking status. Consequently, it is a clinical tool for physicians to make more informed decisions around the clinical management of an IPN.
To visual how this might be accomplished, we demonstrate how the cancer probability score generated by the classifier can be related to cancer risk (see FIG. 11)
At a given classifier score, some percentage of all cancer nodules will have a smaller score. This is the sensitivity of the classifier. For example, at classifier score 0.8, 47% of cancer patients have a lower score, at classifier score 0.7, 28% of cancer patients have a lower score, at classifier score 0.5, only 9% are lower and finally at score 0.25, only 4% are lower. This enables a physician to interpret a patient's classifier score in terms of relative risk.
The Molecular Foundations of the Classifier
The goal was to identify the molecular signature of a malignant pulmonary nodule by selecting proteins that were the cooperative, robustly detected by SRM and stable within the classifier. How well associated with lung cancer is the derived classifier? Is there a molecular foundation for the perturbation of these 13 proteins in blood? And finally, how unique is the classifier among other possible protein combinations?
To answer these questions the 13 proteins of the classifier were submitted for pathway analysis using IPA (Ingenuity Systems, www.ingenuity.com). The first step was to work from outside the cell inwards to identify the transcription factors most likely to cause a modulation of these 13 proteins. The five most significant were FOS, NRF2, AHR, HD and MYC. FOS is common to many forms of cancer. However, NRF2 and AHR are associated with lung cancer, response to oxidative stress and lung inflammation. MYC is associated with lung cancer and response to oxidative stress while HD is associated with lung inflammation and response to oxidative stress.
The 13 classifier proteins are also highly specific to these three networks (lung cancer, response to oxidative stress and lung inflammation). This is summarized in FIG. 10 where the classifier proteins (green), transcription factors (blue) and the three merged networks (orange) are depicted. Only ISLR is not connected through these three lung specific networks to the other proteins, although it is connected through cancer networks not specific to cancer. In summary, the modulation of the 13 classifier proteins can be tracked back to a few transcription factors specific to lung cancer, lung inflammation and oxidative stress networks.
To address the question of classifier uniqueness, every classifier from the 21 robust and cooperative proteins was formed (Table 20). Due to the computational overhead, these classifiers could not be fully trained by Monte Carlo cross validation, consequently, only estimates of their performance could be obtained. Five high preforming alternative classifiers were identified and then fully trained. The classifier and the five high performing alternatives appear in Table 20. The frequency of each protein appears in the tally column, in particular, the first 11 proteins appear in 4 out of the 6 classifiers. These 11 proteins have significantly higher cooperative scores than the remaining proteins. By this analysis it appears that there is a core group of proteins that form the blood signature of a malignant nodule.







TABLE 20







The classifier and the high performing alternatives; coefficients for proteins


the respective panels are shown.

















Panel
Panel
Panel
Panel
Panel
Protein
Cooperative


Protein
Classifier
110424
130972
126748
109919
60767
Tally
Score


















Constant
36.16
27.72
27.69
23.47
21.32
23.17
—
—


ALDOA
−0.8
−0.67
−0.87
−0.83
−0.64
−0.68
6
1.3


COIA1
−1.56
−1.04
−1.68
−1.37
−0.94
−1.2
6
3.7


TSP1
0.53
0.53
0.39
0.42
0.47
0.41
6
1.8


FRIL
0.39
0.45
0.39
0.41
0.41
0.41
6
2.8


LRP1
−1.59
−0.84
−1.32
1.15
−0.84
−0.87
6
4.0


GRP78
1.41
1.14
1.31
−0.34
0.78
0.6
6
1.4


ISLR
1.4
1.03
1.08
0.75
0.74

5
1.4


IBP3

−0.23
−0.21
−0.38
−0.33
−0.54
5
3.4


TETN
−1.79
−1.23
−1.99
−1.26


4
2.5


PRDX1
−0.34
−0.38


−0.36
−0.4
4
1.5


LG3BP
−0.58

−0.61

−0.38
−0.48
4
4.3


CD14


0.99
1.08

1.4
3
4.0


BGH3
1.73

1.67
−0.83


3
1.8


KIT




−0.31
−0.56
3
1.4


GGH




0.44
0.52
3
1.3


AIFM1


−0.51



1
1.4


FIBA
0.31





1
1.1


GSLG1
−0.7





1
1.2


ENPL






0
1.1


EF1A1






0
1.2


TENX






0
1.1









This result suggests that there is a core group of proteins that define a high performance classifier, but alternative panels exist. However, changes in panel membership affect the tradeoff between NPV and ROR.
Example 7
Materials and Methods
Assay Development Candidates Sourced from Tissue
Patient samples obtained from fresh lung tumor resections were collected from Centre Hospitalier de l'Université de Montreal and McGill University Health Centre under IRB approval and with informed patient consent. Samples were obtained from the tumor as well as from distal normal tissue in the same lung lobe. Plasma membranes of each pair of samples were then isolated from the epithelial cells of 30 patients (19 adenocarcinoma, 6 squamous, 5 large cell carcinoma) and endothelial cells of 38 patients (13 adenocarcinoma, 18 squamous, 7 large cell carcinoma) using immune-affinity protocols. Golgi apparatus were isolated from each pair of samples from 33 patients (18 adenocarcinoma, 14 squamous, 1 adenosquamous) using isopycnic centrifugation followed by ammonium carbonate extraction. Plasma membrane isolations and Golgi isolations were then analyzed by tandem mass spectrometry to identify proteins overexpressed in lung cancer tissue over normal tissue, for both plasma membranes and Golgi.
Assay Development Candidates Sourced from Literature
Candidate lung cancer biomarkers were identified from two public and one commercial database: Entrez, NBK3836, UniProt and NextBio. Terminologies were predefined for the database queries which were automated using PERL scripts. The mining was carried out on May 6, 2010 (UniProt), May 17, 2010 (Entrez) and Jul. 8, 2010 (NextBio), respectively. Biomarkers were then assembled and mapped to UniProt identifiers.
Evidence of Presence in Blood
The tissue-sourced and literature-source biomarker candidates were required to have evidence of presence in blood. For evidence by mass spectrometry detection, three datasets were used. HUPO9504 contains 9504 human proteins identified by tandem mass spectrometry [13]. HUPO889, a higher confidence subset of HUPO9504, contains 889 human proteins [18]. The PeptideAtlas (November 2009 build) was also used. A biomarker candidate was marked as previously detected if it contained at least one HUPO889, or at least two HUPO9504 peptides, or at least two PeptideAtlas peptides.
In addition to direct evidence of detection in blood by mass spectrometry, annotation as secreted proteins or as single-pass membrane proteins [19] were also accepted as evidence of presence in blood. Furthermore, proteins in UniProt or designation as plasma proteins three programs for predicting whether or not a protein is secreted into the blood were used. These programs were TMHMM [20], SignalP [21] and SecretomeP [22]. A protein was predicted as secreted if TMHMM predicted the protein had one transmembrane domain and SignalP predicted the transmembrane domain was cleaved; or TMHMM predicted the protein had no transmembrane domain and either SignalP or SecretomeP predicted the protein was secreted.
SRM Assay Development
SRM assays for 388 targeted proteins were developed based on synthetic peptides, using a protocol similar to those described in the literature [15, 23, 24]. Up to five SRM suitable peptides per protein were identified from public sources such as the PeptideAtlas, Human Plasma Proteome Database or by proteotypic prediction tools [25] and synthesized. SRM triggered MS/MS spectra were collected on an ABSciex 5500 QTrap for both doubly and triply charged precursor ions. The obtained MS/MS spectra were assigned to individual peptides using MASCOT (cutoff score ≧15) [26]. Up to four transitions per precursor ion were selected for optimization. The resulting corresponding optimal retention time, declustering potential and collision energy were assembled for all transitions. Optimal transitions were measured on a mixture of all synthetic peptides, a pooled sample of benign patients and a pooled sample of cancer patients. Transitions were analyzed in batches, each containing up to 1750 transitions. Both biological samples were immuno-depleted and digested by trypsin and were analyzed on an ABSciex 5500 QTrap coupled with a reversed-phase (RP) high-performance liquid chromatography (HPLC) system. The obtained SRM data were manually reviewed to select the two best peptides per protein and the two best transitions per peptide. Transitions having interference with other transitions were not selected. Ratios between intensities of the two best transitions of peptides in the synthetic peptide mixture were also used to assess the specificity of the transitions in the biological samples. The intensity ratio was considered as an important metric defining the SRM assays.
Processing of Plasma Samples
Plasma samples were sequentially depleted of high- and medium-abundance proteins using immuno-depletion columns packed with the IgY14-Supermix resin from Sigma. The depleted plasma samples were then denatured, digested by trypsin and desalted. Peptide samples were separated using a capillary reversed-phase LC column (Thermo BioBasic 18 KAPPA; column dimensions: 320 μm×150 mm; particle size: 5 μm; pore size: 300 Å) and a nano-HPLC system (nanoACQUITY, Waters Inc.). The mobile phases were (A) 0.2% formic acid in water and (B) 0.2% formic acid in acetonitrile. The samples were injected (8 μl) and separated using a linear gradient (98% A to 70% A over 19 minutes, 5 μl/minute). Peptides were eluted directly into the electrospray source of the mass spectrometer (5500 QTrap LC/MS/MS, AB Sciex) operating in scheduled SRM positive-ion mode (Q1 resolution: unit; Q3 resolution: unit; detection window: 180 seconds; cycle time: 1.5 seconds). Transition intensities were then integrated by software MultiQuant (AB Sciex). An intensity threshold of 10,000 was used to filter out noisy data and undetected transitions.
Plasma Samples Used for Discovery and Validation Studies
Aliquots of plasma samples were provided by the Institut Universitaire de Cardiologie et de Pneumologie de Quebec (IUCPQ, Hospital Laval), New York University, the University of Pennsylvania, and Vanderbilt University (see Table 17). Subjects were enrolled in clinical studies previously approved by their Ethics Review Board (ERB) or Institutional Review Boards (IRB), respectively. In addition, plasma samples were provided by study investigators after review and approval of the sponsor's study protocol by the respective institution's IRB as required. Sample eligibility for the proteomic analysis was based on the satisfaction of the study inclusion and exclusion criteria, including the subject's demographic information, the subject's corresponding lung nodule radiographic characterization by chest computed tomography (CT), and the histopathology of the lung nodule obtained at the time of diagnostic surgical resection. Cancer samples had a histopathologic diagnosis of either non-small cell lung cancer (NSCLC), including adenocarcinoma, squamous cell, large cell, or bronchoalveolar cell carcinoma and a radiographic nodule of 30 mm or smaller. Benign samples, including granulomas, hamartomas and scar tissue, were also required to have a radiographic nodule of 30 mm or smaller and either histopathologic confirmation of being non-malignant or radiological confirmation in alignment with clinical guidelines. To ensure the accuracy of the clinical data, independent monitoring and verification of the clinical data associated with both the subject and lung nodule were performed in accordance with the guidance established by the Health Insurance Portability and Accountability Act (HIPAA) of 1996 to ensure subject privacy.
Study Design
The objective of the study design was to eliminate clinical and technical bias. Clinically, cancer and benign samples were paired so that they were from the same site, same gender, nodule sizes within 10 mm, age within 10 years, and smoking history within 20 pack years. Up to 15 pairs of matched cancer and benign samples per batch were assigned iteratively to processing batches until no statistical bias was demonstrable based on age, gender or nodule size.
Paired samples within each processing batch were further randomly and repeatedly assigned to positions within the processing batch, until the absolute values of the corresponding Pearson correlation coefficients between position and gender, nodule size, and age were less than 0.1. Afterwards, each pair of cancer and benign samples was randomized to their relative positions. To provide a control for sample batching, three 200 μl aliquots of a pooled human plasma standard (HPS) (Bioreclamation, Hicksville, N.Y.) were positioned at the beginning, middle and end of each processing batch, respectively. Samples within a batch were analyzed together.
Logistic Regression Model
The logistic regression classification method [27] was used to combine a panel of transitions into a classifier and to calculate a classification probability score between 0 and 1 for each sample. The probability score (Ps) of a sample was determined as Ps=1/[1+exp(−α−⊖i=1Nβi*{hacek over (I)}i,s)], where {hacek over (I)}i,s was the logarithmically transformed (base 2), normalized intensity of transition i in sample s, βi was the corresponding logistic regression coefficient, α was a classifier-specific constant, and N was the total number of transitions in the classifier. A sample was classified as benign if Ps was less than a decision threshold. The decision threshold can be increased or decreased depending on the desired NPV. To define the classifier, the panel of transitions (i.e. proteins), their coefficients, the normalization transitions, classifier coefficient α and the decision threshold must be learned (i.e. trained) from the discovery study and then confirmed using the validation study.
Discovery of the Rule-Out Classifier
A summary of the 143 samples used for classifier discovery appears in Table 17 and processed as described above.
Protein transitions were normalized as described above. Transitions that were not detected in at least 50% of the cancer samples or 50% of the benign samples were eliminated leaving 117 transitions for further consideration. Missing values for these transitions were replaced by half the minimum detected value over all samples for that transition.
The next step was finding the set of most cooperative proteins. The cooperative score of a protein is the number of high performing panels it participates in divided by the number of such panels it could appear on by chance alone. Hence, a cooperative score above 1 is good, and a score below 1 is not. The cooperative score for each protein is estimated by the following procedure:
One million random panels of 10 proteins each, selected from the 117 candidates, were generated. Each panel of 10 proteins was trained using the Monte Carlo cross validation (MCCV) method with a 20% hold-off rate and one hundred sample permutations per panel) to fit a logistic regression model and its performance assessed by partial AUC [28].
By generating such a large number of panels, we sample the space of classifiers sufficiently well to find some high performers by chance. The one hundred best random panels (see Table 2) out of the million generated were kept and for each of the 117 proteins we determined how frequently each occurred on these top panels. Of the 117 proteins, 36 had frequency more than expected by chance, after endogenous normalizers were removed. (Table 22) The expected number of panels on which a protein would appear by chance is 100*10/117=8.33. The cooperative score for a protein is the number of panels it appears on divided by 8.33.


















TABLE 21













Coefficient
Coefficient






Official


Co-

SEQ
(Discovery)
(Final)

Predicted



Protein
Gene
Cooperative

efficient

ID
alpha =
alpha =
Tissue
Concentration


Category
(UniProt)
Name
Score
Partial AUC
CV
Transition
NO
36.16
26.25
Candidate
(ng/ml)


























Classifier
TSP1_HUMAN
THBS1
1.8
0.25
0.24
GFLLLASLR_495.31_559.40
22
0.53
0.44

510


Classifier
COIA1_HUMAN
COL18A1
3.7
0.16
0.25
AVGLAG-
11
−1.56
−0.91

35








TFR_446.26_721.40







Classifier
ISLR_HUMAN
ISLR
1.4
0.32
0.25
ALPGTPVASS-
14
1.40
0.83

—








QPR_640.85_841.50







Classifier
TETN_HUMAN
CLEC3B
2.5
0.26
0.26
LDTLAQE-
20
−1.79
−1.02

58000








VALLK_657.39_330.20







Classifier
FRIL_HUMAN
FTL
2.8
0.31
0.26
LGG-
24
0.39
0.17
Secreted, Epi, Endo
12








PEAGLGEYLFER_804.40_913.40







Classifier
GRP78_HUMAN
HSPA5
1.4
0.27
0.27
TWNDPSVQQDIK_715.85_260.20
23
1.41
0.55
Secreted, Epi, Endo
100


Classifier
ALDOA_HUMAN
ALDOA
1.3
0.26
0.28
ALQASALK_401.25_617.40
7
−0.80
−0.26
Secreted, Epi
250


Classifier
BGH3_HUMAN
TGFBI
1.8
0.21
0.28
LTLLAPLNSVFK_658.40_804.50
8
1.73
0.54
Epi
140


Classifier
LG3BP_HUMAN
LGALS3BP
4.3
0.29
0.29
VE-
25
−0.58
−0.21
Secreted
440








IFYR_413.73_598.30







Classifier
LRP1_HUMAN
LRP1
4.0
0.13
0.32
TVLWPNGLSLDIPAGR_855.00_400.20
15
−1.59
−0.83
Epi
20


Classifier
FI-
FGA
1.1
0.31
0.35
NSLFEYQK_514.76_714.30
26
0.31
0.13

130000



BA_HUMAN












Classifier
PRDX1_HUMAN
PRDX1
1.5
0.32
0.37
QITVNDLPVGR_606.30_428.30
16
−0.34
−0.26
Epi
60


Classifier
GSLG1_HUMAN
GLG1
1.2
0.34
0.45
IIIQESALDYR_660.86_338.20
27
−0.70
−0.44
Epi, Endo
—


Robust
KIT_HUMAN
KIT
1.4
0.33
0.46





8.2


Robust
CD14_HUMAN
CD14
4.0
0.33
0.48




Epi
420


Robust
EF1A1_HUMAN
EEF1A1
1.2
0.32
0.56




Secreted, Epi
61


Robust
TENX_HUMAN
TNXB
1.1
0.30
0.56




Endo
70


Robust
AIFM1_HUMAN
AIFM1
1.4
0.32
0.70




Epi, Endo
1.4


Robust
GGH_HUMAN
GGH
1.3
0.32
0.81





250


Robust
IBP3_HUMAN
IGFBP3
3.4
0.32
1.82





5700


Robust
ENPL_HUMAN
HSP90B1
1.1
0.29
5.90




Secreted, Epi, Endo
88


Non-
ERO1A_HUMAN
ERO1L
6.2






Secreted, Epi, Endo
—


Robust













Non-
6PGD_HUMAN
PGD
4.3






Epi, Endo
29


Robust













Non-
ICAM1_HUMAN
ICAM1
3.9







71


Robust













Non-
PTPA_HUMAN
PPP2R4
2.1






Endo
3.3


Robust













Non-
NCF4_HUMAN
NCF4
2.0






Endo
—


Robust













Non-
SEM3G_HUMAN
SEMA3G
1.9







—


Robust













Non-
1433T_HUMAN
YWHAQ
1.5






Epi
180


Robust













Non-
RAP2B_HUMAN
RAP2B
1.5






Epi
—


Robust













Non-
MMP9_HUMAN
MMP9
1.4







28


Robust













Non-
FOLH1_HUMAN
FOLH1
1.3







—


Robust













Non-
GSTP1_HUMAN
GSTP1
1.3






Endo
32


Robust













Non-
EF2_HUMAN
EEF2
1.3






Secreted, Epi
30


Robust













Non-
RAN_HUMAN
RAN
1.2






Secreted, Epi
4.6


Robust













Non-
SODM_HUMAN
SOD2
1.2






Secreted
7.1


Robust













Non-
DSG2_HUMAN
DSG2
1.1






Endo
2.7


Robust









The 36 most cooperative proteins are listed in Table 22.



















TABLE 22













Coefficient
Coefficient






Official


Co-

SEQ
(Discovery)
(Final)

Predicted



Protein
Gene
Cooperative

efficient

ID
alpha =
alpha =
Tissue
Concentration


Category
(UniProt)
Name
Score
Partial AUC
CV
Transition
NO
36.16
26.25
Candidate
(ng/ml)


























Classifier
TSP1_HUMAN
THBS1
1.8
0.25
0.24
GFLLLASLR_495.31_559.40
22
0.53
0.44

510


Classifier
COIA1_HUMAN
COL18A1
3.7
0.16
0.25
AVGLAGTFR_446.26_721.40
11
−1.56
−0.91

35


Classifier
ISLR_HUMAN
ISLR
1.4
0.32
0.25
ALPGTPVASS-
14
1.40
0.83

—








QPR_640.85_841.50







Classifier
TETN_HUMAN
CLEC3B
2.5
0.26
0.26
LDTLAQE-
20
−1.79
−1.02

58000








VALLK_657.39_330.20







Classifier
FRIL_HUMAN
FTL
2.8
0.31
0.26
LGG-
24
0.39
0.17
Secreted, 
12








PEAGLGEYLFER_804.40_913.40







Classifier
GRP78_HUMAN
HSPA5
1.4
0.27
0.27
TWNDPSVQQDIK_715.85_260.20
23
1.41
0.55
Secreted, Epi, Endo
100


Classifier
ALDOA_HUMAN
ALDOA
1.3
0.26
0.28
ALQASALK_401.25_617.40
7
−0.80
−0.26
Secreted, Epi
250


Classifier
BGH3_HUMAN
TGFBI
1.8
0.21
0.28
LTLLAPLNSVFK_658.40_804.50
8
1.73
0.54
Epi
140


Classifier
LG3BP_HUMAN
LGALS3BP
4.3
0.29
0.29
VEIFYR_413.73_598.30
25
−0.58
−0.21
Secreted
440


Classifier
LRP1_HUMAN
LRP1
4.0
0.13
0.32
TVLWPNGLSLDIPAGR_855.00_400.20
15
−1.59
−0.83
Epi
20


Classifier
FIBA_HUMAN
FGA
1.1
0.31
0.35
NSLFEYQK_514.76_714.30
26
0.31
0.13

130000


Classifier
PRDX1_HUMAN
PRDX1
1.5
0.32
0.37
QITVNDLPVGR_606.30_428.30
16
−0.34
−0.26
Epi
60


Classifier
GSLG1_HUMAN
GLG1
1.2
0.34
0.45
IIIQESALDYR_660.86_338.20
27
−0.70
−0.44
Epi, Endo
—


Robust
KIT_HUMAN
KIT
1.4
0.33
0.46





8.2


Robust
CD14_HUMAN
CD14
4.0
0.33
0.48




Epi
420


Robust
EF1A1_HUMAN
EEF1A1
1.2
0.32
0.56




Secreted,
61












Epi



Robust
TENX_HUMAN
TNXB
1.1
0.30
0.56




Endo
70


Robust
AIFM1_HUMAN
AIFM1
1.4
0.32
0.70




Epi, Endo
1.4


Robust
GGH_HUMAN
GGH
1.3
0.32
0.81





250


Robust
IBP3_HUMAN
IGFBP3
3.4
0.32
1.82





5700


Robust
ENPL_HUMAN
HSP90B1
1.1
0.29
5.90




Secreted, Epi, Endo
88


Non-Robust
ERO1A_HUMAN
ERO1L
6.2






Secreted, Epi, Endo
—


Non-Robust
6PGD_HUMAN
PGD
4.3






Epi, Endo
29


Non-Robust
ICAM1_HUMAN
ICAM1
3.9







71


Non-Robust
PTPA_HUMAN
PPP2R4
2.1






Endo
3.3


Non-Robust
NCF4_HUMAN
NCF4
2.0






Endo
—


Non-Robust
SEM3G_HUMAN
SEMA3G
1.9







—


Non-Robust
1433T_HUMAN
YWHAQ
1.5






Epi
180


Non-Robust
RAP2B_HUMAN
RAP2B
1.5






Epi
—


Non-Robust
MMP9_HUMAN
MMP9
1.4







28


Non-Robust
FOLH1_HUMAN
FOLH1
1.3







—


Non-Robust
GSTP1_HUMAN
GSTP1
1.3






Endo
32


Non-Robust
EF2_HUMAN
EEF2
1.3






Secreted,
30












Epi



Non-Robust
RAN_HUMAN
RAN
1.2






Secreted,
4.6












Epi



Non-Robust
SODM_HUMAN
SOD2
1.2






Secreted
7.1


Non-Robust
DSG2_HUMAN
DSG2
1.1






Endo
2.7









The set of 36 cooperative proteins was further reduced to a set of 21 proteins by manually reviewing raw SRM data and eliminating proteins that did not have robust SRM transitions due to low signal to noise or interference.
Proteins were iteratively eliminated from the set of 21 proteins until a classifier with the optimal partial AUC was obtained. The criteria for elimination was coefficient stability. In a logistic regression model each protein has a coefficient. In the process of training the model the coefficient for each protein is determined. When this is performed using cross validation (MCCV), hundreds of coefficient estimates for each protein are derived. The variability of these coefficients is an estimate of the stability of the protein. At each step the proteins were trained using MCCV (hold out rate 20%, ten thousand sample permutations per panel) to a logistic regression model and their stability measured. The least stable protein was eliminated. This process continued until a 13 protein classifier with optimal partial AUC was reached.
Finally, the 13 protein classifier was trained to a logistic regression model by MCCV (hold out rate 20%, twenty thousand sample permutations). The thirteen proteins for the rule-out classifier are listed in Table 18 along with their highest intensity transition and model coefficient.
Selection of a Decision Threshold
Assuming the cancer prevalence of lung nodules is prev, the performance of a classifier (NPV and ROR) on the patient population with lung nodules was calculated from sensitivity (sens) and specificity (spec) as follows:




  
    
      
        
          NPV
          =
          
            
              
                (
                
                  1
                  -
                  prev
                
                )
              
              *
              spec
            
            
              
                prev
                *
                
                  (
                  
                    1
                    -
                    sens
                  
                  )
                
              
              +
              
                
                  (
                  
                    1
                    -
                    prev
                  
                  )
                
                *
                spec
              
            
          
        
        ,
      
    
    
      
        (
        1
        )
      
    
  
  
    
      
        
          PPV
          =
          
            
              prev
              *
              sens
            
            
              
                prev
                *
                sens
              
              +
              
                
                  (
                  
                    1
                    -
                    prev
                  
                  )
                
                *
                
                  (
                  
                    1
                    -
                    spec
                  
                  )
                
              
            
          
        
        ,
      
    
    
      
        (
        2
        )
      
    
  
  
    
      
        ROR
        =
        
          
            prev
            *
            
              (
              
                1
                -
                sens
              
              )
            
          
          +
          
            
              (
              
                1
                -
                prev
              
              )
            
            *
            
              spec
              .
            
          
        
      
    
    
      
        (
        3
        )
      
    
  




The threshold separating calls for cancer or benign samples was then selected as the probability score with NPV≧90% and ROR≧20%. As we expect the classifier's performance measured on the discovery set to be an overestimate, the threshold is selected to be a range, as performance will usually degrade on an independent validation set.
Validation of the Rule-Out Classifier
52 cancer and 52 benign samples (see Table 17) were used to validate the performance of the 13 protein classifier. Half of the samples were placed in pre-determined processing batches analyzed immediately after the discovery samples and the other half of samples were analyzed at a later date. This introduced variability one would expect in practice. More specifically, the three HPS samples run in each processing batch were utilized as external calibrators. Details on HPS calibration are described below.
Calibration by HPS Samples
For label-free MS approach, variation on signal intensity between different experiments is expected. To reduce this variation, we utilized HPS samples as an external standard and calibrated the intensity between the discovery and validation studies. Assume that {hacek over (I)}i,s is the logarithmically transformed (base 2), normalized intensity of transition i in sample s, {hacek over (I)}i,dis and {hacek over (I)}i,val are the corresponding median values of HPS samples in the discovery and the validation studies, respectively. Then the HPS corrected intensity is

Ĩi,s={hacek over (I)}i,s−{hacek over (I)}i,val+{hacek over (I)}i,dis 

Consequently, assume that the probability for cancer of a clinical sample in the validation study is predicted as prob by the classifier. Then the HPS corrected probability of cancer of the clinical sample is calculated as follows:




  
    
      probability
      corrected
    
    =
    
      1
      
        1
        +
        
          e
          
            -
            
              S
              corrected
            
          
        
      
    
  




  where




  
    
      S
      corrected
    
    =
    
      S
      -
      
        S
        
          HPS
          ,
          val
        
      
      +
      
        S
        
          HPS
          ,
          dis
        
      
    
  




  and




  
    S
    =
    
      ln
      ⁢
      
        
          prob
          
            1
            -
            prob
          
        
        .
      
    
  



Here SHPS,dis and SHPS,val were the median value of S of all HPS samples in the discovery and validation studies, respectively.
Statistical Analysis
All statistical analyses were performed with Stata, R and/or MatLab.
Depletion Column Drift
We observed an increase of signal intensity as more and more samples were depleted by the same column. We used transition intensity in HPS samples to quantify this technical variability. Assuming Ii,s was the intensity of transition i in a HPS sample s, the drift of the sample was defined as




  
    
      drift
      s
    
    =
    
      median
      ⁡
      
        (
        
          
            
              I
              
                i
                ,
                s
              
            
            -
            
              
                I
                ^
              
              s
            
          
          
            
              I
              ^
            
            s
          
        
        )
      
    
  
  ,




where Îi was the mean value of Ii,s among all HPS samples that were depleted by the same column and the median was taken over all detected transitions in the sample. Then the drift of the column was defined as

driftcol=median(drifts>0)−median(drifts<0).

Here the median was taken over all HPS samples depleted by the column. If no sample drift was greater or less than zero, the corresponding median was taken as 0. The median column drift was the median of drifts of all depletion columns used in the study.
Identification of Endogenous Normalizing Proteins
The following criteria were used to identify a transition as a normalizer:

    
    
        Possessed the highest median intensity of all transitions from the same protein.
        Detected in all samples.
        Ranked high in reducing median technical CV (median CV of transition intensities that were measured on HPS samples) as a normalizer.
        Ranked high in reducing median column drift that was observed in sample depletion.
        Possessed low median technical CV and low median biological CV (median CV of transition intensities that were measured on clinical samples).

Six transitions were selected and appear in Table 23.

    
    









TABLE 23







Panel of endogenous normalizers.














Median
Median




SEQ ID
Technical
Column


Normalizer
Transition
NO
CV (%)
Drift (%)














PEDF_HUMAN
LQSLFDSPDFSK_692.34_593.30
28
25.8
6.8


MASP1_HUMAN
TGVITSPDFPNPYPK_816.92_258.10
 6
26.5
18.3


GELS_HUMAN
TASDFITK_441.73_710.40
 5
27.1
16.8


LUM_HUMAN
SLEDLQLTHNK_433.23_499.30
29
27.1
16.1


C163A_HUMAN
INPASLDK_429.24_630.30
30
26.6
14.6


PTPRJ_HUMAN
VITEPIPVSDLR_669.89_896.50
31
27.2
18.2



Normalization by Panel of Transitions

25.1
9.0



Without Normalization

32.3
23.8









Data Normalization
A panel of six normalization transitions (see Table 23) were used to normalize raw SRM data for two purposes: (A) to reduce sample-to-sample intensity variations within same study and (B) to reduce intensity variations between different studies. For the first purpose, a scaling factor was calculated for each sample so that the intensities of the six normalization transitions of the sample were aligned with the corresponding median intensities of all HGS samples. Assuming that Ni,s is the intensity of a normalization transition i in sample s and {circumflex over (N)}i the corresponding median intensity of all HGS samples, then the scaling factor for sample s is given by Ŝ/Ss, where




  
    S
    s
  
  =
  
    median
    ⁡
    
      (
      
        
          
            N
            
              1
              ,
              s
            
          
          
            
              N
              ^
            
            1
          
        
        ,
        
          
            N
            
              2
              ,
              s
            
          
          
            
              N
              ^
            
            2
          
        
        ,
        ...
        ⁢
        
          
        
        ,
        
          
            N
            
              6
              ,
              s
            
          
          
            
              N
              ^
            
            6
          
        
      
      )
    
  




is the median of the intensity ratios and Ŝ is the median of Ss over all samples in the study. For the second purpose, a scaling factor was calculated between the discovery and the validation studies so that the median intensities of the six normalization transitions of all HGS samples in the validation study were comparable with the corresponding values in the discovery study. Assuming that the median intensities of all HGS samples in the two studies are {circumflex over (N)}i,dis and {circumflex over (N)}i,val, respectively, the scaling factor for the validation study is given by





  R
  =
  
    median
    ⁡
    
      (
      
        
          
            
              N
              ^
            
            
              1
              ,
              dis
            
          
          
            
              N
              ^
            
            
              1
              ,
              val
            
          
        
        ,
        
          
            
              N
              ^
            
            
              2
              ,
              dis
            
          
          
            
              N
              ^
            
            
              2
              ,
              val
            
          
        
        ,
        ...
        ⁢
        
          
        
        ,
        
          
            
              N
              ^
            
            
              6
              ,
              dis
            
          
          
            
              N
              ^
            
            
              6
              ,
              val
            
          
        
      
      )
    
  




Finally, for each transition of each sample, its normalized intensity was calculated as

Ĩi,s=Ii,s*R*Ŝ/Ss 

where Ii,s was the raw intensity.

