Background:While clinical outcomes following immunotherapy have shown an association with tumor mutationload using whole exome sequencing (WES), its clinical applicability is currently limited by cost and bioinformaticsrequirements.Methods:We developed a method to accurately derive the predicted total mutation load (PTML) within individualtumors from a small set of genes that can be used in clinical next generation sequencing (NGS) panels. PTML wasderived from the actual total mutation load (ATML) of 575 distinct melanoma and lung cancer samples andvalidated using independent melanoma (n= 312) and lung cancer (n= 217) cohorts. The correlation of PTMLstatus with clinical outcome, following distinctimmunotherapies, was assessed using the Kaplan–Meier method.Results:PTML (derived from 170 genes) was highly correlated with ATML in cutaneous melanoma and lungadenocarcinoma validation cohorts (R2=0.73andR2= 0.82, respectively). PTML was strongly associated withclinical outcome to ipilimumab (anti-CTLA-4, three cohorts) and adoptive T-cell therapy (1 cohort) clinical outcome inmelanoma. Clinical benefit from pembrolizumab (anti-PD-1) in lung cancer was also shown to significantlycorrelate with PTML status (log rankPvalue < 0.05 in all cohorts).Conclusions:The approach of using small NGS gene panels, already applied to guide employment of targetedtherapies, may have utility in the personalized use of immunotherapy in cancer.

Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set / Roszik, Jason; Haydu, Lauren E.; Hess, Kenneth R.; Oba, Junna; Joon, Aron Y.; Siroy, Alan E.; Karpinets, Tatiana V.; Stingo, Francesco C.; Baladandayuthapani, Veera; Tetzlaff, Michael T.; Wargo, Jennifer A.; Chen, Ken; Forget, Marie-Andrée; Haymaker, Cara L.; Chen, Jie Qing; Meric-Bernstam, Funda; Eterovic, Agda K.; Shaw, Kenna R.; Mills, Gordon B.; Gershenwald, Jeffrey E.; Radvanyi, Laszlo G.; Hwu, Patrick; Futreal, P.Andrew; Gibbons, Don L.; Lazar, Alexander J.; Bernatchez, Chantale; Davies, Michael A.; Woodman, Scott E.. - In: BMC MEDICINE. - ISSN 1741-7015. - ELETTRONICO. - 14:(2016), pp. 1-8. [10.1186/s12916-016-0705-4]

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

STINGO, FRANCESCO CLAUDIO;
2016

Abstract

Background:While clinical outcomes following immunotherapy have shown an association with tumor mutationload using whole exome sequencing (WES), its clinical applicability is currently limited by cost and bioinformaticsrequirements.Methods:We developed a method to accurately derive the predicted total mutation load (PTML) within individualtumors from a small set of genes that can be used in clinical next generation sequencing (NGS) panels. PTML wasderived from the actual total mutation load (ATML) of 575 distinct melanoma and lung cancer samples andvalidated using independent melanoma (n= 312) and lung cancer (n= 217) cohorts. The correlation of PTMLstatus with clinical outcome, following distinctimmunotherapies, was assessed using the Kaplan–Meier method.Results:PTML (derived from 170 genes) was highly correlated with ATML in cutaneous melanoma and lungadenocarcinoma validation cohorts (R2=0.73andR2= 0.82, respectively). PTML was strongly associated withclinical outcome to ipilimumab (anti-CTLA-4, three cohorts) and adoptive T-cell therapy (1 cohort) clinical outcome inmelanoma. Clinical benefit from pembrolizumab (anti-PD-1) in lung cancer was also shown to significantlycorrelate with PTML status (log rankPvalue < 0.05 in all cohorts).Conclusions:The approach of using small NGS gene panels, already applied to guide employment of targetedtherapies, may have utility in the personalized use of immunotherapy in cancer.
2016
14
1
8
Roszik, Jason; Haydu, Lauren E.; Hess, Kenneth R.; Oba, Junna; Joon, Aron Y.; Siroy, Alan E.; Karpinets, Tatiana V.; Stingo, Francesco C.; Baladandayuthapani, Veera; Tetzlaff, Michael T.; Wargo, Jennifer A.; Chen, Ken; Forget, Marie-Andrée; Haymaker, Cara L.; Chen, Jie Qing; Meric-Bernstam, Funda; Eterovic, Agda K.; Shaw, Kenna R.; Mills, Gordon B.; Gershenwald, Jeffrey E.; Radvanyi, Laszlo G.; Hwu, Patrick; Futreal, P.Andrew; Gibbons, Don L.; Lazar, Alexander J.; Bernatchez, Chantale; Davies, Michael A.; Woodman, Scott E.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1071816
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