Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

Abstract

Tumor mutational burden (TMB), defi ned as the number of somatic mutations per megabase of interrogated genomic sequence, varies across malignancies. Panel sequencing-based estimates of TMB have largely replaced whole-exome sequencing-derived TMB in the clinic. Retrospective evidence suggests that TMB can predict the effi cacy of immune checkpoint inhibitors, and data from KEYNOTE-158 led to the recent FDA approval of pembrolizumab for the TMBhigh tumor subgroup. Unmet needs include prospective validation of TMB cutoffs in relationship to tumor type and patient outcomes. Furthermore, standardization and harmonization of TMB measurement across test platforms are important to the successful implementation of TMB in clinical practice.Signifi cance: Evaluation of TMB as a predictive biomarker creates the need to harmonize panel-based TMB estimation and standardize its reporting. TMB can improve the predictive accuracy for immunotherapy outcomes, and has the potential to expand the candidate pool of patients for treatment with immune checkpoint inhibitors. Responses were observed across tumor types, and microsatellite instability-high (MSI-H) status did not account for all of the increased clinical benefi t in the TMB-high subgroup ( 24 ).On the basis of these data, the FDA approved pembrolizumab monotherapy for the subgroup of solid-tumor patients with TMB ≥10 mut/Mb who are treatment-refractory and lack satisfactory alternative treatment options. Although data from KEYNOTE-158 demonstrated a role for TMB in selection of patients for cancer treatment, important issues remain,

Immune-Related Mutational Landscape and Gene Signatures: Prognostic Value and Therapeutic Impact for Head and Neck Cancer

“…It is worth noting that TMB does not always correlate with ICI responsiveness and its applicability should be considered with caution due to important limitations as a predictive biomarker, especially when used in isolation. Major challenges for TMB utility and its limitations have been reported and critically discussed in excellent recent studies and review articles [ 29 , 37 , 38 , 39 , 40 ]. A composite predictor that also includes other critical variables, such as PD-L1 IHC, immune-related mutational and epigenetic landscapes as well as gene expression signatures, MHC and T cell receptor repertoire, clonality of neoantigens and tumor heterogeneity, is urgently needed [ 33 , 38 ].…”

High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types

“…11 While evidence does support treatment of certain hypermutator cancer subsets with ICB, such as successes in patients with MSI-high (MSI-H) colorectal tumors, 12,13 it is unclear if this principle is generalizable across cancer types, and what is an optimal threshold, if any, for qualifying a cancer as TMB-H for ICB treatment selection. 2,7,[14][15][16] Furthermore, accumulating evidence indicates that TMB assessment and bioinformatics interpretation vary across different targeted sequencing panels, likely in a cancer typedependent manner. 17 In this work, we first demonstrate classification of tumors as TMB-H varies in a cancer typespecific manner across different DNA sequencing assays/ approaches in over 10 000 patients spanning 31 cancer types from The Cancer Genome Atlas (TCGA).…”

Cancer Immunotherapy Update: FDA-Approved Checkpoint Inhibitors and Companion Diagnostics

“…Patients with TMB-H (≥ 10 mut/Mb) were found to have an ORR of 29% and patients with TMB ≥ 13 mut/Mb achieved an ORR of 37%. The higher mutational burden within a tumor is expected to correspond to a higher level of immunogenic neopeptides that would drive T cell-mediated anti-tumor immunity (35)(36)(37).…”