Isolation of Membrane Proteins from Tissues
Endothelial plasma membrane proteins were isolated from normal and tumor lung tissue samples that were obtained from fresh lung resections. Briefly, tissues were washed in buffer and homogenates were prepared by disrupting the tissues with a Polytron. Homogenates were filtered through a 180-μm mesh and filtrates were centrifuged at 900×g for 10 min, at 4° C. Supernatants were centrifuged on top of a 50% (w:v) sucrose cushion at 218,000×g for 60 min at 4° C. to pellet the membranes. Pellets were resuspended and treated with micrococcal nuclease. Membranes from endothelial cells were incubated with a combination of anti-thrombomodulin, anti-ACE, anti-CD34 and anti-CD144 antibodies, and then centrifuged on top of a 50% (w:v) sucrose cushion at 280,000×g for 60 min at 4° C. After pellets were resuspended, endothelial cell plasma membranes were isolated using MACS microbeads, treated with potassium iodide to remove cytoplasmic peripheral proteins.
Epithelial plasma membrane proteins from normal and tumor lung tissue samples were isolated from fresh lung resections. Tissues were washed and homogenates as described above for endothelial plasma membrane proteins preparation. Membranes from epithelial cells were labeled with a combination of anti-ESA, anti-CEA, anti-CD66c and anti-EMA antibodies, and then centrifuged on top of a 50% (w:v) sucrose cushion at 218,000×g for 60 min at 4° C. Epithelial cell plasma membranes were isolated using MACS microbeads and the eluate was centrifuged at 337,000×g for 30 minutes at 4° C. over a 33% (w:v) sucrose cushion. After removing the supernatant and sucrose cushion, the pellet was resuspended in Laemmli/Urea/DTT.
Isolation of Secreted Proteins from Tissues
Secreted proteins were isolated from normal and tumor lung tissue samples that were isolated from fresh lung resections. Tissues were washed and homogenized using a Polytron homogenization. The density of the homogenates was adjusted to 1.4 M with concentrated sucrose prior to isolating the secretory vesicles by isopycnic centrifugation at 100,000×g for 2 hr at 4° C. on a 0.8 and 1.2 M discontinuous sucrose gradient. Vesicles concentrating at the 0.8/1.2 M interface were collected and further incubated for 25 minutes with 0.5 M KCl (final concentration) to remove loosely bound peripheral proteins. Vesicles were recuperated by ultracentrifugation at 150,000×g for one hour at 4° C. and then opened with 100 mM ammonium carbonate pH 11.0 for 30 minutes at 4° C. Secreted proteins were recovered in the supernatant following a 1-hour ultracentrifugation at 150,000×g at 4° C.
Preparation of IgY14-SuperMix Immunoaffinity Columns
Immunoaffinity columns were prepared in-house using a slurry containing a 2:1 ratio of IgY14 and SuperMix immunoaffinity resins, respectively (Sigma Aldrich). Briefly, a slurry (10 ml, 50%) of mixed immunoaffinity resins was added to a glass chromatography column (Tricorn, GE Healthcare) and the resin was allowed to settle under gravity flow, resulting in a 5 ml resin volume in the column. The column was capped and placed on an Agilent 1100 series HPLC system for further packing (20 minutes, 0.15M ammonium bicarbonate, 2 ml/min). The performance of each column used in the study was then assessed by replicate injections of aliquots of HPS sample. Column performance was assessed prior to beginning immunoaffinity separation of each batch of clinical samples.
IgY14-Sumermix Immunoaffinity Chromatography
Plasma samples (60 μl) were diluted (0.15M ammonium bicarbonate, 1:2 v/v, respectively) and filtered (0.2 μm AcroPrep 96-well filter plate, Pall Life Sciences) prior to immunoaffinity separation. Dilute plasma (90 μl) was separated on the IgY14-SuperMix column connected to an Agilent 1100 series HPLC system using a three buffers (loading/washing: 0.15M ammonium bicarbonate; stripping/elution: 0.1M glycine, pH 2.5; neutralization: 0.01M Tris-HCl, 0.15M NaCl, pH 7.4) with a load-wash-elute-neutralization-re-equilibration cycle (36 minutes total time). The unbound and bound fractions were monitored using a UV absorbance (280 nm) and were baseline resolved after separation. Only the unbound fraction containing the low abundance proteins was collected for downstream processing and analysis. Unbound fractions were lyophilized prior to enzymatic digestion.
Enzymatic Digestion of Low Abundance Proteins
Low abundance proteins were reconstituted under mild denaturing conditions (200 μl of 1:1 0.1M ammonium bicarbonate/trifluoroethanol v/v) and allowed to incubate (30 minutes, room temperature, orbital shaker). Samples were then diluted (800 μl of 0.1M ammonium bicarbonate) and digested with trypsin (Princeton Separations; 0.4 μg trypsin per sample, 37° C., 16 hours). Digested samples were lyophilized prior to solid-phase extraction.
Solid-Phase Extraction
Solid phase extraction was used to reduce salt and buffer contents in the samples prior to mass spectrometry. The lyophilized samples containing tryptic peptides were reconstituted (350 μl 0.01M ammonium bicarbonate) and allowed to incubate (15 minutes, room temperature, orbital shaker). A reducing agent was then added to the samples (30 μl 0.05M TCEP) and the samples were incubated (60 minutes, room temperature). Dilute acid and a low percentage of organic solvent (375 μl 90% water/10% acetonitrile/0.2% trifluoroacetic acid) were added to optimize the solid phase extraction of peptides. The extraction plate (Empore C18, 3M Bioanalytical Technologies) was conditioned according to manufacturer protocol. Samples were loaded onto the solid phase extraction plate, washed (500 μl 95% water/5% acetonitrile/0.1% trifluoroacetic acid) and eluted (200 μl 52% water/48% acetonitrile/0.1% trifluoroacetic acid) into a collection plate. The eluate was split into two equal aliquots and each aliquot was taken to dryness in a vacuum concentrator. One aliquot was used immediately for mass spectrometry, while the other was stored (−80° C.) and used as needed. Samples were reconstituted (12 μl 90% water/10% acetonitrile/0.2% formic acid) just prior to LC-SRM MS analysis.
Inclusion and Exclusion Criteria
Plasma samples were eligible for the studies if they were (A) obtained in EDTA tubes, (B) obtained from subjects previously enrolled in IRB-approved studies at the participating institutions, and (C) archived, e.g. labeled, aliquotted and frozen, as stipulated by the study protocols. The samples must also satisfy the following inclusion and exclusion criteria:

    
    
        1) Inclusion Criteria:
        2) Sample eligibility was based on clinical parameters, including the following subject, nodule and clinical staging parameters:
        
            a) Subject
            
                i) age ≧40
                ii) any smoking status, e.g. current, former, or never
                iii) co-morbid conditions, e.g. COPD
                iv) prior malignancy with a minimum of 5 years in clinical remission
                v) prior history of skin carcinomas—squamous or basal cell
            
            
            b) Nodule
            
                i) Radiology
                
                    (1) size ≧4 mm and ≦70 mm (up to Stage 2B eligible)
                    (2) any spiculation or ground glass opacity
                
                
                ii) pathology
                
                    (1) malignant—adenocarcinoma, squamous, or large cell
                    (2) benign—inflammatory (e.g. granulomatous, infectious) or non-inflammatory (e.g. hamartoma)
                
                
            
            
            c) Clinical stage
            
                i) Primary tumor: ≦T2 (e.g. 1A, 1B, 2A and 2B)
                ii) Regional lymph nodes: NO or N1 only
                iii) Distant metastasis: MO only
            
            
        
        
        3) Exclusion Criteria
        
            a) Subject: prior malignancy within 5 years of IPN diagnosis
            b) Nodule:
            
                i) size data unavailable
                ii) for cancer or benign SPNs, no pathology data available
                iii) pathology—small cell lung cancer
            
            
            c) Clinical stage
            
                i) Primary tumor: ≧T3
                ii) Regional lymph nodes: ≧N2
                iii) Distant metastasis: ≧M1
            
            
        
        
    
    


Power Analysis for the Discovery Study
The power analysis for the discovery study was based on the following assumptions: 1) The overall false positive rate (α) was set to 0.05. 2) {hacek over (S)}idák correction for multiple testing was used to calculate the effective αeff for testing 200 proteins, i.e.,




  
    i
    .
    e
    .
  
  ,
  
    
  
  ⁢
  
    
      α
      eff
    
    =
    
      1
      -
      
        
          
            1
            -
            α
          
          200
        
        .
      
    
  




3) The effective sample size was reduced by a factor of 0.864 to account for the larger sample requirement for the Mann-Whitney test than for the t-test. 4) The overall coefficient of variation was set to 0.43 based on a previous experience. 5) The power (1−β) of the study was calculated based on the formula for the two-sample, two-sided t-test, using effective αeff and effective sample size. The power for the discovery study was tabulated in Table 24 by the sample size per cohort and the detectable fold difference between control and disease samples.








TABLE 24







Cohort size required to detect protein


fold changes with a given probability.










Detectable Protein Fold Difference












Cohort Size
1.25
1.5
1.75
2














20
0.011
0.112
0.368
0.653


30
0.025
0.277
0.698
0.925


40
0.051
0.495
0.905
0.992


50
0.088
0.687
0.977
0.999


60
0.129
0.812
0.994
1


70
0.183
0.902
0.999
1


80
0.244
0.953
1
1


90
0.302
0.977
1
1


100
0.369
0.99
1
1









Power Analysis for the Validation Study
Sufficient cancer and benign samples are needed in the validation study to confirm the performance of the rule-out classifier obtained from the discovery study. We are interested in obtaining the 95% confidence intervals (CIs) on NPV and ROR for the rule-out classifier. Using the Equations in the Selection of a Decision Threshold section herein, one can derive sensitivity (sens) and specificity (spec) as functions of NPV and ROR, i.e.,

sens=1−ROR*(1−NPV)/prev,

spec=ROR*NPV/(1−prev),

where prev is the cancer prevalence in the intended use population. Assume that the validation study contains NC cancer samples and NB benign samples. Based on binomial distribution, variances of sensitivity and specificity are given by

var(sens)=sens*(1−sens)/NC 

var(spec)=spec*(1−spec)/NB 

Using the Equations in the Selection of a Decision Threshold section herein, the corresponding variances of NPV and ROR can be derived under the large-sample, normal-distribution approximation as





  
    
      var
      ⁡
      
        (
        NPV
        )
      
    
    =
    
      
        
          
            NPV
            2
          
          ⁡
          
            (
            
              1
              -
              NPV
            
            )
          
        
        2
      
      ⁡
      
        [
        
          
            
              var
              ⁡
              
                (
                sens
                )
              
            
            
              
                (
                
                  1
                  -
                  sens
                
                )
              
              2
            
          
          +
          
            
              var
              ⁡
              
                (
                spec
                )
              
            
            
              spec
              2
            
          
        
        ]
      
    
  
  ,
  
    

  
  ⁢
  
    
      var
      ⁡
      
        (
        ROR
        )
      
    
    =
    
      
        
          prev
          2
        
        *
        
          var
          ⁡
          
            (
            sens
            )
          
        
      
      +
      
        
          
            (
            
              1
              -
              prev
            
            )
          
          2
        
        *
        
          
            var
            ⁡
            
              (
              spec
              )
            
          
          .
        
      
    
  




The two-sided 95% CIs of NPV and ROR are then given by ±zα/2√{square root over (var(NPV))} and ±zα/2√{square root over (var(ROR))}, respectively, where zα/2=1.959964 is the 97.5% quantile of the normal distribution. The anticipated 95% CIs for the validation study were tabulated in Table 25 by the sample size (NC=NB=N) per cohort.








TABLE 25







The 95% confidence interval (CI) of NPV as a function of cohort


size. The corresponding 95% CI of ROR is also listed. The prevalence


was set at 28.5%. The expected NPV and ROR were set to values in


the discovery study, i.e., 90% and 52%, respectively.










95% CI of
95% CI of


Cohort Size
NPV (± %)
ROR (± %)












10
12.5
22.1


20
8.8
15.7


30
7.2
12.8


40
6.2
11.1


50
5.6
9.9


60
5.1
9.0


70
4.7
8.4


80
4.4
7.8


90
4.2
7.4


100
3.9
7.0


150
3.2
5.7


200
2.8
5.0









Calculation of Q-Values of Peptide and Protein Assays
To determine the false positive assay rate the q-values of peptide SRM assays were calculated as follows. Using the distribution of Pearson correlations between transitions from different proteins as the null distribution (FIG. 7), an empirical p-value was assigned to a pair of transitions from the same peptide, detected in at least five common samples otherwise a value of ‘NA’ is assigned. The empirical p-value was converted to a q-value using the “qvalue” package in Bioconductor. Peptide q-values were below 0.05 for all SRM assays presented in Table 6.
The q-values of protein SRM assays were calculated in the same way except Pearson correlations of individual proteins were calculated as those between two transitions from different peptides of the protein. For proteins not having two peptides detected in five or more common samples, their q-values could not be properly evaluated and were assigned ‘NA’.
Impact of Categorical Confounding Factors







TABLE 26







Impact of categorical confounding factors on classifier score.












Cancer
p-value
Benign
p-value















Gender
# Female
70
0.786*
68
0.387*



Median score
0.701

0.570




(quartile range)
(0.642-0.788)

(0.390-0.70) 




# Male
54

55




Median
0.736

0.621




(quartile range)
(0.628-0.802)

(0.459-0.723)



Smoking
# Never
8
0.435**
34
0.365**


Status
Median score
0.664

0.554




(quartile range)
(0.648-0.707)

(0.452-0.687)




# Past
98

73




Median
0.703

0.586




(quartile range)
(0.618-0.802)

(0.428-0.716)




# Current
17

13




Median score
0.749

0.638




(quartile range)
(0.657-0.789)

(0.619-0.728)






*p-value by Mann-Whitney test


**p-value by Kruskal-Wallis test






Impact of Continuous Confounding Factors







TABLE 27







Impact of continuous confounding factors on classifier score.












