Pilot Study: Metabolic and Microbial Profiling of Lung Cancer
Phase: NA
Conditions: Nonsmall Cell Lung Cancer, Nonsmall Cell Lung Cancer Stage
Sponsor: Wake Forest University Health Sciences

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
 
 
 
 
Principal Investigator: 
 
Andrew Bishop, PhD, Assistant Professor 
 
 
 
 
 
Internal Medicine/ Molecular Medicine 
 
 
 
 
 
Wake Forest Baptist Health 
 
 
 
 
 
Medical Center Blvd 
 
 
 
 
 
Winston Salem, NC 27157 
 
 
Molecular Medicine  
Co-Investigator(s):  
 
Kimberly Reeves, PhD, Assistant Professor 
Internal Medicine/ Molecular Medicine 
 
Tony Reeves, PhD, Assistant Professor 
Internal Medicine/ Molecular Medicine 
  
 
Pulmonary Co-Investigator(s): 
Travis Dotson, MD, Assistant Professor 
Internal Medicine/ Pulmonary and Critical Care 
 
Christina Bellinger, MD, Assistant Professor 
Internal Medicine/ Pulmonary and Critical Care 
  
 
Cardiothoracic surgery  
Co-Investigator(s):  
 
Leonard (Jim) Wudel, MD, Associate Professor 
Surgical Sciences/ Cardiothoracic surgery 
 
 
 
Biostatistician: 
Ralph D’Agostino Jr. PhD, Professor 
 
Biostatistical Sciences/ Comprehensive Cancer Center 
 
 
 
Study Team: 
Ashley Wudel, RN, Clinical Coordinator  
 
 
 
Sharon Averill, RN, Clinical Research Nurse 
 
 
Justin Prescott, Graduate student 
 
 
Regulatory Contact:  
 
Emily Teal, MA 
 
 
 
 
 
Comprehensive Cancer Center 
 
Protocol Editor: 
 
 
Megan Farmer, MS 
 
 
 
 
 
Comprehensive Cancer Center 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
 
 
 
 
Participating Institution(s): Wake Forest Baptist Health 
 
 
Version Date: 
 
09/03/2019 
Amended: 
 
 
 
 
 
ClinicalTrials.gov:    
NCT03998189 
 
 
Confidential

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
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Table of Contents 
SCHEMA ..................................................................................................................................................... 5 
1.0 
Introduction and Background .......................................................................................................... 6 
2.0 
Objectives ........................................................................................................................................ 6 
2.1 
Primary Objective ........................................................................................................................ 7 
2.2 
Secondary Objective .................................................................................................................... 7 
3.0 
Study Population .............................................................................................................................. 7 
3.1 
Inclusion Criteria ......................................................................................................................... 7 
3.2 
Exclusion Criteria ........................................................................................................................ 7 
3.2.1 
Patients who are have taken antibiotics within two weeks. ......................................................... 7 
3.2.2 
Patients who are on continuous supplemental oxygen ................................................................ 7 
3.2.3 
Patients currently undergoing active treatment for other malignancies. ..................................... 7 
3.2.4 
Subjects who are unable or unwilling to provide consent. .......................................................... 7 
3.3 
Inclusion of Women and minorities ............................................................................................ 7 
4.0 
Methods ........................................................................................................................................... 8 
4.1 
Registration Procedures ............................................................................................................... 8 
4.2 
Experimental or Data Collection Methods .................................................................................. 8 
4.3 
Study-Related Activities ............................................................................................................ 12 
5.0 
Outcome Measures......................................................................................................................... 12 
5.1 
Primary Outcomes ..................................................................................................................... 12 
5.2 
Secondary Outcomes ................................................................................................................. 13 
6.0 
Analytic Plan .................................................................................................................................. 13 
6.1 
Sample Size and Power ............................................................................................................. 13 
6.2 
Analysis of Primary Outcome ................................................................................................... 14 
6.3 
Analysis of Secondary Outcomes .............................................................................................. 14 
6.4 
Accrual Rate .............................................................................................................................. 15 
6.5 
Length of Study ......................................................................................................................... 16 
7.0 
Data Management .......................................................................................................................... 16 
8.0 
Confidentiality and Privacy ........................................................................................................... 16 
9.0 
Data Safety and Monitoring ........................................................................................................... 16 
10.0 
WFUHS IRB AE Reporting Requirements ............................................................................... 17 
11.0 
Reporting of Unanticipated Problems, Adverse Events or Deviations .......................................... 17 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
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Appendix A – Subject Eligibility Checklist ............................................................................................ 19 
Appendix B – Protocol Registration Form ............................................................................................. 20 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
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SCHEMA 
 
 
 
 
 
Metabolomics and 
microbial metagenomic 
sequencing analysis 
Suspected primary 
stage I-III non-small 
cell lung cancer 
 
Pre-surgery: 
exhaled breath 
condensate (EBC), 
exhaled breath 
volatiles (EBV), saliva, 
blood, urine collection 
Surgical 
tumor 
removal 
Post-surgery 
(1 mo.): 
EBC, EBV, 
saliva, blood, 
urine 
collection 
Register 
Primary Outcomes: 
Assess feasibility of adding these non-invasive 
collections to pre- and post-surgery 
Secondary Outcomes: 
Assess changes in metabolites and microbial 
populations between lung cancer patients’ 
biosamples  pre- and post-surgery 
 
Identification of lung cancer stage specific 
signatures 
Identify signatures which associate with 
pulmonary function 
Advanced 
Tumor Bank 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
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1.0 
Introduction and Background 
 
Lung cancer (LC) is the second most common type of cancer and leading cause of cancer-related 
deaths in the United States (1). It is estimated that there will be over 120,000 new cases in 2018 
(1). Early detection leads to earlier treatment, and early LC stage detection has clear benefit in 
survival rates(2). Clinical tools for diagnostic screening of LC are limited, but efforts have been 
made to develop molecular techniques to identify LC during bronchoscopy procedures (3). The 
National Lung Screening Trial screened patients at high-risk of LC by computed tomography (CT) 
scans (4). Mortality rates were reduced through the screening, but a number of challenges to CT 
screening were noted. For example, one in four patients showed incidental abnormalities in the 
lung potentially not representing LC, and further invasive tests including biopsies and/or surgery 
were required to rule out cancer(4). Additionally, the uncertainty surrounding incidental findings 
is suspected to lead to further patient stress, which may further impact the patient’s health.  
 
