Novel genomic signature predictive of response to immune checkpoint blockade: A pan-cancer analysis from project Genomics Evidence Neo-plasia Information Exchange (GENIE)

Nishwant Swami; William L Hwang; Jimmy A Guo; Hannah Hoffman; Matthew C Abramowitz; Ziad Elbakouny; Himisha Beltran; Fallon Chipidza; Toni Choueiri; Alan Dal Pra; Franklin Huang; Salma Kaochar; Philip Kantoff; Daniel W Kim; Amar U Kishan; Erin Kobetz; Catherine Marinac; Lorelei A Mucci; Vinayak Muralidhar; Alan Pollack; Nina N Sanford; Edward M Schaeffer; Daniel E Spratt; Shuang G Zhao; Timothy R Rebbeck; Paul L Nguyen; Felix Y Feng; Brandon A Mahal; Mohammed Alshalalfa

doi:10.1016/j.cancergen.2021.08.004

Novel genomic signature predictive of response to immune checkpoint blockade: A pan-cancer analysis from project Genomics Evidence Neo-plasia Information Exchange (GENIE)

Cancer Genet. 2021 Nov:258-259:61-68. doi: 10.1016/j.cancergen.2021.08.004. Epub 2021 Aug 28.

Authors

Nishwant Swami¹, William L Hwang², Jimmy A Guo³, Hannah Hoffman⁴, Matthew C Abramowitz⁵, Ziad Elbakouny⁶, Himisha Beltran⁶, Fallon Chipidza⁶, Toni Choueiri⁶, Alan Dal Pra⁵, Franklin Huang⁷, Salma Kaochar⁸, Philip Kantoff⁹, Daniel W Kim⁶, Amar U Kishan¹⁰, Erin Kobetz⁵, Catherine Marinac⁶, Lorelei A Mucci¹¹, Vinayak Muralidhar⁶, Alan Pollack⁵, Nina N Sanford¹², Edward M Schaeffer¹³, Daniel E Spratt¹⁴, Shuang G Zhao¹⁴, Timothy R Rebbeck¹⁵, Paul L Nguyen⁶, Felix Y Feng⁷, Brandon A Mahal¹⁶, Mohammed Alshalalfa¹⁷

Affiliations

¹ University of Massachusetts Medical School, Worcester, MA, USA.
² Massachusetts General Hospital, Boston, MA, USA; Broad Institute, Cambridge MA, USA.
³ Massachusetts General Hospital, Boston, MA, USA; Broad Institute, Cambridge MA, USA; University of California at San Francisco, San Francisco, CA, USA.
⁴ Massachusetts General Hospital, Boston, MA, USA.
⁵ University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, USA.
⁶ Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, USA.
⁷ University of California at San Francisco, San Francisco, CA, USA.
⁸ Baylor College of Medicine, Houston, TX, USA.
⁹ Memorial Sloan Kettering Cancer Center, New York, NY, USA.
¹⁰ University of California Los Angeles, Los Angeles, CA, USA.
¹¹ Harvard T. H. Chan School of Public Health, Boston, MA, USA.
¹² University of Texas Southwestern Medical Center, Dallas, TX, USA.
¹³ Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
¹⁴ University of Michigan, Ann Arbor, MI, USA.
¹⁵ Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, USA; Harvard T. H. Chan School of Public Health, Boston, MA, USA.
¹⁶ University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, USA. Electronic address: [email protected].
¹⁷ University of California at San Francisco, San Francisco, CA, USA; Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA, USA. Electronic address: [email protected].

PMID: 34551377
DOI: 10.1016/j.cancergen.2021.08.004

Abstract

Background: High tumor mutation burden (TMB) and total mutation count (TMC) can be predictive of better response to immune checkpoint blockade (ICB). Nevertheless, TMB and TMC are limited by variation across cancers and inconsistent definitions due to different profiling methods (targeted vs whole genome sequencing). Our objective was to identify genomic alterations (GAs) associated with ICB response and builds a novel genomic signature predictive of ICB response, independent of TMB/TMC.

Methods: This was a pan-cancer next generation sequencing (NGS)-association study using January 2014-May 2016 data from AACR Project Genomics Evidence Neo-plasia Information Exchange (GENIE). Participants included 6619 patients with metastatic or un-resectable cancer across 9 cancer types (including 1572 ICB-treated patients). GA data was collected using next-generation sequencing (NGS) assays and downloaded from cbioportal.org. Predictive analyses for ICB response were performed to develop the signature (ImmGA).

Results: GAs in 16 genes were associated with improved OS in ICB-treated patients (p < 0.005). 13 GAs were associated with an OS benefit in ICB-treated patients (P_interaction < 0.05); these genes composed the ImmGA signature. High ImmGA score (≥2 alterations out of 13 predictive GAs) was associated with better OS in ICB-treated patients (AHR:0.67, 95%CI [0.6-0.75], p = 1.4e^-12), even after accounting for TMC (P_interaction = 8e^-16). High ImmGA was associated with better OS in ICB-treated patients across most cancers and across different ICB treatment modalities.

Conclusion: A novel signature predictive of ICB response (ImmGA) was developed from 13 GAs. Further investigation of the utility of ImmGA for treatment and trial selection is warranted.

Keywords: Biomarkers; Immunotherapy; Neoplasms; Tumor.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Biomarkers, Tumor / genetics*
Follow-Up Studies
Gene Expression Regulation, Neoplastic / drug effects*
High-Throughput Nucleotide Sequencing / methods*
Humans
Immune Checkpoint Inhibitors / therapeutic use*
Mutation*
Neoplasms / drug therapy
Neoplasms / genetics
Neoplasms / pathology*
Prognosis
Survival Rate

Substances

Biomarkers, Tumor
Immune Checkpoint Inhibitors