Coefficient of




Correlation
linear fit (95% CI)
p-value














Age
All
0.198
0.003
0.002





 (0.001-0.005)




Cancer
0.012
0.000
0.893





(−0.003-0.003)




Benign
0.248
0.004
0.006





 (0.001-0.007)



Nodule
All
−0.057
−0.002
0.372


size


(−0.005-0.002)




Cancer
−0.013
0.000
0.889





(−0.005-0.004)




Benign
−0.055
−0.001
0.542





(−0.006-0.003)



Pack-
All
0.154
0.001
0.019


year


 (0.00-0.002)




Cancer
0.060
0.000
0.520





(−0.001-0.001)




Benign
0.108
0.001
0.254





 (0.00-0.002)










Example 8
A Systems Biology-Derived, Blood-Based Proteomic Classifier for the Molecular Characterization of Pulmonary Nodules
Summary
Each year millions of pulmonary nodules are discovered by computed tomography but remain undiagnosed as malignant or benign. As the majority of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. This invention presents a 13-protein blood-based classifier for the identification of benign nodules. Using a systems biology strategy, 371 protein candidates were identified and selected reaction monitoring (SRM) assays developed for each. The SRM assays were applied in a multisite discovery study (n=143) with benign and cancer plasma samples matched on nodule size, age, gender and clinical site. Rather than identify the best individual performing proteins, the 13-protein classifier was formed from proteins performing best on panels. The classifier was validated on an independent set of plasma samples (n=104) demonstrating high negative predictive value (92%) and specificity (27%) sufficiently high to obviate one-in-four patients with benign nodules from invasive procedures. Importantly, validation performance on a non-discovery clinical site showed NPV of 100% and specificity of 28%, arguing for the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, FOS) highly associated with lung cancer, lung inflammation and oxidative stress networks. Remarkably, the classifier score was independent of patient nodule size, smoking history and age. As these are the currently used risk factors for clinical management of pulmonary nodules, the application of this molecular test would provide a powerful complementary tool for physicians to use in lung cancer diagnosis.
Rationale
Computed tomography (CT) identifies millions of pulmonary nodules annually with many being undiagnosed as malignant or benign. The vast majority of these nodules are benign, but due to the threat of cancer, a significant number of patients with benign nodules undergo unnecessary invasive medical procedures costing the healthcare system billions of dollars annually. Consequently, there is a high unmet need for a non-invasive clinical test that can identify benign nodules with high probability.
Presented is a 13-protein plasma test, or classifier, for identifying benign nodules. To develop the classifier, a systems biology approach based on the supposition that biological networks in tumors become disease-perturbed and alter the expression of their cognate proteins was adopted. This systems approach employs a variety of strategies to identify blood proteins that directly reflect lung cancer-perturbed networks.
First, candidate biomarkers prioritized for inclusion on the classifier were those proteins secreted by or shed from the cell surface of lung cancer cells in contrast to normal lung cells. These are proteins both associated with lung cancer and also most likely to be emitted by a malignant pulmonary nodule into blood. The literature was also surveyed to identify blood proteins associated with lung cancer. In total, an initial list of 388 protein candidates for inclusion on the classifier were derived from these three sources.
Another system-driven approach was to prioritize the 388 protein candidates for inclusion on the classifier by how frequently they appear on high performing protein panels, as opposed to their individual diagnostic performance. This strategy is motivated by the intent to capture the integrated behavior of proteins within lung cancer-perturbed networks. Proteins that appear frequently on high performing panels are called cooperative proteins. This is a defining step in the discovery of the classifier as the most cooperative proteins are often not the proteins with best individual performance.
Third, the classifier is deconstructed in terms of its relationship to lung cancer networks. Ideally, the classifier consists of multiple proteins from multiple lung cancer-perturbed networks. We conjecture that measuring multiple proteins from the same lung cancer associated pathway increases the signal-to-noise ratio thus enhancing performance of the classifier.
Selected reaction monitoring (SRM) mass spectrometry (MS) was utilized to measure the concentrations of the candidate proteins in plasma. SRM is a form of MS that monitors predetermined and highly specific mass products, called transitions, of particularly informative (proteotypic or protein-specific) peptides of targeted proteins. Briefly, SRM assays for proteins are based on the high reproducibility of peptide ionization, the foundation of MS. During a SRM analysis, the mass spectrometer is programmed to monitor for transitions of the specific protein(s) being assayed. The resulting chromatograms are integrated to provide quantitative or semi-quantitative protein abundance information. The benefits of SRM assays include high protein specificity, large multiplexing capacity, and both rapid and reliable assay development and deployment. SRM has been used for clinical testing of small molecule analytes for many years, and recently in the development of biologically relevant assays. Exceptional public resources exist to accelerate SRM assay development including the PeptideAtlas, the Plasma Proteome Project, the SRM Atlas and the PeptideAtlas SRM Experimental Library.
In accordance with evolving guidelines for clinical test development, the classifier was discovered (n=143) and validated (n=104) using independent plasma sets from multiple clinical sites consistent with an intended use population of patients with lung nodules, defined as round opacities up to 30 mm in size. In contrast to other biomarker studies, utilizing biospecimens associated with the broad clinical spectrum of lung cancer (Stages I to IV), the cancer plasma samples analyzed were limited to Stage IA, which corresponds to the intended use population of lung nodules of size 30 mm or less. The classifier yielded a performance amendable to further clinical stratification of the intended use by parameters such as age, smoking history or nodule size, as guided by a clinician's diagnostic needs.
Validated performance of the 13-protein classifier demonstrated a negative predictive value (NPV) of 92% and a specificity of 27%. For clinical utility, the classifier must reliably and frequently provide information that can participate in a physician's decision to avoid an invasive procedure. High NPV is required to ensure that the classifier reliably identifies benign nodules. Equivalently, malignant nodules are rarely (8% or less) reported as benign by the classifier. A specificity of 27% implies that one-in-four patients with a benign nodule can avoid invasive procedures, and so, frequently provides information of clinical utility. All validation samples were independent of discovery samples, and 37 came from a new clinical site. Performance on the samples from the new site demonstrated a NPV of 100% and a specificity of 28% suggesting that the classifier performance extends to new clinical settings. Remarkably, the classifier score is demonstrated to be independent of the patient's age, smoking history and nodule size, thereby complementing current clinical risk factors with an informative molecular dimension for evaluating the disease status of a pulmonary nodule.
Results
Table 28 presents the steps taken in the refinement of the initial 388 protein candidates down to the set of 13 classifier proteins used for validation and performance assessment. The results are presented in the same sequence.







TABLE 28







Steps in refining the 388 candidates


down to the 13-protein classifier








Number of



Proteins
Refinement











388
Lung cancer associated protein candidates



sourced from tissue and literature.


371
Number of the 388 protein candidates



successfully developed into a SRM assay.


190
Number of the 371 SRM protein assays detected



in plasma.


125
Number of the 190 SRM protein assays detected



in at least 50% of cancer or 50% of benign



discovery samples.


36
Number of the 125 detected proteins that were



cooperative.


21
Number of the 36 cooperative proteins with



robust SRM assays (i.e. no interfering signals,



good signal-to-noise, etc.)


13
Number of the 21 robust and cooperative



proteins with stable logistic regression



coefficients.









Selection of Biomarker Candidates for Assay Development.
To identify lung cancer biomarkers in blood that are shed or secreted from lung tumor cells, proteins overexpressed on the cell surface or over-secreted from lung cancer tumor cells relative to normal lung cells were identified from freshly resected lung tumors using organelle isolation techniques combined with mass spectrometry. In addition, an extensive literature search for lung cancer biomarkers was performed using public and private resources. Both the tissue-sourced biomarkers and literature-sourced biomarkers were required to have evidence of previous detection in blood. The tissue (217) and literature (319) candidates overlapped by 148 proteins, resulting in a list of 388 protein candidates.
Development of SRM Assays.
Standard synthetic peptide techniques were used to develop a 371-protein multiplexed SRM assay from the 388 protein candidates. For 17 of the candidates, appropriate synthetic peptides could not be developed or confidently identified. The 371 SRM assays were applied to plasma samples from patients with pathologically confirmed benign nodules and pathologically confirmed malignant lung nodules to determine how many of the 371 proteins could be detected in plasma. A total of 190 SRM assays were able to detect their target proteins in plasma (51% success rate). This success rate (51%) compares very favorably to similar efforts (16%) to develop large scale SRM assays for the detection of diverse cancer markers in blood. Of the 190 proteins detected in blood, 114 were derived from the tissue-sourced candidates and 167 derived from the literature-sourced candidates (91 protein overlap). It is conjectured that the 49% of candidate proteins not detected in blood were present, but below the level of detection of the technology.
Classifier Discovery.
A summary of the features of the 143 samples used for classifier discovery appears in Table 29. Samples were obtained from three clinical sites to avoid overfitting to a single clinical site. Participating clinical sites were Institut Universitaire de Cardiologie et de Pneumologie de Quebec (IUCPQ), New York University (NYU) and University of Pennsylvania (UPenn). All samples were selected to be consistent with intended use, specifically, having nodule size 30 mm or less. Cancer and benign samples were pathologically confirmed.







TABLE 29







Clinical characteristics of subjects and nodules in the discovery and


validation studies














Cancer n
Benign n
p value
Cancer n
Benign n
p value









Characteristics
Discovery Study
Validation Study
















Subjects
72
71

52
52



Age (year)*
65
64
0.46†
63
62
0.03†



(59-72)
(52-71)

(60-73)
(56-67)



Gender


1.00‡


0.85‡


Male
29
28

25
27



Female
43
43

27
25



Smoking History








Status


0.006‡


0.006‡


Never§
5
19

3
15



Former
60
44

38
29



Current
6
6

11
7



No Data
1
2

0
1



Pack-Year*¶
37
20
0.001†
40
27
0.09†



(20-52)
 (0-40)

(19-50)
 (0-50)



Nodules








Size (mm)*
13
13
0.69†
16
15
0.68†



(10-16)
(10-18)

(13-20)
(12-22)



Source


1.00‡


0.89‡


IUCPQ||
14
14

13
12



New York
29
28

6
9



Pennsylvania
29
29

14
13



Vanderbilt
0
0

19
18



Histopathology








Benign Diagnosis








Granuloma
—
48

—
26



Hamartoma
—
9

—
6



Scar
—
2

—
2



Other**
—
12

—
18



Cancer Diagnosis








Adenocarcinoma
41
—

25
—



Squamous Cell
3
—

15
—



Large Cell
0
—

2
—



Bronchioloalveolar
3
—

0
—



(BAC)








Adenocarcinoma/BAC
21
—

5
—



Other††
4
—

5
—





*Data shown are median values with quartile ranges indicated in parentheses.


†Mann-Whitney test.


‡Fisher's exact test.


§A never smoker is defined as an individual who has a lifetime history of smoking less than 100 cigarettes.


¶A pack-year is defined as the product of the total number of years of smoking and the average number of packs of cigarettes smoked daily. Pack-year data were not available for 4 cancer and 6 benign subjects in the discovery set and 2 cancer and 3 benign subjects in the validation set.


||IUCPQ is the Institute Universitaire de Cardiologie et de Pneumologie de Quebec.


**For the discovery study, the Benign Diagnosis “Other” category included: amyloidosis, n = 2; fibroelastic nodule, n = 1; fibrosis, n = 1; hemorrhagic infarct, n = 1; lymphoid aggregate, n = 1; organizing pneumonia, n = 3; pulmonary infarct, n = 1; sclerosing hemangioma, n = 1; and subpleural fibrosis with benign lymphoid hyperplasia, n = 1. For the validation study, the Benign Diagnosis “Other” category included: amyloidosis, n = 1; bronchial epithelial cells, n = 4; bronchiolitis interstitial fibrosis, n = 1; emphysematous lung, n = 1; fibrotic inflammatory lesion, n = 1; inflammation, n = 1; parenchymal intussusception, n = 1; lymphangioma, n = 1; mixed lymphocytes and histiocytes, n = 1; normal parenchyma, n = 1; organizing pneumonia, n = 1; pulmonary infarct, n = 2; respiratory bronchiolitis, n = 1; and squamous metaplasia, n = 1.


††For the discovery study, the non-small cell lung cancer (NSCLC) Diagnosis “Other” category included: adenocarcinoma squamous cell mixed, n = 1; large cell squamous cell mixed, n = 1; pleomorphic carcinoma, n = 1, and not specified, n = 1. For the validation study, the NSCLC Diagnosis “Other” category included: carcinoid, n = 2; large cell squamous cell mixed, n = 1; and not specified, n = 2.






Benign and cancer samples were paired by matching on age, gender, nodule size and clinical site to avoid bias during SRM analysis and also to ensure that the biomarkers discovered were not markers of age, gender, nodule size or clinical site.
The 371-protein SRM assay was applied to the 143 discovery samples and the resulting transition data were analyzed to derive a 13-protein classifier using a logistic regression model (Table 30). The key step in this refinement (Table 28) was the identification of 36 cooperative proteins of which 21 had robust SRM signal. A protein was deemed cooperative if found more frequently on the best performing panels than expected by chance alone, with the significance determined using the following statistical estimation procedure. Briefly, a million random 10-protein panels were generated and the frequency of each protein among the best performing panels (p value ≦10−4) was calculated. These proteins were sampled from the list of 125 proteins reproducibly detected in either benign samples or in cancer samples (see Table 28). Full details of the estimation procedure and the full discovery process are described in Materials and Methods in Example 9. Importantly, the 13-protein classifier was fully defined before validation was performed.







TABLE 30







The 13-protein logistic regression classifier










Protein

SEQ ID
Co-


(Human)
Transition
NO
efficient













LRP1
TVLWPNGLSLDIPAGR_855.00_400.20
15
−1.59


BGH3
LTLLAPLNSVFK_658.40_804.50
 8
1.73


COIA1
AVGLAGTFR_446.26_721.40
11
−1.56


TETN
LDTLAQEVALLK_657.39_330.20
20
−1.79


TSP1
GFLLLASLR_495.31_559.40
22
0.53


ALDOA
ALQASALK_401.25_617.40
 7
−0.80


GRP78
TWNDPSVQQDIK_715.85_260.20
23
1.41


ISLR
ALPGTPVASSQPR_640.85_841.50
14
1.40


FRIL
LGGPEAGLGEYLFER_804.40_913.40
24
0.39


LG3BP
VEIFYR_413.73_598.30
25
−0.58


PRDX1
QITVNDLPVGR_606.30_428.30
16
−0.34


FIBA
NSLFEYQK_514.76_714.30
26
0.31


GSLG1
IIIQESALDYR_660.86_338.20
27
−0.70





Constant (α) equals to 36.16.






Classifier Validation.
A total of 52 cancer and 52 benign samples (Table 29) were used to validate the performance of the 13-protein classifier. All validation samples were from different patients than the discovery samples. In addition, 36% of the validation samples were sourced from a new fourth clinical site, Vanderbilt University (Vanderbilt). A new clinical site participating in the validation study provides greater confidence that the classifier's performance generalizes beyond the discovery study. The remaining validation samples were selected randomly from the discovery sites. Samples were selected to be consistent with intended use and matched as in the discovery study.
The classifier was applied to the validation samples and analyzed (Materials and Methods in Example 9). The performance of the classifier is presented in FIG. 12 in terms of negative predictive value (NPV) and specificity (SPC), as these are the two most clinically relevant measures. NPV is the population-based probability that a nodule predicted to be benign by the classifier is truly benign. As the NPV is representative of the classifier's performance on the intended use population, it can be calculated from the classifier's sensitivity, specificity and the estimated cancer prevalence (20%) in the intended use population. Specificity is the percentage of benign nodules that are predicted to be benign by the classifier. The classifier generates a cancer probability score, ranging from 0 to 1. Any reference value in this range can be defined so that a sample is predicted to be benign if the sample's classifier score is below the reference value, or predicted to be malignant if the sample's classifier score is above the reference value. The reference value used in practice depends primarily on the physician and his/her minimum required NPV. For the purposes of illustration we assume that the NPV requirement is 90%.
At reference value 0.43, the classifier has NPV of 96%+/−4% and specificity of 45%+/−13% on the discovery samples, where 95% confidence intervals are reported. At the same reference value of 0.43, the classifier has NPV of 92%+/−7% and specificity of 27%+/−12% on the validation samples. Table 31 reports the classifier's performance for discovery and validation sample sets and for multiple lung cancer prevalences. For each lung cancer prevalence, the reference value was selected to ensure NPV is 90% or more.







TABLE 31







Performance of the classifier in discovery and validation at three cancer


prevalences














Prevalence
Reference
Sensitivity
Specificity
NPV
PPV


Dataset
(%)
Value
(%)
(%)
(%)
(%)
















Discovery
20
0.43
93
45
96
30


(n = 143)
25
0.37
96
38
96
34



30
0.33
96
34
95
38


Validation
20
0.43
90
27
92
24


(n = 104)
25
0.37
92
23
90
29



30
0.33
94
21
90
34


Vander
20
0.43
100
28
100
26


bilt
25
0.37
100
22
100
30


(n = 37)
30
0.33
100
17
100
34





NPV is negative predictive value.


PPV is positive predictive value.






