75P - Target identification by TIME phenotypes

Background

We have previously shown that a large gene set can classify the tumor immune microenvironment (TIME) of patients with epithelial tumors into one of three phenotypes: immune inflamed (immune hot), immuno-suppressive (immune cold), or immune inert (immune cold). Given that the tumor is constantly evolving, identifying key genes where changes in mutation or cell signaling is associated with a change in phenotypes can lead to identification of potential new targets. Here we show three bioinformatic methodologies for this purpose.

Methods

The first approach to identifies genes hypermethylated in one phenotype and hypomethylated in another as methylation patterns can be surrogates for mutations that drive a transition across the TIME. Second, MUTECT2 annotation was used to detect high impact mutations which were associated with the phenotypes. Third, the IntAct database was used to identify potential cell signaling pairings where one gene was associated with patients classified as hot and a second gene classified patients as cold. All three of these approaches yielded genes with known targeted therapies in active clinical trials.

Results

The methylation approach identified VEGFR2 as a gene where mutations could likely affect TIME phenotypes. VEGFR2 is routinely used in clinical trials and has an approved drug on the market (ramucirumab), and mutations in VEGFR2 are seen to affect patient prognosis and drug sensitivity. With the MUTECT2 approach, mutations in STK11 were identified. Several clinical trials are examining the role that mutations in STK11 can play in altering the treatment paradigm between immunotherapy and KRAS targeted therapy. Finally, the IntAct approach identified the interaction between CRCX4 and CXCL12, known to be involved in TIME evolution and with several clinical trials being conducted designed to disrupt this interaction.

Conclusions

Utilizing a curated gene list to classify patients into TIME phenotypes and then using bioinformatic methods to find genes which may be driving transition across immune of phenotypes resulted in identifying known and well validated targets from current clinical trials. The results argue for a more careful examination of other genes that resulted from this methodology as potential targets for immunotherapy.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Oncocyte.

Disclosure

R. Seitz: Financial Interests, Personal, Advisory Role: Oncocyte, Inc. B. Ring, C. Cronister: Financial Interests, Personal, Full or part-time Employment: Oncocyte, Inc.