Abstract 5182: Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response

Abstract

Abstract Immune checkpoint inhibitors (ICI) have become the standard of care as a first- or second-line systemic treatment for patients with various cancer types. Unfortunately, many patients do not respond to this treatment and the occurrence of immune-related adverse events varies widely. There is an urgent need for robust predictive biomarkers for ICI response to identify patients with likely clinical benefit from this costly treatment. Several biomarkers, aimed at predicting response to ICI, have been proposed, including PD-L1 expression, tumor mutation burden (TMB), infiltration of cytotoxic T-cells in the tumor, Microsatellite Instability (MSI) and expression of various immune gene signatures. However, these individual biomarkers have suboptimal performance and multiple complementary omics technologies are required to properly quantify them. Integration of these multi-omic biomarkers has proven valuable in increasing the accuracy and robustness of ICI response prediction.We have developed a computational pipeline that determines the expressed mutation burden (eTMB), MSI status, fraction of infiltrating immune cells and various immune gene expression signatures directly from the RNA-sequencing profile of the tumor. Each biomarker is quantified using a dedicated algorithm that applies machine learning (eTMB and MSI) or computational deconvolution (infiltrating immune cells) and integrates external data sources to maximize performance. Algorithm performance has been validated on large cohorts of tumor RNA-sequencing data with matching gold standard quantification of said biomarkers. As a proof of concept, we applied our pipeline to tumor RNA-sequencing data of 45 gastric cancer patients treated with pembrolizumab. Responders showed significantly higher eTMB scores and were enriched among the MSI-H patients. Fractions of CD8 T-cells and M1 Macrophages, together with interferon gamma and cytotoxic T-cell gene expression signatures, were significantly increased in responders. Notably, integrating these biomarkers into a single prediction model improved prediction of response to checkpoint inhibition therapy compared to predictions based on eTMB or MSI alone. Taken together, our approach enables the quantification of various ICI response biomarkers from a single omics layer (RNA-sequencing) and can contribute to ICI response prediction. Citation Format: Pieter Mestdagh, Pieter-Jan Van Dam, Frederick De Baene, Carolina Fierro, Elise Van Hoof, Emmanuel Rivière, Hanne Dangreau, Reindert Van Cauwenberge, Jo Vandesompele. Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5182.