Abstract 5955: In Silico dissection of immune infiltrate signatures that are detected by DetermaIO, a predictor of response to immune therapy

Abstract

Abstract Background: The composition of tumors includes not only malignant but also immune, stromal and other cell types. Understanding this dynamic tumor immune microenvironment (TIME) is important to guide treatment and develop novel therapies and markers. We have previously validated immuno-oncology gene expression signatures (DetermaIO (DTIO) and a 101-gene algorithm), that predict efficacy of checkpoint inhibitors (ICI) based on distinguishing immunomodulatory (IM), mesenchymal stem-like (MSL), and mesenchymal (M) phenotypes. In this study we used TCGA and clinical cohorts to identify immune infiltrate populations within these defined TIME spaces and their association with ICI treatment. Methods: We derived novel human immune infiltrate signatures from a translation of murine ImmGen cell populations and a search for conserved co-expression of immune markers across multiple tumors. In total, 20 tumors from TCGA were employed for derivation and analysis encompassing 7163 unique samples. These novel signatures were compared to published immune infiltrate signatures and then their association with ICI efficacy and each other assessed in three cohorts treated with ICI therapy, IMvigor210 and an additional bladder cohort, comprising 272 and 89 patients with censored outcome results, and a melanoma cohort (N=105). Results: The ImmGen analysis created 35 immune cell signatures and pan-tumor conserved co-expression of immune markers created eight signatures. The co-expression signatures often contained a mixed population of cell-type markers, though largely dominated by either myeloid or lymphoid markers. These signatures showed highly reproducible proportions of samples with strong expression between train and test TCGA sets. Most immune signatures had their highest representation in IM and DTIO+ tumors, however there was also consistent identification of presumptive immune infiltrate presence in MSL, M and DTIO negative cases. Two of the conserved co-expression signatures, one comprised of B-cell markers, and the other of T cell and other lymphoid markers, were associated with ICI efficacy in IMvigor210 and validated in the other “real-world” bladder cohort (B-cell: OR=0.8, p=0.022, T lymphoid: OR=0.7, p=0.005). Both signatures also had significant association with outcome in the cohort with clinical response outcomes, being strongest in patients after treatment had initiated. Conclusions: These cell-type signatures may be identifying novel immune infiltrate populations that co-exist within the tumor immune microenvironment and are potentially predictive of ICI response. The two signatures were not independent of DTIO in either cohort, suggesting that the 27-gene algorithm DTIO largely incorporates this information. This analysis begins to dissect the complex physiology of the tumor immune microenvironment that mediates response to immune therapy. Citation Format: Brian Z. Ring, Catherine T. Cronister, Robert S. Seitz, Douglas T. Ross, Brock Schweitzer, David R. Gandara. In Silico dissection of immune infiltrate signatures that are detected by DetermaIO, a predictor of response to immune therapy. [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 5955.