Targeted Next Generation Sequencing Identifies Markers of Response to PD 1 Blockade
Mutational load, by whole exome sequencing, can correlate with immunotherapy responses. Assessing melanoma mutational load of a fraction of the genome, by hybrid capture based NGS, provided an accurate surrogate for WES determinations, and predicted response to anti PD 1. Therapeutic antibodies blocking programmed death 1 and its ligand (PD 1/PD L1) induce durable responses in a substantial fraction of melanoma patients. We sought to determine whether the number and/or type of mutations identified using a next generation sequencing (NGS) panel available in the clinic was correlated with response to anti PD 1 in melanoma. Using archival melanoma samples from anti PD 1/PD L1 treated patients, we performed hybrid capture based NGS on 236 315 genes and T cell receptor (TCR) sequencing on initial and validation cohorts from two centers. Patients who responded to anti PD 1/PD L1 had higher mutational loads in an initial cohort (median, 45.6 vs. 3.9 mutations/MB; P 0.003) and a validation cohort (37.1 vs. 12.8 mutations/MB; P 0.002) compared with nonresponders. Response rate, progression free survival, and overall survival were superior in the high, compared with intermediate and low, mutation load groups. Melanomas with NF1 mutations harbored high mutational loads (median, 62.7 mutations/MB) and high response rates (74% whereas BRAF/NRAS/NF1 wild type melanomas had a lower mutational load. In these archival samples, TCR clonality did not predict response. Mutation numbers in the 315 genes in the NGS platform strongly correlated with those detected by whole exome sequencing in The Cancer Genome Atlas samples, but was not associated with survival. In conclusion, mutational load, as determined by an NGS platform available in the clinic, effectively stratified patients by likelihood of response. This approach may provide a clinically feasible predictor of response to anti PD 1/PD L1. Cancer Immunol Res; 4(11) 959 67. (c)2016 AACR.
Cancer Immunology Research