Tumor Mutational Burden as a Predictor of Response to Immunotherapy

Share this content:
A single definition of TMB as a predictor of survival benefit from immune checkpoint inhibitors across cancer types is not feasible, according to authors.
A single definition of TMB as a predictor of survival benefit from immune checkpoint inhibitors across cancer types is not feasible, according to authors.

A retrospective study of more than 7000 patients with a variety of cancers demonstrated an association between the number of somatic nonsynonymous mutations (ie, mutations observed in a cancer specimen that result in a modified protein sequence) as assessed by a specific targeted exome sequencing platform and improved overall survival following treatment with immune checkpoint inhibitor therapy.1 This study was published online on January 14, 2019 in Nature Genetics.

The search for robust predictors of clinical benefit from immune checkpoint inhibitors is an active area of research. Potential biomarkers of response to immunotherapy include PD-L1 protein expression, although limitations to its use have been identified. Small cohort studies have also shown an association between tumor mutational burden (TMB) — defined in this study as the total number of somatic nonsynonymous mutations normalized to the total number of megabases sequenced — and benefit from immune checkpoint inhibitors.

Researchers at Memorial Sloan Kettering Cancer Center, New York, New York, evaluated the clinical and genomic data from a large heterogeneous cohort of patients with advanced/mostly metastatic cancer (non-small cell lung cancer, melanoma, renal cell carcinoma, bladder cancers, head and neck squamous cell cancer, and glioma) who had undergone tumor sequencing with the MSK-IMPACT platform, which uses targeted exome sequencing to interrogate a specific subset of 468 cancer-related genes using both tumor tissue and matched normal tissue as a control. In this cohort, 1662 and 5371 patients received or did not receive prior treatment with an immune checkpoint inhibitor (including atezolizumab, avelumab, durvalumab, ipilimumab, nivolumab, pembrolizumab, or tremelimumab as monotherapy or in combination), respectively.

Since TMB has been shown to vary across tumor types, the researchers defined subgroups of TMB, such as the top 10% or 20% TMB, within specific histologies. They subsequently demonstrated an association between higher TMB percentiles and improved overall survival with immune checkpoint inhibitor therapy in most cancer histologies investigated, with glioma as a notable exception. In addition, by comparison with the immunotherapy-naive group, they showed that this finding was not attributable to a general prognostic benefit of high TMB. Although glioma was an exception, the researchers concluded that “these data indicate that the association between TMB and improved survival after [immune checkpoint inhibition] is likely to be present in most cancer histologies.”

Despite this conclusion, the authors wrote that these results suggested that a universal definition of “high TMB” across cancer types would not be feasible. Furthermore, optimal TMB cutoffs for clinical use were not determined in this study, and the TMB cutoff within a cancer type is likely to vary by the sequencing platform employed. “Future studies that integrate other genomic or pathologic biomarkers may allow for the development of an even more optimized predictive test to inform clinical decisions on the use of immune checkpoint inhibitors,” the authors concluded.

Reference

  1. Samstein RM, Lee CH, Shoushtari AN, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types [published online January 14, 2019]. Nat Genet. doi: 10.1038/s41588-018-0312-8

Related Resources

You must be a registered member of Cancer Therapy Advisor to post a comment.

Sign Up for Free e-newsletters



Regimen and Drug Listings

GET FULL LISTINGS OF TREATMENT Regimens and Drug INFORMATION

Bone Cancer Regimens Drugs
Brain Cancer Regimens Drugs
Breast Cancer Regimens Drugs
Endocrine Cancer Regimens Drugs
Gastrointestinal Cancer Regimens Drugs
Gynecologic Cancer Regimens Drugs
Head and Neck Cancer Regimens Drugs
Hematologic Cancer Regimens Drugs
Lung Cancer Regimens Drugs
Other Cancers Regimens
Prostate Cancer Regimens Drugs
Rare Cancers Regimens
Renal Cell Carcinoma Regimens Drugs
Skin Cancer Regimens Drugs
Urologic Cancers Regimens Drugs