BLOOD-BASED PROTEIN BIOMARKER PANEL FOR EARLY AND ACCURATE DETECTION OF CANCER

Methods and compositions for accurate blood biomarker panel-based detection of cancer, e.g., breast cancer, and sub-typing, e.g., using ultrasensitive immunoassays, e.g., digital ELISA.

TECHNICAL FIELD

Described herein are methods and compositions for accurate blood biomarker panel-based detection of cancer, e.g., breast cancer, and subtyping, e.g., using ultrasensitive immunoassays, e.g., digital ELISA.

BACKGROUND

Breast cancer is the second leading cause of cancer death in females in the United States (1).

SUMMARY

Described herein are methods and compositions for accurate blood biomarker panel-based detection of cancer, e.g., breast cancer, and subtyping, e.g., using ultrasensitive immunoassays, e.g., digital ELISA, on blood samples. Thus provided herein are methods that include obtaining a sample comprising blood (e.g., whole blood, serum, or plasma) from a subject, and determining a level of at least 2, 3, 4, 5, 10, 15, 20, or all 24 biomarkers as listed in Table Ain the sample. In some embodiments, the biomarkers comprise at least MICA, CA125, and CD25. In some embodiments, the biomarkers comprise at least HER3, HSP70, CYR61, and LCN2. In some embodiments, the biomarkers comprise at least ER, HER3, HER4, CXCL10, CYR61, P21, MICA, CD25, IL-6, and CA125.

In some embodiments, the methods include calculating a score for the subject based on the level of the biomarkers, wherein a score above a threshold score indicates that the subject has or is at risk of developing cancer.

In some embodiments, the methods include calculating a score for the subject based on the level of the biomarkers, and comparing the score to subtype reference scores for known subtypes of breast cancer and identifying a subject who has a score that is comparable to the subtype reference as having that subtype of breast cancer.

In some embodiments, the methods include recommending or sending the subject for additional evaluation, e.g., by imaging and/or biopsy.

In some embodiments, the methods include administering a treatment for breast cancer to a subject who has been identified as having or at risk of developing breast cancer. In some embodiments, the treatment comprises chemotherapy, hormone therapy, immunotherapy, radiation, or surgical resection.

DETAILED DESCRIPTION

Large-scale breast cancer screening programs have been widely implemented because early detection and treatment can improve patient outcomes (2). However, detecting breast cancer early and accurately is challenging due to limitations in conventional detection methods, such as mammography, which suffer from high false-positive and false-negative rates (3-11). Additionally, current screening methods do not provide any disease-relevant molecular information and thus are limited in their ability to distinguish between benign and malignant breast tumors. Since breast cancer is a highly heterogeneous disease, detection methods that provide molecular information are promising for early and accurate detection. Thus, advances in breast cancer detection can reduce patient morbidity by preventing unnecessary invasive biopsies, which arise from screen-detected false positives. Advances in detection methods will also enable timely intervention for cancers that require treatment, thereby improving patient outcomes.

Liquid biopsies for cancer detection are particularly promising since they provide molecular information and are minimally invasive (12, 13). Currently, efforts to develop liquid biopsies for breast cancer mainly rely on detecting circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) (14-16). However, applying these two classes of biomarkers to early cancer detection is challenging because the tumor must be relatively large to produce sufficient quantities of ctDNA or CTCs that can be detectable in blood (17-19). Proteins are particularly promising biomarkers since they are directly involved in biological processes that are dysregulated in disease and are also abundant in the cell. Furthermore, plasma proteins have been shown to be indicators of health status (20, 21). Previous studies have developed blood tests for breast cancer detection; however, these attempts have limited accuracy, particularly for early stage breast cancer detection (22, 23). Thus, developing a test using circulating proteins may improve our ability to accurately detect breast cancer (24).

Described herein is a blood protein biomarker panel for breast cancer detection. In some embodiments, the methods use analytically robust Single Molecule Array (Simoa) immunoassays (25, 26). Using gene expression data from The Cancer Genome Atlas (TCGA) (27), we showed that the biomarkers were able to distinguish between breast cancer and other types of cancer in tumor tissues. We then developed and analytically validated assays for these biomarkers in blood and showed that the panel can distinguish between healthy and breast cancer patients in a small preliminary cohort (n=49). We then applied the biomarker panel to a second, larger cohort of healthy and newly diagnosed, treatment-naïve breast cancer patients (n=197).

The results reported here provide evidence that circulating proteins can accurately detect breast cancer. This was especially encouraging given that most of the breast cancer subjects had tumors consistent with early-stage disease, an important consideration for detection and screening methods. For the model using 24 biomarkers plus age, the overall AUC was 0.95 (95% CI 0.92-0.98) and 88% of subjects were correctly classified, with 87% sensitivity and 90% specificity. This compares favorably with mammography, which has a false-negative rate of about 20% (7-11). Additionally, over 50% of patients screened annually for 10 years in the U.S. will have a false-positive mammogram, which requires further evaluation with a biopsy (3-6). Decreasing the mammogram screen-detected false-positives would reduce unnecessary invasive diagnostic surgical procedures and overall patient morbidity. Furthermore, the model using the 24 biomarkers plus age showed greater net benefit across a wide range of threshold probabilities compared to the other models, suggesting that diagnostic decisions made with information from the panel of markers could be superior to those made without it.

We also downselected the most informative markers and showed that HER3, HSP70, CYR61, and LCN2 are especially important biomarkers, with an AUC of 0.87 (95% CI 0.81-0.92) for this four biomarker panel.