It is apparent that complex diseases, like cancer, require powerful and sensitive technologies to 
uncover underlying mechanisms of the disease and even to detect the presence of the disease. A 
metabolomics approach is able to obtain a snapshot of biological and molecular mechanisms 
underlying a disease process by capturing metabolic profiles that are indicative of cellular 
metabolism. Current profiling technology results in the identification of hundreds to thousands of 
metabolites, which can be used as an unbiased analysis of biochemical pathways and potentially 
target biological dysfunction(5) (6) (7). A number of studies have utilized metabolomics 
approaches to profile LC (8). Many of these studies focused on tumor versus non-tumor tissue or 
serum in cancer patients versus healthy controls. We seek to assess metabolic signatures in samples 
from patients before and after surgical removal of their tumor. 
  
Recent studies have also focused on the link between cancer and the human microbiota, with special 
interest in determining how the oral microbiota contributes to the pathogenesis of various cancer 
types, including LC(9). It is well-established that the lungs are not sterile, and scientists have 
recently demonstrated that the lung microbiome is very similar to the oral microbiome(10); (11); 
(12). Furthermore, periodontal disease is associated with LC risk(13); (14), suggesting an 
association between the oral microbiome and LC risk, but further studies are needed to determine 
how oral microbial variations contribute to LC(9). The most informative approach to assessing the 
human microbiota is through shotgun metagenomic sequencing, which allows the characterization 
of microorganisms down to the species or even strain level and provides information on the putative 
function of the microbiota. Collecting saliva before and after surgical resection offers a noninvasive 
approach for studying the role oral microbiota play in LC, which has not previously been done. 
 
Utilizing these unbiased approaches will be pivotal to understanding the molecular dysregulation 
in LC and will potentially lead to the discovery of novel signatures within non-invasive biosamples 
that can be used as a diagnostic tool for identifying and characterizing LC. Our long-term goal is 
to translate findings from this study into diagnostics for the clinic, giving clinicians robust 
information to identify at-risk patients prior to the observation of symptoms. The need for a non-
invasive, low-risk, highly-specific diagnostic tool for LC is still evident. By profiling patients prior 
to and post-surgical resection, we will identify potential molecular markers that may be indicative 
of LC. 
2.0 
Objectives 
 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Page 7 of 20 
 
This pilot study will establish non-invasive sample collections, including breath, saliva, blood and 
urine pre-surgery and at the patient’s one-month post-surgery follow-up visit. Patients with 
suspected non-small cell lung cancer (NSCLC) stage I-III will be recruited from Co-I Jim 
Wudel’s clinic.  
2.1 
Primary Objective 
2.1.1 To evaluate the feasibility of adding non-invasive sample collections in the pre-
surgical setting and at the post-surgery follow-up visit. 
2.2 
Secondary Objective  
2.2.1 To identify and assess metabolic and microbial signatures collected at pre- and 
post-surgery and determine which are indicative of LC.    
2.2.2 To identify signatures which are associated with LC stage 
2.2.3 To identify signatures which are impacted by patient’s pulmonary function status 
3.0 
Study Population 
 
3.1 
Inclusion Criteria 
 
3.1.1 Male and female patients age >18 years, of all racial and ethnic origins, with 
suspected NSCLC stages I, II, and III, as evident through radiographic evidence and felt 
acceptable to undergo surgical resection. 
 
3.1.2 Patients who have the ability to understand and the willingness to sign a written 
consent form. 
3.2 
Exclusion Criteria 
3.2.1 Patients who are have taken antibiotics within two weeks. 
3.2.2 Patients who are on continuous supplemental oxygen. 
3.2.3 Patients currently undergoing active treatment for other malignancies. 
3.2.4 Subjects who are unable or unwilling to provide consent. 
3.3 
Inclusion of Women and minorities 
3.3.1 Women and men of all races and ethnicity who meet the above-described 
eligibility criteria are eligible for this trial.  
 
3.3.2 
 The study consent form will also be provided in Spanish for Spanish-speaking 
participants. Based on WFBCCC population estimates, approximately 51% of 
participants to will be women. Translating this to our sample size estimate of 90, 
we plan to enroll  45 women and  45 men. Similarly, we expect approximately 
6.6% of study participants to be Hispanic/Latino (N=6), 13.3% Black or African 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Page 8 of 20 
 
American (N=12), 0.48% American Indian/Alaska Native (N=1), and 1.8% 
Asian (N=1).  
4.0 
Methods 
 
4.1 
Registration Procedures 
All patients entered into any WFBCCC trial, whether treatment, companion, or cancer 
control trial, must be linked with a study protocol in EPIC within 24 hours of Informed 
Consent. Patients must be registered prior to the initiation of the study.   
 
You must perform the following steps in order to ensure prompt registration of your 
patient: 
 
1.0 
Complete the Eligibility Checklist (Appendix A). 
2.0 
Complete the Protocol Registration Form (Appendix B). 
3.0 
Alert the Cancer Center registrar by phone, and then send the signed Informed 
Consent Form, Eligibility Checklist and Protocol Registration Form to the 
registrar, either by fax or e-mail. 
 
