Tumor Mutation Burden: Promises and Challenges A Perspective from the IASLC Pathology Committee

Immune checkpoint inhibitor therapies have revolutionized the management of patients with non-small cell lung carcinoma (NSCLC) and have led to unprecedented improvements in response rates and survival in a subset of a patients with this fatal disease. However, the available therapies work only for a minority of patients, are associated with substantial societal cost, and may lead to significant immune-related adverse events. Therefore, patient selection must be optimized through use of relevant biomarkers. PD-L1 protein expression by immunohistochemistry is widely used today for selection of PD-1 inhibitor therapy in NSCLC patients, however this approach lacks both robust sensitivity and specificity for predicting response. Tumor mutation burden (TMB), or the number of somatic mutations derived from next generation sequencing techniques, has been widely explored as an alternative or complementary biomarker for response to immune checkpoint inhibitors. In theory, a higher TMB increases the probability of tumor neoantigen production and, therefore, likelihood of immune recognition and tumor cell killing. While TMB alone is a simplistic surrogate of this complex interplay, it is a quantitative variable that can be relatively readily measured using currently available sequencing techniques. A large number of clinical trials and retrospective analyses employing both tumor and blood-based sequencing tools have examined the performance of TMB as a predictive biomarker and in many cases show a correlation between high TMB and immune checkpoint inhibitor response rates and progression-free survival. Many challenges remain prior to implementation of TMB as a biomarker in clinical practice. These include: identification of therapies whose response is best informed by TMB status; robust definition of a predictive TMB cutpoint; acceptable sequencing panel size and design; need for robust technical and informatic rigor to generate precise and accurate TMB measurements across different laboratories. Finally, effective prediction of response to immune checkpoint inhibitor therapy will likely require integration of TMB with a host of other potential biomarkers, including tumor genomic driver alterations, tumor-immune milieu, and other features of the host immune system. This perspective piece will review the current clinical evidence for TMB as a biomarker and address the technical sequencing considerations and ongoing challenges to use of TMB in routine practice.