The panel of protein biomarkers substantially outperformed any single protein. We observed an AUC of 0.95 for a panel using the 24 biomarkers plus age, 0.87 for a panel using the four most informative markers plus age, and 0.77 for HSP70 and age, which was the best-performing single marker. The full panel had better discrimination, calibration, and improvement in diagnostic decision-making by net benefit (51) than the four biomarker panel using the most informative markers. Furthermore, the panels performed substantially better than any individual marker. For a given biomarker, the concentrations in the breast cancer and healthy groups largely overlapped, indicating that the ability to distinguish between breast cancer and healthy subjects depends on the cumulative effect of multiple markers. These results indicate that the full panel is critical for accurately detecting breast cancer.

Finally, as shown herein some of the biomarkers can be used for distinguishing between molecular subtypes of breast cancer. MICA, CA125, and

CD25 were the top three most informative protein biomarkers in blood for subtyping (FIG.12), with an AUC of 0.96 (95% CI 0.91-1.00) using this three-marker panel (FIG.3E).

The blood tests described herein can be used, e.g., individually or in combination with another clinical modality, such as mammography, to improve the accuracy of breast cancer screening.

Methods of Diagnosis

Included herein are methods for diagnosing breast cancer, and/or determining the subtype of breast cancer present in a subject. The methods rely on detection of a biological marker or a plurality of protein biological markers as described herein, e.g., as shown in Table A. In some embodiments, the present methods provide blood tests for breast cancer detection and diagnosis using circulating protein biomarkers.

Proteins are responsible for cell growth, proliferation, signaling, motility, metabolic processes, and regulate tumorigenesis via cell adhesion, invasion, and migration. Additionally, proteins modulate the immune system's response to cancer.

Therefore, protein signatures involved in breast cancer pathophysiology are extremely promising for breast cancer detection and diagnosis. Provided herein is a panel of protein biomarkers associated with breast cancer. These biomarkers are involved in various biological processes including angiogenesis, proliferative signaling, and metastasis.

In some embodiments, the methods include determining levels of at least 3, 4, 5, 10, 15, 20, or all 24 of the biomarkers in Table A. In some embodiments, the biomarkers comprise at least MICA, CA125, and CD25. In some embodiments, the biomarkers comprise at least HER3, HSP70, CYR61, and LCN2. In some embodiments, the biomarkers comprise at least ER, HER3, HER4, CXCL10, CYR61, P21, MICA, CD25, IL-6, and CA125. In some embodiments, where multiple isoforms of a biomarker exist, a method that detects all of the isoforms is used.

The methods include obtaining a sample from a subject, and evaluating the presence and/or level of a breast cancer biomarker in the sample.

The methods can also include comparing the presence and/or level with one or more references, e.g., a control reference that represents a normal level of the breast cancer biomarker, e.g., a level in an unaffected subject, and/or a disease reference that represents a level of the proteins associated with breast cancer, e.g., a level in a subject having breast cancer. In some embodiments, the level provides for differential diagnosis, e.g., is a level in a subject having a known type of breast cancer (e.g., ER+ or TNBC). Suitable reference values can include those shown in Table 1.

As used herein the term “sample”, when referring to the material to be tested for the presence of a biological marker using the method of the invention, includes inter alia whole blood, plasma, or serum. If needed, various methods are well known within the art for the identification and/or isolation and/or purification of a biological marker from a sample. An “isolated” or “purified” biological marker is substantially free of cellular material or other contaminants from the cell or tissue source from which the biological marker is derived, i.e. partially or completely altered or removed from the natural state through human intervention. For example, proteins contained in the sample can be isolated according to standard methods, for example using lytic enzymes, chemical solutions, or isolated by protein-binding resins following the manufacturer's instructions.

The presence and/or level of a protein can be evaluated using methods known in the art. In preferred embodiments, the methods include the use of highly sensitive or ultrasensitive and preferably multiplex detection methods including Meso Scale Discovery (MSD); Single-Molecule Arrays (SIMOA); Single-Molecule Counting (SMC); LUMINEX; SOMAscan Assays; mass spectrometry (e.g., MALDI-MS) and mass cytometry (e.g., CyTOF) (see, e.g., Cohen and Walt, Chem. Rev. 2019, 119, 293-321).

In some embodiments, the protein biomarkers in blood for breast cancer detection are measured using SIMOA assays (25, 26). SIMOA assays have several advantages over the conventional ELISA, the current gold standard for protein detection in blood. First, SIMOA is 1000× more sensitive than ELISA and allows for quantification of analytes present at low concentrations (25). SIMOA can detect protein concentrations as low as 10−19M compared to conventional ELISA's ability to detect only 10−12M. Second, due to the high sensitivity of SIMOA, the serum samples can be more dilute, which reduces non-specific binding that arises from matrix effects (53, 54). Third, SIMOA has a wide dynamic range that spans four orders of magnitude in concentration, and thus a single assay can be used to detect both low and high abundance markers (55). In some embodiments, the SIMOA technique achieves this high sensitivity by digitally counting the number of molecules in a sample by labeling and physically isolating each immunocomplex into femtoliter-sized wells (FIGS.4A-D). These advantages provide for detection and quantification of blood biomarkers for developing a robust biomarker panel.

