18F-FSPG PET/CT and Integrated Biomarkers for Early Lung Cancer Detection in Patients With Indeterminate Pulmonary Nodules
Phase: PHASE2
Conditions: Cigarette Smoker, Current Smoker, Former Smoker, Multiple Pulmonary Nodules, Pulmonary Nodule
Sponsor: Andrei Iagaru

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Co-Investigators 
Brian Shaller, MD 
Alice Fan, MD 
Andrei Iagaru, MD 
Biostatistician 
Jarrett Rosenberg, PhD 
Project Manager 
IRB-46607 
LUN0106 
NCT03824535 
IND 125516 
Version date:  08 June 2023 
Initial SRC Approval Date – 30 October 2018 
18F-FSPG PET/CT and Integrated Biomarkers for Early Lung Cancer Detection in Patients with Indeterminate Pulmonary 
Nodules 
18F-FSPG PET/CT and Integrated Biomarkers for Early Lung Cancer Detection in Patients with 
Indeterminate Pulmonary Nodules 
Protocol Director 
Carina Mari Aparici, MD 
300 Pasteur Dr, H2200 
Stanford, CA 94305-5281 
drmari@stanford.edu 

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TABLE OF CONTENTS 
SCHEMA ....................................................................................................................................................... 4 
LIST OF ABBREVIATIONS AND DEFINITION OF TERMS ........................................................................... 5 
1. 
OBJECTIVES ........................................................................................................................................ 6 
1.1. 
Primary Objective ............................................................................................................................... 6 
1.2. 
Secondary Objective .......................................................................................................................... 6 
2. 
BACKGROUND ..................................................................................................................................... 6 
2.1. 
Study Disease .................................................................................................................................... 6 
2.2. 
Imaging agent .................................................................................................................................... 6 
2.3. 
Clinicaltrials.gov ................................................................................................................................. 7 
2.4. 
Rationale ............................................................................................................................................ 7 
2.5. 
Preliminary results .............................................................................................................................. 8 
2.6. 
Study Design .................................................................................................................................... 10 
3. 
PARTICIPANT SELECTION AND ENROLLMENT PROCEDURES ..................................................... 11 
3.1. 
Inclusion Criteria .............................................................................................................................. 12 
3.2. 
Exclusion Criteria ............................................................................................................................. 13 
3.3. 
Informed Consent Process ............................................................................................................... 15 
3.3.1. 
Primary Completion ...................................................................................................................... 18 
3.3.2. 
Study Completion: ........................................................................................................................ 18 
4. 
IMAGING AGENT AND PROCEDURE INFORMATION ...................................................................... 18 
4.1. 
Imaging Agent and Imaging Procedure ............................................................................................ 18 
5. 
STUDY PROCEDURES ....................................................................................................................... 20 
6. 
STUDY CALENDAR ............................................................................................................................ 21 
7. 
COMPENSATION ................................................................................................................................ 21 
8. 
ADVERSE EVENTS AND REPORTING PROCEDURES .................................................................... 21 
8.1. 
Risks and Potential Adverse Events ................................................................................................. 21 
8.2. 
Adverse Event Reporting ................................................................................................................. 22 
9. 
REGULATORY CONSIDERATIONS ................................................................................................... 22 
9.1. 
Investigational New Drug Application (IND) ...................................................................................... 22 
9.2. 
Institutional Review of Protocol ........................................................................................................ 22 
9.3. 
Data Management Plan.................................................................................................................... 22 
9.4. 
Data and Safety Monitoring Plan ...................................................................................................... 22 

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10. 
MEASUREMENTS ........................................................................................................................... 23 
10.1. Primary Outcome Measure (ClinicalTrials.gov)................................................................................. 23 
10.2. Secondary Outcome Measure (ClinicalTrials.gov) ............................................................................ 23 
10.3. Measurement Methods..................................................................................................................... 23 
11. 
STATISTICAL CONSIDERATIONS .................................................................................................. 24 
11.1. Statistical Design and Data Analysis ................................................................................................ 24 
11.2. Sample Size ..................................................................................................................................... 25 
11.3. Accrual estimates ............................................................................................................................. 26 
11.4. Criteria for future studies .................................................................................................................. 26 
12. 
REFERENCES ................................................................................................................................ 27 

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18F-FSPG PET/CT and Integrated Biomarkers for Early Lung Cancer Detection in Patients with Indeterminate Pulmonary Nodules 
SCHEMA 
 
• 
All PET imaging studies and image analysis will take place at Stanford (total of 120 patients). 
• 
Nasal brushings and blood samples will also take place at Stanford according to our own protocols. Eventually these 
samples will be combined with other cohorts (total of > 900 patients) for collective analysis with our collaborators at Boston 
University. 

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LIST OF ABBREVIATIONS AND DEFINITION OF TERMS 
 
 
CBC 
Complete blood count 
CI 
Confidence interval 
CRF 
Case report/Record form 
CTCAE 
Common Terminology Criteria for Adverse Events 
DSMB 
Data Safety Monitoring Board 
18F 
Fluorine-18 
FDG 
Fluorodeoxyglucose 
FSPG 
(4S)-4-(3-18F-Fluoropropyl)-L-Glutamate 
IND 
Investigational New Drug 
IRB 
Institutional Review Board 
IV 
Intravenous 
LDCT 
Low dose computed tomography 
NPV 
Negative Predictive Value 
PET/CT 
Positron emission tomography – computed tomography 
PPV 
Positive Predictive Value 
ROC 
Receiver-Operative-Characteristic 
SAE 
Serious adverse event 
SUV 
Standard Uptake Value 

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1. OBJECTIVES 
1.1. 
Primary Objective 
Comparison of 18F-FSPG accumulation with 18F-FDG accumulation to assess whether 
(4S)-4-(3-18F-Fluoropropyl)-L-Glutamate (18F-FSPG) PET is better at discriminating 
between benign and malignant nodules. 
 
