Tumor- and T cell–intrinsic mechanisms of immune checkpoint inhibition (ICI) response were uncovered.
Major Finding: Tumor- and T cell–intrinsic mechanisms of immune checkpoint inhibition (ICI) response were uncovered.
Approach: A meta-analysis combined whole-exome sequencing and transcriptomic data from more than 1,000 patients.
Impact: This work establishes a database that can be utilized to elucidate determinants of ICI response.
Immune checkpoint inhibitors (ICI) have revolutionized the standard of care for many tumor types, but there remains a need to identify biomarkers of response. Because ICIs activate adaptive immunity irrespective of tumor cell–intrinsic pathways, Litchfield, Reading, Puttick, and colleagues hypothesized that a meta-analysis of studies across various cancers would uncover clinically relevant biomarkers that underpin ICI response across tumor types. Raw whole-exome sequencing and transcriptomic data from twelve published ICI studies were reprocessed through a uniform bioinformatics pipeline and aggregated into a database dubbed CPI1000+ consisting of data from 1,008 patients representing seven cancer types. Definitions of “responders” and “nonresponders” were harmonized across individual cohorts using RECIST criteria, and analysis of 55 previously reported biomarkers of ICI response revealed that the dominant biomarker associated with ICI response was clonal tumor mutation burden (TMB). A machine learning algorithm was trained on 11 biomarkers that achieved pan-cancer significance, including clonal TMB and CXCL9 expression, as well as tobacco, UV, and APOBEC mutation signatures. Notably, the resulting multivariable predictive model performed significantly better than a TMB-only model in independent validation cohorts. Several mutational signatures were found to be associated with response to ICI, including a UV signature that was associated with dinucleotide variants, shown to be linked with more radical amino acid substitutions and potentially resulting in the generation of more immunogenic epitopes. In investigating specific genomic loci, analysis of somatic copy-number gains and losses indicated that loss of TRAF2 at the 9q34 locus was associated with ICI sensitivity. Comparison of focal amplification of oncogenes between responders and nonresponders suggested a predictive association between CCND1 amplification and ICI resistance. Lastly, to investigate additional biomarkers of ICI response, transcriptomic analysis of neoantigen-reactive CD8+ T cells was performed, showing that CXCL13 and CCR5 were upregulated in neoantigen-reactive CD8+ T cells and selectively expressed in CPI1000+ responders. Together, this comprehensive meta-analysis provides a set of biomarkers underlying pan-cancer ICI response.
Litchfield K, Reading JL, Puttick C, Thakkar K, Abbosh C, Bentham R, et al. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition. Cell 2021;184:596–614.E14.
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