In some embodiments, mass spectrometry, and particularly matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS), are used for the detection of biomarkers. (See U.S. Pat. Nos. 5,118,937; 5,045,694; 5,719,060; 6,225,047). In some embodiments, other methods can be used, e.g., standard electrophoretic and quantitative immunoassay methods for proteins, including but not limited to, Western blot; enzyme linked immunosorbent assay (ELISA); Enzyme-Linked Immunospot (ELISPOT); biotin/avidin type assays; protein array detection, e.g., protein microarrays; radio-immunoassay; immunohistochemistry (IHC); immune-precipitation assay; flow cytometry/FACS (fluorescent activated cell sorting); Proximity Ligation Assay (PLA); lateral flow assay; surface plasmon resonance (SPR); optical imaging; and mass spectrometry (Kim (2010) Am J Clin Pathol 134: 157-162; Yasun (2012) Anal Chem 84(14): 6008-6015; Brody (2010) Expert Rev Mol Diagn 10(8): 1013-1022; Philips (2014) PLOS One 9(3): e90226; Pfaffe (2011) Clin Chem 57(5): 675-687; Cohen and Walt, Chem. Rev. 2019, 119, 293-321). The methods typically include revealing labels such as fluorescent, chemiluminescent, radioactive, and enzymatic or dye molecules that provide a signal either directly or indirectly. As used herein, the term “label” refers to the coupling (i.e. physical linkage) of a detectable substance, such as a radioactive agent or fluorophore (e.g. phycoerythrin (PE) or indocyanine (Cy5)), to an antibody or probe, as well as indirect labeling of the probe or antibody (e.g. horseradish peroxidase, HRP) by reactivity with a detectable substance.

In some embodiments, the presence and/or level of the biomarker(s) is comparable to the presence and/or level of the protein(s) in the disease reference, and the subject has one or more symptoms associated with breast cancer, then the subject has breast cancer. In some embodiments, the subject has no overt signs or symptoms of breast cancer, but the presence and/or level of one or more of the proteins evaluated is comparable to the presence and/or level of the protein(s) in the disease reference, then the subject has breast cancer or an increased risk of developing breast cancer. In some embodiments, once it has been determined that a person has breast cancer, or has an increased risk of developing breast cancer, then a treatment, e.g., as known in the art or as described herein, can be administered.

Suitable reference values can be determined using methods known in the art, e.g., using standard clinical trial methodology and statistical analysis. The reference values can have any relevant form. In some cases, the reference comprises a predetermined value for a meaningful level of the biomarker(s), e.g., a control reference level that represents a normal level of the biomarker(s), e.g., a level in an unaffected subject or a subject who is not at risk of developing a disease described herein, and/or a disease reference that represents a level of the proteins associated with breast cancer, e.g., a level in a subject having breast cancer.

The predetermined level can be a single cut-off (threshold) value, such as a median or mean, or a level that defines the boundaries of an upper or lower quartile, tertile, or other segment of a clinical trial population that is determined to be statistically different from the other segments. It can be a range of cut-off (or threshold) values, such as a confidence interval. It can be established based upon comparative groups, such as where association with risk of developing disease or presence of disease in one defined group is a fold higher, or lower, (e.g., approximately 2-fold, 4-fold, 8-fold, 16-fold or more) than the risk or presence of disease in another defined group. It can be a range, for example, where a population of subjects (e.g., control subjects) is divided equally (or unequally) into groups, such as a low-risk group, a medium-risk group and a high-risk group, or into quartiles, the lowest quartile being subjects with the lowest risk and the highest quartile being subjects with the highest risk, or into n-quantiles (i.e., n regularly spaced intervals) the lowest of the n-quantiles being subjects with the lowest risk and the highest of the n-quantiles being subjects with the highest risk.

In some embodiments, the predetermined level is a level or occurrence in the same subject, e.g., at a different time point, e.g., an earlier time point.

Subjects associated with predetermined values are typically referred to as reference subjects. For example, in some embodiments, a control reference subject does not have breast cancer, does not have a risk of developing breast cancer, or does not later develop breast cancer.

A disease reference subject is one who has (or has an increased risk of developing) breast cancer. An increased risk is defined as a risk above the risk of subjects in the general population.

Thus, in some cases, where the biomarker is decreased in cancer (see Table 1), the level of the biomarker(s) in a subject being less than or equal to a reference level of the biomarker(s) is indicative of the presence or risk of developing breast cancer, and the level of the biomarker(s) in a subject being greater than or equal to the reference level of the biomarker(s) is indicative of the absence of disease or normal risk of the disease.

In other cases, where the biomarker is increased in cancer (see Table 1), the level of the biomarker(s) in a subject being greater than or equal to the reference level of the biomarker(s) is indicative of the presence or risk of developing breast cancer, and the level of the biomarker(s) in a subject being less than or equal to a reference level of the biomarker(s) is indicative of the absence of disease or normal risk of the disease.

As noted below, to build the diagnostic model, the outcome was binary breast cancer case status (breast cancer versus healthy). Age and protein markers were modeled as continuous predictors. The values were log transformed and a logistic regression model was used to classify breast cancer and healthy subjects. To assess the classification accuracy of each particular model, subjects with a predicted probability of at least 50% were assigned as predicted to have cancer, while those below 50% were predicted to be healthy. A subject's predicted case status for a given model was then compared to the observed case status.

Thus, in some embodiments, to assess whether a subject has breast cancer in the clinic, the method can include first log transforming the biomarker values and then assigning a predicted probability, e.g., using a logistic regression model, to produce a probability score. If a subject has a predicted probability score above a selected threshold, e.g., at least 50%, the subject would be predicted to have cancer (e.g., assigned to a cancer category). If the predicted probability score is below the selected threshold, e.g., 50%, the subject would be predicted to be healthy (e.g., assigned to a healthy category).

In some embodiments, the levels of the biomarkers are used to calculate a score, e.g., along with one or more additional variable, e.g., age. The score can be calculated, e.g., using an algorithm such as summation, or weighted summation, of the (normalized) levels of the biomarkers. Specific algorithms can be identified using known statistical methods including PCA, linear regression, SVM (support vector machine), decision tree, KNN (K-nearest neighbors), K-means, gradient boosting, or random forest methods.