1.2. 
Secondary Objective 
To develop and validate early lung cancer detection biomarkers that would directly impact 
the growing need to integrate imaging and non-invasive molecular diagnostics for 
indeterminate pulmonary nodules and allow physicians to avoid unnecessary invasive 
procedures in patients with benign lung disease. We will work with collaborators at Boston 
University to develop prediction models that could identify patients with lower likelihood of 
malignancy who can be followed by CT surveillance, instead of invasive lung biopsy. 
 
2. BACKGROUND 
2.1. 
Study Disease 
Lung cancer remains the leading cause of cancer death, in large part due to our inability to 
intercept the disease process prior to its progression to an advanced state. Cancer 
interception refers to actively interrupting the cancer development process and decreasing 
the rate of clinical presentation with advanced disease. The rate-limiting steps impeding 
effective lung cancer interception are 1) a lack of understanding of the earliest targetable 
molecular events in lung carcinogenesis; 2) the challenge of developing highly sensitive and 
specific methods for early detection; and 3) the lack of markers for predicting or monitoring 
the efficacy of interception approaches. The USPSTF recommends annual screening for 
lung cancer with low-dose computed tomography (LDCT) in adults aged 55 to 80 years who 
have a 30 pack-year smoking history and currently smoke or have quit within the past 
15 years. With the LDCT screening a growing number of lung nodules is being detected. 
The nodule size range in screen-eligible individuals generally represents intermediate risk 
where there is considerable clinical uncertainty about diagnostic approach. A sensitive 
biomarker with high negative predictive value would enable physicians to avoid unnecessary 
procedures in patients with benign lung disease, avoiding substantial risks and costs. 
2.2. 
Imaging agent 
While other radiopharmaceuticals are available, 18F-fluorodeoxyglucose (18F-FDG) is still 
the primary radiotracer clinically used for the evaluation of malignancies with PET/CT. 
18F-FDG allows the non-invasive visualization of the glycolytic pathway in cells. While 
effective for many cancer types, it also has many limitations including poor uptake in certain 
cancer types, prominent background uptake in the brain, kidneys, and often the 
gastrointestinal tract, and prominent uptake in benign inflammatory or infectious processes. 
 
New insights into other metabolic pathways utilized by tumor versus normal cells have led to 
the development of a new classes of cancer diagnostics and therapeutics. Many tumor 
types are dependent on the glutaminolytic pathway. After glutamine enters the cell, 

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glutaminase converts it to glutamate, which is subsequently used in several metabolic 
pathways in the cancer cell, including as a substrate for the Krebs Cycle and for redox 
reactions. This pathway is an alternative to the better-known glycolytic pathway mentioned 
above. Some cancers that depend on glutamine as an energy source demonstrate high 
levels of glutaminase expression and a high intratumoral ratio of glutamate-to-glutamine. 
Other glutamine-dependent tumors have normal glutaminase activity but other changes that 
may result in increased glutamine utilization. Lung cancers are described to be dependent 
on the glutaminolytic pathway (1). 
(4S)-4-(3-[18F]fluoropropyl)-L-glutamate (18F-FSPG, prior alias BAY 94-9392) is a novel 
agent for PET imaging and is specifically transported into cells via system xC-. This 
exchanger binds the amino acids glutamate and cystine with similar affinity and is able to 
transport both into the cell by exchange of abundant intracellular glutamate. Intracellular 
cystine is rapidly reduced to two molecules of cysteine, which are either used in glutathione 
(GSH) biosynthesis or contribute directly to redox maintenance. The 18F-FSPG molecule, 
as a glutamate analog, is perfectly suited to provide important non-invasive information 
about tumor biology that is uniquely different from anatomic imaging such as CT or MRI and 
from 18F-FDG. 18F-FSPG has been shown to be taken up by non-small cell lung cancers 
(2). Our aim is to evaluate this imaging agent in the indeterminate lung nodule setting and 
determine if it can be used to identify early lung cancers. 
2.3. 
Clinicaltrials.gov 
This study is registered at ClinicalTrials.gov. 
2.4. 
Rationale 
There is a growing need for markers to identify the small number of malignant pulmonary 
nodules from the large number of benign nodules identified by chest CT. We propose to 
develop integrated clinical, molecular and imaging-based models for lung cancer in older 
smokers with nodules 7 to 30 mm at elevated risk for lung cancer based on lung cancer 
screening eligibility criteria. These indeterminate pulmonary nodules (IPNs) may have been 
detected in the context of screening or incidentally as part of routine practice. While a 
number of clinical risk models exist (3, 4), this nodule size range in screen-eligible 
individuals generally represents intermediate risk where there is considerable clinical 
uncertainty about diagnostic approach. A sensitive biomarker with high negative predictive 
value would enable physicians to avoid unnecessary procedures in patients with benign lung 
disease, avoiding substantial risks and costs. Our hypothesis is that we can improve current 
lung cancer detection by incorporating detailed information about nodule imaging 
characteristics and molecular data from surrogate tissues (nose and plasma) into risk 
models. The key advances over our prior and ongoing funded work are 1) detection of 
tumor-associated mutations in plasma; 2) focusing on complete sequencing-based 
characterization of the nasal transcriptome; 3) performing single-cell RNA-seq of nasal 
epithelium to identify the cellular architecture of cancer-associated expression; 
4) incorporating imaging characteristics of the nodule uncovered by advanced semantic and 
computational methods; 5) piloting study of 18F-FSPG PET tracer; and 6) incorporating each 
of these data types together with clinical data into an algorithm that can trigger appropriate 
diagnostic workup of IPN patients. 

