Working Towards Precision Medicine for the Tumor MicroenvironmentKristen Strand-Tibbitts1, Kyung Kim2, Jung Yong Hong2, Seung Tae Kim2, Jeeyun Lee2 and Laura Benjamin1

1 Oncologie, Inc., Waltham, MA. 2Department of Hematology-Oncology, Samsung Medical Center, Seoul, Korea

INTRODUCTION

Immune therapies for cancer have generated an enhanced focus on controlling cancer through modulation of biologiesassociated with the tumor microenvironment, rather than the traditional approach of targeting cancer cell biology. As more and more targeted therapies are designed to modulate the tumor microenvironment, we need a better understanding of microenvironmental heterogeneity in human cancer. Similar to what has been done to describe patient subsets based on their cancer biology using DNA and RNA signatures, we are working to describe patient subsets based on their microenvironment biology. The ultimate goal is to find effective means of identifying patients for novel therapeutic treatments that target biological pathways that regulate the non-neoplastic cells and drive cancer progression.

RESULTS

Presented at SITC; National Harbor ,MD; 08Nov2019

METHODS

• Gene expression from Gastric Cancer tumor biopsies from the ACRG (Cristescu et al 2015), TCGA (Zhang et al 2014), and Singapore cohort (Lei et al 2013) were analyzed with respect to genes involved in stromal biology. We have defined four unique stromal phenotypes.

• The four stromal phenotypes were evaluated for prognostic potential by Kaplan Meier Analysis of Overall Survival (OS) available in the ACRG/TCGA and Singapore datasets.

Figure 1 Comparison of the prevalence of four stromal subtypes derived in the three public Gastric cancer datasets (ACRG, TCGA and Singapore)

The four stromal phenotypes show similar prevalence in the three public datasets. There are slightly more samples that are classified as Group 2 (34-23 %) and Group 4 subgroup is the least prevalent (15-24 %).

Figure 2a Four stromal phenotypes have different prognostic risk in the ACRG data N=298

Figure 2b Four stromal phenotypes have different prognostic risk in the TCGA data N=388

Figure 2c Four stromal phenotypes have different prognostic risk in the Singapore cohort data N=192

Figure 2a-c Survival Risk curves for the four stromal phenotypes in the ACRG, TCGA and Singapore data show worse prognosis for the Group 3 or Group 4 phenotypes compared to the group 1 phenotype .

Figure 3a Age in the ACRG Figure 3b Age in the TCGA Figure 3c Age in the Singapore Cohort

Figure 3d Gender not associated with Stromal Class in the ACRG

Figure 3d Gender not associated with Stromal Class in the TCGA

Figure 3d Gender not associated with Stromal Class in the Singapore

CONCLUSIONS

Future clinical trials to prospectively test these four unique phenotypes as predictive biomarkers for therapy is planned. Oncologie has initiated a clinical trial with patients with second line or greater Gastric Cancer with the combination of Bavituximab/Pembrolizumab (NCT04099641). Predose tumor samples will be collected and classified into the four stromal phenotypes and patient outcome will be compared to see if any of the four phenotypes show enhanced response.

Group 1N=85

Group 2 N=98

Group 3N=69

Group 4N=46

OverallN= 298*

Stage 1 6 (7.1%) 8 (8.2 %) 9 (13.0 %) 7 (15.2 %) 30 (10 %)

2 26 (30.6%) 29 (29.6 %) 25 (36.2 %) 16 (34.8 %) 96 (32.2%)

3 29 (34.1%) 33 (33.7 %) 20 (29.0 %) 13 (29.0 %) 95 (31.8%)

4 24 (28.2 %) 28 (28.6 %) 15 (21.7 %) 10 (21.7 %) 77 (25.8 %)

*298 of 300 subjects reported stage of disease

Table 1 Stromal Phenotypes and Stage of Disease for the ACRG

Group 1N=86

Group 2N=117

Group 3 N=100

Group 4 N=72

OverallN= 375*

Stage 1 13 (15.1%) 24 (20.5%) 7 ( 7.0%) 7 (9.7%) 51 (13.6%)

2 29 (33.7%) 34 ( 29.1%) 32 ( 32.0%) 26 ( 36.1%) 121 (32.3%)

3 34 (39.5%) 49 ( 41.9%) 52 ( 52.0%) 30 ( 41.7%) 165 (44.0%)

4 10 ( 11.6%) 10 ( 8.5%) 9 ( 9.0%) 9 ( 12.5%) 38 (10.1%)

*375 of 388 subjects reported stage of disease

Table 2 Stromal Phenotypes and Stage of Disease for the TCGA

Group 1N=40

Group 2N=54

Group 3N=51

Group 4 N=47

OverallN= 192

Stage 1 8 (20.0%) 12 (22.2%) 1 ( 2.0%) 10 (21.3%) 31 (16.1%)

2 3 (7.5 %) 10 ( 18.5%) 9 ( 17.6%) 7 (14.9%) 29 (15.1%)

3 19 (47.5 %) 17 ( 31.5%) 16 (31.4 %) 20 (42.6%) 72 (37.5%)

4 10 ( 25.0%) 15 ( 27.8%) 25 ( 49.0%) 10 ( 21.3%) 60 (31.3%)

Table 3 Stromal Phenotypes and Stage of Disease for the Singapore Cohort

Legend Figures 3a-d and Table 1, 2 and 3 Stromal phenotypes appear to be independent of age, gender and

stage of disease