The performance of the 13-protein classifier on validation samples from the new clinical site (Vanderbilt) is a great indicator of the classifier's performance on future samples, and a strong sign that the classifier is not overfit to the three discovery sites. The NPV and specificity on the Vanderbilt samples are 100% and 28%, respectively, at the same reference value 0.43.
FIG. 13 presents the application of the classifier to all 247 discovery and validation samples. FIG. 13 compares the clinical risk factors of smoking (measured in pack years) and nodule size (proportional to the diameter of each circle) to the classifier score assigned to each sample. Nodule size does not appear to increase with the classifier score. Indeed, both large and small nodules are spread across the classifier score spectrum. To quantify this observation, the Pearson correlation between the classifier score and nodule size, smoking history pack-year and age were calculated and found to be insignificant (Table 32). The implication of this observation is remarkable. The classifier provides information on the disease status of a pulmonary nodules that is independent of the three currently used risk factors for malignancy (age, smoking history and nodule size), and thus provides incremental molecular information of great added clinical value. For a similar plot of nodule size vs. classifier score, see FIG. 15.







TABLE 32





Impact of clinical characteristics on classifier score







Continuous Clinical Characteristics













Sample
Pearson
Coefficient of
95% CI* of
p-value on


Characteristics
Group
Correlation
Linear Fit
Coefficient
Coefficient










Subject












Age
All
0.190
0.005
 (0.002, −0.008)
0.003



Cancer
0.015
0.000
(−0.004, −0.004)
0.871



Benign
0.227
0.005
 (0.001, −0.010)
0.012


Smoking
All
0.185
0.002
 (0.000, −0.003)
0.005


History
Cancer
0.089
0.001
(−0.001, −0.002)
0.339


Pack-Years
Benign
0.139
0.001
 (0.000, −0.003)
0.140







Nodule












Size
All
−0.071
−0.003
(−0.008, −0.002)
0.267



Cancer
−0.081
−0.003
(−0.009, −0.003)
0.368



Benign
−0.035
−0.001
(−0.008, −0.005)
0.700










Categorical Clinical Characteristics













Classifier

p-value on

p-value on


Characteristics
Score
Cancer
Cancer
Benign
Benign





Gender


0.477†

0.110†


Female
Median
0.786

0.479




(quartile range)
(0.602-0.894)

(0.282-0.721)



Male
Median
0.815

0.570




(quartile range)
(0.705-0.885)

(0.329-0.801)



Smoking


0.652†

0.539†


History







Status







Never
Median
0.707

0.468




(quartile range)
(0.558-0.841)

(0.317-0.706)



Past
Median
0.804

0.510




(quartile range)
(0.616-0.892)

(0.289-0.774)



Current
Median
0.790

0.672




(quartile range)
(0.597-0.876)

(0.437-0.759)









The Molecular Foundations of the Classifier.
To address the biological relevance of the 13 classifier proteins, they were submitted for pathway analysis using IPA (Ingenuity Systems, www.ingenuity.com). It is identified that the transcription regulators most likely to cause a modulation of these 13 proteins. Using standard IPA analysis parameters, the four most significant (see Materials and Methods in Example 9) nuclear transcription regulators were FOS (proto-oncogene c-Fos), NF2L2 (nuclear factor erythroid 2-related factor 2), AHR (aryl hydrocarbon receptor) and MYC (myc proto-oncogene protein). These proteins regulate 12 of the 13 classifier proteins, with ISLR being the exception (see below).
FOS is common to many forms of cancer. NF2L2 and AHR are associated with lung cancer, oxidative stress response and lung inflammation. MYC is associated with lung cancer and oxidative stress response. These four transcription regulators and the 13 classifier proteins, collectively, are also highly associated (p-value 1.0e−07) with the same three biological networks, namely, lung cancer, lung inflammation and oxidative stress response. This is summarized in FIG. 14 where the classifier proteins (green), transcription regulators (blue) and the three merged networks (orange) are depicted. Only ISLR (Immunoglobulin superfamily containing leucine-rich repeat protein) is not connected through these three networks to other classifier proteins, although it is connected through cancer networks not specific to lung. In summary, the modulation of the 13 classifier proteins can be linked back to a few transcription regulators highly associated with lung cancer, lung inflammation and oxidative stress response networks; three biological processes reflecting aspects of lung cancer.
The present invention distinguishes itself in multiple ways. First, the performance of the 13-protein classifier achieves intended use performance requirements with NPV (and sensitivity) of at least 90% or higher in validation, across multiple prevalence estimates (see Table 31). Second, intended use population samples (nodule size 30 mm or less and/or Stage IA) were used in discovery and validation, in contrast to prior studies where non-intended use samples ranging from Stage I to Stage IV were used. In some cases, nodule size information was not disclosed in prior work. Third, the 13-protein classifier was demonstrated to provide a score that is independent of the currently used cancer risk parameters of nodule size, smoking history and age.
The utilization of SRM technology enables global interrogation of proteins associated with lung cancer processes in contrast to technologies such as those that multiplex antibodies where it is often not feasible to multiplex hundreds of candidate markers for a specific disease.
Clinical Study Designs.
The design and conduct of biomarker studies is necessarily impacted by the eventual intended use population and performance requirements for the clinical test. Emerging guidelines help in the design of studies that have greater chance of translating into clinical impact. In the design of the discovery and validation studies presented here, four requirements were especially important. First, conducting a multiple clinical site discovery study enabled us to determine those proteins robust to variations introduced by differences in site-to-site sample processing and management, as well as from any biological differences in the populations being served by the different site hospitals. Such a design is critical as site-to-site sources of variations can often exceed biological signal. Second, utilizing intended use samples, as defined by age, smoking history and nodule size, in discovery and validation phases enabled us to obtain a realistic estimate of the performance envelop of the classifier. Third, careful matching of cancer and benign cohorts on age, gender, nodule size and clinical site was critical in not only avoiding bias, but in the discovery and validation of a classifier that provides a score independent of these clinical factors as well as smoking history. Fourth, validation samples were from different patients than the discovery samples. Furthermore, 36% of the validation samples were from an entirely new clinical site, a critical validation step to show that results are not overfit to the sites used in the discovery phase. Performance on samples from the new clinical site was exceptionally high (NPV of 100%, specificity of 28%), yielding a high level of confidence in the performance of the test in clinical practice.
Systems Biology and Blood Signatures.
The integration of a systems biology approach to biomarker discovery with SRM technology enabled the simultaneous exploration of a large number of lung cancer relevant proteins, resulting in a highly sensitive classifier. The systems approach employed several strategies.
First, proteins secreted or shed from the cell surface of lung cancer cells were identified (i.e. tissue-sourced) as these are likely lung cancer perturbed proteins to be detected in blood. Of the classifier's 13 proteins, seven were tissue-sourced, demonstrating that tissue-sourcing is an effective method for prioritizing proteins for SRM assay development.
A second systems driven approach was the identification of the most cooperative protein biomarkers. Cooperative proteins are those that may not be the best individual performers but appear frequently on high performance panels. Motivating this approach is the desire to derive a classifier with multiple proteins from multiple lung cancer associated networks. By monitoring multiple proteins and networks, it was expected that the classifier would be highly sensitive to the circulating signature of a malignant nodule, as demonstrated in validation.
There are two confirmations of the effectiveness of the cooperative protein approach. A pathway analysis demonstrated that the classifier proteins are likely modulated by a small number of transcription regulators (AHR, NF2L2, MYC, FOS) highly associated with lung cancer, lung inflammation and oxidative stress response networks/processes. Chronic lung inflammation and oxidative stress response are both linked to NSCLC development. A strength of the classifier is that it monitors multiple proteins from these multiple lung cancer associated processes. This multiple protein, multiple process survey accounts for the high sensitivity of the classifier for detecting the circulating signature emitted by malignant nodules, and so, high NPV when the classifier calls a nodule benign.
The second validation of the cooperative approach is a direct comparison to traditional biomarker strategies. Typically, proteins are shortlisted in the discovery process by filtering on individual diagnostic performance. To contrast the difference between filtering proteins based on strong individual performance as opposed to frequency on high performance panels, we calculated a p-value for each protein using the Mann-Whitney non-parametric test. Only 2 of the 36 cooperative proteins had a p-value below 0.05, a commonly used significance threshold for measuring individual performance. More importantly, we derived a “p-classifier” using the same steps for the 13-protein classifier derivation (see Table 28 and Materials and Methods in Example 9) except that the Mann Whitney p-value was used in place of cooperative score. The p-classifier achieved NPV 96% and specificity 18% in discovery and NPV 91% and specificity 19% in validation as compared to the 13-protein classifier performance of NPV 96% and specificity 45% in discovery and NPV 92% and specificity 27% in validation. Note that the reference value thresholds were selected to ensure NPV of at least 90%. Hence, we expect similar high NPV performance between the 13-protein cooperative classifier and the p-classifier. Specificity is the performance measure where a comparison can be made. This is where a significant drop in performance from the 13-protein cooperative classifier to the p-classifier is observed. This confirms that the best individual protein performers are not necessarily the best proteins for classifiers
Most Informative Proteins.
Which proteins in the classifier are most informative? To answer this question all possible classifiers were constructed from the set of robust cooperative proteins and their performance measured. The frequency of each protein among the 100 best performing panels was determined. Four proteins (LRP1, COIA1, ALDOA, LG3BP) were highly enriched with 95% of the 100 best classifiers having at least three of these four proteins (p-value <1.0e−100). Seven of eight proteins (LRP1, COIA1, ALDOA, LG3BP, BGH3. PRDX1, TETN, ISLR) appeared together on over half of all the best classifiers (p-value <1.0e−100). Note that the 13-protein classifier contains additional proteins as they further increase performance, likely by measuring proteins in the same three lung cancer networks (lung cancer, lung inflammation and oxidative stress). The conclusion is that high performance panels of cooperative proteins for pulmonary nodule characterization are similar in composition to one another with a preference for a set of particularly informative (cooperative) proteins.
In summary, by integrating systems biology strategies for biomarker discovery (tissue-sourced candidates with cancer relevance, cooperative proteins, multiple proteins from multiple lung cancer associated networks), enabling technologies (SRM for global proteomic interrogation) and clinical focus (designing studies for intended use), this invention identifies a 13-protein proteomic classifier that provides molecular insight into the disease status of pulmonary nodules.
Example 9
Materials and Methods
Identification of Candidate Plasma Proteins.
Two approaches were employed to identify candidate proteins for a lung cancer classifier, including analysis of the proteome of lung tissues with a histopathologic diagnosis of NSCLC and a search of literature databases for lung cancer-associated proteins. All candidate proteins were also assessed for evidence of blood circulation and satisfied one or more requirement(s) for the evidence.
Analysis of Plasma Samples Using SRM-MS.
Briefly, the protocol for SRM-MS analysis of plasma aliquots included immunodepletion on IgY14-Supermix resin columns (Sigma) of medium- and high-abundance proteins, denaturation, trypsin digestion, and desalting, followed by reversed-phase liquid chromatography and SRM-MS analysis of the obtained peptide samples.
Development of SRM Assays.
SRM assays for candidate proteins were developed based on synthetic peptides, as previously described. After identification and synthesis of up to five suitable peptides per protein, SRM triggered MS/MS spectra were collected on a 5500 QTrap® mass spectrometer for both doubly and triply charged precursor ions. The obtained MS/MS spectra were assigned to individual peptides using MASCOT and with a minimum cutoff score of 15. Up to four transitions per precursor ion were then selected for optimization. The resulting corresponding optimal retention time, declustering potential and collision energy were assembled for all transitions. Optimal transitions were measured on a mixture of all synthetic peptides and on two pooled plasma samples, each obtained from ten subjects with either benign or malignant, i.e. NSCLC, lung nodules at the Institut Universitaire de Cardiologie et de Pneumologie de Quebec (IUCPQ, Quebec, Canada). All subjects provided informed consent and contributed biospecimens in studies approved by the institution's Ethics Review Board (ERB). Plasma samples were processed as described above. Batches of 1750 transitions were analyzed by SRM-MS, with SRM-MS data manually reviewed to select the two best peptides per protein and the two best transitions per peptide. The intensity ratio, defined as the ratio between the intensities of the two best transitions of a peptide in the synthetic peptide mixture, was used to assess the specificity of the transitions in a biological sample. Transitions demonstrating interference with other transitions were not selected. A method to ensure the observed transitions corresponded to the peptides and proteins they were intended to measure was developed. In particular, 93% of peptide transitions developed had an error rate below 5%.
Discovery Study Design.
A retrospective, multi-center, case-control study was performed using archival K2-EDTA plasma aliquots previously obtained from subjects who provided informed consent and contributed biospecimens in studies approved by the Ethics Review Board (ERB) or the Institutional Review Boards (IRB) at the IUCPQ or New York University (New York, N.Y.) and the University of Pennsylvania (Philadelphia, Pa.), respectively. In addition, plasma samples were provided by study investigators after review and approval of the sponsor's study protocol by the respective institution's ERB or IRB, as required. Sample eligibility for the proteomic analysis was based on the satisfaction of the study inclusion and exclusion criteria, including the subject's demographic information; the subject's corresponding lung nodule radiographic characterization by chest CT scan and a maximal linear dimension of 30 mm; and the histopathology of the lung nodule obtained at the time of diagnostic surgical resection, i.e. either NSCLC or a benign, i.e. non-malignant, process. Each cancer-benign sample pair was matched, as much as possible among eligible samples, by gender, nodule size (±10 mm), age (±10 years), smoking history pack-years (±20 pack-years), and by center. Independent monitoring and verification of the clinical data associated with both the subject and lung nodule were performed in accordance with the guidance established by the Health Insurance Portability and Accountability Act (HIPAA) of 1996 to ensure subject privacy. The study was powered with a probability of 92% to detect 1.5 fold differences in protein abundance between malignant and benign lung nodules.
Logistic Regression Model.
The logistic regression classification method was used to combine a panel of transitions into a classifier and to calculate a classification probability score between 0 and 1 for each sample. The probability score (Ps) of a sample was determined as

Ps=1/[1+exp(−α−Σi=1Nβi*{hacek over (I)}i,s)],  (1)

where {hacek over (I)}i,s was the logarithmically transformed (base 2), normalized intensity of transition i in sample s, βi was the corresponding logistic regression coefficient, α was a classifier-specific constant, and N was the total number of transitions in the classifier. A sample was classified as benign if Ps was less than a reference value or cancer otherwise. The reference value can be increased or decreased depending on the desired NPV. To define the classifier, the panel of transitions (i.e. proteins), their coefficients, the normalization transitions, classifier coefficient α and the reference value must be learned (i.e. trained) from the discovery study and then confirmed using the validation study.