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2.5. 
Preliminary results 
Detection of mutations in circulating tumor DNA. We have used both targeted and whole 
genome methods (5) to identify genomic alterations in the plasma of healthy individuals and 
patients with cancer. Most recently, we have used the TEC-seq approach described above 
to examine plasma specimens from 44 healthy individuals: no mutations were observed in 
the cancer driver genes analyzed in our panel. We next analyzed plasma samples from 
lung cancer (n = 64), breast cancer (n = 46), colorectal cancer (n = 42), and ovarian cancer 
patients (n = 42). These untreated patients were largely diagnosed at stage I and II. We 
found that the concentration of ctDNA in plasma from cancer patients was ~29 ng/mL, 
significantly higher than that observed in healthy individuals (n = 44; 7 ng/mL; P = 0.001). 
By TEC-seq, we detected cancer driver mutations in plasma from over half of stage I and II 
colorectal, ovarian, lung, and breast cancer patients. The affected genes and mutation 
frequencies in the lung cancer patient plasma was similar to what has previously been 
reported (Figure 1). On average, 2.1 alterations were observed in each patient with 
detectable ctDNA, with lung cancers having a higher number of alterations per case. We 
next evaluated 216 alterations in 100 patients where matched tumor tissue and blood cells 
were available. Overall, 82 of the 100 patients (82%) had at least one alteration observed in 
the circulation that was identical to that in the tumor specimen. We therefore expect that the 
fraction of cancer patients with a ctDNA-detected mutation will increase as the TEC-seq 
panel is expanded to include more cancer-associated mutations. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1. Mutation frequencies in cfDNA of lung cancer patients. The ctDNA mutational frequencies (bars) are similar to 
previously reported tumor mutation frequencies (dots) from COSMIC for 76 out of 81 genes analyzed (P > 0.05 for 76 of 81 genes, 

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Nasal epithelial gene expression differences associated with lung cancer. We have 
previously established the feasibility of detecting lung cancer via nasal epithelial gene 
expression (6) using nasal microarray data from current or former smokers undergoing 
bronchoscopy for suspect lung cancer (as part of our validation of the Percepta™ bronchial 
genomic test for lung cancer) (7, 8). We derived a 30-gene nasal biomarker that in an 
independent test set significantly improved the performance of a clinical risk model for lung 
cancer (6). This proof-of-concept nasal biomarker was developed in the clinical setting of 
bronchoscopy, potentially enriching for higher risk patients with larger lesions. The important 
next step is to build a biomarker in the clinical setting of all indeterminate lung nodules and 
to incorporate additional molecular and imaging features. 
Single cell RNA-seq of bronchial airway epithelium. To explore the value of single cell 
transcriptomics in understanding smoking-related airway injury, we performed scRNA-seq of 
~1200 cells from 6 healthy current and 6 never smokers using CEL-Seq (9). Global 
clustering using t-SNE indicates cell subsets enriched for known airway cell-type markers 
(Figure 2). Goblet cells (MUC5AC+) are significantly more abundant in current smokers, 
whereas basal (KRT5+) and club cells (SCGB1A1+) are significantly more abundant in the 
airways of never smokers. To explore lung-cancer associated airway gene expression, we 
have performed scRNA-seq on ~1200 cells from 12 patients undergoing bronchoscopy for 
suspect lung cancer. We find expression of many genes in our bronchial airway gene 
expression biomarker (7) localizes to t-SNE cell clusters, suggesting that biomarker 
performance might be enhanced by assessing specific airway cell types. 
Semantic and quantitative imaging variables for diagnostic risk of lung cancer. Dr. Aberle 
has developed logistic regression models combining clinical and semantic imaging variables 
in 11,128 CT-detected nodules from the NLST and found that semantic imaging features 
significantly improves the performance of clinical risk models of disease in cross validation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2. Single-cell RNA-seq of the normal bronchial epithelium. t-SNE clustering of single cell transcriptomes from 6 current 
and 6 never smokers. Left: Cells are colored by smoking status, with black cells from smokers and grey cells from nonsmokers. 
Right: Cells are colored by the expression levels of known cell type markers. 

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from 0.76 (95% CI: 0.74-0.79) to 0.89 (95% CI: 0.87-0.90). In separate work, Dr. Aberle 
has identified quantitative imaging features in 146 ever smokers aged 50 to 80 years with 
incidentally-detected nodules from the UCLA EMR with high prediction performance 
(AUC-.86) in cross validation. Our preliminary data establishes the potential of both 
semantic and quantitative feature-based models for lung cancer. 
 
18F-FSPG PET tracer. Hijacking energy metabolism is a hallmark of cancer (10). 18F-FSPG 
is a glutamate analogue tracer that is transported into cells via the system xC– transporter, 
which maintains the cellular redox environment (11). This transporter is also important in 
tumor growth, progression and treatment resistance (12, 13). 18F-FSPG visualizes distinct 
tumor biology compared to 18F-FDG, which measures cellular glucose uptake (14). Studies 
at Stanford have shown that lung cancer is well visualized by 18F-FSPG with low 
background uptake (2, 15). New data suggest that 18F-FSPG has lower uptake in many 
inflammatory conditions (16), which can severely limit 18F-FDG PET accuracy (Table 1). 
We therefore hypothesize here that 18F-FSPG is a superior tracer to assess the likelihood of 
cancer in pulmonary nodules. 
Relevance of preliminary data: The preliminary data above establishes 1) our ability to 
measure a panel of mutations in ctDNA in early stage lung cancer, setting the stage for 
evaluating the panel as an early detection tool in the IPN setting; 2) nasal gene-expression 
as a potential diagnostic for lung cancer; 3) our ability to perform scRNA-seq of airway 
epithelial brushings in order to characterize the heterogeneity of the field; 4) semantic and 
quantitative imaging markers as diagnostic in the IPN setting; 5) the potential for novel 
molecular imaging markers to improve upon existing PET markers. 
2.6. 
Study Design 
120 patients with indeterminate lung nodules will be enrolled in the study at Stanford. Blood 
and nasal brushings will be collected once. The samples from these 120 patients will be part 
of a bigger cohort of over 900 patients for collaborative data analysis. 
All samples will be de-identified before shipment or other data sharing with collaborators 
takes place. An additional 70 patients who may or may not have smoking history will be 
imaged at Stanford and analyzed with the other 50 patients enrolled at Stanford. 
All 120 patients will be imaged once by 18F-FSPG PET/CT as part of this open-label, 
nonrandomized, single-dose explorative study and once by 18F-FDG PET/CT. PET/CT 
imaging will be scheduled > 24 hours apart to allow for imaging agent washout, and not 
more than two weeks apart to allow for optimal image comparison. 
Clinical diagnosis (cancer / benign disease) is based on 1) histopathology of biopsy/surgical 
specimen or 2) surveillance by CT scans for a period of 2 years to confirm that there is no 
 
 
 
 
Table 1. SUVmax with standard deviation for non-malignant lung 

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growth of the nodule. All patients without histopathological diagnosis will undergo CT 
surveillance for a 2-year period by clinical standards. 
3. PARTICIPANT SELECTION AND ENROLLMENT PROCEDURES 
Inclusion and Exclusion Criteria are provided on the Eligibility Checklist, following, and which 
may be extracted for use in screening potential subjects. 
The following Participant Eligibility Checklist will be completed in its entirety for each subject 
prior to registration. The completed, signed, and dated checklist will be retained in the 
subject’s study file, and the study’s Regulatory Binder. 
Pursuant to Stanford Medicine SOP “Confirmation of Participant Eligibility in Clinical Trials,” 
the treating Physician (investigator); the Study Coordinator; and an Independent Reviewer 
will verify that the subject’s eligibility is accurate; complete;  and legible in source records. 
A description of the eligibility verification process will be included in the EPIC or other 
Electronic Medical Record progress note. 
 
 

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3.1. Participant Eligibility Checklists 
For each prospective study participant that is screened, the appropriate checklist will be printed, 
the results recorded, and filed in the respective subject binder or file. It is anticipated that not all 
prospective study participants will be enrolled. 
Eligibility Checklist (Group 1: Subjects with smoking history) 
I. 
Protocol Information 
 
Protocol Title: 18F-FSPG PET/CT and Integrated Biomarkers for Early Lung Cancer 
Detection in Patients with Indeterminate Pulmonary Nodules 
Group Group 1: Subjects with smoking history 
eProtocol number: 
OnCore number: 
IRB-46607 
LUN0106 
Principal 
Investigator: Carina Mari Aparici, MD 
 
II. 
Subject Information 
 
Subject name / Unique ID: 
/ 
Gender 
Male 
Female 
 
III.  
Study Information 
Inclusion Criteria 
Prospective Participant Must MATCH 
ALL these Inclusion Criteria to be Eligible 
Yes 
No 
Supporting 
Documentation * 
1. Pulmonary nodule between the size of 7 to 30 mm. 
Subjects with multiple nodules may be eligible if the 
dominant nodule is 7 to 30 mm. 
 
 
2. ≥ 45 years old 
 
 
3. Current or former cigarette smoker, with 
≥ 20 pack years 
 
 
4. Documented informed consent 
 
 

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Exclusion Criteria 
 
Prospective Participants Must NOT Match 
ANY of These Exclusion Criteria 
Yes 
No 
Supporting 
Documentation * 
1. History or previous diagnosis of lung cancer 
 
 
2. Any other current or previous malignancy within the 
past 5 years other than exceptions as determined 
by the opinion of the treating MD and PI, which 
include: 
• 
Adequately treated basal cell or squamous cell 
skin cancer 
• 
Carcinoma in situ of cervix 
• 
Prostate cancer with stable PSA level for >3 
years, or 
• 
Other neoplasm that, in the opinion of the 
treating MD and PI, will not interfere with study- 
specific endpoints 
 
 
3. Pregnant or nursing (per department of radiology 
policy) 
 
 
* All subject files must include supporting documentation to confirm subject eligibility. The 
method of confirmation can include, but is not limited to, laboratory test results, radiology test 
results, subject self-report, and medical record review. 
V. 
Statement of Eligibility 
By signing this form of this trial I verify that this subject is: 
eligible / 
ineligible for 
participation in the study. This study is approved by the Stanford Cancer Institute Scientific 
Review Committee, the IRB of record, and has finalized financial and contractual agreements as 
required by Stanford School of Medicine’s Research Management Group. 
 
Study Coordinator printed name: 
Date: 
Signature: 
Investigator printed name: 
Date: 
Signature: 
Triple-check reviewer printed name: 
Date: 
Signature: 

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Eligibility Checklist (Group 2: Smokers and Non-Smokers) 
I. 
Protocol Information 
 
Protocol Title: 18F-FSPG PET/CT and Integrated Biomarkers for Early Lung Cancer 
Detection in Patients with Indeterminate Pulmonary Nodules 
Group Group 2: Smokers and non-smokers 
eProtocol number: 
OnCore number: 
IRB-46607 
LUN0106 
Principal 
Investigator: Carina Mari Aparici, MD 
 
II. 
Subject Information 
 
Subject name / Unique ID: 
/ 
Gender 
Male 
Female 
 
III.  
Study Information 
Inclusion Criteria 
Prospective Participant Must MATCH 
ALL these Inclusion Criteria to be Eligible 
Yes 
No 
Supporting 
Documentation * 
1. Pulmonary nodule between the size of 7 to 30 mm. 
Subjects with multiple nodules may be eligible if the 
dominant nodule is 7 to 30 mm. 
 
 
2. ≥ 45 years old 
 
 
3. Documented informed consent 
 
 

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Exclusion Criteria 
 
Prospective Participants Must NOT Match 
ANY of These Exclusion Criteria 
Yes 
No 
Supporting 
Documentation * 
1. History or previous diagnosis of lung cancer 
 
 
2. Any other current or previous malignancy within the 
past 5 years other than exceptions as determined by 
the opinion of the treating MD and PI, which include: 
• 
Adequately treated basal cell or squamous cell 
skin cancer 
• 
Carcinoma in situ of cervix 
• 
Prostate cancer with stable PSA level for >3 
years, or 
• 
Other neoplasm that, in the opinion of the 
treating MD and PI, will not interfere with study- 
specific endpoints 
 
 
3. Pregnant or nursing (per department of radiology 
policy) 
 
 
* All subject files must include supporting documentation to confirm subject eligibility. The 
method of confirmation can include, but is not limited to, laboratory test results, radiology test 
results, subject self-report, and medical record review. 
V. 
Statement of Eligibility 
By signing this form of this trial I verify that this subject is: 
eligible / 
ineligible for 
participation in the study. This study is approved by the Stanford Cancer Institute Scientific 
Review Committee, the IRB of record, and has finalized financial and contractual agreements as 
required by Stanford School of Medicine’s Research Management Group. 
 
