Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition.

Checkpoint inhibitors (CPIs) augment adaptive immunity. Systematic pan-tumor analyses may reveal the relative importance of tumor-cell-intrinsic and microenvironmental features underpinning CPI sensitization. Here, we collated whole-exome and transcriptomic data for >1,000 CPI-treated patients across seven tumor types, utilizing standardized bioinformatics workflows and clinical outcome criteria to validate multivariable predictors of CPI sensitization. Clonal tumor mutation burden (TMB) was the strongest predictor of CPI response, followed by total TMB and CXCL9 expression. Subclonal TMB, somatic copy alteration burden, and histocompatibility leukocyte antigen (HLA) evolutionary divergence failed to attain pan-cancer significance. Dinucleotide variants were identified as a source of immunogenic epitopes associated with radical amino acid substitutions and enhanced peptide hydrophobicity/immunogenicity. Copy-number analysis revealed two additional determinants of CPI outcome supported by prior functional evidence: 9q34 (TRAF2) loss associated with response and CCND1 amplification associated with resistance. Finally, single-cell RNA sequencing (RNA-seq) of clonal neoantigen-reactive CD8 tumor-infiltrating lymphocytes (TILs), combined with bulk RNA-seq analysis of CPI-responding tumors, identified CCR5 and CXCL13 as T-cell-intrinsic markers of CPI sensitivity.

Using a pan-cancer atlas to investigate tumour associated macrophages as regulators of immunotherapy response.

The paradigm for macrophage characterization has evolved from the simple M1/M2 dichotomy to a more complex model that encompasses the broad spectrum of macrophage phenotypic diversity, due to differences in ontogeny and/or local stimuli. We currently lack an in-depth pan-cancer single cell RNA-seq (scRNAseq) atlas of tumour-associated macrophages (TAMs) that fully captures this complexity. In addition, an increased understanding of macrophage diversity could help to explain the variable responses of cancer patients to immunotherapy. Our atlas includes well established macrophage subsets as well as a number of additional ones. We associate macrophage composition with tumour phenotype and show macrophage subsets can vary between primary and metastatic tumours growing in sites like the liver. We also examine macrophage-T cell functional cross talk and identify two subsets of TAMs associated with T cell activation. Analysis of TAM signatures in a large cohort of immune checkpoint inhibitor-treated patients (CPI1000 + ) identify multiple TAM subsets associated with response, including the presence of a subset of TAMs that upregulate collagen-related genes. Finally, we demonstrate the utility of our data as a resource and reference atlas for mapping of novel macrophage datasets using projection. Overall, these advances represent an important step in both macrophage classification and overcoming resistance to immunotherapies in cancer.

Quantifying the impact of immunotherapy on RNA dynamics in cancer.

BACKGROUND: Checkpoint inhibitor (CPI) immunotherapies have provided durable clinical responses across a range of solid tumor types for some patients with cancer. Nonetheless, response rates to CPI vary greatly between cancer types. Resolving intratumor transcriptomic changes induced by CPI may improve our understanding of the mechanisms of sensitivity and resistance. METHODS: We assembled a cohort of longitudinal pre-therapy and on-therapy samples from 174 patients treated with CPI across six cancer types by leveraging transcriptomic sequencing data from five studies. RESULTS: Meta-analyses of published RNA markers revealed an on-therapy pattern of immune reinvigoration in patients with breast cancer, which was not discernible pre-therapy, providing biological insight into the impact of CPI on the breast cancer immune microenvironment. We identified 98 breast cancer-specific correlates of CPI response, including 13 genes which are known IO targets, such as toll-like receptors TLR1, TLR4, and TLR8, that could hold potential as combination targets for patients with breast cancer receiving CPI treatment. Furthermore, we demonstrate that a subset of response genes identified in breast cancer are already highly expressed pre-therapy in melanoma, and additionally we establish divergent RNA dynamics between breast cancer and melanoma following CPI treatment, which may suggest distinct immune microenvironments between the two cancer types. CONCLUSIONS: Overall, delineating longitudinal RNA dynamics following CPI therapy sheds light on the mechanisms underlying diverging response trajectories, and identifies putative targets for combination therapy.

Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance.

Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, TSTR, characterized by heat shock gene expression. TSTR cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8+ cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of TSTR cells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery.