Analysis of Variables Predicting Pathological Complete Response and Immune Related Adverse Events in Patients With Resectable Non-Small Cell Lung Cancer Receiving Neoadjuvant Immunotherapy With Chemotherapy - A Prospective Cohort Study
Conditions: Non-small Cell Lung Cancer Stage IB (Resectable), Non-small Cell Lung Cancer Stage II (Resectable), Non-small Cell Lung Cancer Stage ⅢA (Resectable)
Sponsor: London Health Sciences Centre Research Institute OR Lawson Research Institute of St. Joseph's

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ITLE 
Analysis of Variables Predicting Pathological Complete Response and 
Immune related adverse events in Patients with Resectable Non-Small 
Cell Lung Cancer receiving Neoadjuvant Immunotherapy with 
Chemotherapy – A Prospective Cohort Study 
‘Pre-PLaN’ 
 
 
Principal Investigator: Dr. Mark Vincent 
 
Co-Investigators: Dr. Saurav Verma 
Dr. Daniel Breadner 
Dr. Christopher Goodman 
 
 
 

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Table of Contents 
TITLE ..................................................................................................................................... 1 
1.0 
INTRODUCTION ...................................................................................................................3 
2.0   AIM ........................................................................................................................................7 
3.0   HYPOTHESIS ............................................................................................................................7 
4.0   OBJECTIVES .............................................................................................................................7 
4.1   Primary objectives ......................................................................................................................................... 7 
4.2.  Secondary objectives .................................................................................................................................... 7 
5.0   ENDPOINTS .............................................................................................................................8 
5.1   Primary endpoints ......................................................................................................................................... 8 
5.2   Secondary endpoints ..................................................................................................................................... 8 
5.3   Definition of endpoints ................................................................................................................................. 8 
6.0   STUDY POPULATION ...............................................................................................................9 
6.1   Inclusion criteria ............................................................................................................................................ 9 
6.2   Exclusion criteria ......................................................................................................................................... 10 
6.3   Participant discontinuation ......................................................................................................................... 10 
7.0   STUDY DESIGN ...................................................................................................................... 10 
7.1   Study schema .............................................................................................................................................. 10 
7.2   Data collection ............................................................................................................................................ 11 
7.3   Data analysis ............................................................................................................................................... 12 
7.4   Sample size justification .............................................................................................................................. 12 
7.5   Peripheral blood collection for ctDNA and biomarker analysis .................................................................. 14 
7.6   Treatment details ........................................................................................................................................ 14 
7.7   Feasibility .................................................................................................................................................... 15 
7.8   Data safety monitoring committee ............................................................................................................. 15 
8.0   EXPECTED OUTCOMES ........................................................................................................... 15 
9.0    SIGNIFICANCE ...................................................................................................................... 15 
10.0   NOVELTY ............................................................................................................................. 16 
11.0   ETHICAL CONSIDERATIONS .................................................................................................. 16 
11.1   Institutional Review Board (IRB) / Research Ethics Board (REB) ............................................................... 16 
11.2   Informed Consent ..................................................................................................................................... 16 
11.3   Confidentiality of Participant Records ...................................................................................................... 17 
12.0   REFERENCES ........................................................................................................................ 18 
 
 
 

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1.0   INTRODUCTION 
Lung cancer is the leading cause of cancer-related death in both men and women. Non-small cell lung 
cancer (NSCLC) accounts for 85% of lung cancers. About 30% present with ‘early stage’ lung cancer (stages 
I-IIIA). The implementation of screening programs for lung cancer will increase the incidence and 
proportion of patients with early-stage lung cancer. Surgical resection has traditionally been and 
continues to be the standard-of-care (SoC), yet most patients still relapse and die. Adjuvant (post-
operative) chemotherapy offers modest additional survival benefit (1), and there remains equipoise in the 
relative value of adjuvant vs neoadjuvant (pre-operative) chemotherapy (2). Recently, Health Canada 
approved neoadjuvant immunotherapy (nivolumab, a type of immune checkpoint inhibitor (ICI)) with 
platinum-based chemotherapy in patients with resectable (IB-IIIA) NSCLC, based on a recent large 
prospective trial (CheckMate 816), showing better event-free survival (EFS) and overall survival (OS), and 
markedly increased pathological complete responses (pCR), vs chemotherapy alone (3,4) (Figure 1 and 2). 
Achievement of pCR resulted in markedly prolonged EFS (i.e., a very low risk of relapse, hazard ratio (HR) 
0.13) compared to any other response status; response status, therefore, seems to be a critically 
important early endpoint with considerable predictive power for the presence or absence of later events 
(Figure 3). Actually, pCR has been used as a surrogate of survival in neoadjuvant therapy trials in numerous 
disease sites. Further trials and updates (e.g., NADIM II) have been confirmatory, which has propelled 
neoadjuvant chemoimmunotherapy towards becoming the new SoC (5–7). pCR rates in CM816 and 
NADIM II were 24% and 37% respectively with neoadjuvant nivolumab plus chemotherapy compared to 
2.2% and 7% with neoadjuvant chemotherapy alone (3,5,7). This approach also led to improved major 
pathological responses (MPR), overall response rates (ORR), EFS and OS in CheckMate 816, and improved 
3-year-progression free survival (PFS) and OS in NADIM II.  
 

