Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set

Abstract

BackgroundWhile clinical outcomes following immunotherapy have shown an association with tumor mutation load using whole exome sequencing (WES), its clinical applicability is currently limited by cost and bioinformatics requirements.MethodsWe developed a method to accurately derive the predicted total mutation load (PTML) within individual tumors from a small set of genes that can be used in clinical next generation sequencing (NGS) panels. PTML was derived from the actual total mutation load (ATML) of 575 distinct melanoma and lung cancer samples and validated using independent melanoma (n = 312) and lung cancer (n = 217) cohorts. The correlation of PTML status with clinical outcome, following distinct immunotherapies, was assessed using the Kaplan–Meier method.ResultsPTML (derived from 170 genes) was highly correlated with ATML in cutaneous melanoma and lung adenocarcinoma validation cohorts (R2 = 0.73 and R2 = 0.82, respectively). PTML was strongly associated with clinical outcome to ipilimumab (anti-CTLA-4, three cohorts) and adoptive T-cell therapy (1 cohort) clinical outcome in melanoma. Clinical benefit from pembrolizumab (anti-PD-1) in lung cancer was also shown to significantly correlate with PTML status (log rank P value < 0.05 in all cohorts).ConclusionsThe approach of using small NGS gene panels, already applied to guide employment of targeted therapies, may have utility in the personalized use of immunotherapy in cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-016-0705-4) contains supplementary material, which is available to authorized users.

Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy

“…To date, the best predictive biomarkers identified include total tumor mutational load (Roszik et al, 2016; Snyder et al, 2014) as well as markers of an effective immune infiltrate within a tumor signifying a “hot” tumor microenvironment, typified by increased number of CD8+ cytotoxic T lymphocytes in proximity to programmed death receptor ligand-1 (PD-L1) positive cells (Taube et al, 2014; Tumeh et al, 2014). Mutational load is highly relevant, as tumors with a higher mutational load exhibit higher levels of neoantigens capable of inducing anti-tumor immune responses – translating into a higher likelihood of response to immune checkpoint blockade across several cancer types (Rizvi et al, 2015; Snyder et al, 2014; Van Allen et al, 2015).…”

T cell exhaustion: from pathophysiological basics to tumor immunotherapy

“…They performed a phase II clinical trial in which they were able to investigate that mismatch-repair deficiency predicted clinical effect of pembrolizumab in patients suffering from colorectal carcinoma [195], implying that response rates and clinical benefit from anti-PD1 therapies is correlating with high non-synonymous mutation load, which associates with the presence of tumor associated neoantigens [195, 196]. It was suggested that there is a general correlation of mutation load within tumor DNA and efficacy of immune checkpoint inhibition, irrespective of targeting PD-1 or its ligand, likely by an increased expression of tumor associated neoantigens [195–197]. While tumors with deficiencies in DNA mismatch-repair were found to have a better response toPD-1 blockade [195], it will certainly be clinically relevant to assess other surrogate markers which predict response to immune checkpoint blockade.…”

Predictive factors of response to immunotherapy—a review from the Spanish Melanoma Group (GEM)

“…A retrospective study showed that small sets of genes could be used in substitution of whole genome sequencing to predict response to ipilimumab (33). Tumor burden can be combined with other factors to improve predictive accuracy.…”