Lung Nodule Classifier Development. The goal of the discovery study was to derive a multivariate classifier with a target performance sufficient for clinical utility in the intended use population, i.e. a classifier having an NPV of 90% or higher. This goal was incorporated in the data analysis strategies. The classifier development included the following: normalization and filtering of raw SRM-MS data; identification of candidate proteins that occurred with a high frequency in top-performing panels; evaluation of candidate proteins based on SRM-MS signal quality; selection of candidate proteins for the final classifier based on their stability in performance; and training to a logistic regression model to derive the final classifier. Table 28 provides a summary overview of the primary steps.
Normalization of raw SRM-MS data was performed to reduce sample-to-sample intensity variations using a panel of six endogenous proteins. After data normalization, SRM-MS data were filtered down to transitions having the highest intensities of the corresponding proteins and satisfying the criterion for detection in a minimum of 50% of the cancer or 50% of the benign samples. A total of 125 proteins satisfied these criteria of reproducible detection. Missing values were replaced by half the minimum detected values of the corresponding transitions in all samples.
Remaining transitions were then used to identify proteins, defined as cooperative proteins, that occurred with high frequency on top-performing protein panels. The cooperative proteins were derived using the following estimation procedure as it is not computational feasible to evaluate the performance of all possible protein panels.
Monte Carlo cross validation (MCCV) (36) was performed on 1×106 panels, each panel comprised of 10 randomly selected proteins and fitted to a logistic regression model, as described above, using a 20% holdout rate and 102 sample permutations. The receiver operating characteristic (ROC) curve of each panel was generated and the corresponding partial area under the ROC curve (AUC) but above the boundary of sensitivity being 90%, defined as the partial AUC (37, 38), was used to assess the performance of the panel. By focusing on the performance of individual panels at high sensitivity region, the partial AUC allows for the identification of panels with high and reliable performance on NPV. The candidate proteins that occurred in the top 100 performing panels with a frequency greater than that expected by chance were identified as cooperative proteins. For each protein the cooperative score is defined as its frequency on the 100 high performance panels divided by the expected frequency. Highly cooperative proteins had a score of 1.75 or higher (the corresponding one-sided p value <0.05) while non-cooperative proteins had a score of 1 or less. Note that one million panels were sampled to ensure that the 100 top performing panels were exceptional (empirical p value ≦10−4). In addition, panels of size 10 were used in this procedure based on empirical evidence that larger panels did not change the resulting list of cooperative proteins. We also wanted to avoid overfitting the logistic regression model. In total, 36 cooperative proteins were identified, including 15 highly cooperative proteins.
Raw chromatograms of all transitions of cooperative proteins were then manually reviewed. Proteins with low signal-to-noise ratios and/or showing evidence of any interference were removed from further consideration for the final classifier. In total, 21 cooperative and robust proteins were identified.
Remaining candidate proteins were then evaluated in an iterative, stepwise procedure to derive the final classifier. In each step, MCCV was performed using a holdout rate of 20% and 104 sample permutations to train the remaining candidate proteins to a logistic regression model and to assess the variability, i.e. stability, of the coefficient derived for each protein by the model. The protein having the least stable coefficient was identified and removed. Proteins for the final classifier were identified when the corresponding partial AUC was optimal. Seven of the 13 proteins in the final classifier were highly cooperative.
Proteins in the final classifier were further trained to a logistic regression model by MCCV with a holdout rate of 20% and 2×104 sample permutations.
Lung Nodule Classifier Validation.
The design of the validation study was identical to that of the discovery study, but involved K2-EDTA plasma samples associated with independent subjects and independent lung nodules not evaluated in the discovery study. Additional specimens were obtained from Vanderbilt University (Nashville, Tenn.) with similar requirements for patient consent, IRB approval, and satisfaction of HIPAA requirements. Of the 104 total cancer and benign samples in the validation study, half were analyzed immediately after the discovery study, while the other half was analyzed later. The study was powered to observe the expected 95% confidence interval (CI) of NPV being 90±8%.
The raw SRM-MS dataset in the validation study was normalized in the same way as the discovery dataset. Variability between the discovery and the validation studies was mitigated by utilizing human plasma standard (HPS) samples in both studies as external calibrator. Missing data in the validation study were then replaced by half the minimum detected values of the corresponding transitions in the discovery study. Transition intensities were applied to the logistic regression model of the final classifier learned previously in the training phase, from which classifier scores were assigned to individual samples. The performance of the lung nodule classifier on the validation samples was then assessed based on the classifier scores.
IPA Pathway Analysis.
Standard parameters were used. Specifically, in the search for nuclear transcription regulators, requirements were p-value <0.01 with a minimum of 3 proteins modulated. Significance was determined using a right-tailed Fisher's exact test using the IPA Knowledge Database as background.
Candidate Biomarker Identification.
Candidate Biomarkers Identified by Tissue Proteomics.
Specimens of resected NSCLC (adenocarcinoma, squamous cell and large cell) lung tumors and non-adjacent normal tissue in the same lobe were obtained from patients who provided informed consent in studies approved by the Ethics Review Boards at the Centre Hospitalier de l'Université de Montreal and the McGill University Health Centre.
The proteomic analyses of lung tumor tissues targeted membrane-associated proteins on endothelial cells (adenocarcinoma, n=13; squamous cell, n=18; and large cell, n=7) and epithelial cells (adenocarcinoma, n=19; squamous cell, n=6; and large cell, n=5), and those associated with the Golgi apparatus (adenocarcinoma, n=13; squamous cell, n=15; and large cell, n=5).
Membrane proteins from endothelial cells or epithelial cells and secreted proteins were isolated from normal or tumor tissues from fresh lung resections after washing in buffer and disruption with a Polytron to prepare homogenates. The cell membrane protocol included filtration using 180 μm mesh and centrifugation at 900×g for 10 min at 4° C., supernatants prior to layering on 50% (w:v) sucrose and centrifugation at 218,000×g for 1 h at 4° C. to pellet the membranes. Membrane pellets were resuspended and treated with micrococcal nuclease, and incubated with the following antibodies specified by plasma membrane type: endothelial membranes (anti-thrombomodulin, anti-ACE, anti-CD34 and anti-CD144 antibodies); epithelial membranes (anti-ESA, anti-CEA, anti-CD66c and anti-EMA antibodies), prior to centrifugation on top of a 50% (w:v) sucrose cushion at 280,000×g (endothelial) or 218,000×g (epithelial) for 1 h at 4° C. After pellet resuspension, plasma membranes were isolated using MACS microbeads. Endothelial plasma membranes were treated with KI to remove cytoplasmic peripheral proteins. The eluate of epithelial plasma membranes was centrifuged at 337,000×g for 30 min at 4° C. over a 33% (w:v) sucrose cushion, with resuspension of the pellet in Laemmli/Urea/DTT after removal of the supernatant and sucrose cushion.
To isolate secreted tissue proteins, the density of the tissue homogenates (prepared as described above) was adjusted to 1.4 M sucrose prior to isolating the secretory vesicles by isopycnic centrifugation at 100,000×g for 2 h at 4° C. on a 0.8 and 1.2 M discontinuous sucrose gradient. Vesicles concentrating at the 0.8/1.2 M interface were collected and further incubated for 25 min with 0.5 M KCl to remove loosely bound peripheral proteins. Vesicles were recuperated by ultracentrifugation at 150,000×g for 1 h at 4° C. and then opened with 100 mM (NH4)HCO3 (pH 11.0) for 30 min at 4° C. Secreted proteins were recovered in the supernatant following ultracentrifugation at 150,000×g for 1 h at 4° C.
Membrane or secreted proteins were then analyzed by CellCarta® (Caprion, Montreal, Québec) proteomics platform, including digestion by trypsin, separation by strong cation exchange chromatography, and analysis by reversed-phase liquid chromatography coupled with electrospray tandem mass spectrometry (MS/MS). Peptides in the samples were identified by database searching of MS/MS spectra using MASCOT and quantified by a label-free approach based on their signal intensity in the samples, similar to those described in the literature. Proteins whose tumor-to-normal abundance ratio was either ≧1.5 or ≦2⁄3 were then identified as candidate biomarkers.
Candidate Biomarkers Identified by Literature Searches. Automated literature searches using predefined terms and automated PERL scripts were performed on the following databases: UniProt on May 6, 2010, Entrez, NBK3836 on May 17, 2010, and NextBio on Jul. 8, 2010. Biomarker candidates were compiled and mapped to UniProt identifiers using the UniProt Knowledge Base.
Presence of Candidate Biomarkers in the Blood.
The tissue- and literature-identified biomarker candidates were required to demonstrate documented evidence in the literature or a database as a soluble or solubilized circulating protein. The first criterion was evidence by mass spectrometry detection, with a candidate designated as previously detected by the following database-specific criteria: a minimum of 2 peptides in HUPO9504, which contains 9,504 human proteins identified by MS/MS; a minimum of 1 peptide in HUPO889, which is a higher confidence subset of HUPO9504 containing 889 human proteins; or at least 2 peptides in Peptide Atlas (November 2009 build). The second criterion was annotation as either a secreted or single-pass membrane protein in UniProt. The third criterion was designation as a plasma protein in the literature. The fourth criterion was prediction as a secreted protein based on the use of various programs: prediction by TMHMM as a protein with one transmembrane domain, which however is cleaved based on prediction by SignalP; or prediction by TMHMM as having no transmembrane domain and prediction by either SignalP or SecretomeP as a secreted protein. All candidate proteins satisfied one or more of the criteria.
Study Designs and Power Analyses.
Sample, Subject and Lung Nodule Inclusion and Exclusion Criteria.
The inclusion criteria for plasma samples were collection in EDTA-containing blood tubes; obtained from subjects previously enrolled in the Ethics Review Board (ERB) or the Institutional Review Boards (IRB) approved studies at the participating institutions; and archived, e.g. labeled, aliquoted and frozen, as stipulated by the study protocols.
The inclusion criteria for subjects were the following: age ≧40; any smoking status, e.g. current, former, or never; any co-morbid conditions, e.g. chronic obstructive pulmonary disease (COPD); any prior malignancy with a minimum of 5 years in clinical remission; any prior history of skin carcinomas, e.g. squamous or basal cell. The only exclusion criterion was prior malignancy within 5 years of lung nodule diagnosis.
The inclusion criteria for the lung nodules included radiologic, histopathologic and staging parameters. The radiologic criteria included size ≧4 mm and ≦30 mm, and any spiculation or ground glass opacity. The histopathologic criteria included either diagnosis of malignancy, e.g. non-small cell lung cancer (NSCLC), including adenocarcinoma (and bronchioloalveolar carcinoma (BAC), squamous, or large cell, or a benign process, including inflammatory (e.g. granulomatous, infectious) or non-inflammatory (e.g. hamartoma) processes. The clinical staging parameters included: primary tumor: ≦T1 (e.g. 1A and 1B); regional lymph nodes: N0 or N1 only; distant metastasis: M0 only. The exclusion criteria for lung nodules included the following: nodule size data unavailable; no pathology data available, histopathologic diagnosis of small cell lung cancer; and the following clinical staging parameters: primary tumor: ≧T2, regional lymph nodes: ≧N2, and distant metastasis: ≧M1.
Sample Layout.
Up to 15 paired samples per batch were assigned randomly and iteratively to experimental processing batches until no statistical bias was demonstrable on age, gender or nodule size. Paired samples within each processing batch were further randomly and repeatedly assigned to positions within the processing batch until the absolute values of the corresponding Pearson correlation coefficients between position and age, gender and nodule size were less than 0.1. Each pair of cancer and benign samples was then randomized to their relative positions in the batch. To provide a positive control for quality assessment, three 200 μl aliquots of a pooled human plasma standard (HPS) (Bioreclamation, Hicksville, N.Y.) were positioned at the beginning, middle and end of each processing batch, respectively. Samples within a batch were analyzed together: sequentially during immunodepletion and SRM-MS analysis but in parallel during denaturing, digestion, and desalting.
Power Analysis for the Classifier Discovery Study.
The power analysis for the discovery study was based on the following assumptions: (A) The overall false positive rate (α) was set to 0.05. (B) {hacek over (S)}idák correction for multiple testing was used to calculate the effective αeff for testing 200 proteins, i.e.




  
    i
    .
    e
    .
    
      
    
    ⁢
    
      α
      eff
    
  
  =
  
    1
    -
    
      
        
          1
          -
          α
        
        200
      
      .
    
  




(C) The effective sample size was reduced by a factor of 0.864 to account for the larger sample requirement for the Mann-Whitney test than for the t-test (13). (D) The overall coefficient of variation was set to 0.43 based on a previous experience. (E) The power (1−β) of the study was calculated based on the formula for the two-sample, two-sided t-test, using effective αeff and effective sample size.

Power Analysis for the Classifier Validation Study.
Sufficient cancer and benign samples are needed in the validation study to confirm the performance of the lung nodule classifier obtained from the discovery study. We are interested in obtaining the 95% confidence intervals (CIs) on NPV and specificity for the classifier. Assuming the cancer prevalence of lung nodules is prev, the negative predictive value (NPV) and the positive predictive value (PPV) of a classifier on the patient population with lung nodules were calculated from sensitivity (sens) and specificity (spec) as follows:




  
    
      
        NPV
        =
        
          
            
              (
              
                1
                -
                prev
              
              )
            
            *
            spec
          
          
            
              prev
              *
              
                (
                
                  1
                  -
                  sens
                
                )
              
            
            +
            
              
                (
                
                  1
                  -
                  prev
                
                )
              
              *
              spec
            
          
        
      
    
    
      
        (
        
          S
          ⁢
          
            
          
          ⁢
          1
        
        )
      
    
  
  
    
      
        PPV
        =
        
          
            prev
            *
            sens
          
          
            
              prev
              *
              sens
            
            +
            
              
                (
                
                  1
                  -
                  prev
                
                )
              
              *
              
                (
                
                  1
                  -
                  spec
                
                )
              
            
          
        
      
    
    
      
        (
        
          S
          ⁢
          
            
          
          ⁢
          2
        
        )
      
    
  




Using Eq. (S1) above, one can derive sensitivity as a function of NPV and specificity, i.e.





  
    
      
        sens
        =
        
          1
          -
          
            
              
                1
                -
                NPV
              
              NPV
            
            ⁢
            
              
                1
                -
                prev
              
              prev
            
            ⁢
            spec
          
        
      
    
    
      
        (
        
          S
          ⁢
          
            
          
          ⁢
          3
        
        )
      
    
  




Assume that the validation study contains NC cancer samples and NB benign samples. Based on binomial distribution, variances of sensitivity and specificity are given by





  
    
      
        
          var
          ⁡
          
            (
            sens
            )
          
        
        =
        
          sens
          *
          
            
              (
              
                1
                -
                sens
              
              )
            
            /
            
              N
              C
            
          
        
      
    
    
      
        (
        S4
        )
      
    
  
  
    
      
        
          var
          ⁡
          
            (
            spec
            )
          
        
        =
        
          spec
          *
          
            
              (
              
                1
                -
                spec
              
              )
            
            /
            
              N
              B
            
          
        
      
    
    
      
        (
        S5
        )
      
    
  




Using Eqs. (S1, S2) above, the corresponding variances of NPV and PPV can be derived under the large-sample, normal-distribution approximation as





  
    
      
        
          
            var
            ⁡
            
              (
              NPV
              )
            
          
          =
          
            
              
                
                  NPV
                  2
                
                ⁡
                
                  (
                  
                    1
                    -
                    NPV
                  
                  )
                
              
              2
            
            ⁡
            
              [
              
                
                  
                    var
                    ⁡
                    
                      (
                      sens
                      )
                    
                  
                  
                    
                      (
                      
                        1
                        -
                        sens
                      
                      )
                    
                    2
                  
                
                +
                
                  
                    var
                    ⁡
                    
                      (
                      spec
                      )
                    
                  
                  
                    spec
                    2
                  
                
              
              ]
            
          
        
        ,
      
    
    
      
        (
        S6
        )
      
    
  
  
    
      
        
          var
          ⁡
          
            (
            PPV
            )
          
        
        =
        
          
            
              
                
                  PPV
                  2
                
                ⁡
                
                  (
                  
                    1
                    -
                    PPV
                  
                  )
                
              
              2
            
            ⁡
            
              [
              
                
                  
                    var
                    ⁡
                    
                      (
                      sens
                      )
                    
                  
                  
                    sens
                    2
                  
                
                +
                
                  
                    var
                    ⁡
                    
                      (
                      spec
                      )
                    
                  
                  
                    
                      (
                      
                        1
                        -
                        spec
                      
                      )
                    
                    2
                  
                
              
              ]
            
          
          .
        
