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Richardson RB, Anghel CV, Deng DS. Profound synchrony of age-specific incidence rates and tumor suppression for different cancer types as revealed by the multistage-senescence model of carcinogenesis. Aging (Albany NY) 2021; 13:23545-23578. [PMID: 34695806 PMCID: PMC8580351 DOI: 10.18632/aging.203651] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 09/07/2021] [Indexed: 12/27/2022]
Abstract
The age-specific trend of cancer incidence rates, but not its magnitude, is well described employing the multistage theory of carcinogenesis by Armitage and Doll in combination with the senescence model of Pompei and Wilson. We derived empirical parameters of the multistage-senescence model from U.S. Surveillance, Epidemiology, and End Results (SEER) incidence data from 2000–2003 and 2010–2013 for The Cancer Genome Atlas (TCGA) cancer types. Under the assumption of a constant tumor-specific transition rate between stages, there is an extremely strong linear relationship (P < 0.0001) between the number of stages and the stage transition rate. The senescence tumor suppression factor for 20 non-reproductive cancers is remarkably consistent (0.0099±0.0005); however, five female reproductive cancers have significantly higher tumor suppression. The peak incidence rate for non-reproductive cancers occurs at a younger age for cancers with fewer stages and their carcinogenic stages are of longer duration. Driver gene mutations are shown to contribute on average only about a third of the carcinogenic stages of different tumor types. A tumor’s accumulated incidence, calculated using a two-variable (age, stage) model, is strongly associated with intrinsic cancer risk. During both early adulthood and senescence, the pace of tumor suppression appears to be synchronized across most cancer types, suggesting the presence of overlapping evolutionary processes.
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Affiliation(s)
- Richard B Richardson
- Radiobiology and Health Branch, Canadian Nuclear Laboratories (CNL), Chalk River Laboratories, Chalk River, ON K0J 1J0, Canada.,Medical Physics Unit, Cedars Cancer Centre, McGill University Health Centre - Glen Site, Montreal, QC H4A 3J1, Canada
| | - Catalina V Anghel
- Computational Techniques Branch, Canadian Nuclear Laboratories (CNL), Chalk River Laboratories, Chalk River, ON K0J 1J0, Canada
| | - Dennis S Deng
- Computational Techniques Branch, Canadian Nuclear Laboratories (CNL), Chalk River Laboratories, Chalk River, ON K0J 1J0, Canada
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Salcedo A, Tarabichi M, Espiritu SMG, Deshwar AG, David M, Wilson NM, Dentro S, Wintersinger JA, Liu LY, Ko M, Sivanandan S, Zhang H, Zhu K, Ou Yang TH, Chilton JM, Buchanan A, Lalansingh CM, P'ng C, Anghel CV, Umar I, Lo B, Zou W, Simpson JT, Stuart JM, Anastassiou D, Guan Y, Ewing AD, Ellrott K, Wedge DC, Morris Q, Van Loo P, Boutros PC. A community effort to create standards for evaluating tumor subclonal reconstruction. Nat Biotechnol 2020; 38:97-107. [PMID: 31919445 PMCID: PMC6956735 DOI: 10.1038/s41587-019-0364-z] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 11/18/2019] [Indexed: 02/03/2023]
Abstract
Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.
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Affiliation(s)
- Adriana Salcedo
- Ontario Institute for Cancer Research, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Maxime Tarabichi
- The Francis Crick Institute, London, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
| | | | - Amit G Deshwar
- The Edward S. Rogers Senior Department of Electrical & Computer Engineering, Toronto, Canada
| | - Matei David
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Stefan Dentro
- The Francis Crick Institute, London, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
| | | | - Lydia Y Liu
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Minjeong Ko
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Kaiyi Zhu
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Tai-Hsien Ou Yang
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - John M Chilton
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
| | - Alex Buchanan
- Oregon Health & Sciences University, Portland, OR, USA
| | | | | | | | - Imaad Umar
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Bryan Lo
- Ontario Institute for Cancer Research, Toronto, Canada
| | - William Zou
- Ontario Institute for Cancer Research, Toronto, Canada
| | | | - Joshua M Stuart
- Department of Biomolecular Engineering, Center for Biomolecular Sciences and Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Dimitris Anastassiou
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Adam D Ewing
- Mater Research Institute, University of Queensland, Woolloongabba, Queensland, Australia
| | - Kyle Ellrott
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
- Oregon Health & Sciences University, Portland, OR, USA
| | - David C Wedge
- Big Data Institute, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Quaid Morris
- Ontario Institute for Cancer Research, Toronto, Canada
- Donnelly Centre, University of Toronto, Toronto, Canada
- Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Vector Institute for Artificial Intelligence, Toronto, Canada
| | - Peter Van Loo
- The Francis Crick Institute, London, UK
- Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Paul C Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Urology, University of California, Los Angeles, Los Angeles, CA, USA.
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA.
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA.
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Anghel CV, Quon G, Haider S, Nguyen F, Deshwar AG, Morris QD, Boutros PC. ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles. BMC Bioinformatics 2015; 16:156. [PMID: 25972088 PMCID: PMC4429941 DOI: 10.1186/s12859-015-0597-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 04/27/2015] [Indexed: 01/23/2023] Open
Abstract
Background Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer. Results To simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets. Conclusions The ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0597-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Catalina V Anghel
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, Suite 510, M5G 0A3, ON, Canada.
| | - Gerald Quon
- Department of Computer Science, University of Toronto, 10 King's College Road, Room 3303, M5S 3G4, Toronto, ON, Canada.
| | - Syed Haider
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, Suite 510, M5G 0A3, ON, Canada. .,Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford, OX3 7DQ, United Kingdom.
| | - Francis Nguyen
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, Suite 510, M5G 0A3, ON, Canada.
| | - Amit G Deshwar
- Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College, Room SFB540, Toronto, M5S 3G4, ON, Canada.
| | - Quaid D Morris
- Department of Computer Science, University of Toronto, 10 King's College Road, Room 3303, M5S 3G4, Toronto, ON, Canada. .,Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College, Room SFB540, Toronto, M5S 3G4, ON, Canada. .,Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Room 4396, Toronto, M4S 1A8, ON, Canada. .,The Donnelly Centre, 160 College Street, Room 230, Toronto, M5S 3E1, ON, Canada.
| | - Paul C Boutros
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, Suite 510, M5G 0A3, ON, Canada. .,Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, M5G 1L7, ON, Canada. .,Department of Pharmacology and Toxicology, University of Toronto, 1 King's College Circle, Toronto, M5S 1A8, ON, Canada.
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