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




Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set - Download this document for free, or read online. Document in PDF available to download.

BMC Medicine

, 14:168

Translational Oncology

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 R = 0.73 and R = 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.

KeywordsMelanoma Lung cancer Total mutation load CTLA-4 PD-1 Immunotherapy AbbreviationsATMLactual total mutation load

LUADlung adenocarcinoma

NGSnext generation sequencing

OSoverall survival

PFSprogression-free survival

PTMLpredicted total mutation load

SKCMskin cutaneous melanoma

WESwhole exome sequencing

Electronic supplementary materialThe online version of this article doi:10.1186-s12916-016-0705-4 contains supplementary material, which is available to authorized users.

Download fulltext PDF



Author: Jason Roszik - Lauren E. Haydu - Kenneth R. Hess - Junna Oba - Aron Y. Joon - Alan E. Siroy - Tatiana V. Karpinets - F

Source: https://link.springer.com/







Related documents