Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence

Author:

Klempner Samuel J.12,Fabrizio David3,Bane Shalmali4,Reinhart Marcia4,Peoples Tim5,Ali Siraj M.3,Sokol Ethan S.3,Frampton Garrett3,Schrock Alexa B.3,Anhorn Rachel3,Reddy Prasanth3

Affiliation:

1. The Angeles Clinic and Research Institute, Los Angeles, California, USA

2. Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA

3. Foundation Medicine, Inc., Cambridge, Massachusetts, USA

4. Analysis Group, Inc., Menlo Park, California, USA

5. Amgen Inc., Thousand Oaks, California, USA

Abstract

Abstract Treatment with immune checkpoint inhibitors (ICPIs) extends survival in a proportion of patients across multiple cancers. Tumor mutational burden (TMB)—the number of somatic mutations per DNA megabase (Mb)—has emerged as a proxy for neoantigen burden that is an independent biomarker associated with ICPI outcomes. Based on findings from recent studies, TMB can be reliably estimated using validated algorithms from next-generation sequencing assays that interrogate a sufficiently large subset of the exome as an alternative to whole-exome sequencing. Biological processes contributing to elevated TMB can result from exposure to cigarette smoke and ultraviolet radiation, from deleterious mutations in mismatch repair leading to microsatellite instability, or from mutations in the DNA repair machinery. A variety of clinical studies have shown that patients with higher TMB experience longer survival and greater response rates following treatment with ICPIs compared with those who have lower TMB levels; this includes a prospective randomized clinical trial that found a TMB threshold of ≥10 mutations per Mb to be predictive of longer progression-free survival in patients with non-small cell lung cancer. Multiple trials are underway to validate the predictive values of TMB across cancer types and in patients treated with other immunotherapies. Here we review the rationale, algorithm development methodology, and existing clinical data supporting the use of TMB as a predictive biomarker for treatment with ICPIs. We discuss emerging roles for TMB and its potential future value for stratifying patients according to their likelihood of ICPI treatment response.

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

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