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

Kevin Litchfield,Dhruva Biswas,Sunil Kumar Saini,José Afonso Guerra-Assunção,Charles Swanton,Virginia Turati,Krupa Thakkar,Sergio A Quezada,Matthew D Hellmann,Samra Turajlic,Nicholas Mcgranahan,Maise Al Bakir,James L Reading,Clare Puttick,Javier Herrero,James Larkin,Chris Abbosh,Peter Van Loo,Thomas B.K Watkins,Robert Bentham,Andrew Rowan,Andrew J.S Furness,Tariq Enver,Rachel Rosenthal,Lucía Conde ,Emilia L Lim ,Se‐Hoon Lee ,Sine Reker Hadrup

doi:10.1016/j.cell.2021.01.002

Abstract

SummaryCheckpoint 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.

Highlights

To date, multiple biomarkers have been associated with immune checkpoint inhibitor (CPI) response, which can be broadly grouped into four categories: (1) sources of antigen that elicit T cell responses, (2) mechanisms of immune evasion that drive resistance, (3) host factors, and (4) markers of immune infiltration
Benchmarking of previously reported biomarkers of CPI response Samples were processed from raw sequencing reads, and standardized processing/quality control procedures were executed as described in STAR methods
We note Z score conversion has been applied in other large-scale tumor mutation burden (TMB) projects (Vokes et al, 2019), and as a control all analyses were repeated without Z score conversion, with the top-ranked biomarkers found to be the same

Summary

Introduction

Multiple biomarkers have been associated with immune checkpoint inhibitor (CPI) response, which can be broadly grouped into four categories: (1) sources of antigen that elicit T cell responses, (2) mechanisms of immune evasion that drive resistance, (3) host factors, and (4) markers of immune infiltration. Despite these promising insights, large-scale studies of.

Results

Discussion

Conclusion