Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer

Mark Zucker; Maria A Perry; Samuel I Gould; Arielle Elkrief; Anton Safonov; Rohit Thummalapalli; Miika Mehine; Debyani Chakravarty; A Rose Brannon; Marc Ladanyi; Pedram Razavi; Mark T A Donoghue; Yonina R Murciano-Goroff; Kristiana Grigoriadis; Nicholas McGranahan; Mariam Jamal-Hanjani; Charles Swanton; Yuan Chen; Ronglai Shen; Sarat Chandarlapaty; David B Solit; Nikolaus Schultz; Michael F Berger; Jason Chang; Adam J Schoenfeld; Francisco J Sánchez-Rivera; Ed Reznik; Chaitanya Bandlamudi

doi:10.1016/j.cell.2024.11.010

Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer

Cell. 2024 Dec 12:S0092-8674(24)01327-8. doi: 10.1016/j.cell.2024.11.010. Online ahead of print.

Authors

Mark Zucker¹, Maria A Perry², Samuel I Gould³, Arielle Elkrief⁴, Anton Safonov⁵, Rohit Thummalapalli⁵, Miika Mehine⁶, Debyani Chakravarty², A Rose Brannon⁶, Marc Ladanyi⁶, Pedram Razavi⁵, Mark T A Donoghue⁷, Yonina R Murciano-Goroff⁵, Kristiana Grigoriadis⁸, Nicholas McGranahan⁹, Mariam Jamal-Hanjani¹⁰, Charles Swanton¹¹, Yuan Chen¹², Ronglai Shen¹², Sarat Chandarlapaty¹³, David B Solit¹⁴, Nikolaus Schultz¹⁵, Michael F Berger¹⁶, Jason Chang⁶, Adam J Schoenfeld⁵, Francisco J Sánchez-Rivera³, Ed Reznik¹⁷, Chaitanya Bandlamudi¹⁸

Affiliations

¹ Computational Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
² Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
³ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁴ Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁵ Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁶ Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁷ Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
⁸ Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London, London, UK; Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK.
⁹ Cancer Research UK Lung Cancer Centre of Excellence, University College London, London, UK; Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK.
¹⁰ Cancer Research UK Lung Cancer Centre of Excellence, University College London, London, UK; Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK; Department of Medical Oncology, University College London Hospitals, London, UK.
¹¹ Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London, London, UK; Department of Medical Oncology, University College London Hospitals, London, UK.
¹² Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
¹³ Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
¹⁴ Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
¹⁵ Computational Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, New York, NY, USA.
¹⁶ Computational Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, New York, NY, USA.
¹⁷ Computational Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address: [email protected].
¹⁸ Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address: [email protected].

PMID: 39701102
DOI: 10.1016/j.cell.2024.11.010

Abstract

The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.

Keywords: KEAP1; Knudson's two-hit; biallelic inactivation; biallelic loss; cancer genomics; clinical sequencing; lung cancer; pancancer; predictive biomarkers; tumor suppressor genes.