*Protocol Registration is open from 8:30 AM - 4:00 PM, Monday-Friday. 
 
4.0 
Fax/e-mail ALL eligibility source documents with registration. Patients will not 
be registered without all required supporting documents. 
Note: If labs were performed at an outside institution, provide a printout of the results. 
Ensure that the most recent lab values are sent. 
 
To complete the registration process, the Registrar will:   
 
 
assign a patient study number 
 
register the patient on the study 
 
 
4.2 
Experimental or Data Collection Methods 
 
4.2.1 
Clinical data collection: 
All patients will be recruited within the Wake Forest Medical Center. 
Medical history will be obtained from the patients charts to identify demographics (age, race, 
ethnicity, zip code), oncologic history (date of diagnosis and cancer type), and medical history of 
disease. Pulmonary function tests (PFTs) will be performed by Wake Forest Baptist Health 
Pulmonary Function Lab as a standard of care for pre-surgical assessment. PFTs will be accepted 
from other certified laboratories within six months of their appointmen. Pulmonary function is 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
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defined by undergoing standard pulmonary function studies per 
American Thoracic Society recommendations.  The collection of 
these pulmonary function studies is often done as part of the pre-
surgical assessment collected by a cardiothoracic surgical 
team. Also, the collection of pulmonary functions studies can help 
with the classification and treatment of underlying obstructive lung 
disease that can be crucial to post-operative management. 
  At pre- and post- surgery, the research nurse will obtain current 
medications, food/ingestion survey and tobacco use information. 
This information will be necessary to identify any exogenous factors 
confounding both metabolic and microbial signatures. Following 
surgery, information from the patient’s final pathology report will 
be obtained to provide information on the tumor site location, 
characteristics of the cancer removed, and 
stage of the cancer.  
4.2.2 
Non-invasive 
sample 
collections:  
Pre-surgery follow-up collection: During 
the patient’s surgery consultation meeting 
with Dr. Wudel,  Upon entering the are the 
patient will be given a receptacle for 
collection of a urine specimen. Given that 
blood collection may be seen as invasive to 
the patient, the patient will be prompted for a 
blood sample. If the patient agrees, the 
research nurse will collect 1 redtop vacutainer 
serum tube and 1 purple top blood tube from 
the patient. We will then initiate the saliva 
and exhaled breath volatiles (EBV) and 
exhaled breath condensate (EBC) collection. For the saliva collection, patients will be given a saliva 
collection receptacle. This receptacle (SalivaBio – Passive Drool) allow for sterile transfer of saliva 
from the patient’s mouth into collection tubes.  For exhaled breath collection, we will utilize the 
ReCIVA (Figure 1, Owlstone medical), breath collection device. The device will be attached to the 
in-house medical air supply (yellow) with airflow set to 40L/min per the company’s 
recommendation (Figure 2). One-time use masks will be placed over the patient’s mouth and nose.  
The patient will be allowed to breathe normally to acclimate themselves to the mask and airflow. 
The patient will be allowed to hold the device, if desired, and a 
head strap will be placed around their head for additional support 
and comfort. Once the patient is acclimated (<1 min), they will be 
prompted to start the collection. Pre-loaded collection parameters 
will be set with the device software to simultaneously collect four 
tubes of exhaled breath from patient’s expired breath. Each metal 
tube (pictured in Figure 1) is packed with material to bind volatile 
chemicals. Once collection begins, the system will learn the 
breathing pattern of the patient (25 sec) and will adjust the 
collection pumps accordingly. Breath collection will take between 
5-7 min, depending on the patient’s breathing rate and total 
collection volume. This study will collection 500 mL of exhaled 
breath on each of the four tubes. All exhaled breath samples will 
be stored at 4°C until analysis. For exhaled breath condensate, we 
Figure 1: ReCIVA breath collection device 
Figure 2: Clinic breath gas collection setup 
Figure 3: R-tube breath 
collection device 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Page 10 of 20 
 
will utilize the R-tube collection device (Respiratory Research Inc.) (Figure 3). The R-tube is a 
one-time use apparatus that utilizes a metal cooling sleeve, placed over the plastic tube, to cool the 
patient’s breath, creating condensation build up within the device. The patient will breathe through 
the mouth piece for 10 min. Following the collection, a metal plunger will be used to consolidate 
the condensate within the collection tube and the EBC sample can be transferred to a fresh sterile 
tube. The saliva, urine and blood tube will be transported to the research lab at room temperature 
and processed within two hours.  All biofluids will be aliquoted, frozen and stored at -80° C until 
analysis. 
 
Surgical tumor removal: In addition to our non-invasive collections, as part of the Advanced 
Tumor Bank protocol, the tumor tissue will be collected and banked. We will obtain tumor tissue 
samples from the Advanced Tumor Bank following processing.  
 
Post-surgery follow-up collection: 24-48 hours prior to their 4-week in-clinic follow-up 
appointment, patients will be called to remind them for their 4-week follow-up, and follow-up 
collections will be performed in Dr. Wudel’s clinic, 4th floor of the Cancer Center. Upon entering 
the clinic, the patient will be taken the research nurse visit room. The research nurse will obtain 
current medications and tobacco use information. The patient will be prompted for a urine sample. 
If the patient again agrees to a blood sample, the research nurse will collect 1 redtop vacutainer 
serum tube and 1 purpletop vacutainer EDTA tube from the patient. We will initiate the saliva and 
exhaled breath collection as described above. Following all of the collections, the patient will be 
given their incentives for participating in the study.  
 
