Abstract 6574: Systematic analysis of the predictive gene expression signatures of immunotherapies across multiple cancer types using IOSig

Abstract

Abstract Immune checkpoint inhibitors (ICI) have provided durable responses in a subset of patients in multiple solid tumor types. However, developing robust and reproducible predictive gene signatures for ICIs remains a challenge partly due to the lack of systematic comparisons across a large pan-cancer transcriptomics compendia. We recently developed Immuno-Oncology Signatures Explorer (IOSig), a user-friendly web tool, to allow users to query and explore predictive biomarkers from a large collection of pan-cancer patient samples treated with ICIs (> 2500 patient samples from 40 studies). Here, we investigated six solid tumors: kidney, melanoma, bladder, head and neck squamous cell, gastrointestinal, and non-small cell lung cancers tissue-based RNA-seq datasets in IOSig. The 2175 samples, from 45 cohorts, were Z-normalized with the genes from each signature being averaged to assign a sample a single score per signature. Area Under the Receiver Operating Characteristic (AUROC) curve was used to summarize the gene signatures’ ability to predict response to ICIs. We focus on the top 10 predictive gene signatures for each cancer type. There were 22 unique gene signatures from the top 10 of the six cancer types. Overall, 17 signatures were predictive in GI, NSCLC, melanoma and HNSCC. Many of these signatures were enriched in immune activation and inflammatory signatures. Conversely, the MYC, DNA damage repair, and proliferation signatures are only predictive in bladder cancer. No predictive signatures were associated with kidney cancer. Interestingly, the immune activation signatures were highly predictive in the GI cancer cohorts in our study, as the cohorts were enriched with patients having high tumor mutational burdens and microsatellite instability. In summary, we used IOSig to analyze the predictive ability of previously published gene signatures across 6 cancer types. Some of these signatures warrant further investigation in a cancer-specific manner. A table showing the AUROC value of the 22 selected gene signatures in each cancer type. Signature Melanoma Bladder Kidney NSCLC HNSC GI Cancer Average Chemokines 0.657 0.563 0.526 0.721 0.657 0.870 0.665 TIP Hot 0.657 0.571 0.544 0.737 0.592 0.866 0.661 ifng18 0.674 0.557 0.584 0.680 0.589 0.871 0.659 Ipi_Neo 0.660 0.567 0.563 0.675 0.602 0.859 0.654 Rooney 0.669 0.569 0.568 0.661 0.646 0.797 0.652 Chaurio 0.670 0.572 0.570 0.679 0.570 0.834 0.649 ifng6 0.655 0.593 0.553 0.627 0.635 0.823 0.648 ifng-effector 0.672 0.566 0.567 0.686 0.564 0.830 0.648 Roh 0.661 0.551 0.565 0.697 0.548 0.856 0.646 NRS 0.649 0.512 0.588 0.687 0.605 0.818 0.643 effector_t 0.679 0.567 0.579 0.670 0.524 0.835 0.642 impres 0.619 0.516 0.577 0.706 0.555 0.847 0.637 mMDSC 0.643 0.524 0.558 0.706 0.557 0.797 0.631 Ock 0.640 0.563 0.586 0.579 0.578 0.827 0.629 Davoli 0.665 0.546 0.569 0.655 0.514 0.822 0.629 MHC_1 0.644 0.593 0.550 0.574 0.553 0.806 0.620 MHC_2 0.661 0.533 0.554 0.659 0.437 0.802 0.608 gMDSC 0.582 0.477 0.526 0.706 0.458 0.695 0.574 DNA_damage_repair 0.432 0.598 0.555 0.383 0.499 0.361 0.471 Proliferation 0.434 0.655 0.530 0.370 0.420 0.372 0.463 Mitoscore 0.422 0.588 0.374 0.418 0.580 0.312 0.449 MYC 0.367 0.607 0.503 0.381 0.482 0.303 0.441 Citation Format: Samuel Coleman, Caroline Wheeler, Rebecca Hoyd, Louis Denko, Ching-Nung Lin, Muhammad Z. Fadlullah, Siwen Hu-Lieskovan, Christine Chung, Ahmad A. Tarhini, Daniel Spakowicz, Aik Choon Tan. Systematic analysis of the predictive gene expression signatures of immunotherapies across multiple cancer types using IOSig [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6574.