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Figure 1. Event-free survival in patients in CM816 (3). 
 
Figure 2. Pathological complete response in CM816 (3). 
 
Figure 3. Event-free Survival in Patients with or without a Pathological Complete Response (3). 
In NADIM II trial survival benefit is especially observed in patients with tumors with positive PD-
L1 expression and in those patients achieving pCR. As an exploratory analysis in NADIM II (only 28 patients 
with full data), multimodal analysis of variables predicting the response to neoadjuvant treatment 
revealed a model which could predict pCR with an area under curve (AUC) of 0.76 (8). Notably their model 

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included histology, mutational profile, radiomics, and the neutrophil/lymphocyte ratio.  Such a model is 
not sufficiently accurate to base clinical decisions on.  
We propose to improve upon this, by means of a prospective single cohort study to analyze the 
predictors of pCR in a larger cohort of our patients on the same neoadjuvant immunotherapy plus 
chemotherapy but employing a range of parameters that are both much wider and considerably more 
sophisticated. We intend to make use of parameters that reflect both the tumor itself, as well as the 
integrity of immune system of the host, obviously a critical determinant of immune-mediated efficacy, yet 
strangely neglected in the literature. We will then seek to develop an initial integrated predictive model 
for pCR with a good sensitivity/specificity, as a prelude to testing (and if necessary, refining) the model in 
future work, with a much larger sample. If subsequently validated, such a model would have several 
important applications.  
Noting that immune-related adverse events (irAEs) are strongly associated with efficacy in 
multiple trials across several disease sites  (9–12), we will not only be including the emergence of on-
treatment irAEs in our pCR modeling, but also develop an additional model as a subsidiary aim to predict 
irAEs themselves. The patient’s baseline immune status is a critical and heretofore largely neglected 
common determinant of both efficacy and irAEs, and clearly deserves more attention. The incidence of ≥ 
grade 3 adverse events (AEs) was 33.5% in CheckMate 816, and there is currently no available way to 
predict their occurrence. 
We would be doing a more comprehensive and sophisticated assessment of both the immune 
status and the cancer, pre-treatment, than has been reported thus far, and using cutting-edge genomic, 
flow cytometric, proteomic, pathologic and radiomic technology, will enable a much better predictive 
system of both benefit and harm, and reveal novel insights into host-tumor biology. This will include 
demographic, clinical and radiological (PET, radiomics (13), sarcopenia index) data. We would collect 
laboratory data including neutrophil/lymphocyte ratio, flow cytometry for neutrophils and lymphocyte 
subsets, gene expression profiling, circulating tumor cells (CTCs), baseline/serial ctDNA, LAG3-expressing 
T cells, lactate dehydrogenase (LDH) and iso-enzymes (for LDH-A), leukemia inhibitory factor (LIF) (14), 
cytokines especially interleukin-6 (IL-6) (15), interleukin-7 (16), serum protein electrophoresis, tumor 
markers and growth/differentiation factor-15 (GDF-15) (17). The LIPI score (Derived NLR 
(neutrophils/(leucocytes-neutrophils) ratio) and lactate dehydrogenase (LDH)) have been correlated with 
the efficacy of ICIs in lung cancer (18). The pathologic assessment will also include multiplex analysis for 
analysis of intra- and peri-tumoral microenvironment cell-type analysis, tumor-infiltrating lymphocyte 