For example, in some embodiments, an exemplary model uses a logistic regression analysis wherein each variable (biomarker, X) gets a weight (B). In the exemplary equation below, the weights (B) are calculated for each marker, and there can be unique B values for each of the biomarkers, e.g., for each of the 24 biomarkers and age (25 in total).

In the clinic, the measured biomarker values (X values) can be used to obtain a probability score a patient will have cancer by plugging in the measured biomarker values (X) into the equation and then calculating a probability value (P). In some embodiments, the clinical procedure to obtain the individual's probability of having breast cancer would be as follows:

First, blood would be drawn from the screenee. Second, the screenee's blood concentration of each biomarker protein in the panel would be measured using Simoa. Third, the screenee's predicted probability of having breast cancer would be calculated based on a logistic regression formula with a dependent variable of the natural log of [(probability of having breast cancer)/(probability of not having breast cancer)], and with independent variables of age and each biomarker in the panel. The predicted probability could then inform discussions between the screenee and physician as to how best to proceed, such as a decision that no further follow-up is necessary or to pursue confirmatory radiologic imaging.

For the 24-marker panel, the model parameter estimates based on the Tufts sample with 197 participants were as follows, with age measured in years, CA15-3 and CA19-9 measured in units/mL, and all other markers measured in pg/mL:

For the 4-marker panel identified via cross validation, the model parameter estimates based on the Tufts sample with 197 participants were as follows, with age measured in years and all markers measured in pg/mL:

In some embodiments, the amount by which the level (or score) in the subject is less than the reference level (or score) is sufficient to distinguish a subject from a control subject, and optionally is a statistically significantly less than the level (or score) in a control subject. In cases where the level (or score) of the biomarker(s) in a subject being equal to the reference level (or score) of the biomarker(s), the “being equal” refers to being approximately equal (e.g., not statistically different).

The predetermined value can depend upon the particular population of subjects (e.g., human subjects) selected. For example, an apparently healthy population will have a different ‘normal’ range of levels of the biomarker(s) than will a population of subjects which have, are likely to have, or are at greater risk to have, a disorder described herein. Accordingly, the predetermined values selected may take into account the category (e.g., sex, age, health, risk, presence of other diseases) in which a subject (e.g., human subject) falls. Appropriate ranges and categories can be selected with no more than routine experimentation by those of ordinary skill in the art.

In characterizing likelihood, or risk, numerous predetermined values can be established.

Breast cancer is typically categorized into one of three major subtypes, based on the presence or absence of molecular markers for estrogen or progesterone receptors and human epidermal growth factor 2 (ERBB2; formerly HER2): hormone receptor positive/ERBB2 negative, ERBB2 positive, and triple-negative (tumors lacking all 3 standard molecular markers); see, e.g., Waks and Winer, JAMA. 2019 Jan. 22; 321(3): 288-300. In addition, the present methods can be used to make a differential diagnosis between estrogen receptor positive (ER+) and triple negative breast cancer (TNBC). In these methods, at least MICA, CA125, and CD25, or at least ER, HER3, HER4, CXCL10, CYR61, P21, MICA, CD25, IL-6, and CA125, are determined and used to identify whether a subject has ER+breast cancer or TNBC. Exemplary coefficients for the 10- and 3-marker panels are as follows:

Thus, in some embodiments, the model is used to identify presence of ER+ subtype. The model provides the log-odds of having an ER+ breast tumor versus not having breast cancer at all, and the predicted probability for an individual having ER+ breast cancer as compared to no breast cancer at all. For triple-negative subtype, the model provides the log-odds of having a triple-negative breast tumor versus not having breast cancer at all, and the predicted probability for an individual having triple-negative breast cancer as compared to no breast cancer at all.

The present methods can also be used to identify subjects for further evaluation, e.g., for imaging (e.g., mammogram or ultrasound) and/or biopsy, to confirm a cancer diagnosis.

Methods of Treatment

The methods described herein include methods for the treatment of breast cancer. Generally, the methods include selecting and optionally administering a therapeutically effective amount of a treatment for breast cancer to a subject who has been determined to be in need of such treatment by a method described herein. Treatments for breast cancer include radiation, surgical resection, chemotherapy, hormone/endocrine therapy, and immunotherapy.

In some embodiments, where a subject is identified as likely to have ER+ breast cancer, a treatment comprising administration of endocrine therapy (e.g., tamoxifen, toremifene, fulvestrant, Aromatase inhibitors (AIs) (e.g., Letrozole (Femara), Anastrozole (Arimidex), or Exemestane (Aromasin)) or ovarian suppression, e.g., by oophorectomy or LHRH analogs) and optionally chemotherapy (e.g., as above or phosphoinositide 3-kinase (PI3K), mechanistic target of rapamycin (mTOR), or cyclin-dependent kinase (CDK) 4/6 inhibitors or Poly(ADP-ribose) polymerase (PARP) inhibitors)) is selected and optionally administered (see Waks and Winer, JAMA. 2019 Jan. 22; 321(3): 288-300).

EXAMPLES

Materials and Methods

The following materials and methods were used in the Example set forth herein.

