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Qing T, Mohsen H, Marczyk M, Ye Y, O'Meara T, Zhao H, Townsend JP, Gerstein M, Hatzis C, Kluger Y, Pusztai L. Germline variant burden in cancer genes correlates with age at diagnosis and somatic mutation burden. Nat Commun 2020; 11:2438. [PMID: 32415133 PMCID: PMC7228928 DOI: 10.1038/s41467-020-16293-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 04/21/2020] [Indexed: 11/24/2022] Open
Abstract
Cancers harbor many somatic mutations and germline variants, we hypothesized that the combined effect of germline variants that alter the structure, expression, or function of protein-coding regions of cancer-biology related genes (gHFI) determines which and how many somatic mutations (sM) must occur for malignant transformation. We show that gHFI and sM affect overlapping genes and the average number of gHFI in cancer hallmark genes is higher in patients who develop cancer at a younger age (r = -0.77, P = 0.0051), while the average number of sM increases in increasing age groups (r = 0.92, P = 0.000073). A strong negative correlation exists between average gHFI and average sM burden in increasing age groups (r = -0.70, P = 0.017). In early-onset cancers, the larger gHFI burden in cancer genes suggests a greater contribution of germline alterations to the transformation process while late-onset cancers are more driven by somatic mutations.
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Affiliation(s)
- Tao Qing
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
| | - Hussein Mohsen
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Michal Marczyk
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
- Data Mining Division, Silesian University of Technology, Gliwice, Poland
| | - Yixuan Ye
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Tess O'Meara
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
| | - Hongyu Zhao
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Jeffrey P Townsend
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Mark Gerstein
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA
- Department of Computer Science, Yale University, New Haven, CT, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, USA
| | - Christos Hatzis
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA
- Bristol-Myers Squibb, New York, NY, USA
| | - Yuval Kluger
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
- Department of Pathology, School of Medicine, Yale University, New Haven, CT, USA
- Program of Applied Mathematics, Yale University, New Haven, CT, USA
| | - Lajos Pusztai
- Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA.
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