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subsets e.g., killer CD8+, Tregs, etc., M1 vs M2 macrophages, N1 vs N2 neutrophils, dendritic cells, cancer-
associated fibroblasts; angiogenesis score; spatial transcriptomics for highly focused and detailed gene 
expression analysis matched to cellular infiltrate (19,20) and immunohistochemistry (IHC) for suspected 
biomarkers (LSD1, CD47). A large next-generation sequencing (NGS) lung panel, specifically including the 
usual driver mutations, STK-11, and tumor mutation burden (TMB) would be done. Most of above 
parameters have a prognostic and/or predictive value in lung cancer. 
At an institutional/systems level, this new standard of care involves complex interdisciplinary 
collaboration between thoracic surgeons, respirologists, medical oncologists, pathologists, radiologists, 
nuclear-medicine specialists, interventional radiologists, medical geneticists, and immunologists. The 
demands placed on the system will increase and the smooth patient flow through this novel care-path will 
be challenging. Previously, patients with early resectable lung cancer (based on imaging (PET-CT), were 
eligible for upfront surgery and then adjuvant chemotherapy. Now, ideally these patients should have an 
upfront biopsy (which may take few weeks for the intervention and another few weeks for the pathology 
and molecular results) before beginning neo-adjuvant chemotherapy and immunotherapy. With this 
neoadjuvant paradigm the standard of care demands interdisciplinary co-ordination to make decisions in 
the best interests of the patients, keeping in mind patient variables, tumor variables, as well as the 
complex logistics of waiting lists, biopsy, pathology/molecular result wait times etc. There should be a 
stress test of the care-delivery system, providing unprecedented opportunities to identify and fix 
emergent problems under a rigorously defined protocol designed to reflect future standard of care. 
We propose a prospective single cohort study to analyze the predictors of pCR and irAEs in 
patients receiving neoadjuvant immunotherapy and chemotherapy before surgery. We will then develop 
a predictive model that can predict pCR with a good sensitivity and specificity. If validated in subsequent 
studies such a model could be used to select patients who are more likely to benefit with neoadjuvant 
immunotherapy and chemotherapy approach. Such decision support tool will help choose the right 
patients for treatment with neoadjuvant chemo-immunotherapy versus upfront surgery.  
This project, besides having the scientific goals described in the paragraph above, will help 
improve the timeliness and coordination of care, and serve as a test-run for the future standardized 
implementation of this paradigm in our center and elsewhere. In summary, this study is a perfect example 
of coordination of ‘bench (para-clinical departments)’ and ‘bedside (clinical departments)’ working 
together in real time to improve clinical care and patient outcomes – and at the same time answering a 
very important clinical question of how to predict which resectable lung cancer patients can best benefit 

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from neoadjuvant treatment with immunotherapy and chemotherapy and which among them are more 
likely to have a serious immune related adverse event. 
2.0   AIM 
To develop a predictive model to predict which patients will develop irAEs and who will achieve a pCR 
with neoadjuvant chemoimmunotherapy treatment for resectable NSCLC. 
3.0   HYPOTHESIS  
A model combining predictive variables for pCR in patients with resectable NSCLC treated with 
neoadjuvant immunotherapy and chemotherapy can predict a pCR with an AUC of at least 0.8. 
4.0   OBJECTIVES 
4.1   Primary objectives 
1. To explore a combination of baseline and treatment-emergent potentially predictive variables, 
relating to both the tumor and the patient’s immune status, including clinicopathological, blood-
based, tissue-based, molecular, gene-expression, and radiological, in patients with early-stage 
NSCLC participant to neoadjuvant immunotherapy and chemotherapy. 
2. To curate these data by univariate and multivariate analyses, and to identify those variables 
independently useful in predicting pCR and occurrence irAEs in these patients. 
3. To parsimoniously combine these independently predictive factors into integrated models with 
adequate sensitivity and specificity, enabling both clinical utility and enhanced scientific insights.   
4.2.  Secondary objectives 
1. To assess pCR, MPR, objective response rates (ORRs), EFS, OS, chemotherapy related toxicity and 
irAEs in patients with resectable NSCLC treated with neoadjuvant immunotherapy and 
chemotherapy. 
2. To evaluate exploratory and entirely novel potential biomarkers for predicting pCR, MPR, ORR, 
EFS and irAEs. 
3. To assess if a post-treatment (pre-surgery) complete metabolic response by F18-FDG-PET-CT and 
a blood-only molecular residual disease assay can accurately predict a pCR. 
4. To assess whether liquid biopsy for molecular residual disease during follow-up can predict a 
recurrence of lung cancer. 