All samples collected from this study will be banked and will have the potential to be used for 
future scientific experimentation, which may include additional genetic, proteomic, and 
metabolomic analyses not described in the data collection section below.  
 
Research Data Collection:  
Data generation: Unbiased metabolomics analysis of bio-volatiles and biofluids has the potential 
to identify 100-1000’s of metabolites in any single sample. Our group has successfully assessed 
bio-volatiles and biofluids by unbiased metabolomics (15) (16) Although there is no single method 
that allows for the complete analysis of the metabolite, utilizing multiple modes of sample 
preparation and analysis will potentially identify a number of classes of compounds relevant to this 
study. The bio-volatile that will potentially be identified are various hydrocarbons, aldehydes, 
ketones, and alcohols. Through our biofluids analysis we will potentially identify amino acids, 
sugars, nucleotides, citric acid cycle intermediates through our GCMS analysis while our LCMS 
analysis will identify a wide range of lipids (e.g. ceramides, sphingomyelins, cholesteryl esters, 
oxysterols, lyso- and phospholipids, mono-, di- and triacylglycerols, galactosyl- and 
glucuronyllipids, bile acids, steroids, eicosanoids/oxylipins, very short chain fatty acids).  
Metagenomics assessment of the microbiome will identify populations of microbes that are present 
in patients at the time of collections. We will map sequencing reads to a reference database 
consisting of complete genomes for prokaryotes (bacteria and archaea), eukaryotes (fungi and 
protozoa) and viruses to identify changes in these populations down to the species or strain level. 
We will also identify potentially active functional profiles of the microbiota identified in the 
samples. We will perform a de novo assembly of the reads to identify protein coding regions, and 
the coding regions will subsequently be annotated with Pfam domains. The abundance of functional 
categories will then be estimated to build a functional profile for each sample 
 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
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Breath metabolomics analysis: Exhaled breath samples will be analyzed using a Markes thermal 
desorber, TD100-xr, to inject breath samples onto an Agilent two-dimensional GC system 7890B 
in tandem with LECO’s Pegasus 4D Time-of-Flight instrument system (TD-GCxGC-TOFMS). 
The collected spectra will be processed using LECO’s ChromaTOF version 4.72 software. Initial 
data processing includes peak deconvolution, 1st and 2nd dimension retention time recordings, signal 
to noise ratio calculation, unique mass detection, peak area determination, and compound 
identification based on the spectral pattern similarity match against established mass spectral 
reference libraries (NIST14, 2014 National Institute of Standard Technology, NIH, USA). 
Identified spectral matches will be aligned using R-program R2DGC (17) to determine common 
metabolites in patients prior to and after surgery. MetaboAnalyst 4.0 will be employed to normalize 
and transform the data for univariate and multivariate statistical analysis (18). Additional statistical 
analysis may be performed using Graphpad Prism 8 (Graphpad Software, Inc) 
 
Biofluid and tissue metabolomic analysis: Saliva, serum, urine and tissue aliquots will be 
prepared for analysis by standard processing according to published methodology(5); (19). Two 
platforms of unbiased metabolomics analysis will be completed using both a Q Exactive GC system 
and a Q Exactive LC system. Utilizing two chromatographic methodologies will maximize our 
metabolite identification coverage.  The collected spectra will be processed using Thermo 
Scientific™ TraceFinder™ 4.1 software. The initial data processing will include baseline 
correction, peak deconvolution, retention index, signal to noise ratio calculation, unique mass 
detection, peak area determination, and compound identification based on the spectral pattern 
similarity match against established mass spectral reference libraries (NIST14, National Institute 
of Standard Technology, NIH and a high-resolution mass spectral library from TraceFinder™ 
software). Identified spectral matches will be identified for pre- and post-surgery following 
metabolomics standards initiative (MSI) guidelines for metabolite identification (Level 2), with a 
match score requirement of equal to or greater than 80%. Aligned metabolomic data analysis 
software, MetaboAnalyst 4.0, will be employed to normalize and transform the data for univariate 
and multivariate statistical analysis (18). Additional statistical analysis may be performed using 
Graphpad Prism 8 (Graphpad Software, Inc) 
Biofluid and tissue microbiome analysis: DNA will be extracted from each blood, saliva and lung 
tumor tissue sample using the QIAamp® UCP Pathogen Mini Kit (Qiagen) with extended 
homogenization of the tissue samples using the Mini-Beadbeater-96 cell disrupter (Biospec 
Products). The quantity and quality of the DNA extracts will be determined using a DeNovix DS-
11 FX+ Spectrophotometer/Fluorometer and Agilent 4200 TapeStation, respectively. The DNA 
will then be prepared for shotgun metagenomic sequencing using the Nextera® DNA Flex Library 
Preparation Kit (Illumina). DNA libraries will be normalized and quantified prior to pooling to 
ensure equal library representation. Pooled DNA libraries will then be sequenced using the Illumina 
NovaSeq 6000 S1 Reagent Kit and sequencer, generating 2 x 150 bp paired-end reads. Raw 
sequence reads (FASTQ files) will be imported into the Microbial Genomics Pro Suite within the 
CLC Genomics Workbench (version 11.0.1; https://www.qiagenbioinformatics.com) for QC, 
adapter trimming, and analysis. High-quality (≥Q30) reads for each sample will be aligned to those 
in a blank extraction sample (negative control) that will be processed from DNA extraction through 
library preparation to identify and remove contaminating sequences. The filtered reads will then be 
aligned to a reference database consisting of complete genomes in the RefSeq database for 
prokaryotes (bacteria and archaea), eukaryotes (fungi and protozoa) and viruses for taxonomic 
profiling. The filtered reads will also be assembled into contigs de novo, and MetaGeneMark (20) 
will be used to identify protein-coding regions within the reads to predict functional diversity of 
the microbiota identified in the samples. The coding regions will be annotated with Pfam domains 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Page 12 of 20 
 
and GO terms, and the abundance of functional categories will be estimated to build a functional 
profile for each sample.  
 