      
    
    
      
        (
        S7
        )
      
    
  




The two-sided 95% CIs of sensitivity, specificity, NPV and PPV are then given by ±zα/2√{square root over (var(sens))}, ±zα/2√{square root over (var(spec))}, ±zα/2√{square root over (var(NPV))} and ±zα/2√{square root over (var(PPV))}, respectively, where zα/2=1.959964 is the 97.5% quantile of the normal distribution.
Experimental Procedures.
Immunoaffinity Chromatography.
An immunoaffinity column was prepared by adding 10 ml of a 50% slurry containing a 2:1 ratio of IgY14 and SuperMix resins (Sigma Aldrich), respectively, to a glass chromatography column (Tricorn, GE Healthcare) and allowed to settle by gravity, yielding a 5 ml volume of resin in the column. The column was capped and placed on an HPLC system (Agilent 1100 series) for further packing with 0.15 M (NH4)HCO3 at 2 ml/min for 20 min, with performance assessed by replicate injections of HPS aliquots. Column performance was assessed prior to immunoaffinity separation of each sample batch.
To isolate low abundance proteins, 60 μl of plasma were diluted in 0.15M (NH4)HCO3 (1:2 v/v) to a 180 μl final volume and filtered using a 0.2 μm AcroPrep 96-well filter plate (Pall Life Sciences). Immunoaffinity separation was conducted on a IgY14-SuperMix column connected to an HPLC system (Agilent 1100 series) using 3 buffers (loading/washing: 0.15 M (NH4)HCO3; stripping/elution: 0.1 M glycine, pH 2.5; and neutralization: 0.01 M Tris-HCl and 0.15 M NaCl, pH 7.4) with a cycle comprised of load, wash, elute, neutralization and re-equilibration lasting 36 min. The unbound and bound fractions were monitored at 280 nm and were baseline resolved after separation. Unbound fractions (containing the low abundance proteins) were collected for downstream processing and analysis, and lyophilized prior to enzymatic digestion.
Enzymatic Digestion and Solid-Phase Extraction.
Lyophilized fractions containing low abundance proteins were digested with trypsin after being reconstituted under mild denaturing conditions in 200 μl of 1:1 0.1 M (NH4)HCO3/trifluoroethanol (TFE) (v/v) and then allowed to incubate on an orbital shaker for 30 min at RT. Samples were diluted in 800 μl of 0.1 M (NH4)HCO3 and digested with 0.4 μg trypsin (Princeton Separations) per sample for 16 h at 37° C. and lyophilized. Lyophilized tryptic peptides were reconstituted in 350 μl of 0.01 M (NH4)HCO3 and incubated on an orbital shaker for 15 min at RT, followed by reduction using 30 μl of 0.05 M TCEP and incubation for 1 h at RT and dilution in 375 μl of 90% water/10% acetonitrile/0.2% trifluoroacetic acid. The extraction plate (Empore C18, 3M Bioanalytical Technologies) was conditioned according to the manufacturer's protocol, and after sample loading were washed in 500 μl of 95% water/5% acetonitrile/0.1% trifluroacetic acid and eluted by 200 μl of 52% water/48% acetonitrile/0.1% trifluoroacetic acid into a collection plate. The eluate was split into 2 equal aliquots and was taken to dryness in a vacuum concentrator. One aliquot was used immediately for mass spectrometry, while the other was stored at −80° C. Samples were reconstituted in 12 μl of 90% water/10% acetonitrile/0.2% formic acid just prior to LC-SRM MS analysis.
SRM-MS Analysis.
Peptide samples were separated using a capillary reversed-phase LC column (Thermo BioBasic 18 KAPPA; column dimensions: 320 μm×150 mm; particle size: 5 μm; pore size: 300 Å) and a nano-HPLC system (nanoACQUITY, Waters Inc.). The mobile phases were (A) 0.2% formic acid in water and (B) 0.2% formic acid in acetonitrile. The samples were injected (8 μl) and separated using a linear gradient (98% A to 70% A) at 5 μl/minute for 19 min. Peptides were eluted directly into the electrospray source of the mass spectrometer (5500 QTrap LC/MS/MS, AB Sciex) operating in scheduled SRM positive-ion mode (Q1 resolution: unit; Q3 resolution: unit; detection window: 180 seconds; cycle time: 1.5 seconds). Transition intensities were then integrated by software MultiQuant (AB Sciex). An intensity threshold of 10,000 was used to filter out non-specific data and undetected transitions.
Normalization and Calibration of Raw SRM-MS Data.
Definition of Depletion Column Drift.
Due to changes in observed signal intensity after repetitive use of each immunoaffinity column, the column's performance was assessed by quantifying the transition intensity in the control HPS samples. Assuming Ii,s was the intensity of transition i in an HPS sample s, the drift of the sample was defined as




  
    
      
        
          
            drift
            s
          
          =
          
            median
            ⁡
            
              (
              
                
                  
                    I
                    
                      i
                      ,
                      s
                    
                  
                  -
                  
                    
                      I
                      ^
                    
                    s
                  
                
                
                  
                    I
                    ^
                  
                  s
                
              
              )
            
          
        
        ,
      
    
    
      
        (
        
          S
          ⁢
          
            
          
          ⁢
          8
        
        )
      
    
  




where Îi was the mean value of Ii,s among all HPS samples that were depleted by the same column, and the median was taken over all detected transitions in the sample. The column variability, or drift, was defined as

driftcol=median(drifts>0)−median(drifts<0).  (S9)

Here the median was taken over all HPS samples depleted by the column. If no sample drift were greater or less than zero, the corresponding median was taken as 0. The median column drift was the median of drifts of all depletion columns used in the study.

Identification of Endogenous Normalizing Proteins.
The following criteria were used to identify a transition of a normalization protein: (A) possession of the highest median intensity of all transitions from the same protein; (B) detected in all samples; (C) ranking high in reducing median technical coefficient of variation (CV), i.e. median CV of transition intensities that were measured on HPS samples, as a normalizer; (D) ranking high in reducing median column drift that was observed in sample depletion; and (E) possession of low median technical CV and low median biological CV, i.e. median CV of transition intensities that were measured on clinical samples. Six endogenous normalizing proteins were identified and are listed in Table 33.







TABLE 33







List of endogenous normalizing proteins














Median
Median


Normalizing

SEQ ID
Technical
Column


Protein
Transition
NO
CV (%)
Drift (%)














PEDF_HUMAN
LQSLFDSPDFSK_692.34_593.30
28
25.8
6.8


MASP1_HUMAN
TGVITSPDFPNPYPK_816.92_258.10
6
26.5
18.3


GELS_HUMAN
TASDFITK_441.73_710.40
5
27.1
16.8


LUM_HUMAN
SLEDLQLTHNK_433.23_499.30
29
27.1
16.1


C163A_HUMAN
INPASLDK_429.24_630.30
30
26.6
14.6


PTPRJ_HUMAN
VITEPIPVSDLR_669.89_896.50
31
27.2
18.2



Normalization by Panel of Transitions

25.1
9.0



Without Normalization

32.3
23.8









Normalization of Raw SRM-MS Data.
Six normalization transitions were used to normalize raw SRM-MS data to reduce sample-to-sample intensity variations within same study. A scaling factor was calculated for each sample so that the intensities of the six normalization transitions of the sample were aligned with the corresponding median intensities of all HPS samples. Assuming that Ni,s is the intensity of a normalization transition i in sample s and {circumflex over (N)}i the corresponding median intensity of all HPS samples, then the scaling factor for sample s is given by Ŝ/Ss, where




  
    
      
        
          S
          s
        
        =
        
          median
          ⁡
          
            (
            
              
                
                  N
                  
                    1
                    ,
                    s
                  
                
                
                  
                    N
                    ^
                  
                  1
                
              
              ,
              
                
                  N
                  
                    2
                    ,
                    s
                  
                
                
                  
                    N
                    ^
                  
                  2
                
              
              ,
              ...
              ⁢
              
                
              
              ,
              
                
                  N
                  
                    6
                    ,
                    s
                  
                
                
                  
                    N
                    ^
                  
                  6
                
              
            
            )
          
        
      
    
    
      
        (
        S10
        )
      
    
  




is the median of the intensity ratios and Ŝ is the median of Ss over all samples in the study. Finally, for each transition of each sample, its normalized intensity was calculated as

Ĩi,s=Ii,s*Ŝ/Ss  (S11)

where Ii,s was the raw intensity.

Calibration by Human Plasma Standard (HPS) Samples.
For a label-free MS approach, variation on signal intensity between different experiments is expected. To reduce this variation, we utilized HPS samples as an external standard and calibrated the intensity between the discovery and validation studies. Assume that {hacek over (I)}i,s is the logarithmically transformed (base 2), normalized intensity of transition i in sample s, {hacek over (I)}i,dis and {hacek over (I)}i,val are the corresponding median values of HPS samples in the discovery and the validation studies, respectively. Then the HPS corrected intensity is

Ĩi,s={hacek over (I)}i,s−{hacek over (I)}i,val+{hacek over (I)}i,dis  (S12)

Calculation of q-Values of Peptide and Protein Assays. In the development of SRM assays, it is important to ensure that the transitions detected correspond to the peptides and proteins they were intended to measure. Computational tools such as mProphet (15) enable automated qualification of SRM assays. We introduced a complementary strategy to mProphet that does not require customization for each dataset. It utilizes expression correlation techniques (16) to confirm the identity of transitions from the same peptide and protein with high confidence. In FIG. 16, a histogram of the Pearson correlations between every pair of transitions in the assay is presented. The correlation between a pair of transitions is obtained from their expression profiles over all samples in the discovery study. As expected, transitions from the same peptide are highly correlated. Similarly, transitions from different peptide fragments of the same protein are also highly correlated. In contrast, transitions from different proteins are not highly correlated, which enables a statistical analysis of the quality of a protein's SRM assay.
To determine the false positive assay rate we calculated the q-values (17) of peptide SRM assays. Using the distribution of Pearson correlations between transitions from different proteins as the null distribution (FIG. 16), an empirical p-value was assigned to a pair of transitions from the same peptide, detected in at least five common samples. A value of ‘NA’ is assigned if the pair of transitions was detected in less than five common samples. The empirical p-value was converted to a q-value using the “qvalue” package in Bioconductor (www.bioconductor.org/packages/release/bioc/html/qvalue.html). We calculated the q-values of protein SRM assays in the same way except Pearson correlations of individual proteins were calculated as those between two transitions from different peptides of the protein. For proteins not having two peptides detected in five or more common samples, their q-values could not be properly evaluated and were assigned ‘NA’. If the correlation of transitions from two peptides from the same protein is above 0.5 then there was less than a 3% probability that the assay is false.
Most 36 cooperative proteins are shown in table below.







TABLE 34





Cooperative classifiers
























Official
Co-

Co-
Fre-




Protein
Gene
operative
Partial
efficient
quen-
Transition for


Category
(UniProt)
Name
Score
AUC
CV
cy
Quantitation





Classifier
TSP1_HUMAN
THBS1
1.8
0.25
0.24
59
GFLLLASLR_495.31_559.40


Classifier
COIA1_HUMAN
COL18A1
3.7
0.16
0.25
91
AVGLAGTFR_446.26_721.40


Classifier
ISLR_HUMAN
ISLR
1.4
0.32
0.25
64
ALPGTPVASSQPR_640.85_841.50


Classifier
TETN_HUMAN
CLEC3B
2.5
0.26
0.26
67
LDTLAQEVALLK_657.39_330.20


Classifier
FRIL_HUMAN
FTL
2.8
0.31
0.26
53
LGGPEAGLGEYLFER_804.40_913.40


Classifier
GRP78_HUMAN
HSPA5
1.4
0.27
0.27
40
TWNDPSVQQDIK_715.85_260.20


Classifier
ALDOA_HUMAN
ALDOA
1.3
0.26
0.28
88
ALQASALK_401.25_617.40


Classifier
BGH3_HUMAN
TGFBI
1.8
0.21
0.28
69
LTLLAPLNSVFK_658.40_804.50


Classifier
LG3BP_HUMAN
LGALS3BP
4.3
0.29
0.29
76
VEIFYR_413.73_598.30


Classifier
LRP1_HUMAN
LRP1
4.0
0.13
0.32
93
TVLWPNGLSLDIPAGR_855.00_400.20


Classifier
FIBA_HUMAN
FGA
1.1
0.31
0.35
11
NSLFEYQK_514.76_714.30


Classifier
PRDX1_HUMAN
PRDX1
1.5
0.32
0.37
68
QITVNDLPVGR_606.30_428.30


Classifier
GSLG1_HUMAN
GLG1
1.2
0.34
0.45
23
IIIQESALDYR_660.86_338.20


Robust
KIT_HUMAN
KIT
1.4
0.33
0.46
28
YVSELHLTR_373.21_263.10


Robust
CD14_HUMAN
CD14
4.0
0.33
0.48
73
ATVNPSAPR_456.80_527.30


Robust
EF1A1_HUMAN
EEF1A1
1.2
0.32
0.56
52
IGGIGTVPVGR_513.30_428.30


Robust
TENX_HUMAN
TNXB
1.1
0.30
0.56
22
YEVTVVSVR_526.29_759.50


Robust
AIFM1_HUMAN
AIFM1
1.4
0.32
0.70
6
ELWFSDDPNVTK_725.85_558.30


Robust
GGH_HUMAN
GGH
1.3
0.32
0.81
43
YYIAASYVK_539.28_638.40


Robust
IBP3_HUMAN
IGFBP3
3.4
0.32
1.82
58
FLNVLSPR_473.28_685.40


Robust
ENPL_HUMAN
HSP90B1
1.1
0.29
5.90
22
SGYLLPDTK_497.27_460.20


Non-
ERO1A_HUMAN
ERO1L
6.2



VLPFFERPDFQLFTGNK_685.70_318.20


Robust









Non-
6PGD_HUMAN
PGD
4.3



LVPLLDTGDIIIDGGNSEYR_1080.60_897.40


Robust









Non-
ICAM1_HUMAN
ICAM1
3.9



VELAPLPSWQPVGK_760.93_342.20


Robust









Non-
PTPA_HUMAN
PPP2R4
2.1



FGSLLPIHPVTSG_662.87_807.40


Robust









Non-
NCF4_HUMAN
NCF4
2.0



GATGIFPLSFVK_618.85_837.50


Robust









Non-
SEM3G_HUMAN
SEMA3G
1.9



LFLGGLDALYSLR_719.41_837.40


Robust









Non-
1433T_HUMAN
YWHAQ
1.5



TAFDEAIAELDTLNEDSYK_1073.00_748.40


Robust









Non-
RAP2B_HUMAN
RAP2B
1.5



VDLEGER_409.21_603.30


Robust









Non-
MMP9_HUMAN
MMP9
1.4



AFALWSAVTPLTFTR_840.96_290.20


Robust









Non-
FOLH1_HUMAN
FOLH1
1.3



LGSGNDFEVFFQR_758.37_825.40


Robust









Non-
GSTP1_HUMAN
GSTP1
1.3



ALPGQLKPFETLLSQNQGGK_709.39_831.40


Robust









Non-
EF2_HUMAN
EEF2
1.3



FSVSPVVR_445.76_470.30


Robust









Non-
RAN_HUMAN
RAN
1.2



LVLVGDGGTGK_508.29_591.30


Robust









Non-
SODM_HUMAN
SOD2
1.2



NVRPDYLK_335.52_260.20


Robust









Non-
DSG2_HUMAN
DSG2
1.1



GQIIGNFQAFDEDTGLPAHAR_753.04_299.20


Robust




















P Value








SEQ
(Mann-



Predicted




ID
Whitney
Transition for
Peptide
Tissue
Concentration



Category
NO
test)
Qualification
Q Value
Candidate
(ng/ml)