Study Coordinator printed name: 
Date: 
Signature: 
Investigator printed name: 
Date: 
Signature: 
Triple-check reviewer printed name: 
Date: 
Signature: 

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3.2. 
Informed Consent Process 
All participants must be provided a consent form describing the study with sufficient 
information for participants to make an informed decision regarding their participation. 
Participants must sign the IRB approved informed consent prior to participation in any study 
specific procedure. The participant must receive a copy of the signed and dated consent 
document. The original signed copy of the consent document must be retained in the 
medical record or research file. 
Study Timeline 
3.2.1. Primary Completion: 
The study will reach primary completion 36 months from the time the study opens to 
accrual. 
3.2.2. Study Completion: 
The study will reach study completion 60 months from the time the study opens to 
accrual. 
4. IMAGING AGENT AND PROCEDURE INFORMATION 
4.1. 
Imaging Agent and Imaging Procedure 
Patients enrolled in this study will undergo a one-time 18F-FSPG PET/CT procedure which 
includes a single IV injection of 18F-FSPG. Patients will also undergo one 18F-FDG PET/CT 
as part of their standard of care. The two PET/CT procedure will be scheduled > 24 hours 
apart to allow for imaging agent washout, and not more than two weeks apart. 
The nuclear physician or designee is responsible for ensuring that deliveries of the imaging 
agent and other materials from the manufacturing site are correctly received, recorded, 
handled, and stored safely and properly in accordance with all applicable regulatory 
guidelines, and used in accordance with this protocol. 
Unless otherwise agreed, imaging agent vials must be destroyed on-site after use. The 
transport containers have to be returned to the manufacturing site. A list of imaging agent or 
other materials that were returned, or destroyed, must be prepared and signed by the 
principal investigator or designee. If there are any discrepancies, an explanation for these 
should also be provided 
Physical, Chemical and Pharmaceutical Properties of the Imaging Agent 
IUPAC name: 
(S)- 4-(3-[18F]Fluoropropyl)-L-glutamic acid 
Chemical name: 
(2S, 4S)-2-Amino-4-(3-[18F]-fluoro propyl) pentane dioic acid 
Empirical formula: 
C8H14FNO4 
Molecular weight: 
206.2 g/mol 
The active pharmaceutical ingredient for PET/CT imaging is the compound 18F-FSPG that is 
labeled with the radioactive fluoride isotope 18F. 18F has a half-life of 110 minutes. The 
radioactive substance and the final imaging product are produced on-site at the study site 

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according to the radiolabeling and purification procedure described in the site Standard 
Operating Procedures (SOPs) and the Investigational New Drug application (IND). The 
Department of Chemistry, Manufacturing, Controls (CMC) personnel of Stanford will train 
qualified radiochemists/pharmacists with respect to the imaging agent production and 
handling. The precursor PI-020 and a non-radioactive standard used as a reference for 
Quality Assurance, and corresponding Certificates of Analysis will be provided by Piramal 
Imaging, SA. 
Each batch of 18F-FSPG produced must meet criteria listed in the specification for identity, 
purity, concentration, specific activity and pH before being released. Manufacturing and 
quality control testing will be checked and documented accordingly. Sterility tests will be 
conducted as control of the validated production process after release of the product 
according to the established procedures. 
The final product will be formulated as sterile solution for IV injection. The radioactivity of 
the final product will be verified before injection using a suitable counter, as established on 
site. 
Further details can be found in the Piramal Investigator's Brochure. 
Dosage and Administration 
Study participants will be administered 18F-FSPG under the direct supervision of a nuclear 
physician or designee. For administration of 18F-FSPG, access into a large vein (e.g., 
antecubital vein) should be established using a suitable indwelling catheter. To avoid 
extravasation of 18F-FSPG, correct localization of the catheter must be ensured by a test 
injection of normal saline prior to the injection of 18F-FSPG. 
Each study participant will receive a single IV injection of 18F-FSPG with a total activity 
amounting to 300 MBq (8.1 mCi) ± 10%. This single IV injection will contain a maximum 
mass dose of the diagnostic test agent (total: radioactive plus non-radioactive “cold” 
substance) of no more than 50 µg and a total volume of up to 10 mL. 18F-FSPG will be 
administered manually via IV injection followed by a 10 mL saline flush. The PET/CT scan 
will be conducted about 60 minutes after the injection of 18F-FSPG. 
Packaging and Labeling 
18F-FSPG for Injection will be provided as a sterile solution in a glass vial, contained in a 
lead-shielded transport container. Provided is a representation of the 18F-FSPG for Injection 
label to be used on both the vial and the lead-shielded transport container. 

