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

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. IMPLICATIONS FOR PRACTICE: Tumor mutational burden (TMB) is a newly established independent predictor of immune checkpoint inhibitor (ICPI) treatment outcome across multiple tumor types. Certain next-generation sequencing-based techniques allow TMB to be reliably estimated from a subset of the exome without the use of whole-exome sequencing, thus facilitating the adoption of TMB assessment in community oncology settings. Analyses of multiple clinical trials across several cancer types have demonstrated that TMB stratifies patients who are receiving ICPIs by response rate and survival. TMB, alongside other genomic biomarkers, may provide complementary information in selecting patients for ICPI-based therapies.

Keywords: Antibodies/therapeutic use; DNA; DNA mutational analysis; Genes; Neoplasm; Programmed cell death 1 receptor; Sequence analysis.

Figure 1

Example of targeted NGS panel‐based TMB calculation. Adapted from Spigel et al. 30. ^aPredicted drivers are mutations thought to be responsible for oncogenesis in a tumor. Abbreviations: Mb, megabase; NGS, next‐generation sequencing; TMB, tumor mutational burden.

Figure 2

Timeline of TMB biomarker development. Abbreviations: 1L, first‐line; 2L, second‐line; CGP, comprehensive genomic profiling; FDA, U.S. Food and Drug Administration; I‐O, immune‐oncology; NCCN, National Comprehensive Cancer Network; NSCLC, non‐small cell lung cancer; PD‐1, programmed cell death protein 1; PD‐L1, programmed death‐ligand 1; SCLC, small cell lung cancer; TMB, tumor mutational burden.

Figure 3

Interaction of high TMB with other cancer biomarkers. An analysis of Foundation Medicine's FoundationCore database (data on file) was undertaken to understand the relative prevalence of biomarkers that play a predictive role in immunotherapy decisions for patients with non‐small cell lung cancer (NSCLC). Through September 2018, there were 9,347 NSCLC samples with Foundation Medicine testing (FoundationOne and FoundationOne CDx) that also underwent PD‐L1 testing. The relative distribution of EGFR and/or ALK alterations, TMB ≥10 mutations per megabase, and PD‐L1 positive is shown here. Prevalence of each of the biomarkers in all patients with NSCLC (n = 35,370), regardless of PD‐L1 testing, was determined with EGFR alterations found in 14.1% and ALK alterations in 2.9%; this appears similar to the rates observed in the smaller subset of patients with concurrent PD‐L1 assessment. Overall, the overlap is limited, indicating a need to assess each of these biomarkers when making immunotherapy decisions in the NSCLC setting. Abbreviations: ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; PD‐L1, programmed death‐ligand 1; TMB, tumor mutational burden.

Figure 4

Degree of overlap between high TMB and PD‐L1 varies based on the presence of other alterations among patients with non‐small cell lung cancer (NSCLC). Among NSCLC samples with Foundation Medicine testing that also underwent PD‐L1 testing (n = 9,347; described in Fig. 3), the relative overlap between TMB ≥10 mutations per megabase and PD‐L1 is highest in patients with multiple genomic alterations as well as KRAS, BRAF, and MET alterations and lowest in patients with ALK and RET alterations. Abbreviations: ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; PD‐L1, programmed death‐ligand 1; TMB, tumor mutational burden.