Classifier
22
0.23
GFLLLASLR_495.31_318.20
1.90E−05

510



Classifier
11
0.16
AVGLAGTFR_446.26_551.30
6.70E−04

35



Classifier
14
0.74
ALPGTPVASSQPR_640.85_440.30
4.40E−03

—



Classifier
20
0.14
LDTLAQEVALLK_657.39_871.50
3.70E−05

58000



Classifier
24
0.19
LGGPEAGLGEYLFER_804.40_525.30
4.30E−05
Secreted,
12








Epi,









Endo




Classifier
23
0.44
TWNDPSVQQDIK_715.85_288.10
1.80E−03
Secreted,
100








Epi,









Endo




Classifier
 7
0.57
ALQASALK_401.25_489.30
3.70E−05
Secreted,
250








Epi




Classifier
 8
0.57
LTLLAPLNSVFK_658.40_875.50
1.40E−04

140



Classifier
25
0.45
VEIFYR_413.73_485.30
2.80E−05
Secreted
440



Classifier
15
0.26
TVLWPNGLSLDIPAGR_855.00_605.30
1.40E−04
Epi
20



Classifier
26
0.57
NSLFEYQK_514.76_315.20
1.90E−05

130000



Classifier
16
0.24
QITVNDLPVGR_606.30_770.40
1.90E−05
Epi
60



Classifier
27
0.27
IIIQESALDYR_660.86_724.40
6.70E−03
Epi,
—








Endo




Robust
32
0.27
YVSELHLTR_373.21_526.30
2.40E−03

8.2



Robust
33
0.72
ATVNPSAPR_456.80_386.20
4.30E−04
Epi
420



Robust
34
0.53
IGGIGTVPVGR_513.30_628.40
4.50E−04
Secreted,
61








Epi




Robust
 2
0.54
YEVTVVSVR_526.29_660.40
1.10E−03
Endo
70



Robust
35
0.20
ELWFSDDPNVTK_725.85_875.40
3.70E−02
Epi,
1.4








Endo




Robust
36
0.24
YYIAASYVK_539.28_567.30
1.70E−03

250



Robust
 4
0.04
FLNVLSPR_473.28_359.20
2.80E−05

5700



Robust
37
0.57
SGYLLPDTK_497.27_573.30
1.10E−03
Secreted,
88








Epi,









Endo




Non-
38
0.06
VLPFFERPDFQLFTGNK_685.70_419.20
1.20E−02
Secreted,
—



Robust




Epi,









Endo




Non-
39
0.03
LVPLLDTGDIIIDGGNSEYR_1080.60_974.50
5.50E−03
Epi,
29



Robust




Endo




Non-
40
0.31
VELAPLPSWQPVGK_760.93_413.20
2.80E−02

71



Robust









Non-
41
0.26
FGSLLPIHPVTSG_662.87_292.10
1.90E−03
Endo
3.3



Robust









Non-
42
0.11
GATGIFPLSFVK_618.85_690.40
7.90E−04
Endo
—



Robust









Non-
43
0.20
LFLGGLDALYSLR_719.41_538.30
1.10E−03

—



Robust









Non-
44
0.69
TAFDEAIAELDTLNEDSYK_1073.00_969.50
1.10E−02
Epi
180



Robust









Non-
45
0.34
VDLEGER_409.21_361.20
1.20E−03
Epi
—



Robust









Non-
46
0.36
AFALWSAVTPLTFTR_840.96_589.30
4.00E−03

28



Robust









Non-
47
0.06
LGSGNDFEVFFQR_758.37_597.30
5.80E−03

—



Robust









Non-
48
0.46
ALPGQLKPFETLLSQNQGGK_709.39_261.20
1.70E−04
Endo
32



Robust









Non-
49
0.79
FSVSPVVR_445.76_557.30
1.10E−02
Secreted,
30



Robust




Epi




Non-
50
0.27
LVLVGDGGTGK_508.29_326.20
2.80E−03
Secreted,
4.6



Robust




Epi




Non-
51
0.86
NVRPDYLK_335.52_423.30
2.40E−02
Secreted
7.1



Robust









Non-
52
0.08
GQIIGNFQAFDEDTGLPAHAR_753.04_551.30
5.70E−03
Endo
2.7



Robust















A P-classifier using the same steps for the 13-protein classifier derivation (see Table 28 and Materials and Methods in Example 9) except that the Mann Whitney p-value was used in place of cooperative score was also derived.








TABLE 35








P-Classifiers




















P










Value







Official

SEQ
(Mann-

Coef-
Cooper-



Protein
Gene

ID
Whitney
Coefficient
ficient
ative


Category
(UniProt)
Name
Transition for Quantitation
NO
test)
(α = 27.24)
CV
Protein


















P-Classifier
FRIL_HUMAN
FTL
LGGPEAGLGEYLFER_804.40_913.40
24
0.19
0.39
0.21
Yes


P-
TSP1_HUMAN
THBS1
GFLLLASLR_495.31_559.40
22
0.23
0.48
0.21
Yes


Classifier










P-
LRP1_HUMAN
LRP1
TVLWPNGLSLDIPAGR_855.00_400.20
15
0.26
−0.81
0.22
Yes


Classifier










P-
PRDX1_HUMAN
PRDX1
QITVNDLPVGR_606.30_428.30
16
0.24
−0.51
0.24
Yes


Classifier










P-
TETN_HUMAN
CLEC3B
LDTLAQEVALLK_657.39_330.20
20
0.14
−1.08
0.27
Yes


Classifier










P-
TBB3_HUMAN
TUBB3
ISVYYNEASSHK_466.60_458.20
19
0.08
−0.21
0.29
No


Classifier










P-
COIA1_HUMAN
COL18A1
AVGLAGTFR_446.26_721.40
11
0.16
−0.72
0.29
Yes


Classifier










P-
GGH_HUMAN
GGH
YYIAASYVK_539.28_638.40
36
0.24
0.74
0.33
Yes


Classifier










P-
A1AG1_HUMAN
ORM1
YVGGQEHFAHLLILR_584.99_263.10
53
0.27
0.30
0.36
No


Classifier










Robust
AIFM1_HUMAN
AIFM1
ELWFSDDPNVTK_725.85_558.30
35
0.20


Yes


Robust
AMPN_HUMAN
ANPEP
DHSAIPVINR_374.54_402.20
54
0.16


No


Robust
CRP_HUMAN
CRP
ESDTSYVSLK_564.77_347.20
55
0.17


No


Robust
GSLG1_HUMAN
GLG1
IIIQESALDYR_660.86_338.20
27
0.27


Yes


Robust
IBP3_HUMAN
IGFBP3
FLNVLSPR_473.28_685.40
 4
0.04


Yes


Robust
KIT_HUMAN
KIT
YVSELHLTR_373.21_263.10
32
0.27


Yes


Robust
NRP1_HUMAN
NRP1
SFEGNNNYDTPELR_828.37_514.30
56
0.22


No


Non-
6PGD_HUMAN
PGD
LVPLLDTGDIIIDGGNSEYR_1080.60_897.4
39
0.03


Yes


Robust










Non-
CH10_HUMAN
HSPE1
VLLPEYGGTK_538.80_751.40
57
0.07


No


Robust










Non-
CLIC1_HUMAN
CLIC1
FSAYIK_364.70_581.30
 9
0.14


No


Robust










Non-
COF1_HUMAN
CFL1
YALYDATYETK_669.32_827.40
58
0.08


No


Robust










Non-
CSF1_HUMAN
CSF1
ISSLRPQGLSNPSTLSAQPQLSR_813.11_600.30
59
0.23


No


Robust










Non-
CYTB_HUMAN
CSTB
SQVVAGTNYFIK_663.86_315.20
60
0.16


No


Robust










Non-
DMKN_HUMAN
DMKN
VSEALGQGTR_509.27_631.40
61
0.17


No


Robust










Non-
DSG2_HUMAN
DSG2
GQIIGNFQAFDEDTGLPAHAR_753.04_299.20
52
0.08


Yes


Robust










Non-
EREG_HUMAN
EREG
VAQVSITK_423.26_448.30
62
0.16


No


Robust










Non-
ERO1A_HUMAN
ERO1L
VLPFFERPDFQLFTGNK_685.70_318.20
38
0.06


Yes


Robust










Non-
FOLH1_HUMAN
FOLH1
LGSGNDFEVFFQR_758.37_825.40
47
0.06


Yes


Robust










Non-
ILEU_HUMAN
SERPINB1
TYNFLPEFLVSTQK_843.94_379.20
63
0.09


No


Robust










Non-
K1C19_HUMAN
KRT19
FGAQLAHIQALISGIEAQLGDVR_803.11_274.20
64
0.17


No


Robust










Non-
LYOX_HUMAN
LOX
TPILLIR_413.28_514.40
65
0.22


No


Robust










Non-
MMP7_HUMAN
MMP7
LSQDDIK_409.72_705.30
66
0.23


No


Robust










Non-
NCF4_HUMAN
NCF4
GATGIFPLSFVK_618.85_837.50
42
0.11


Yes


Robust










Non-
PDIA3_HUMAN
PDIA3
ELSDFISYLQR_685.85_779.40
67
0.04


No


Robust










Non-
PTGIS_HUMAN
PTGIS
LLLFPFLSPQR_665.90_340.30
68
0.06


No


Robust










Non-
PTPA_HUMAN
PPP2R4
FGSLLPIHPVTSG_662.87_807.40
41
0.26


Yes


Robust










Non-
RAN_HUMAN
RAN
LVLVGDGGTGK_508.29_591.30
50
0.27


Yes


Robust










Non-
SCF_HUMAN
KITLG
LFTPEEFFR_593.30_261.20
69
0.16


No


Robust










Non-
SEM3G_HUMAN
SEMA3G
LFLGGLDALYSLR_719.41_837.40
43
0.20


Yes


Robust










Non-
TBA1B_HUMAN
TUBA1B
AVFVDLEPTVIDEVR_851.50_928.50
70
0.15


No


Robust










Non-
TCPA_HUMAN
TCP1
IHPTSVISGYR_615.34_251.20
71
0.17


No


Robust










Non-
TERA_HUMAN
VCP
GILLYGPPGTGK_586.80_284.20
72
0.29


No


Robust










Non-
TIMP1_HUMAN
TIMP1
GFQALGDAADIR_617.32_717.40
73
0.26


No


Robust










Non-
TNF12_HUMAN
TNFSF12
AAPFLTYFGLFQVH_805.92_700.40
74
0.29


No


Robust










Non-
UGPA_HUMAN
UGP2
LVEIAQVPK_498.80_784.50
75
0.08


No


Robust









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## Claims

1. A method of determining the likelihood that a pulmonary nodule in a subject is not lung cancer, comprising:
(a) measuring an abundance of a plurality of transitions in a blood sample obtained from the subject, wherein each transition consists of a precursor ion m/z and a fragment ion m/z thereof, wherein said plurality of transitions comprise at least 3 transitions comprising a transition of ALQASALK (SEQ ID NO: 7) comprising m/z 401.25 and m/z 617.40, a transition of LGGPEAGLGEYLFER (SEQ ID NO: 24) comprising m/z 804.40 and m/z 913.40, and a transition of VEIFYR (SEQ ID NO: 25) comprising m/z 413.73 and m/z 598.30, and wherein said measuring is performed by selected reaction monitoring mass spectrometry;
(b) calculating a probability of lung cancer score based on the abundance of a plurality transitions of step (a); and
(c) ruling out lung cancer for the subject if the score in step (b) is lower than a pre-determined score.

2. The method of claim 1, wherein said plurality of transitions further comprises at least one transition selected from the group consisting of a transition of AVGLAGTFR (SEQ ID NO: 11) comprising m/z 446.26 and m/z 721.40, a transition of GFLLLASLR (SEQ ID NO: 22) comprising m/z 495.31 and m/z 559.40, a transition of LTLLAPLNSVFK (SEQ ID NO: 8) comprising m/z 658.40 and m/z 804.50, a transition of NSLFEYQK (SEQ ID NO: 26) comprising m/z 514.76 and m/z 714.30, a transition of IIIQESALDYR (SEQ ID NO: 27) comprising m/z 660.86 and m/z 338.20, a transition of TWNDPSVQQDIK (SEQ ID NO: 23) comprising m/z 715.85 and m/z 260.20, a transition of LDTLAQEVALLK (SEQ ID NO: 20) comprising m/z 657.39 and m/z 330.20, a transition of QITVNDLPVGR (SEQ ID NO: 16) comprising m/z 606.30 and m/z 428.30, and a transition of ATVNPSAPR (SEQ ID NO: 33) comprising m/z 456.80 and m/z 527.30.

3. The method of claim 1, wherein when lung cancer is ruled out the subject does not receive a treatment protocol.

4. The method of claim 3, wherein said treatment protocol is a pulmonary function test (PFT), pulmonary imaging, a biopsy, a surgery, a chemotherapy, a radiotherapy, or any combination thereof.

5. The method of claim 4, where said pulmonary imaging is an x-ray, a chest computed tomography (CT) scan, or a positron emission tomography (PET) scan.

6. The method of claim 1, wherein said pulmonary nodule has a diameter of less than or equal to 3 cm.

7. The method of claim 1, wherein said pulmonary nodule has a diameter of about 0.8 cm to 3.0 cm.

8. The method of claim 1, wherein said score is calculated from a logistic regression model applied to the transition measurements.

9. The method of claim 1, wherein said score is determined as Ps=1/[1+exp(−α−Σi=1Nβi*{hacek over (I)}i,s)], where {hacek over (I)}i,s is logarithmically transformed and normalized intensity of transition i in said sample (s), βi is the corresponding logistic regression coefficient, α was a panel-specific constant, and N was the total number of transitions in said panel.

10. The method of claim 8, further comprising normalizing the transition measurements.

11. The method of claim 10, wherein the transition measurements are normalized by one or more transitions selected from the group consisting of a transition of LQSLFDSPDFSK (SEQ ID NO:28) comprising m/z 692.34 and m/z 593.30, a transition of TGVITSPDFPNPYPK (SEQ ID NO:6) comprising m/z 816.92 and m/z 258.10, a transition of TASDFITK (SEQ ID NO:5) comprising m/z 441.73 and m/z 710.40, a transition of SLEDLQLTHNK (SEQ ID NO:29) comprising m/z 433.23 and m/z 499.30, a transition of INPASLDK (SEQ ID NO:30) comprising m/z 429.24 and m/z 630.30, and a transition of VITEPIPVSDLR (SEQ ID NO:31) comprising m/z 669.89 and m/z 896.50.

12. The method of claim 1, wherein said blood sample is selected from the group consisting of plasma, serum, and whole blood.

13. The method of claim 1, wherein the determining the likelihood that a pulmonary nodule in a subject is not lung cancer is determined by the sensitivity, specificity, negative predictive value or positive predictive value associated with the score.

14. The method of claim 1, wherein said score determined in step (c) has a negative predictive value (NPV) that is at least about 80%.

15. A method of determining the likelihood that a pulmonary nodule in a subject is not lung cancer, comprising:
(a) measuring an abundance of a plurality of transitions in a blood sample obtained from the subject, wherein each transition consists of a precursor ion m/z and a fragment ion m/z thereof, wherein said plurality of transitions comprise a transition of ALQASALK (SEQ ID NO: 7) comprising m/z 401.25 and m/z 617.40, a transition of LGGPEAGLGEYLFER (SEQ ID NO: 24) comprising m/z 804.40 and m/z 913.40, a transition of VEIFYR (SEQ ID NO: 25) comprising m/z 413.73 and m/z 598.30, a transition of GFLLLASLR (SEQ ID NO: 22) comprising m/z 495.31 and m/z 559.40, and a transition of AVGLAGTFR (SEQ ID NO: 11) comprising m/z 446.26 and m/z 721.40, and wherein said measuring is performed by selected reaction monitoring mass spectrometry;
(b) calculating a probability of lung cancer score based on the abundance of a plurality of transitions of step (a); and
(c) ruling out lung cancer for the subject if the score in step (b) is lower than a pre-determined score.

16. The method of claim 15, wherein said plurality of transitions further comprises at least one transition selected from the group consisting of a transition of LTLLAPLNSVFK (SEQ ID NO: 8) comprising m/z 658.40 and m/z 804.50, a transition of NSLFEYQK (SEQ ID NO: 26) comprising m/z 514.76 and m/z 714.30, a transition of IIIQESALDYR (SEQ ID NO: 27) comprising m/z 660.86 and m/z 338.20, a transition of TWNDPSVQQDIK (SEQ ID NO: 23) comprising m/z 715.85 and m/z 260.20, a transition of LDTLAQEVALLK (SEQ ID NO: 20) comprising m/z 657.39 and m/z 330.20, a transition of QITVNDLPVGR (SEQ ID NO: 16) comprising m/z 606.30 and m/z 428.30, and a transition of ATVNPSAPR (SEQ ID NO: 33) comprising m/z 456.80 and m/z 527.30.