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Imaging Agent Logistics and Accountability 
The imaging agent will be prepared at Stanford at the following facility under the supervision 
of Director Frederick T Chin, PhD: 
Cyclotron & Radiochemistry Facility 
Lucas Building 
Molecular Imaging Program at Stanford (MIPS) 
1201 Welch Road, Room PS049 
Stanford, CA 94305 
P: 650-725-4182 
F: 650-618-0415 
The imaging agent will be requested via a standard online radiotracer request form received 
by the MIPS Cyclotron & Radiochemistry Facility at Stanford University. The agent will be 
hand delivered from the Radiochemistry Facility at the Lucas Center to the Hot Lab in the 
Nuclear Medicine and Molecular Imaging Clinic at Stanford Hospital. 
Appropriate documentation (for order/receipt, dosage instructions and release for human 
use) will be provided with each delivery of imaging agent which will contain the batch 
number, time of preparation, and radioactive concentration of injection (MBq/mL) at the time 
of calibration preparation. The final quality control processes for the imaging agent will 
occur and be documented before the substance is administered to the subject at the 
imaging clinic. The qualified person at the manufacturing site will ensure that the product 
quality meets the defined criteria and will declare this in a document for release, to be 
received by the investigator (or designated personnel) at the imaging clinic. The imaging 
agent will not be administered to the subject without release for human use. 
The investigator (or designated personnel) will confirm receipt of the imaging agent in writing 
and will use the imaging agent only within the framework of this clinical study and in 
accordance with this study protocol. For each subject he/she will keep a record of the 
imaging agent dispensed including the tear off label of the dispensed vial, a printout 
providing relevant parameters of synthesis and formulation of the imaging agent, and all 
other accompanying forms to the imaging agent. These documents are to be filed in the 
investigator site file. 
Receipt, distribution, and return of the imaging agent must be properly documented on the 
forms provided by the manufacturer giving the following information: study protocol number, 
sender, receiver, date, mode of transport, quantity, batch number, expiration date, and 
retest date, if applicable. 
5. STUDY PROCEDURES 
Phlebotomy: 
Blood will be collected once by a standard venipuncture. Blood will be collected in 
chemistry tubes (EDTA) and cell preservative tubes (Streck) to be stored in the Canary 
Center at Stanford biorepository and to be sent to our Boston University collaborators for 
processing and analysis. A total of 30 mL will be collected in a one-time blood draw. Blood 
draw will take place within 1 month after the patient has been consented. 

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Nasal Brushing: 
Patient will be asked to blow their nose. A small speculum is used to widen the nostril and a 
tiny brush (the size of a Q tip) is put in through the speculum and rotated for 3 seconds. 
This brush is then removed, and a second brush is inserted and rotated for 3 seconds. 
Finally, the speculum and brush are removed from the nose. Nasal brushing will take place 
within 1 month after the patient has been consented. 
18F-FSPG PET/CT scan: 
The patient will be injected with the radiotracer and allowed to rest for about 60 minutes. 
He/she will then be placed in the PET/CT and a static whole-body scan will be obtained from 
the vertex of the skull to the mid-thighs, starting at 60 (± 10) minutes after radiotracer 
injection. 
6. STUDY CALENDAR 
 
Visit 1 
Within 1 month 
from Visit 1 
(can be Visit 2/3) 
Visit 2 or 3* 
Visit 3 or 2* 
Within 
24 to 72 hours after 
18F-FSPG PET/CT 
Informed consent 
X 
Nasal Brushing 
X 
Blood draw 
X 
18F-FDG PET/CT 
X 
18F-FSPG PET/CT 
X 
Patient follow-up 
X 
 
* & 
: PET/CT exams is not specified and may be reversed. PET/CT exams will be 
scheduled at least 24 hours apart, and no more than 2 weeks apart. 
 
7. COMPENSATION 
Study participants will be compensated with a total of $500 for the time it takes them to 
complete the study procedures, including time and transportation for the additional visit to 
the clinic for the PET/CT procedure. 
8. ADVERSE EVENTS AND REPORTING PROCEDURES 
8.1. 
Risks and Potential Adverse Events 
The risk associated with phlebotomy is small and includes discomfort, a bruise at the point 
where the blood is taken, redness, infection, and a rare risk of fainting. The risk associated 

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with the nasal brushing procedure is also small and includes minor discomfort and 
temporary nose bleed. 
18F-FSPG has been administered to over 65 cancer patients world-wide with no adverse 
events reported. As such, there are no major risks that are known or expected with this 
radiopharmaceutical. However, general risks to keep in mind include the radiation 
exposure. The expected radiation exposure of an 18F-FSPG scan in this study is expected 
to be similar to or less than that of an 18F-FDG scan. Other risks to patients are mainly 
related to the intravenous (IV) injection. Although the risk is small, intravenous injection can 
cause infection, injection site discomfort, and bruising. 
8.2. 
Adverse Event Reporting 
Patients will be contacted for follow up within 24 to 72hours after the 18F-FSPG PET/CT, 
Adverse events will be graded according to CTCAE v5. Both Serious and Non-Serious 
Adverse Events will be clearly noted in source documentation. The Protocol Director (PD) 
or designee will assess each Adverse Event (AE) to determine whether it is unexpected 
according to the Informed Consent, Protocol Document, or Investigator’s Brochures, and 
related to the investigation. 
9. REGULATORY CONSIDERATIONS 
9.1. 
Investigational New Drug Application (IND) 
This study will be submitted to 18F-FSPG IND 125516, held by Dr Andrei Iagaru. 
9.2. 
Institutional Review of Protocol 
The protocol, the proposed informed consent and all forms of participant information related 
to the study (e.g. advertisements used to recruit participants) will be reviewed and approved 
by the Stanford IRB and Scientific Review Committee (SRC). Any changes made to the 
protocol will be submitted as a modification and will be approved by the IRB and SRC prior 
to implementation. The Protocol Director will disseminate the protocol amendment 
information to all participating investigators. 
Sample management 
PET imaging analysis will be performed at Stanford. The blood and nasal samples will be 
transferred to Boston University for the RNA and DNA sequencing analyses. 
9.3. 
Data Management Plan 
We will use OnCore and REDCap for data storage and all paper documents will be stored in 
a binder in a locked cabinet on Stanford premises. 
9.4. 
Data and Safety Monitoring Plan 
During the clinical investigation, the Protocol Director will evaluate the progress of the trial, 
including periodic assessments of data quality and timeliness, participant recruitment, 
accrual and retention, participant risk versus benefit, and other factors that can affect study 
outcome. The study team meets bi-weekly to review the study conduct and status. 
The Stanford Cancer Institute Data and Safety Monitoring Committee (DSMC) will audit 
study related activities at least annually in accordance with the DSMC SOP to determine 