Study Design

In this study, we sought to develop a blood-based protein biomarker panel for breast cancer detection using analytically robust Simoa assays. We identified 24 biomarker candidates and developed and analytically validated the Simoa assays. We used mRNA expression levels in tumor tissues for these biomarkers from TCGA to further confirm that our selected biomarkers are indicative of breast cancer when compared to other cancers. We then used a first, preliminary sample cohort (n=49) of healthy and breast cancer patients and measured the 24 protein biomarker candidates in serum using the Simoa assays we developed. We then sought to validate our results in a second larger cohort. We initiated a sample collection at Tufts Medical Center. All subjects in this cohort were female and over 40 years old. The breast cancer subjects have not previously received treatment for breast cancer and had tumors generally consistent with early stage disease. We measured the concentrations of the 24 biomarkers in serum using our Simoa assays. We developed a model using a logistic regression analysis with these 24 biomarkers plus age in order to distinguish between the healthy and breast cancer subjects. To downselect the most important markers, we used a backwards selection process and then developed a model using the four most informative markers plus age. As a secondary analysis, we assessed the subtypes correctly classified as cancer by the two models. We also used the TCGA data to identify important biomarkers for distinguishing between the subtypes using the protein biomarkers in blood. We then built a model using a logistic regression analysis in order to determine whether a subject has ER+ or TNBC in serum using protein biomarkers. Informed consent was obtained for all blood samples used in this study.

Biomarker Panel Analysis in Tumor Tissues

mRNA expression data deposited in The Cancer Genome Atlas (TCGA) database (cancergenome.nih.gov/) were obtained and a principal component analysis (PCA) was performed using the Caret package in R version 3.6.2. A total of 9,860 cancer subjects, of which 1,084 are breast cancer subjects, were analyzed. For this analysis, we used 23 out of the 24 biomarkers shown inFIG.1A. We did not include CA19-9 in the analysis due to lack of corresponding mRNA data.

Simoa Assay Description

Simoa assays are bead-based immunoassays with the major advance of signal detection by single molecule counting, which results in ultra-high sensitivity. Antibody-coated capture beads are added in large excess to a sample containing low concentrations of target analyte molecules. Poisson statistics dictate that either one or zero target protein molecules will bind to each bead. The beads are then incubated with a biotinylated detection antibody and streptavidin-β-galactosidase, forming an enzyme-labeled immunocomplex. Then the beads are loaded onto an array of 50 fL sized wells in which each well can hold only one bead. A fluorogenic substrate is added and the wells are sealed with oil, producing a locally high concentration of fluorescent product, thus enabling single molecule quantitation by counting active wells. At high target molecule concentrations, fluorescence intensity of the array is used to determine target concentration, thereby extending the dynamic range of the assay. The signal output is measured on the Simoa instrument using the standard unit of average enzymes per bead (AEB). All Simoa consumables and reagents were purchased from Quanterix Corp.

Development of Ultra-Sensitive Simoa Assays

Capture antibodies were reconstituted and stored according to the instructions provided by the manufacturer. Antibody catalog numbers are provided in Table 1. The antibody was buffer exchanged to remove the storage buffer by first adding 0.13 mg of antibody solution to an Amicon filter (50K, EMD Millipore). Bead Conjugation Buffer (Quanterix) was then added to the filter up to a total volume of 500 μL. The filter device was centrifuged at 14,000× g for 5 minutes. The effluent was discarded and the process was repeated twice. The filter was inverted into a new tube and centrifuged at 1000× g for 2 minutes. The filter was rinsed with 50 μL of Bead Conjugation Buffer and centrifuged at 1000× g for 2 minutes. The concentration of the antibody was measured using a NanoDrop 2000 spectrophotometer. The antibody was diluted to 0.5 mg/mL in Bead Conjugation Buffer and stored on ice until ready for use. 2.8×108carboxylated, 2.7 μm, paramagnetic beads (Quanterix) were transferred into a microtube and washed three times with 200 μL of Bead Wash Buffer (Quanterix). The beads were then washed two times with 200 μL of Bead Conjugation Buffer and re-suspended in 190 μL of Bead Conjugation Buffer. Fresh 10 mg of 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) (ThermoFisher) was reconstituted in 1 mL of Bead Conjugation Buffer just prior to use. To activate the beads, 10 μL of EDC were added to the bead suspension to give a final concentration of 0.5 mg/ml and a final volume of 200 μL. The beads were then placed on a rotator for 30 minutes. The activated beads were then washed with 200 μL of Bead Conjugation Buffer. 200 μL of capture antibody solution was then added to the beads, vortexed, and placed on the rotator for 120 minutes for conjugation. The antibody-conjugated beads were then washed two times with 200 μL of Bead Wash Buffer. The beads were then blocked with 200 μL of Bead Blocking Buffer (Quanterix) and placed on the rotator for 30 minutes. The beads were washed with 200 μL of Bead Wash Buffer, washed with 200 μL of Bead Diluent (Quanterix), and re-suspended in 200 μEL of Bead Diluent. The beads were counted using a Beckman Coulter multi-sizer and stored at 4° C.

Detection antibodies that were not already biotinylated by the vendor were biotinylated for use in Simoa assays as previously described. (56) Briefly, the antibodies were purified using an Amicon filter three times in Biotinylation Reaction Buffer (Quanterix). Antibody concentrations were determined using NanoDrop One Spectrophotometer. Antibodies were conjugated to biotin using EZ-Link NHS-PEG4 Biotin (Thermo Fisher Scientific) using 40× molar excess and incubated for 30 min. The biotinylated antibodies were then purified using an Amicon filter.

Serum samples along with calibration curves were measured using the Simoa HD-1 Analyzer. The calibration curves were fit using a 4PL fit with a 1/y2weighting factor. The calibration curves were used to determine concentrations of the unknown samples. This analysis was done automatically using the software provided by Quanterix with the Simoa HD-1 Analyzer. The limit of detection (LOD) was calculated as the mean of the background plus three times the standard deviation.