The results from the taxonomic and functional profiling will each be merged to estimate similarities 
and differences among the samples. Heat maps will be generated using Euclidean distance and 
clustering based on average linkage to assess functional similarities between samples, and principal 
component analysis (PCA) ordination of Bray-Curtis similarity between samples will be used to 
visualize the microbiota diversity across the samples.  
Data Storage: All research data collected on this study will be stored on Wake Forest Baptist 
Medical Center password protected computers and servers. 
4.3 
Study-Related Activities  
 
a Approximately 1-1.5 mL of EBC will be generated in 10 min of breathing 
b 500 ml of EBV in each of 4 collection tubes will be collected in 5-7 min of breathing 
c 2 ml minimum of saliva will be collected into SalivaBio Passive Drool collection tubes 
d 5-10 ml blood sample in each redtop and purple top vacutainer blood collection tube 
e 25 ml minimum of urine will be collected in sterile specimen containers  
5.0 
Outcome Measures 
5.1 
Primary Outcomes 
5.1.1 
Since the primary objectives is to assess the feasibility of adding these non-
invasive sample collections to pre- and post-surgery visits the following 
outcomes (counts) will be gathered to examine this: 
5.1.1.1 
Number of patients who are screened for trial 
 
 
 
 
Pre- 
Study 
 
Pre-surgery 
Collection 
 
Surgical tumor 
removal 
 
Post-Surgery Follow-up 
 
 
Informed consent 
 
X 
 
 
 
 
 
Demographics 
 
X 
 
 
 
 
 
Medical history 
 
X 
 
 
 
 
Pulmonary Function 
Tests (PFTs) 
 
X 
 
 
 
Concurrent meds 
 
X 
 
 
X 
 
 
Smoking Status 
 
X 
 
X 
 
Breath Collection 
(EBCa/EBVb) 
 
 
X 
 
 
X 
 
Saliva collectionc 
 
 
X 
 
 
X 
 
Blood collectiond 
 
 
X 
 
 
X 
 
 
Urine collectione 
 
 
X 
 
 
X 
 
Tumor collections 
 
 
 
 
X 
 
 
Adverse event 
evaluation 
 
 
X 
 
 
X 

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Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
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5.1.1.1.1 
Among those screened, the number who are eligible 
5.1.1.1.2 
Among those eligible, the number who consent to 
participate 
5.1.1.1.3 
Among those who participate, the number who provide 
pre-surgery samples 
5.1.1.1.4 
Among those who provide pre-surgery samples, the 
number who provide post-surgery samples 
 
5.2 
Secondary Outcomes 
5.2.1 
Since the first secondary objective is to examine metabolic and microbial 
signatures are two time points (pre- and post- surgery) therefore the following 
outcomes will be gathered to examine this: 
5.2.1.1 
Quantitative assessments of metabolic characteristics measured at 
pre-surgery 
5.2.1.2 
Quantitative assessments of microbial assessments measured at pre-
surgery 
5.2.1.3 
Quantitative assessments of metabolic characteristics measured at 
post-surgery 
5.2.1.4 
Quantitative assessments of microbial assessments measured at post-
surgery 
Using these four sets of quantitative assessments, “signatures” will be developed 
(described in statistical analysis section) and compared to see whether there are 
characteristics that remain similar or change when comparing pre- and post- 
surgical measures. 
5.2.2 
Patient LC Stage will be gathered as a secondary outcome.  This outcome will be 
used with the outcome measures gathered for primary outcome 5.1.2 (described 
above) to address the secondary objective (To identify signatures which are 
associated with LC stage). 
5.2.3 
Patient pulmonary function status (assessed by Dr. Wudel) will be gathered as a 
secondary outcome.  This outcome will be used with the outcome measures 
gathered for primary outcome 5.1.2 (described above) to address the secondary 
objective (To identify signatures which are impacted by patient’s pulmonary 
function status). 
6.0 
Analytic Plan 
6.1 
Sample Size and Power 
This is a pilot study therefore; many of the objectives are exploratory in nature.   With this in mind, 
we plan to recruit two cohorts of patients with this study, 45 female and 45 males.  In order to be 
able to examine the questions concerning the possible relationships of metabolic and microbial 
signatures over time and with lung cancer outcomes, both pre- and post- surgery measures are 
needed.  Therefore, each patient must provide both a pre- and post-surgery sample (optional blood 
will be allowed) in order to be used in the signature analyses.  If a post collection is not performed 
or collection is unsatisfactory, the patient will be replaced.  Therefore, our target sample size of 90 
patients (45 male/45 female) is the target for patients with both pre- and post- samples collected. 
 

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First, for the primary objective of assessing feasibility, we can determine the total number of 
patients needed in order to enroll 90 patients.   To examine this question we will determine 1) the 
number of patients screened for the trial, 2) among those screened, the number who are eligible, 3) 
among those eligible, the number who consent to participate, 4) among those who participate, the 
number who provide pre-surgery samples, and 5) among those who provide pre-surgery samples, 
the number who provide post-surgery samples.  Based on our design we will enroll patients until 
90 provide pre- and post- surgery samples.  
 
Since we do not know prospectively how many patients will be needed to enroll 90 we will 
conservatively estimate several possible scenarios and for each scenario describe the width of the 
corresponding 95% Exact Clopper Pearson confidence interval for proportions.   
 
The table below shows different total number of patients needed to enroll 90 for  the 
study, 
beginning with 120 (75% enrollment rate) to 180 (50% enrollment rate). 
 