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5. To document, understand, and resolve impediments to the smooth and timely flow of patients on 
this new standard of care path. 
5.0   ENDPOINTS 
5.1   Primary endpoints 
1) Predictive power of various variables to predict pCR and irAEs. 
2) To determine the predictive power (sensitivity, specificity, receiver operator characteristic (ROC) 
curves) of a model, combining predictive variables, in predicting pCR. 
3) To determine whether a similar model can be constructed to predict irAEs. 
5.2   Secondary endpoints 
1) Assess pCR, MPR, objective response rates (ORR), 24-month EFS, 24-month OS and irAEs. 
2) Exploratory novel potential biomarkers. 
3) Predictive power of post-treatment (pre-surgery) complete metabolic response by F18-FDG-PET-
CT and a blood-only molecular residual disease assay for predicting pCR. 
4) To determine if a liquid biopsy for cancer detection, looking for molecular residual disease, 
during routine follow-up visits can predict a recurrence of lung cancer before or at the same 
time as conventional imaging. 
5) 5a. Time from diagnosis to initiation of treatment 
5b. Time from date of 3rd cycle of chemo-immunotherapy  to date of surgery 
5.3   Definition of endpoints 
• 
Pathologic Complete Response Rate - Pathological complete response (pCR) rate is defined as 
number of participants with absence of residual tumor in lung and lymph nodes at surgery, 
divided by the total number of participants. for each treatment group. Participants who are no 
longer eligible for surgery, or who are on alternative anticancer therapy before surgery, or who 
discontinue the study (e.g. withdraw consent) before surgery are all counted as non-responders.  
For histologic assessment, all tumor and associated lymph node tissue should be sectioned at 1 cm 
intervals. For assessments of pathological response, the percentage of viable tumor cells in at least 1 
section per centimeter of the tumor and lymph node tissue resected should be evaluated. 
• 
Major Pathological Response Rate - Major pathological response (MPR) rate, defined as number 
of participants with ≤ 10% residual tumor in lung and lymph nodes at surgery, divided by the 
number of total participants. Viable tumors with only in situ carcinoma should not be included in 
MPR calculation. Participants who are no longer eligible for surgery, or who are on alternative 

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anti-cancer therapy, or who discontinue the study (e.g. withdraw consent) before surgery are all 
counted as non-responders.  
• 
Event free survival – Event free survival (EFS) defined as the length of time from date of enrolment 
to any of the following events: progression of disease, recurrence disease, or death due to any 
cause. Progression/recurrence will be assessed as per RECIST 1.1. 
• 
Overall Response Rate - Overall response rate (ORR) is defined as proportion of participants 
whose overall radiological response prior to definitive surgery is either a complete response (CR) 
or partial response (PR) per RECIST 1.1 criteria. Participants who received alternative anti-cancer 
therapy before the pre-surgery tumor assessment will be counted as non-responders. 
• 
Overall Survival - Overall survival (OS) is defined as the time between the date of enrolment and 
the date of death due to any cause. For a participant without documentation of death, OS will be 
censored on the last date the participant was known to be alive. 
• 
PD-L1 Protein Expression - PD-L1 expression is defined as the percent of tumor cells membrane 
staining in a minimum of 100 evaluable tumor cells per validated Dako PD-L1 
immunohistochemistry (IHC) assay. This is referred to as quantifiable PD-L1 expression. If the PD-
L1 staining could not be quantified, it is further classified as: 1) Indeterminate: Tumor cell 
membrane staining hampered for reasons attributed to the biology of the tumor tissue sample 
and not because of improper sample preparation or handling. 2) Not evaluable: Tumor tissue 
sample was not optimally collected or prepared and PD-L1 expression is neither quantifiable nor 
indeterminate. Not evaluable can be determined from H&E process before the tumor biopsy 
specimen is sent for PD-L1 evaluation or from the H&E process during PD-L1 evaluation. 
Participants with missing PD-L1 expression are participants with no tumor tissue sample available 
for evaluation. 
6.0   STUDY POPULATION 
6.1   Inclusion criteria 
1. Patients 18 years of age or older. 
2. Participants with histologically confirmed Stage IB (≥ 4 cm), II, IIIA (N2) NSCLC (as per the 8th 
American Joint Committee on Cancer (AJCC)) who are considered to have resectable disease. 
3. Measurable disease according to Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 
1.1). 
4. Participants must have tumor tissue available for PD-L1 immunohistochemical (IHC) testing. 