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whether the study has been conducted in accordance with the protocol, local standard 
operating procedures, FDA regulations, and Good Clinical Practice (GCP).  This may 
include review of regulatory binders, case report forms, eligibility checklists, and source 
documents. In addition, the DSMC will regularly review serious adverse events and protocol 
deviations associated with the research to ensure the protection of human subjects. Results 
of DSMC audits will be communicated to the IRB and the appropriate regulatory authorities 
at the time of continuing review, or in an expedited fashion, as needed. 
10. MEASUREMENTS 
10.1. Primary Outcome Measure (ClinicalTrials.gov) 
    The primary outcome is specificity of 18F-FSPG to discriminate between benign and 
malignant lung nodules, defined as the proportion of benign cases judged to be benign.  With 
an expected 60 benign cases, an observed 90% specificity would have a 95% confidence 
interval of 79% to 96%.  An observed specificity of 75% would have a confidence interval of 
62-85%. 
If a patient has multiple nodules, the per-patient assessment will be based on the 
dominant nodule only, defined as the largest nodule. 
Outcome Title: 18F-FSPG specificity for malignant lung nodules. 
Outcome Description: 18F-FSPG accumulation in lung nodules will be assessed. 
The observed FSPG specificity will be assessed against a fixed value of 75%. 
Timeframe: Up to 5 years. 
Safety Issue: No. 
 
 
10.2. Secondary Outcome Measure (ClinicalTrials.gov) 
The secondary outcome is improved performance of an integrated prediction model to 
discriminate between benign and malignant lung nodules, compared with a clinical 
prediction model. 
Outcome Title: Improved prediction model for lung nodules 
Outcome Description: Improved performance is defined as a p < 0.05 increase in 
the C statistic determined using DeLong’s test for correlated ROC curves. 
Timeframe: Up to 5 years. 
Safety Issue: No. 
 
10.3. Measurement Methods 
For RNA and DNA sequencing the blood and nasal samples will be transferred to Boston 
University. PET imaging analysis will be performed at Stanford. 
Total RNA-seq of Nasal Brushings: RNA sequencing libraries will be prepared from total 
RNA samples using Illumina® TruSeq® Stranded Total RNA Library Preparation Kit. The 
libraries from individual samples will be multiplexed in groups of 4 for cluster generation on 

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the Illumina® cBot® using Illumina® TruSeq® Paired-End Cluster Kit. Each sample will be 
sequenced on the Illumina® HiSeq® 2500 to generate paired-end 75 nucleotide reads. 
De-multiplexing and creation of FASTQ files will be performed using Illumina CASAVA 
software. RNA-Seq reads will be aligned to the human genome (hg19) using STAR, quality 
metrics will be calculated using RSeQC, and gene and transcript expression levels will be 
summarized using RSEM and Ensembl annotation. Gene and transcript filtering will be 
conducted on normalized counts using the mixture model in the SCAN.UPC R package. 
R software will be used to conduct all further analyses.  Batch correction will be done using 
a modified version of our SCAN algorithm29 that is adapted to work with sequencing counts. 
Single Cell RNA-seq of Nasal brushing: Nasal brushings will be subject to Florescent 
Activated Cell Sorting (FACS) (10 markers specific to either immune or epithelial cells) and 
sorted into single cells. Plates of single cells will be processed for single cell RNA 
sequencing using the CEL-Seq2 protocol, 75 bp single-end reads (400K/cell), on 
200 single cells/sample. 
Circulating tumor DNA: ctDNA lung cancer biomarker will be developed using plasma 
samples available on 380 patients (n = 190 each cases and controls). This biomarker will 
then be validated on the remaining 200 samples. We will analyze plasma samples using 
TEC-seq. Tumor WES sequencing will be performed on a subset of training set (n-150) to 
confirm source of ctDNA. Using the same genomic libraries utilized for TEC-seq, we will 
also perform whole genome analyses to identify chromosomal abnormalities using the 
Digital Karyotyping approach. TEC-seq and whole exome seq profile somatic mutations 
which is not considered genetic. 
PET Imaging: Patients will be imaged by 18F-FDG PET/CT and 
18F-FSPG PET/CT. Uncorrected maximum lesion intensity (SUVmax) along with qualitative 
measures (absent, minimal, moderate, or high uptake) will be recorded. These data will be 
analyzed for lung cancer diagnosis using our prospective cohort study design The person 
assessing SUVmax values and making the decisions about “positive” or “negative” will be 
blinded to the cancer status of the patient. We will maintain this by keeping the image 
reader blind to the patient information. 
11. STATISTICAL CONSIDERATIONS 
11.1. Statistical Design and Data Analysis 
 
Clinical diagnosis is based on 1) histopathology of biopsy/surgical specimen or 2) surveillance by 
CT scans for a period of 2 years  to confirm that there is no growth of the nodule. Meta-analysis 
shows that 18F-FDG has a sensitivity and specificity of 89% and 75%  respectively (16). We 
hypothesize that 18F-FSPG is more specific. With an expected 90% specificity for diagnosis with 
18F-FSPG, a sample size of 60 will have 90% power at an alpha-level of 0.05 to demonstrate  a 
significant increase in specificity from a fixed value of 75%. 
 
 
Integrated predictions models: 
Our biomarker discovery and validation will use samples from 930 patients in the intended 
use population collected prior to diagnosis; and subsample from all available cancer and 
non-cancer patients. Through our experience with the NCI Early Detection Research 
Network (EDRN), we have a detailed plan for biomarker discovery and validation, which is 
briefly summarized here. 