Biomarker Measurements in the Preliminary Sample Cohort

Breast cancer serum samples (n=25) and self-reported healthy serum samples (n=24) were obtained from BioIVT. The 24 protein markers were measured in duplicate in the samples using the Simoa assays. The mean of the measurements was calculated and the values were log transformed. A principal component analysis was then performed using the Caret package in R version 3.6.2.

Subject Selection for Developing the Biomarker Panel

Breast cancer patients at Tufts Medical Center were screened and diagnosed with breast cancer via the standard approach, namely, mammography followed by biopsy. Patients who had not undergone surgical and/or therapeutic intervention were eligible. Eligible patients consented to blood donation for the study upon a positive breast cancer diagnosis. Healthy subjects were obtained from the Partner's Biobank, which provides a curated cohort of healthy subjects that were collected at several different hospitals. All subjects were female and over the age of 40 years old. Cases are referred to as breast cancer subjects and non-cases are referred to as healthy subjects.

Statistical Analysis

Blood biomarker levels for 197subjects (100 healthy, 97 cancer) were analyzed. The outcome was binary breast cancer case status (breast cancer versus healthy). Age and protein markers were modeled as continuous predictors. Each marker had up to three replicates per subject. An individual's final marker measurement was the mean of non-missing replicate measurements. When a subject had no observed replicates for a particular marker in a given analysis model, the individual was first assigned an imputed value for the marker using multiple imputation. When a subject had a biomarker level that was below the LOD of a given assay, the value was assigned as the LOD of that assay. The values were log transformed and a logistic regression model was used to classify breast cancer and healthy subjects.

Five-fold cross validation was used to identify a subset of “high performing” markers. To perform the cross validation and marker selection, each of the 197 subjects was randomly assigned to one of five groups. For each of five folds, one group was excluded (test set) and the analysis performed on a combination of the other four groups (training set). Using PROC ADAPTIVEREG in SAS, each fold started in the fold-specific training set from a model of age and all 24 markers and worked backwards to an intercept-only model, with age in the model. The set of predictors yielding the smallest cross validation error was selected as the fold-specific model. The generalized cross validation criterion (GCV) was the measure of the fold-specific model's predictive accuracy. The contribution of each variable to the fold-specific model was measured by its importance, defined as the square root of the GCV value of the fold-specific model from which all basis functions involving the variable have been removed, minus the square root of the GCV value of the selected model, then scaled to set the largest importance value to 100. Markers with an importance of at least 70 in at least three folds were selected as cross-validated markers.

We then compared four models that differed by the set of included predictors: first, age alone; second, age plus HSP70, which was chosen by being the single marker with the greatest AUC; third, age plus four cross validation-selected markers (HSP70, HER3, CYR61, LCN2); and fourth, age plus all 24 markers. For each model, discrimination was assessed by AUC. Calibration was evaluated using LOESS-smoothed calibration plots of observed probability (0 or 1) versus estimated probability of the outcome. We explored the potential improvement in clinical decision-making for each model using decision curves, which plot net benefit versus threshold probability. A threshold probability is the probability designated as the cutoff to define high probability of an outcome, i.e. a positive test result.

To assess the classification accuracy of each particular model, subjects with a predicted probability of at least 50% were assigned as predicted to have cancer, while those below 50% were predicted to be healthy. A subject's predicted case status for a given model was then compared to the observed case status.

To assess our ability to distinguish between the different molecular breast cancer subtypes, we first performed a principal component analysis using mRNA expression levels for 23 biomarkers from the TCGA database. In the TCGA database, tumors are classified as Luminal A (n=412), Luminal B (n=174), Normal (n=25), Basal (n=136), HER2 (n=65). We then assessed the biomarker contribution to the first two principal components using the factoextra package in R and identified the ten most informative markers (top markers) and the ten least informative markers (bottom markers). Using the biomarker measurements in the breast cancer serum samples, we performed a logistic regression analysis using the two panels (top markers and bottom markers) in ER+(n=81) and TNBC (n=10) breast cancer subjects. ER+tumors were defined as having at least 1% of positive staining using immunohistochemistry of tissue biopsies. Triple negative tumors had no expression of ER, PR, or HER2. We then selected the three most informative markers from the model using the top markers and performed another logistic regression analysis using the three marker panel. The three markers were identified in R using the varImp function in the caret package.

Statistical analyses were run using SAS 9.4 (SAS Institute, Cary, NC) and R version 3.6.2. Figures were generated using GraphPad Prism 7 (San Diego, CA). Decision curves, and standard errors to estimate AUC confidence limits, were obtained using R and SAS macros available online (57, 58).

Results

Initial Validation of Candidate Biomarkers for Breast Cancer Detection

We selected 24 biomarker candidates (FIG.1A) for breast cancer detection based on previous studies (28-49). We first assessed whether the biomarkers are associated with breast cancer based on gene expression levels in primary tumor tissues. Principal component analysis (PCA) of mRNA expression data deposited in The Cancer Genome Atlas (TCGA) database showed that the biomarkers were able to distinguish breast cancers from all other cancers (FIG.1B-C). We then developed digital ELISA using Single Molecule Arrays (Simoa) assays for these biomarkers and ensured that the assays are analytically robust by performing rigorous validation tests (FIGS.5-7, Tables S1-S2). Using these Simoa assays, we tested serum samples from a preliminary cohort of female self-reported healthy subjects (n=24) and breast cancer subjects (n=25) (FIG.1D). We showed that this panel can easily distinguish between the healthy and breast cancer subjects. These results suggested that the panel of 24 biomarkers is promising for breast cancer detection. To confirm this result, we sought to investigate whether these biomarkers could be used to detect breast cancer in blood in a larger cohort of newly diagnosed patients who have not received any treatment.