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5. Eastern Cooperative Group (ECOG) Performance Status 0-2.  
6. Able to give informed consent. 
6.2   Exclusion criteria 
1. Presence of locally advanced, unresectable, or metastatic disease.  
2. Participants with known EGFR mutations, ALK or ROS1 translocation. 
3. Participants with active, known, or suspected autoimmune disease (except participants with type 
I diabetes mellitus, residual hypothyroidism due to autoimmune thyroiditis only requiring 
hormone replacement, skin disorders (such as vitiligo, psoriasis, or alopecia) not requiring 
systemic treatment). 
4. Participants with a condition requiring systemic treatment with either corticosteroids ( 10 mg 
daily prednisone or equivalent) or other immunosuppressive medications within 14 days of study 
drug administration. Inhaled or topical steroids are permitted in the absence of active 
autoimmune disease. 
5. Participants with previous malignancies are excluded unless a complete remission was achieved 
at least 3 years prior to study entry and no additional therapy is required or anticipated to be 
required during the study (non-melanoma skin cancer, low grade prostate cancer and other 
indolent malignancies not requiring any treatment and that are unlikely to affect blood-based 
biomarkers are allowed). 
6.3   Participant discontinuation 
Participants may voluntarily discontinue participation in the study at any time. Data previously analyzed 
will not be able to be withdrawn, however, any data not analyzed can be deleted and no further data or 
samples will be collected once a participant has discontinued on the study. 
7.0   STUDY DESIGN 
7.1   Study schema 
This is a prospective cohort study. Consecutive patients with resectable NSCLC meeting eligibility criteria 
will be treated with neoadjuvant nivolumab and chemotherapy. Patients’ demographic, tumor, 
investigation, and treatment data will be collected.  

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Figure 4. Study flow diagram. 
Patients referred to the thoracic surgical component of the Thoracic mutidiciplinary team (MDT) 
who have NSCLC, stages IB – IIIA, will be worked up in the usual manner with routine biopsies and staging 
investigations; and in accordance with the new SOC, they will be offered neoadjuvant nivolumab and 
chemotherapy similar to that used in CM816 trial (i.e. histology-appropriate platinum doublets plus 
nivolumab for 3 cycles prior to definitive surgery). If they accept this general approach, and they do not 
have a known EGFR mutations, or ALK/ROS1 re-arrraangement, they will be offered accrual onto this 
study, in the knowledge that their therapy and subsequent management will not be impacted, except in 
the acquisition of some additional data at baseline and while on systemic therapy. Patients will be 
provided with an informed consent form that describes the data to be collected over and above SOC, the 
impact on their care path (essentially some additional bloodwork and radiology), and the purpose of the 
study. Patients will be compensated for additional travel and parking. 
7.2   Data collection 
Study data will be stored in an electronic REDCap database hosted by Lawson Health Research Institute. 
The collection of data will be oriented around a thorough assessment of the tumor, and the immune status 
of the host, inclusive of both directly and indirectly relevant factors, according to the following 
categorization: clinical variables (e.g. symptoms, performance status, recent medications etc.), 
radiological variables (e.g. results of routine imaging, sarcopenia index, radiomics etc.), hematological 
variables (e.g. complete blood count and differentials, neutrophil/lymphocyte and platelet/lymphocyte 
ratios, flow cytometry etc.), routine blood chemistry (e.g. electrolytes, tumor markers, cytokines etc.), 
pathological variables (e.g. routine H&E, immunohistochemistry, peri-tumoral microenvironment cell-
type analysis, spatial transcriptomics etc.), molecular variables (e.g. next-generation sequencing, ctDNA 
etc.), and fecal microbiome data. 

