Year: 2019
Type: article
Abstract: Immune checkpoint inhibitor (ICI) therapies can achieve meaningful tumor responses in a subset of patients with most types of cancer that have been investigated. However, the majority of patients treated with these drugs do not experience any clinical benefit. Because not all patients benefit from ICIs, and some may experience more meaningful tumor response if treated with chemotherapy or other treatments, there is a compelling need for predictive biomarkers to facilitate more informed selection of therapy. Tumor mutational burden (TMB) is one feature of a tumor that has predictive value for ICI therapy across multiple cancer types. In a pan-cancer analysis of over 1,600 patients, higher TMB was associated with longer survival and higher response rates with ICI therapy. While this effect was seen in the majority of cancer types, indicating that TMB underlies fundamental aspects of immune-mediated tumor rejection, the optimal predictive cut-point varied widely by histology, suggesting that there is unlikely to be one tissue-agnostic definition of high TMB that is useful for predicting ICI response. More comprehensive predictive models integrating TMB with other factors - including genetic, immunologic, and clinicopathologic markers - will be needed to potentially achieve a tissue-agnostic predictor of benefit from ICIs.
Institution: Memorial Sloan Kettering Cancer Center
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Awards: G1676388581, Identifying and targeting neoantigens in aggressive salivary carcinomas, G2051006376, G2695170217, Elucidating genetics of response to immune checkpoint blockade in lung cancer, G4102770861, G4478570997, G5109122429, G5909372135, Dissecting the influence of DNA damage response and homologous recombination deficiencies on tumor immunogenicity, Towards Precision Immuno-Oncology: Unraveling the Genomic Determinants and Mechanisms Underlying Immunotherapy Efficacy and Resistance, G7877325548, Functionalizing recurrent FAT1 mutations and deletions in oral cancer, G874817191, Cancer Center Support Grant
OpenAlex ID: W2966135366