SummaryWe present an exceptional case of a patient with high-grade serous ovarian cancer, treated with multiple chemotherapy regimens, who exhibited regression of some metastatic lesions with concomitant progression of other lesions during a treatment-free period. Using immunogenomic approaches, we found that progressing metastases were characterized by immune cell exclusion, whereas regressing and stable metastases were infiltrated by CD8+ and CD4+ T cells and exhibited oligoclonal expansion of specific T cell subsets. We also detected CD8+ T cell reactivity against predicted neoepitopes after isolation of cells from a blood sample taken almost 3 years after the tumors were resected. These findings suggest that multiple distinct tumor immune microenvironments co-exist within a single individual and may explain in part the heterogeneous fates of metastatic lesions often observed in the clinic post-therapy.Video Abstract

Highlights d Novel mouse system to uncouple tumor mutational load and tumor heterogeneity d Lower tumor heterogeneity leads to decreased tumor growth because of immune rejection d Both clone numbers and their genetic diversity mediate tumor growth and rejection d Tumor heterogeneity is linked to patient survival and checkpoint blockade response

Various computational approaches have been developed for estimating the relative abundance of different cell types in the tumor microenvironment (TME) using bulk tumor RNA data. However, a comprehensive comparison across diverse datasets that objectively evaluates the performance of these approaches has not been conducted. Here, we benchmarked seven widely used tools and gene sets and introduced Consensus TME , a method that integrates gene sets from all the other methods for relative TME cell estimation of 18 cell types. We collected a comprehensive benchmark dataset consisting of pan-cancer data (DNA-derived purity, leukocyte methylation, and hematoxylin and eosin-derived lymphocyte counts) and cell-specific benchmark datasets (peripheral blood cells and tumor tissues). Although none of the methods outperformed others in every benchmark, Consensus TME ranked top three in all cancerrelated benchmarks and was the best performing tool overall. We provide a Web resource to interactively explore the benchmark results and an objective evaluation to help researchers select the most robust and accurate method to further investigate the role of the TME in cancer (www.consensusTME.org). Significance: This work shows an independent and comprehensive benchmarking of recently developed and widely used tumor microenvironment cell estimation methods based on bulk expression data and integrates the tools into a consensus approach.