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7.3   Data analysis 
We will first perform univariable and multivariable Logistic regression model analysis of the features 
associated with the pCR and irAEs to evaluate their importance in predicting pCR and irAEs. The top 
predictors identified will be used as candidate predictors to create an interpretable multimodal deep 
learning model with a good sensitivity/specificity. This model will be able to integrate predictors and 
predict the pCR and irAEs. Model performance will be evaluated using receiver operating characteristic-
area under the curve (AUC) and the precision-recall AUC. Other baseline models, such as random forest 
and LASSO will also be applied to compare model performance. 
7.4   Sample size justification 
Our planned accrual of 60 patients will result in approximately 15 pCRs, assuming an event rate of 25%. 
This will allow us to explore our potential predictors and their univariable associations with pCR and/or 
non-pCR outcomes. The top predictors identified in univariable analyses could be used as candidate 
predictors in a multivariable prediction model. Previous simulation studies have shown that scenarios 
with as few as 5 events per variable can produce estimates with valid confidence interval coverage, type 
I error rate, and relative bias (citation below). This framework would allow for an exploratory multivariable 
model predicting non-pCR with up to 9 of the top candidate predictors identified in univariable analyses, 
and/or up to 3- 4 of the top candidates  predicting pCR. This assumes a two-sided type I error rate of 0.05 
(21). 
 
 

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DEMOGRAPHIC  
CLINICAL  
RADIOLOGY  
BLOOD-BASED  
PATHOLOGY  
 
BASELINE  
 
Age 
Sex/gender  
Ethnicity  
Smoking status  
Alcohol consumption 
Pneumococcal 
vaccination status  
COVID status  
Country of birth 
  
  
  
  
Co-morbidities  
Medications  
ECOG PS  
Weight loss  
Stage  
(AJCC/TNM)  
Body composition  
Physical examination 
EORTC QLQ-C30 
EQ-5D-5L 
 
Sarcopenia Index  
CT scan  
Volumetrics  
SUV (PET/CT)  
Evidence of PE  
Radiomics 
Routine CBCD  
N/L ratio  
Flow (cell subsets)  
Activation marker  
Routine chemistry  
LDH and iso’s  
Protein electrophoresis  
CEA, CA199, CA125  
LIF, CRP, IL6, IL7, GDF-15 
ctDNA  
LIPI score 
Blood banking 
 
Routine H&E,  
IHC (PDL1, ALK,  
ROS1)  
Histotype  
Mulitiplex 
IHC 
for 
TME cell types and 
proteins of interest 
(LAG3, 
LSD1, 
and 
CD47)  
(LHSC algorithm)  
NGS (TMB)  
Spatial transcriptomics 
Fecal microbiome 
(stool sample) 
 
ON-TREATMENT  
 
Smoking status  
Vaccination  
COVID status  
        
Rx-emergent AE’s  
Type of chemo  
Dose intensity  
Use of G-CSF  
Use of PPIs/antibiotics  
Blood transfusions  
Development of  
PE            
CBCD nadir  
 
 
 
AFTER 3 CYCLES  
 
Smoking status  
Vaccination  
COVID status  
Operative fitness   
Physical  
AE assessment  
QoL (global, symptoms)  
  
CT scan  
SUV (PET/CT)  
 
ctDNA  
Routine pre-op  
Blood banking 
 
Biopsy of any 
suspected progression 
sites  
Fecal microbiome 
(stool sample) 
 
AT AND AFTER SURGERY  
 
 
Extent of surgery  
Complications  
Long-term  
EFS/OS  
CT scan  
ctDNA 
N/A  
Blood banking 
Assessment 
of 
path 
response (pCR, MPR)  
IHC for cell types  
Fecal microbiome 
(stool sample)  
 
