The advent of immunotherapy has led to durable clinical responses in a variety of malignancies, but identifying which patients will respond to treatment remains an elusive goal. According to data presented at the 2017 ASCO-SITC Clinical Immuno-Oncology Symposium, however, predictive biomarkers may be close at hand.
Genetic analysis from 2 recent multicohort trials in advanced solid tumors suggests that tumor mutational load and tumor T-cell inflammation, measured by gene expression profiling, are independent determinants of response to anti–PD-1 therapy.
“Nonsynonymous mutational load and neoantigen load, as well as an 18-gene immune-related gene expression profile were significantly associated with overall response to pembrolizumab across multiple indications,” said Tanguy Seiwert, MD, Assistant Professor of Medicine at the University of Chicago, IL. “The predicted clinical utility of both mutational load and gene expression profile was similarly high in these cohorts, suggesting comparable individual diagnostic abilities.”
“Based on these data,” Dr Seiwert added, “mutational load and gene expression profiling may have utility in characterizing responses to anti–PD-1 and other cancer immunotherapies.”
As Dr Seiwert reported, tumor mutational load has been shown to correlate with benefit from CTLA-4 and PD-1 immunotherapies in multiple tumor types, including melanoma, non–small cell lung cancer, colorectal cancer, and urothelial cancer. At the same time, tumor T-cell inflammation, which correlates with T-cell infiltration, gamma interferon expression, and PD-L1 and PD-L2 expression, has been advanced as an alternative biomarker.
Using data from 2 multicenter, multicohort studies (KEYNOTE-012 and KEYNOTE-028), Dr Seiwert and colleagues examined the association between mutational load and progression-free survival in 153 patients with PD-L1–positive tumors (21 different tumor types) who had whole exome sequencing available. Overall response was also evaluated in patients with whole exome sequencing and measurable disease at baseline (n = 135). For tumor T-cell inflammation, expression values for 18 genes were used to determine whether the tumor was inflamed. Finally, the association between mutational load and gene expression profiling was also explored in Moffitt and The Cancer Genome Atlas data sets.
According to Dr Seiwert, objective response to treatment was significantly associated with mutational load (P = .0001), neoepitope load (P = .0001), and gene expression profile (P = .0004). More specifically, at a mutational load cutoff of 102, response rates exceeded 30% in patients with at least 102 mutations. For patients with tumors carrying fewer than 102 mutations, however, response rates dropped to 7%.
Gene expression profiling was also significantly associated with overall survival (P = .0071). In addition, patients with more inflamed tumors had a response rate of 25%, compared with only 2% for those with noninflamed tumors.
Finally, in patients with both high mutational load and tumor inflammation, the response rate exceeded 38%. In patients with low mutational load and noninflamed tumors, however, the response rate fell to 0%.
“Both mutational load and tumor T-cell inflammation are common features of anti–PD-1 responsive tumors,” Dr Seiwert concluded. “Whether used separately or in combination, these markers appear to have a high positive and negative predictive value and may have clinical utility.”