 
 
 
 
 
 
 
As can be seen from the above, the confidence intervals for each scenario are approximately +/- 
10% or less depending on the scenario observed. 
 
For the other primary outcomes, metabolic and microbial assessments taken at two time points (pre- 
and post- surgery), we can determine statistical power in several ways.  One straight-forward 
approach will be to assess the difference in biomarkers (metabolic or microbial) that can be detected 
when comparing pre- and post- surgical assessments within patients.  If there are 90 patients then 
there is 80% power (using an alpha=0.05, 2-sided) to detect a difference in a biomarker measure 
equal to 0.30 standard deviations for that marker. 
 
Since this is a pilot study, the above power calculations suggest that useful data concerning 
feasibility and effect size assessments for biomarkers can be determined in this study.  Multiple 
comparison adjustments are not considered at this time since the focus of examining these effect 
size estimates is to determine an overall ranking of measures, in order to inform future studies 
concerning which signatures may be most promising to examine further.  
6.2 
Analysis of Primary Outcome 
 
To examine the questions of feasibility, we will generate a series of estimates of proportions for 
each question (i.e. proportion of screened who are eligible, etc.).  Next, we will estimate 95% exact 
Clopper-Pearson confidence intervals for each feasibility estimate. 
6.3 
Analysis of Secondary Outcomes 
 
For the analysis of metabolic and microbial measures we will examine each marker assessed both 
at pre- and post- surgery.  We hypothesize that markers that are associated with lung cancer will 
change between the pre- and post- surgery setting, whereas markers that are independent of lung 
cancer will not change between the two time points.  Therefore, a first analysis will be to examine 
# screened and 
eligible 
Number 
enrolled 
Enrollment 
rate 
Lower 95% 
limit 
Upper 95% 
limit  
120 
90 
75% 
0.66 
0.82 
150 
90 
60% 
0.52 
0.68 
180 
90 
50% 
0.42 
0.58 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Page 15 of 20 
 
a series of paired t-tests to determine which markers significantly change between the two time 
points for patients.   
 
In addition to this analysis, we wish to determine whether there are any marker signatures that 
appear in groups of patients.  To do this we will use cluster analysis methods to determine whether 
there are any groups of patients who share similar marker profiles pre-, post- or change from pre- 
to post- for metabolic and microbial measures.  If we are able to identify clusters of patients with 
common marker signatures, we will then determine whether there are other disease characteristics 
that are shared by these patients (i.e., tumor stage, lung function, etc.). This analysis approach will 
also be used below to address the analysis of secondary outcomes.  As stated above, this is a pilot 
study, therefore these cluster analyses will be considered exploratory and hypothesis generating.   
 
To address the two secondary objectives, we will use the same data gathered for the primary 
objective as described above, however we will compare those data to the two secondary outcomes 
measures (LC Stage and patient pulmonary function).  The analytic difference between these two 
measures is that LC stage will be an ordinal variable and pulmonary function is a continuous 
measure. 
 
For LC Stage, we will examine whether pre-surgery, post-surgery or change from pre- to post- 
surgery metabolic or microbial measures are different depending on LC Stage.  To do this we can 
consider the metabolic or microbial measures as outcomes and use a general linear models 
framework to see if LC Stage (included as a class variable, but then examined as an ordinal variable 
using a contrast statement in GLM) is associated with any marker. 
 
In addition to this analysis where individual markers are considered separately, we will use cluster 
analyses (as described above) based on the marker data and then determine whether there are 
associations between cluster membership and LC Stage using 2-way contingency tables. 
 
For pulmonary function, we will use the same approach as for LC Stage, except that pulmonary 
function will be considered as a continuous variable in the model.  Thus, we can examine 
associations (correlations) between the marker measures and pulmonary function.  In addition, 
pulmonary function can be examined at both time-points (pre- and post- surgery) thus we can 
examine the relationships between pulmonary function measures and biomarker measures at each 
time points (and the change in measures over time). 
 
In addition to these analyses, additional exploratory analyses will be examined to determine 
whether 
other 
patient 
level 
characteristics 
(i.e., 
gender, 
age, 
smoking 
history 
(current/former/never), etc.) are associated with different metabolic and/or microbial signatures or 
measures in a general linear model framework. 
 
Since this is a pilot study and the sample size is moderate (n=90), these additional analyses will be 
primarily hypothesis generating to allow us to determine whether there are potential signals in the 
data that may need to be further examined in larger trials. 
6.4 
Accrual Rate 
Based on the large number of anticipated patients for this trial and the non-invasive nature of the 
measurement we anticipate that the majority of eligible patients will participate.  With this in mind, 
we anticipate that we will enroll at least 8 patients per month (2 per week), and thus complete 
accrual within 60 weeks (14 months).  This allows for the possibility that up to 120 patients are 
screened, enrolled and provide pre-surgery data in order to have 90 patients also provide post-

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Page 16 of 20 
 
surgery data (i.e. 75% of patients who provide pre-surgery data also provide post-surgery data).  
We believe this is a conservative estimate for the accrual rate, however we recognize that the trial 
may take longer (or shorter) to be completed based on the actual rate of completion of patients 
(which is one of the questions to be answered by the trial).   With this in mind the shortest 
anticipated time for the trial is 45 weeks (2/week x 45=90) and the longest anticipated time is 90 
weeks, allowing for 50% of patients who enroll and provide pre-surgery data to not provide post-
surgery data.  
 
