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

Jason Roszik, Lauren E. Haydu, Kenneth R. Hess, Junna Oba, Aron Y. Joon, Alan E. Siroy, Tatiana V. Karpinets, Francesco C. Stingo, Veera Baladandayuthapani, Michael T. Tetzlaff, Jennifer A. Wargo, Ken Chen, Marie Andrée Forget, Cara L. Haymaker, Jie Qing Chen, Funda Meric-Bernstam, Agda K. Eterovic, Kenna R. Shaw, Gordon Mills, Jeffrey E. GershenwaldLaszlo G. Radvanyi, Patrick Hwu, P. Andrew Futreal, Don L. Gibbons, Alexander J. Lazar, Chantale Bernatchez, Michael A. Davies, Scott E. Woodman

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

Background: While 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. Methods: We 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. Results: PTML (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). Conclusions: The 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.

Original languageEnglish (US)
Article number168
JournalBMC Medicine
Volume14
Issue number1
DOIs
StatePublished - Oct 25 2016
Externally publishedYes

Fingerprint

Tumor Burden
Immunotherapy
Mutation
Genes
Melanoma
Lung Neoplasms
Exome
Cell- and Tissue-Based Therapy
Computational Biology
Neoplasms
T-Lymphocytes
Costs and Cost Analysis
Skin

Keywords

  • CTLA-4
  • Immunotherapy
  • Lung cancer
  • Melanoma
  • PD-1
  • Total mutation load

ASJC Scopus subject areas

  • Medicine(all)

Cite this

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; 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, Vol. 14, No. 1, 168, 25.10.2016.

Research output: Contribution to journalArticle

Roszik, J, Haydu, LE, Hess, KR, Oba, J, Joon, AY, Siroy, AE, Karpinets, TV, Stingo, FC, Baladandayuthapani, V, Tetzlaff, MT, Wargo, JA, Chen, K, Forget, MA, Haymaker, CL, Chen, JQ, Meric-Bernstam, F, Eterovic, AK, Shaw, KR, Mills, G, Gershenwald, JE, Radvanyi, LG, Hwu, P, Futreal, PA, Gibbons, DL, Lazar, AJ, Bernatchez, C, Davies, MA & Woodman, SE 2016, 'Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set', BMC Medicine, vol. 14, no. 1, 168. https://doi.org/10.1186/s12916-016-0705-4
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 ; 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. / Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set. In: BMC Medicine. 2016 ; Vol. 14, No. 1.
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abstract = "Background: While 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. Methods: We 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. Results: PTML (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). Conclusions: The 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.",
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T1 - Novel algorithmic approach predicts tumor mutation load and correlates with immunotherapy clinical outcomes using a defined gene mutation set

AU - Roszik, Jason

AU - Haydu, Lauren E.

AU - Hess, Kenneth R.

AU - Oba, Junna

AU - Joon, Aron Y.

AU - Siroy, Alan E.

AU - Karpinets, Tatiana V.

AU - Stingo, Francesco C.

AU - Baladandayuthapani, Veera

AU - Tetzlaff, Michael T.

AU - Wargo, Jennifer A.

AU - Chen, Ken

AU - Forget, Marie Andrée

AU - Haymaker, Cara L.

AU - Chen, Jie Qing

AU - Meric-Bernstam, Funda

AU - Eterovic, Agda K.

AU - Shaw, Kenna R.

AU - Mills, Gordon

AU - Gershenwald, Jeffrey E.

AU - Radvanyi, Laszlo G.

AU - Hwu, Patrick

AU - Futreal, P. Andrew

AU - Gibbons, Don L.

AU - Lazar, Alexander J.

AU - Bernatchez, Chantale

AU - Davies, Michael A.

AU - Woodman, Scott E.

PY - 2016/10/25

Y1 - 2016/10/25

N2 - Background: While 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. Methods: We 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. Results: PTML (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). Conclusions: The 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.

AB - Background: While 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. Methods: We 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. Results: PTML (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). Conclusions: The 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.

KW - CTLA-4

KW - Immunotherapy

KW - Lung cancer

KW - Melanoma

KW - PD-1

KW - Total mutation load

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