Blood Biomarker Panels for Breast Cancer Detection in a Newly Diagnosed, Treatment-Naïve Cohort

To assess our ability to detect breast cancer using a blood biomarker panel, we initiated a sample collection at Tufts Medical Center and analyzed serum samples from newly diagnosed patients. Patients were screened by mammography and diagnosed with breast cancer by biopsy. Patients who had a positive breast cancer diagnosis and had not undergone surgical or therapeutic interventions were eligible. Tumor characteristics for breast cancer subjects are given in Table 3. Tumors were generally consistent with early-stage disease, with most being small (T0-T2) and lymph node-negative (N0), and all being non-metastatic (M0). The majority of tumors were estrogen receptor (ER) positive, with a median ER measurement of 95% (interquartile range 85%, 98%) using immunohistochemistry of biopsy specimens. Healthy subjects were obtained from the Partners Biobank, which provides a curated cohort of blood samples from healthy subjects that were collected at several different hospitals. These 197 subjects (100 healthy and 97 breast cancer subjects) were all female and at least 40 years old.

We measured serum biomarker levels in this sample cohort using the 24 Simoa assays. Table 1 andFIG.8present age and biomarker distributions for healthy and breast cancer subjects. Age distributions were similar for the healthy and breast cancer subjects. We then examined whether the biomarker panel could distinguish between healthy and breast cancer subjects using a logistic regression analysis. As shown inFIG.2A, the model using all 24 biomarkers plus age had an area under the curve (AUC) of 0.95 (95% CI 0.92-0.98) while the model using age alone was uninformative with an AUC of 0.51 (95% CI 0.43-0.59). The model using all 24 biomarkers plus age correctly identified 174 of 197 (88%) subjects, with 87% sensitivity and 90% specificity.

We then down-selected the most informative markers using a cross-validation backwards selection process with the 24 protein biomarkers plus age in the model, which yielded HER3, HSP70, CYR61, and LCN2 as the most informative markers (Table 4). The model using these four biomarkers plus age (FIG.2B) had an AUC of 0.87 (95% CI 0.81-0.92). This model correctly identified 165 of 197 (84%) of the subjects, with 85% sensitivity and 83% specificity. The composite cross validation test-set AUC was 0.94 (95% CI 0.92-0.97), showing that the four biomarker panel was well-validated (Table 4). Model calibrations are shown inFIG.9. We also assessed the performance of each of these biomarkers plus age on their own (Table 5). A model of HSP70 plus age (FIG.2C) had an AUC of 0.77 (95% CI 0.71-0.84) and performed better than models of any other individual marker plus age. Compared to models of each individual marker plus age, the model using the four biomarkers plus age performed substantially better. These results suggest that the panel is critical to obtain optimal discrimination and that the individual markers alone are not sufficient to detect breast cancer.FIG.10shows XY scatterplots of the relationship between these four markers. Furthermore, we have included age in our model since the risk of breast cancer increases with age. We show that the concentrations of some biomarkers correlate with age in healthy subjects (FIG.11).

We also assessed the net benefit ratio (FIG.2D) (50-52). For all threshold probabilities of about 10% and above, the model using the 24 biomarkers plus age had a higher net benefit than any alternative model, including a decision to classify all subjects as healthy or, at the other extreme, to classify all subjects as cancer. For threshold probabilities below 10%, the differences in net benefit across the various models were small.

Subtype Analysis Using the Biomarker Candidates

Breast cancer is a heterogeneous disease that consists of different molecular subtypes and thus we sought to evaluate whether our models could accurately classify different breast cancer subtypes as cancer. We identified three subtypes in our breast cancer cohort: ER+ tumors, ER-/HER2+ tumors, and triple negative breast cancers (TNBC). We examined the performance of the 24 biomarker panel and the four biomarker panel that we described in the previous section and found that both models were able to accurately classify the different breast cancer subtypes as cancer (FIG.3A). These results suggest that the panels can be used to generally detect breast cancers of different subtypes. To further confirm these results, we developed new models using the two biomarker panels and two different groups. The first group consisted of healthy and ER+ breast cancer subjects and the second group consisted of healthy and TNBC subjects. We found that all four models had high AUCs (FIG.3B), suggesting that these biomarker panels can accurately distinguish between healthy and breast cancer subtypes.

We next wanted to determine whether the 24 protein biomarkers could distinguish between ER+ and TNBC in blood. Due to our small sample size (for ER+, n=81 and for TNBC, n=10) we sought to downselect and identify the most important biomarkers that could distinguish between the different subtypes. To downselect the markers, we examined mRNA expression levels for the 24 biomarkers in primary tumors by TCGA and observed that the ER+ and TNBC subtypes clustered away from each other (FIG.3C). We selected the top ten markers that contributed the most to the principal components (FIG.3D) and developed a model using these ten protein biomarkers in blood, which provided an AUC of 0.96 (95% CI 0.92-1.00) (FIG.3E).

We identified MICA, CA125, and CD25 as the top three most informative protein biomarkers in blood for subtyping (FIG.12) and observe an AUC of 0.96 (95% CI 0.91-1.00) using this three-marker panel (FIG.3E). Altogether, our results suggest that the protein biomarkers can accurately classify each of several different breast cancer subtypes, and further, that a subset of the 24 biomarkers can distinguish ER+ from TNBC in blood.

