Abstract LB176: Interrogating the single cell immune checkpoint inhibition atlas to decipher ICI response mechanisms

Abstract

Abstract Introduction: Immune Checkpoint Inhibition (ICI) therapies have demonstrated significant clinical success in multiple cancer types. However, many patients do not develop durable responses to ICI treatments and the underlying mechanisms of action are not fully resolved. Recent single cell RNA-seq (scRNA-seq) studies provide a window into the complex interplay between cancer and heterogenous immune cell populations underlying ICI response. However, individual studies are typically focused on specific therapies in specific cancer types making it challenging to identify universal mechanisms and response markers. Approach: To facilitate a more comprehensive study of cell-type specific ICI mechanisms and response determinants, we developed the Single Cell Immune Checkpoint Inhibition Atlas (ICI Atlas) which integrates 27 scRNA-seq datasets (including 17 human and 10 mice datasets) spanning close to 1.9 million cells. Of the 27 datasets, 25 contain both pre- and post-treatment samples, one contains only pre-treatment samples and one contains only post-treatment samples. The Atlas spans over 30 cell types and 16 different cancer types as well as 20 treatment strategies spanning 11 ICI therapies and 28 other drugs. Results: To begin exploring the cell-type specific responses to ICI, we conducted comparative analyses of the ICI Atlas T cells in 4 groups: 1) responders vs. non-responders pre-treatment; 2) responders vs. non-responders post-treatment; 3) responders post-treatment vs. untreated; and 4) non-responders post-treatment vs. untreated leading to 62 pairwise comparisons across ICI Atlas datasets and treatment strategies. First, we found that T cells were either more abundant or more differentiated after ICI treatment and were more differentiated or more abundant in responders (post-treatment) compared to non-responders. We also found that genes previously identified in a meta-analysis of bulk mRNA datasets (Litchfield et al. 2021) as being effective predictors of ICI response are also more highly expressed in T cells of responders post-treatment compared to non-responders or compared to untreated subjects. Using an IFN-related gene signature, we found that post treatment as compared to baseline, IFN genes increased more substantially in responders than in non-responders. Across a large set of studies, we found that the IFN signature clusters T cells into IFN-effector/producers (effector/activated T cells) and IFN-insensitive cells (naive/memory T cells). Finally, using Atlas-derived ICI response signatures in T cells and data from a recently published study of mismatch repair deficient colorectal cancers (MMRd CRC, Pelka et al. 2021), we showed that T cells from MMRd CRC patients recapitulate signatures of ICI responders. Our results suggest that large-scale scRNA-seq atlases can serve as valuable tools for corroborating existing hypotheses, as well as providing key new insights into cell-type specific mechanisms underlying ICI therapy response. Citation Format: Piotr Topolewski, Aleksandra Możwiłło, Marlena Osipowicz, Roy Ronen, Janusz Dutkowski. Interrogating the single cell immune checkpoint inhibition atlas to decipher ICI response mechanisms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB176.