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


Authors: Zucker, M.; Perry, M. A.; Gould, S. I.; Elkrief, A.; Safonov, A.; Thummalapalli, R.; Mehine, M.; Chakravarty, D.; Brannon, A. R.; Ladanyi, M.; Razavi, P.; Donoghue, M. T. A.; Murciano-Goroff, Y. R.; Grigoriadis, K.; McGranahan, N.; Jamal-Hanjani, M.; Swanton, C.; Chen, Y.; Shen, R.; Chandarlapaty, S.; Solit, D. B.; Schultz, N.; Berger, M. F.; Chang, J.; Schoenfeld, A. J.; Sánchez-Rivera, F. J.; Reznik, E.; Bandlamudi, C.
Article Title: Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer
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. © 2024 The Author(s)
Keywords: protein expression; treatment response; survival analysis; gene mutation; overall survival; sequence analysis; single nucleotide polymorphism; somatic mutation; frameshift mutation; genetics; flow cytometry; sensitivity and specificity; genetic analysis; cell proliferation; mouse; phenotype; animal; metabolism; animals; mice; allele; dna damage; gene; progression free survival; gene expression; lung neoplasms; prevalence; genetic variability; alleles; gene frequency; lung cancer; pathology; cell line, tumor; mutational analysis; tumor marker; histology; carcinogenesis; neuroendocrine tumor; prostate cancer; lung tumor; tumor suppressor gene; ubiquitination; genetic transfection; cell culture; genomic instability; heterozygosity; microsatellite instability; tumor cell line; carcinogenicity; egfr gene; tumor growth; tumor suppressor genes; microphthalmia associated transcription factor; k ras protein; bioinformatics; dna extraction; b raf kinase; pik3ca gene; apc protein; genomic dna; genes, tumor suppressor; predictive value; rna sequence; kruppel like factor 4; cancer genomics; cyclin dependent kinase 6; atm gene; germline mutation; lobular carcinoma; zygosity; transcription factor nrf2; cdkn1a gene; biallelic inactivation; guide rna; kelch like ech associated protein 1; keap1 gene; arid1a gene; predictive biomarkers; humans; human; article; differential expression analysis; neutrophil lymphocyte ratio; whole exome sequencing; biomarkers, tumor; hek293t cell line; pan-cancer analysis; keap1; biallelic loss; fat1 gene; cul3 gene; clinical sequencing; kelch-like ech-associated protein 1; keap1 protein, human; antioxidant responsive element; knudson's two-hit; pancancer; smad2 gene; spopgene
Journal Title: Cell
Volume: 188
Issue: 3
ISSN: 0092-8674
Publisher: Cell Press  
Date Published: 2025-02-06
Start Page: 851
End Page: 867.e17
Language: English
DOI: 10.1016/j.cell.2024.11.010
PUBMED: 39701102
PROVIDER: scopus
PMCID: PMC11922039
DOI/URL:
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: Ed Reznik and Chaitanya Bandlamudi -- Source: Scopus
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MSK Authors
  1. David Solit
    783 Solit
  2. Ronglai Shen
    207 Shen
  3. Marc Ladanyi
    1336 Ladanyi
  4. Michael Forman Berger
    775 Berger
  5. Nikolaus D Schultz
    496 Schultz
  6. Angela Rose Brannon
    103 Brannon
  7. Eduard Reznik
    109 Reznik
  8. Pedram Razavi
    184 Razavi
  9. Jason Chih-Peng Chang
    143 Chang
  10. Mark Raymond Zucker
    12 Zucker
  11. Arielle Elkrief
    43 Elkrief
  12. Anton Safonov
    36 Safonov
  13. Yuan Chen
    46 Chen
  14. Miika Mehine
    16 Mehine
  15. Maria Perry
    6 Perry