Figure 5. Pre-PLaN study assessments and detailed data collection schematic [ECOG, Eastern Oncology 
Cooperative Group; PS, performance status; QoL, quality of life; CT, computed tomography, SUV, 
standardized uptake values; PE, pulmonary embolism; CBCD, CBC with differential, TME, tumor 
microenvironment; cell types: tumor cells, angiogenesis score, tumor-infiltrating lymphocytes (TILs) 
(CD8+, CD4+, Tregs), myeloid derived suppressor cells (neutrophil-type, monocyte -type including M1 and 
M2 macrophages); cancer-associated fibroblasts; natural killer (NK) cells, dendritic cells; G-CSF, 
granulocyte colony stimulating factor; GDF-15, growth-differentiation factor 15; PPIs, proton pump 
Inhibitors; PDL1, programmed death ligand-1; LAG3, ; Lymphocyte-activation gene 3; TLS, tertiary 
lymphocyte structures; LIF, Leukemia inhibitory factor; LSD1, Lysine-specific demethylase 1; CMR, 
complete metabolic response; MRD, molecular residual disease; STK11, serine-threonine kinase; TMB, 
tumor mutational burden] 

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7.5   Peripheral blood collection for ctDNA and biomarker analysis 
Plasma will be collected for ctDNA and biomarker analysis at three time-points. At each time-point, four 
10 mL (Paxgene ccfDNA, Streck BCT or K2EDTA) tubes will be drawn. 
• 
Draw #1: Prior to start of neoadjuvant chemotherapy and immunotherapy (Day 1). 
• 
Draw #2: After completion of neoadjuvant chemotherapy and immunotherapy, but before 
surgical resection (2-6 weeks after 3rd cycle).  
• 
Draws #3: 4-6 weeks after surgery. 
5-10 mL of plasma from each collection will be sent to a commercial liquid biopsy provider. The remaining 
5-10 mL of plasma will be used for future confirmatory or correlative studies, which may include blood-
based proteomics or other diagnostic liquid-biopsy testing platforms.  
7.6   Treatment details 
Patients with resectable stage Ib-IIIa NSCLC will be treated with standard of care (as per CheckMate 816), 
neoadjuvant nivolumab plus platinum doublet chemotherapy. It is possible that standard of care may 
evolve in future, but in principle will still broadly involve a combination of PD1/PDL1 inhibitor with 
chemotherapy. 
Eligible participants will be receiving one of the following standard of care chemotherapy regimens: 
Regimen 1 (non-squamous histology only): 
− Pemetrexed 500 mg/m2 IV over 10 minutes or per institutional standard on Day 1 
− Cisplatin 75 mg/m2 IV over 120 minutes or per institutional standard on Day 1, immediately following 
pemetrexed or Carboplatin AUC 5 or 6 IV over 30 minutes or per institutional standard on Day 1 
Regimen 2 (any histology): 
− Paclitaxel 175 or 200 mg/m2 IV over 180 minutes or per institutional standard on Day 1 
− Carboplatin AUC 5 or 6 IV over 30 minutes or per institutional standard on Day 1, immediately following 
paclitaxel 
Regimen 1 (squamous histology and patient cannot tolerate regimen 2): 
− Gemcitabine 1000-1250 mg/m2/day on days 1 and 8 or per institutional standard on Day 1 

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− Cisplatin 75 mg/m2 IV over 120 minutes or per institutional standard on Day 1, immediately following 
pemetrexed or Carboplatin AUC 5 or 6 IV over 30 minutes or per institutional standard on Day 1 
Following the completion of neoadjuvant treatment, all participants who remain operative candidates will 
undergo definitive surgery for their NSCLC within 6-8 weeks after completing neoadjuvant treatment. 
Following definitive surgery, participants in each arm may receive an adjuvant immune check point 
inhibitor and/or adjuvant chemotherapy with or without radiation or no further treatment as per the 
evolving standard of care and institutional standard at the discretion of the investigator. 
7.7   Feasibility 
Our centre treats on an average 50 patients per year with resectable NSCLC who are eligible for 
neoadjuvant therapy. As such, we should be able to meet are enrolment goal in approximately 14 months.  
7.8   Data safety monitoring committee  
This trial does not include a data safety monitoring committee, since the only additional change from 
standard treatment is collection of blood samples. 
8.0   EXPECTED OUTCOMES 
Development of a large and highly detailed data bank containing baseline and on-treatment information, 
to be exploited for the parsimonious derivation of models accurately predicting pCR and IrAEs, and which 
will also be useful for highlighting novel opportunities for therapeutic improvement as well as providing 
an unprecedented ‘deep dive’ into the biology of the host-tumor relationship.   
9.0    SIGNIFICANCE 
• 
An improved model that can predict pCR could help in selecting patients who are most likely to 
benefit with neoadjuvant immunotherapy and chemotherapy. Concerning the model for 
predicting IrAE’s, this is not to suggest such a model, if successful, should be used to deny such 
patients ICIs, but it would allow a more informed consent process, as well as more intensive pro-
active monitoring to avoid the worst outcomes of serious irAEs (which are occasionally fatal) by 
early intervention.      
• 
Identification of patients needing additional treatments (e.g., additional ICI’s like CTLA4 inhibitors 
or LAG3 inhibitors, or radiotherapy) 
• 
Acquisition of important scientific insights into the biological underpinnings of resistance 
• 
Identification of novel targets or strategies to overcome resistance.  

