Antonios Papanicolau-Sengos1, Sarabjot Pabla1, Jeffrey M. Conroy1, 2, Mary Nesline1, Sean T. Glenn1, 3, Blake Burgher1, Jonathan Andreas1, Vincent Giamo1, Moachun Qin1, Grace K. Dy4, Felicia L. Lenzo1, Devin Dressman1, Marc Ernstoff4, Igor Puzanov4, Mark Gardner1, Carl Morrison1, 2,*

1. OmniSeq Inc., 700 Ellicott Street, Buffalo, NY 14203, US, 2. Center for Personalized Medicine, Roswell Park Comprehensive Cancer Center, Elm and Carltons Streets, Buffalo, NY 14263, US, 3. Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Elm and Carltons Streets, Buffalo, NY 14263, US, 4. Department of Medicine, Roswell Park Comprehensive Cancer Center, Elm and Carltons Streets, Buffalo, NY 14263, US

* Dr. Carl Morrison, MD, DVM: Carl.Morrison@OmniSeq.com

ASCO-SITC 2018

Lung Cancer Mutational Profile Correlates with Immune Profile

Copyright 2018 OmniSeq, Inc.

Conclusions

Table 2: PCA associations of KRAS/EGFR wild type with gene rank and category rank.Immunostimulatory (MX1, DDX58, High CD40LG) and Immunosuppressive (ADORA2A,CD39) genes were significantly under-represented.

In NSCLC, KRAS, EGFR, and double WT are immunophenotypicallydistinct:

• KRAS mutants have a trend of immune de-activation• Double WT have a trend of immune activation• EGFR mutants have a mixed profile with a notable trend of under-

representation of checkpoint blockade and anti-inflammatoryprofile.

Notably, the KRAS mutant group had a significant over-representation ofCCR2 and TGFB1 overexpression, both of which are targeted by agentsthat are currently in clinical trials.

A relatively small number of EGFR mutants were included in this study.An expansion of the tested population is necessary to confirm ourfindings. Further breakdown of NSCLC based on primary versusmetastatic status, prior treatment history, other mutations, and othercharacteristics is forthcoming.

Results

Methods

Introduction

Eighty six non-small cell lung cancer samples were tested by NGS using acomprehensive cancer panel for mutational status and an immuneresponse panel which interrogates the expression profile of 54 validatedimmune-related genes1 (Figure 1).

Tumor progression and host immune response are dependent on the tumormicroenvironment (TME), which plays an important role in response totherapy. As immunotherapy continues to demonstrate clinical benefit forlung cancer patients, the development of predictive biomarkers is essentialto guide therapy selection. The mutational and immune landscape of atumor can aid in characterizing the TME. In this study we aim tocharacterize the TME by evaluating the genomic and immune landscape innon-small cell lung cancer (NSCLC) allowing for the identification of newtherapeutic opportunities in patients with NSCLC.

Results

Table 1: PCA associations of mutant KRAS with gene rank and gene category rank.Immunosuppressive genes (CD39, CCR2) were significantly over-represented. Targetedagents for over-represented immunosuppressive and anti-inflammatory markers arelisted.

Tumors evaluated had histologically confirmed primary or metastaticNSCLC. Because of their relatively high numbers, we only consideredactivating KRAS and EGFR mutations. All other cases were consideredwild-type. ALK, RET, and ROS1 fusions were not included. The level ofgene expression was addressed in three distinct ways: gene rank(continuous variable), gene category rank (categorical variable with thefollowing breakdown: very high 95-100, high 85-94, moderate 50-84,low 20-49, very low 0-19), and immune phenotype rank (genes groupedin immune phenotypes, listed in Figure 2). All rankings are the result ofa comparison rank against a reference population of cancer specimensunrelated to this cohort.

Mutant KRAS Associations

Marker/Category Marker Function Targeting Agent P value

Over-represented

CD39 Immunosuppressive None 2.24E-02

DDX58 Immunostimulatory None 3.60E-02

CCR2 Immunosuppressive Plozalizumab; BMS-813160; CCX872-B 4.48E-02

Very high anti-inflammatory response category

NA NA 0.016

High TGFB1 category Anti-inflammatory Fresolisumab; MSB0011359C; NIS793 0.016

Very high MX1 category Immunostimulatory None 0.031

Under-representedLow LAG3 category Co-inhibitory checkpoint NA 0.028

Very low STAT1 category Pro-inflammatory NA 0.020

Double Wildtype Associations

Marker/Category Marker Function Targeting Agent P value

Over-represented

Very low STAT1 category Pro-inflammatory NA 0.005

Very low DDX58 category Immunostimulatory NA 0.023

Low IL10 category Anti-inflammatory NA 0.042

Very low CD27 category Co-stimulatory NA 0.049

Under-represented

ADORA2A Immunosuppressive NA 4.78E-02CD39 Immunosuppressive NA 4.77E-02MX1 Immunostimulatory NA 4.09E-02

Metabolic Immune Escape NA NA 3.45E-02DDX58 Immunostimulatory NA 8.36E-03

Very high MX1 category Immunostimulatory NA 0.038High CD40LG category Co-stimulatory NA 0.033

High ADORA2A category Immunosuppressive NA 0.033Very high AntiInflammatory Response

categoryNA NA 0.032

High TGFB1 category Anti-inflammatory NA 0.025

KRAS

Double WT

EGFR

Figure 1: NGS workflow

Figure 2: Immune phenotypes

References1. Conroy JM, Pabla S, Glenn ST, et al. Analytical Validation of a Next-Generation Sequencing Assay to Monitor Immune Responses in Solid Tumors. J Mol Diagn 2018 Jan;20(1):95-109.

Figure 3: Principal component analysis (PCA) was performed to determine association of

EGFR/KRAS mutations and double wild type (WT) with the immune profile as measured by

the NGS panels. Dimension 1 is KRAS mutant status and dimension 2 is EGFR mutant status.

Thirty six cases were positive for an activating KRAS mutation and nine cases were positive

for an activating EGFR mutation. A single case had both KRAS and EGFR mutations.

Figure 4: Statistical over-representation analysis (v.test) of the three subtypes (KRASmutant, EGFR mutant, and WT) demonstrated a trend of over-representation of all theimmune phenotypes in the double WT group, a trend of under-representation in the KRASmutant group, and a mixture of trends in the EGFR group.

-3 -2 -1 0 1 2 3

Over-representation (v.test)

Immune Phenotypes

T cell Primed Pro-inflammatory Myeloid Suppression Immune Escape Checkpoint Blockade Other Checkpoint Blockade (PD-L1/CTLA4) Anti-inflammatory

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