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Identification of candidate genes from nasal total RNA-seq data:  We will initially select 
20 to 40 candidate genes that significantly discriminate between cases (n = 100) and 
controls (n = 100). We will prioritize candidates to: 1) represent cancer-associated gene 
co-expression patterns, 2) genes whose expression is independent of clinical factors, and 
genes in significantly enriched functional categories. 
Using single cell nasal RNA-seq to refine the nasal gene expression biomarker assay: We 
hypothesize that a subset of airway cells harbor lung-cancer associated gene expression 
differences (or that the distribution of cell types might distinguish the airway of cancer 
patients). We will perform scRNA-seq on cells collected prospectively from 200 patients 
(n = 100 each cases and controls). We will identify cell subpopulations using Celda and test 
for cell type-specific alterations associated with subtype, prognosis or disease activity using 
a strategy similar to that used for bulk RNA-seq data. Additionally, we will test for 
differences in cell population frequency associated with prognosis or disease activity; and 
also determine which cell subpopulations express the prognosis and disease activity 
associated genes identified in the bulk RNA-seq to understand the cellular architecture of 
these disease signatures. For biomarker development, we will interrogate the cellular 
architecture of the expression of the nasal biomarker genes. If biomarker gene expression 
localizes to specific cells, we would subsequently explore whether enriching for these cells 
improves biomarker performance.  Alternatively, if the frequency of a nasal cell type differs 
in patients with lung cancer, we would explore whether quantifying this cell-type’s frequency 
could serve as a lung cancer biomarker. 
Identification of CT features: Similar to the gene expression analysis, our initial goal is to 
identify 5 to 10 candidate semantic or quantitative imaging features that optimally 
discriminate cases and controls. We will centrally process thin-section CT images resulting 
in a high-dimensional dataset of image features based on CAD detection and nodule 
segmentation, and quantitative feature extraction. Semantic analysis of CT scans will be 
conducted by thoracic imagers using an illustrated lexicon to maximize inter-reader 
agreement. A convolutional neural network (CNN) will derive synthetic quantitative 
measures predictive of cancer status via cross validation. The most cancer-discriminating 
quantitative and semantic features will be selected for downstream analysis. 
Data Integration: Using a new set of 150 patients (75 cases and 75 controls), we will subset 
candidate genes and imaging features based on whether they improve (significantly higher 
ROC curve AUC) clinical lung cancer risk models with the goal of picking no more than 
10 genes and 5 imaging features, and prioritizing genes least correlated with imaging 
features or the ctDNA biomarker. We will next use data from 380 new patients (n = 190 
each cases and controls) to train and “lock down” models prior to validation. Model building 
will use a logistic regression framework (incorporating LASSO and ridge regression) and 
cross-validation. We will build models stepwise from clinical-variable-only models by adding 
additional biomarker datatypes (gene expression, imaging, and / or the ctDNA biomarker). 
Cross validation will determine the gain in diagnostic performance from each additional 
datatype, and combinations that do not improve performance will be eliminated. These 
locked down models will be validated in the remaining samples (n = 200). The primary 
endpoint will be calculated after every patient has either completed two years of follow up, 
been diagnosed positive, or died, whichever comes first. The primary endpoint for 
validation of this prediction model will be replication of improved performance. Exploratory 

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endpoints will determine the range of pre-test probabilities for which the models have 
potential clinical utility. 
11.2. Sample Size 
 
Primary outcome: To compare 18F-FSPG PET/CT imaging with 18F-FDG PET/CT imaging 
for indeterminate lung nodules we will evaluate 120 patients. We estimate that 
approximately 60 of the patients will be diagnosed with lung cancer after complete 
diagnostic work-up and ~60 will not have lung cancer. Meta-analysis shows that 18F-FDG 
has a sensitivity and specificity of 89% and 75% respectively (16). We hypothesize that 18F-
FSPG is more specific. With an expected 90% specificity for diagnosis with 18F-FSPG, a 
sample size of 60 will have 90% power at an alpha-level of 0.05 to demonstrate a significant 
increase in specificity from a fixed value of 75%. 
 
Secondary outcome: In total the SU2C consortium cohort will be comprised of 930 study 
participants. Our Stanford group will contribute 120 (60 cases and 60 controls) to the larger 
sample set. Breakdown of the 930 patients are as follows. For the discovery step, sample 
size of 100 cases and 100 controls provides 80% power to detect RNA markers with effect 
sizes of at least 0.52 assuming that we will control the false discovery rate at 10% and 5% of 
markers are differentially expressed. For the refinement step, 75 cases and 75 controls 
provides 81% power to detect markers with a ROC AUC of at least 0.63 assuming an alpha 
level of 0.05. No correction for the multiple hypothesis testing is planned as we will 
re-evaluate top candidates. The sample size of 190 of each group for model building will 
support the following: 
1. This sample size will be sufficient to include approximately 12 to 20 variables overall 
between clinical, imaging, ctDNA and gene expression markers based on conventional 
rules of thumb of 10 to 15 subjects per group per variable; 
2. This will allow us to estimate the AUC for each model with a precision of approximately 
4.5% (95% CI); 
3. Provide 80% power for detecting delta AUCs between models of 0.06 assuming model 
correlation of 0.6. 
For the model testing, 100 subjects in each group will provide AUC precision of ± 0.06 (and 
a precision of between 0.06 and 0.08 for the sensitivity and specificity), as well as a 80% 
power to detect differences in AUCs between models of 0.09. IRBs at the other sites have 
approved the collection of biospecimens and use of clinical / imaging data for this model 
development and validation work. 
11.3. Accrual estimates 
In our clinic on average 30 patients per month are seen with lung nodules. Of these patients 
around 50% have truly indeterminate lung nodules for which clinical management is 
uncertain. We estimate that about 20% of the eligible candidates will consent to participate 
in the study, leading to an accrual of circa 3 patients per month. We expect to be able to 
complete the study (50 patients) within 3 years. 
After the first 2.5 years of the study, we will assess the long-term follow up rate and add 

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additional patients if accrual is not sufficient.  We will not do an interim statistical analysis. 
 
11.4. Criteria for future studies 
If 18F-FSPG PET/CT imaging has a higher specificity for detecting malignant lung nodules 
compared to 18F-FDG PET/CT, a larger study will be planned. 
 
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