TABLE 2Simoa assay reagents. All reagents were obtainedfrom R&D Systems unless otherwise indicated.CaptureDetectorProteinTargetantibodyantibodystandard1ADAM8DY1031DY1031DY10312CA15-310-C03E10-C03F30-1066(Fitzgerald)(Fitzgerald)(Fitzgerald)3CA125DY5609DY5609DY56094CA19-910-CA19B10-CA19A30-AC14(Fitzgerald)(Fitzgerald)(Fitzgerald)5CYR61DY4055DY4055DY40556CD25DY223DY223DY2237CEACAM1DY2244DY2244DY22448CXCL10DY266439904DY266(BioLegend)9EGFDY236DY236DY23610EGFRDYC1854DYC1854DYC185411ERDYC5715DYC5715DYC571512GDF15DY957BAF940DY95713He4DY6274DY6274DY627414HER2DYC1129DYC1129DYC112915HER3DYC234DYC234DYC23416HER4DYC1133DYC1133DYC113317HSP70DYC1663DYC1663DYC166318IL-6MAB206BAF206206IL19LCN2DY1757DY1757DY175720MICADY1300DY1300DY130021P21DYC1047DYC1047DYC104722PRDYC5415DYC5415DYC541523PTX3DY1826DY1826DY182624VEGFAHG0114BAF293DY293(ThermoFisher)

Tumor Characteristics of Breast Cancer Cases (n=97)

TABLE 3Tumor Characteristics.CharacteristicMedian (IQR) or N (%)N MissingCancer Type0Invasive84(87%)In Situ13(13%)Cancer Location2Ductal86(91%)Lobular9(9%)ER, % positive cellsa95(85, 98)1ER Positive Status (>=10%)b77(84%)5PR, % positive cellsa77.5(0, 95)1PR Positive Status (>=10%)b62(70%)9HER2 Positive Status26(30%)11Tumor Size2T011(12%)T161(64%)T217(18%)T34(4%)T42(2%)Lymph Node Metastasis13N055(65%)N126(31%)N23(4%)Tumor Grade1Well Differentiated24(25%)Moderately Differentiated52(54%)Poorly Differentiated20(21%)For categorical variables, category percentages are based on participants with non-missing data for the variable.aIncludes those tumors with measurements in the range of 1-9%.bExcludes those tumors (4 ER, 8 PR) with measurements in the range of 1-9% due to ambiguous nature of tumors with these hormone receptor levels. Receptor-negative status defined as 0%.ER = Estrogen Receptor,IQR = Interquartile Range,PR = Progesterone Receptor

TABLE 4Predictive accuracy and variable importance of five-fold cross validation. Eachparticipant randomly assigned to one of five groups. In each fold, one group was heldout as the test set and the other groups combined served as the training set.Five-fold cross validation (n = 197)Fold 1Fold 2Fold 3Fold 4Fold 5VariableVIVariableVIVariableVIVariableVIVariableVITrainingHSP70100HSP70100HER3100HSP70100HSP70100SetHER395LCN288HSP7096HER389LCN295LCN285HER281LCN284CYR6188HER395CYR6179CA15-380CA19-974LCN280CYR6181CA15-375CA19-970CYR6171CXCL1078EGF79EGF67EGFR63EGF61CEACAM172CEACAM174CA12541CXCL1058CEACAM17VEGF71HE469VEGF0CA12558ADAM82HE466ER34CEACAM10ER0PR1GDF1559ADAM80PR0Age0VEGF0IL-650IL-60Age0CA1250EGF49CXCL100MICA0Age0CA12534Age0Age0PR0AUC, All Test Sets Combined: 0.94 (95% Cl 0.92-0.97)AUC = Area Under Receiver Operating Characteristic Curve,VI = Variable Importance

TABLE 5AUC for models of age and one marker. Each AUC is for a modelof breast cancer (n = 97) and healthy subjects (n =100) with predictors of age and one marker. Predictors werelog-transformed. AUC, area under receiver operating characteristic curve.MarkerAUCADAM80.509CA15-30.594CA19-90.529CA1250.518CD250.526CEACAM10.546CXCL100.536CYR610.675EGF0.661EGFR0.622ER0.555GDF150.522HE40.563HER20.594HER30.772HER40.525HSP700.775IL-60.644LCN20.544MICA0.533p210.505PR0.536PTX30.583VEGF0.623

REFERENCES

7. R. D. Rosenberg, et al., Effects of age, breast density, ethnicity, and estrogen replacement therapy on screening mammographic sensitivity and cancer stage at diagnosis: review of 183,134 screening mammograms in Albuquerque, New Mexico., Radiology 209, 511-518 (1998).

8. K. Kerlikowske, et al., Likelihood ratios for modern screening mammography: risk of breast cancer based on age and mammographic interpretation, Jama 276, 39-43 (1996).

14. A. van de Stolpe, et al., Circulating tumor cell isolation and diagnostics: toward routine clinical use (2011).

24. A. P. Lourenco, et al., A Noninvasive Blood-based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years, Clin. Breast Cancer 17, 516-525.e6 (2017).

39. R. G. Moore, D et al., A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass, Gynecol. Oncol. 112, 40-46 (2009).

41. M. Kamei, et a1.,HE4 Expression Can Be Associated with Lymph Node Metastases and Disease-free Survival in Breast Cancer, Anticancer Res. 4784, 4779-4783 (2010).

45. C. Fang, et al., Serum CA125 is a predictive marker for breast cancer outcomes and correlates with molecular subtypes, Oncotarget 8, 63963-63970 (2017).

64. Moore, R. G. et al. A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecol. Oncol. 112, 40-46 (2009).

68. Ross, J. S. et al. The Her-2/neu gene and protein in breast cancer 2003: biomarker and target of therapy. Oncologist 8, 307-25 (2003).

74. Bauernhofer, T. et al. Role of prolactin receptor and CD25 in protection of circulating T lymphocytes from apoptosis in patients with breast cancer. Br. J. Cancer 88, 1301-1309 (2003).

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