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• 
Will determine if a blood MRD assay can determine recurrence earlier than conventional imaging 
on surveillance. 
10.0   NOVELTY 
The intention to develop models predicting pCR is not per se novel, and indeed we have quoted 
the partly successful but still simple and inadequate NADIM II model above; however, the substantially 
extended scope of the parameters, and their sophistication, which we will employ will elevate this exercise 
to a level that might well achieve both of the critical (and currently unmet) needs for utility in clinical 
decision-making and, of more fundamental importance, highlight the in-depth biological differences 
between those achieving pCR and the rest, such that  by this winnowing process, the key and fundamental 
cause-and-effect relationships can be identified. This latter information could lead to the identification of 
new targets and/or novel opportunities for clinical intervention, to enable a further quantum leap in the 
rate of those achieving pCR. In summary, the vastly increased breadth and depth of parameters for model-
building, the markedly enhanced likelihood they will achieve clinical and scientific utility, the subsidiary 
yet important focus on side effects as well as anti-cancer efficacy, and in particular the use of an in-depth 
assessment of the patient’s immune status, are all individually novel, and in aggregate, unprecedented.         
11.0   ETHICAL CONSIDERATIONS 
The Principal Investigator will obtain ethical approval and clinical trial authorization by competent 
authorities according to local laws and regulations. 
11.1   Institutional Review Board (IRB) / Research Ethics Board (REB) 
The protocol (and any amendments), the informed consent form, and any other written information to 
be given to participants will be reviewed and approved by a properly constituted Institutional Review 
Board (IRB)/Research Ethics Board (REB), operating in accordance with the current federal regulations 
(e.g., Canadian Food and Drug Regulations (C.05.001); US Code of Federal Regulations (21CFR part 56)), 
ICH GCP and local regulatory requirements. A letter to the investigator documenting the date of the 
approval of the protocol and informed consent form will be obtained from the IRB/REB prior to initiating 
the study. Any institution opening this study will obtain REB IRB/REB approval prior to local initiation. 
11.2   Informed Consent 
The written informed consent form to be provided to potential study participants should be approved by 
the IRB/REB and adhere to ICH GCP and the ethical principles that have their origin in the Declaration of 
Helsinki. The investigator is responsible for obtaining written informed consent from each participant, or 

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Page 17 of 19 
if the participant is unable to provide informed consent, the participant’s legally acceptable 
representative, prior to beginning any study procedures and treatment(s). The investigator should inform 
the participant, or the participant’s legally acceptable representative, of all aspects of the study, including 
the potential risks and benefits involved. The participant should be given ample time and opportunity to 
ask questions prior to deciding about participating in the study and be informed that participation in the 
study is voluntary and that they are completely free to refuse to enter the study or to withdraw from it at 
any time, for any reason.  
The informed consent must be signed and dated by the participant, or the participant’s legally 
acceptable representative, and by the person who conducted the informed consent discussion. A copy of 
the signed and dated written informed consent form should be given to the participant or the participant’s 
legally acceptable representative. The process of obtaining informed consent should be documented in 
the patient source documents. 
11.3   Confidentiality of Participant Records 
The names and personal information of study participants will be held in strict confidence and restricted 
to members of the study team. The data coordinator will maintain a confidential participant identification 
list (i.e. master list) during the study. Access to confidential information (i.e., source documents and 
patient records) is only permitted for direct participant management and for those involved in monitoring 
the conduct of the study (i.e., Sponsors, CRO’s, representatives of the IRB/REB, and regulatory agencies). 
The participant’s name will not be used in any public report of the study. 
 
 

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