There are no interim analyses planned for this study.  
6.5 
Length of Study 
Once all patients are enrolled the last follow-up measure will happen 1 month post-surgery, 
therefore we anticipate that the study will be completed within 15 months of when enrollment 
begins (14 months for accrual, one month for final follow-up). 
7.0 
Data Management 
 
Informed consent document 
EPIC 
Protocol registration form 
WISER/OnCore 
Medical History 
WISER/OnCore 
Specimen Collection Form 
WISER/OnCore 
Concurrent Medications Form 
WISER/OnCore 
Tobacco Use Form 
WISER/OnCore 
PFT Form 
WISER/OnCore 
Final Pathology Report Form 
WISER/OnCore 
Adverse Event Log 
WISER/OnCore 
Research Data 
Secure research lab computers and servers 
8.0 
Confidentiality and Privacy 
 
Confidentiality will be protected by collecting only information needed to assess study outcomes, 
minimizing to the fullest extent possible the collection of any information that could directly 
identify subjects, and maintaining all study information in a secure manner. To help ensure 
subject privacy and confidentiality, only a unique study identifier will appear on the data 
collection form. Any collected patient identifying information corresponding to the unique study 
identifier will be maintained on a linkage file, stored separately from the data. The linkage file 
will be kept secure, with access limited to designated study personnel. Following data collection, 
subject identifying information will be destroyed after 5 years, consistent with data validation and 
study design, producing an anonymous analytical data set. Data access will be limited to study 
staff. Data and records will be kept locked and secured, with any computer data password 
protected. No reference to any individual participant will appear in reports, presentations, or 
publications that may arise from the study. 
9.0 
Data Safety and Monitoring 
 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Page 17 of 20 
 
The principal investigator will be responsible for the overall monitoring of the data and safety of 
study participants. The principal investigator will be assisted by other members of the study staff. 
10.0 WFUHS IRB AE Reporting Requirements 
 
Any unanticipated problems involving risks to subjects or others and adverse events shall be 
promptly reported to the IRB, according to institutional policy. Reporting to the IRB is required 
regardless of the funding source, study sponsor, or whether the event involves an investigational or 
marketed drug, biologic or device. Reportable events are not limited to physical injury, but include 
psychological, economic and social harm. Reportable events may arise as a result of drugs, 
biological agents, devices, procedures or other interventions, or as a result of questionnaires, 
surveys, observations or other interactions with research subjects. 
 
All members of the research team are responsible for the appropriate reporting to the IRB and other 
applicable parties of unanticipated problems involving risk to subjects or others. The Principal 
Investigator, however, is ultimately responsible for ensuring the prompt reporting of unanticipated 
problems involving risk to subjects or others to the IRB. The Principal Investigator is also 
responsible for ensuring that all reported unanticipated risks to subjects and others which they 
receive are reviewed to determine whether the report represents a change in the risks and/or benefits 
to study participants, and whether any changes in the informed consent, protocol or other study-
related documents are required. 
 
Any unanticipated problems involving risks to subjects or others occurring at a site where the study 
has been approved by the WFUHS IRB (internal events) must be reported to the WFUHS IRB 
within 7 calendar days of the investigator or other members of the study team becoming aware of 
the event. 
 
Any unanticipated problems involving risks to subjects or others occurring at another site 
conducting the same study that has been approved by the WFUHS IRB (external events) must be 
reported to the WFUHS IRB within 7 calendar days of the investigator or other members of the 
study team becoming aware of the event. 
 
Any event, incident, experience, or outcome that alters the risk versus potential benefit of the 
research and as a result warrants a substantive change in the research protocol or informed consent 
process/document in order to insure the safety, rights or welfare of research subjects. 
11.0 Reporting of Unanticipated Problems, Adverse Events or Deviations 
 
Any unanticipated problems, deviations or protocol changes will be promptly  
reported by the 
principal investigator or designated member of the research team  
to the IRB and sponsor 
or appropriate government agency if appropriate.

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Page 18 of 20 
 
References 
1.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68(1):7-30. 
2.Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC 
Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the 
Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 
2016;11(1):39-51. 
3.Dotson T, Bellinger C. Molecular Diagnostics to Enhance Lung Cancer Screening & Diagnosis. 
Oncology Times. 2016;38(24):30-1. 
4.National Lung Screening Trial Research T, Aberle DR, Berg CD, Black WC, Church TR, 
Fagerstrom RM, et al. The National Lung Screening Trial: overview and study design. Radiology. 
2011;258(1):243-53. 
5.Fiehn O. Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and 
Untargeted Profiling. Curr Protoc Mol Biol. 2016;114:30 4 1- 4 2. 
6.Vasilopoulou CG, Margarity M, Klapa MI. Metabolomic Analysis in Brain Research: 
Opportunities and Challenges. Front Physiol. 2016;7:183. 
7.Fiehn O. Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol. 
2002;48(1-2):155-71. 
8.Bamji-Stocke S, van Berkel V, Miller DM, Frieboes HB. A review of metabolism-associated 
biomarkers in lung cancer diagnosis and treatment. Metabolomics. 2018;14(6). 
9.Chen J, Domingue JC, Sears CL. Microbiota dysbiosis in select human cancers: Evidence of 
association and causality. Semin Immunol. 2017;32:25-34. 
10.Huffnagle GB, Dickson RP. The bacterial microbiota in inflammatory lung diseases. Clin 
Immunol. 2015;159(2):177-82. 
11.Charlson ES, Bittinger K, Haas AR, Fitzgerald AS, Frank I, Yadav A, et al. Topographical 
continuity of bacterial populations in the healthy human respiratory tract. Am J Respir Crit Care 
Med. 2011;184(8):957-63. 
12.Morris A, Beck JM, Schloss PD, Campbell TB, Crothers K, Curtis JL, et al. Comparison of the 
respiratory microbiome in healthy nonsmokers and smokers. Am J Respir Crit Care Med. 
2013;187(10):1067-75. 
13.Vogtmann E, Goedert JJ. Epidemiologic studies of the human microbiome and cancer. Br J 
Cancer. 2016;114(3):237-42. 
14.Zeng XT, Xia LY, Zhang YG, Li S, Leng WD, Kwong JS. Periodontal Disease and Incident 
Lung Cancer Risk: A Meta-Analysis of Cohort Studies. J Periodontol. 2016;87(10):1158-64. 
15.Bishop AC, Libardoni M, Choudary A, Misra B, Lange K, Bernal J, et al. Nonhuman primate 
breath volatile organic compounds associate with developmental programming and cardio-
metabolic status. J Breath Res. 2018;12(3):036016. 
16.Misra BB, Bassey E, Bishop AC, Kusel DT, Cox LA, Olivier M. High Resolution GC/MS 
Metabolomics of Non-Human Primate Serum. Rapid Commun Mass Spectrom. 2018. 
17.Ramaker RC, Gordon ER, Cooper SJ. R2DGC: threshold-free peak alignment and 
identification 
for 
2D 
gas 
chromatography-mass 
spectrometry 
in 
R. 
Bioinformatics. 
2018;34(10):1789-91. 
18.Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, et al. MetaboAnalyst 4.0: towards more 
transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018;46(W1):W486-W94. 
19.Misra BB, Upadhayay,  R.P., Cox, L.A.; Olivier, M. Optimized GC–MS metabolomics for the 
analysis of kidney tissue metabolites. Metabolomics. 2018;14(75). 
20.Zhu W, Lomsadze A, Borodovsky M. Ab initio gene identification in metagenomic sequences. 
Nucleic Acids Res. 2010;38(12):e132. 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Protocol version date 09/03/19 
 
 
 
 
 
Page 19 of 20 
Appendix A – Subject Eligibility Checklist 
 
 
IRB Protocol No. 
WFBCCC Protocol No. 03219 
Study Title: Pilot Study: Metabolic and microbial profiling of lung cancer 
Principal Investigator:  Andrew Bishop, Ph.D. 
 
Inclusion Criteria 
(as outlined in study protocol) 
Criteria 
is met 
Criteria 
is NOT 
met 
Source Used to Confirm * 
(Please document dates and lab 
results) 
Male and female patients age >18 years, of all 
racial and ethnic origins, with suspected non-
small cell lung cancer stages I, II, and III as 
evident through radiographic evidence and felt 
acceptable to undergo surgical resection. 
□ 
□ 
 
Patients who have the ability to understand and 
the willingness to sign a written consent form. 
□ 
□ 
 
Exclusion Criteria 
(as outlined in study protocol) 
Criteria 
NOT 
present 
Criteria 
is 
present 
Source Used to Confirm * 
(Please document dates and lab 
results) 
Patients who are have taken antibiotics within 
two weeks 
□ 
□ 
 
Patients currently undergoing active treatment 
for other malignancies 
□ 
□ 
 
Subjects who are unable or unwilling to provide 
consent 
□ 
□ 
 
Patients who are on continuous supplemental 
oxygen 
□ 
□ 
 
 
This subject is      
 eligible /  ineligible     for participation in this study. 
 
OnCore Assigned PID: ___ ___ ___ ___ ___ ___ ___ ___ 
 
 
 
 
 
Signature of research professional confirming eligibility:_______________________  
Date: ___ ___/___ ___/___ ___ 
 
Signature of Treating Physician**:  _____________________________________  
Date: ___ ___/___ ___/___ ___ 
 
* Examples of source documents include clinic note, pathology report, laboratory results, etc. When listing the 
source, specifically state which document in the medical record was used to assess eligibility. Also include the date 
on the document. Example: “Pathology report, 01/01/14” or “Clinic note, 01/01/14” 
 
**Principal Investigator signature can be obtained following registration if needed 

Pilot Study: Metabolic and microbial profiling of lung cancer  
Wake Forest Baptist Comprehensive Cancer Center (WFBCCC) 
WFBCCC # 03219 
Protocol version date 09/03/19 
 
 
 
 
 
Page 20 of 20 
Appendix B – Protocol Registration Form 
 
DEMOGRAPHICS 
Patient: Last Name: _____________________ 
First Name: ___________________________ 
MRN:          ___ ___ ___ ___ ___ ___ ___ ___ 
DOB (mm/dd/yy): ___ ___ / ___ ___ / ___ ___ 
ZIPCODE:  
___ ___ ___ ___ ___ 
 
 
SEX:   
  Male      Female 
Ethnicity (choose one):   
 Hispanic 
Non-Hispanic 
Race (choose all that 
apply): 
 WHITE           BLACK            ASIAN 
 PACIFIC ISLANDER                 NATIVE AMERICAN 
Height:   ___ ___.___ inches 
Weight: ___ ___ ___.___lbs.(actual) 
Surface Area: ___ ___.___m2   
 
Primary Diagnosis: ____________________________________________________________ 
Date of Diagnosis: ___ ___ /___ ___/___ ___ 
 
Performance Status: ___    ECOG   
 
 
Protocol Registrar can be contact by calling 336-713-6767 between 8:30 AM and 4:00 PM, Monday – Friday. 
 
Completed Eligibility Checklist and Protocol Registration Form must be hand delivered, faxed or e-mailed to the 
registrar at 336-7136772 or registra@wakehealth.edu. 
 
PROTOCOL INFORMATION 
Date of Registration: 
___ ___ / ___ ___ / ___ ___ 
MD Name (last) : 
______________________       
Informed written consent: 
(consent must be signed prior to registration) 
 YES   NO 
 
Date Consent Signed: 
___ ___ / ___ ___ / ___ ___ 
 
PID # (to be assigned by OnCore): 
___ ___ ___ ___ ___ ___ ___ ___