Literature DB >> 20934514

Efficiency of carcinogenesis: is the mutator phenotype inevitable?

Robert A Beckman1.   

Abstract

Cancer development requires multiple oncogenic mutations. Pathogenic mechanisms which accelerate this process may be favored carcinogenic pathways. Mutator mutations are mutations in genetic stability genes, and increase the mutation rate, speeding up the accumulation of oncogenic mutations. The mutator hypothesis states that mutator mutations play a critical role in carcinogenesis. Alternatively, tumors might arise by mutations occurring at the normal rate followed by selection and expansion of various premalignant lineages on the path to cancer. This alternative pathway is a significant argument against the mutator hypothesis. Mutator mutations may also lead to accumulation of deleterious mutations, which could lead to extinction of premalignant lineages before they become cancerous, another argument against the mutator hypothesis. Finally, the need for acquisition of a mutator mutation imposes an additional step on the carcinogenic process. Accordingly, the mutator hypothesis has been a seminal but controversial idea for several decades despite considerable experimental and theoretical work. To resolve this debate, the concept of efficiency has been introduced as a metric for comparing carcinogenic mechanisms, and a new theoretical approach of focused quantitative modeling has been applied. The results demonstrate that, given what is already known, the predominance of mutator mechanisms is likely inevitable, as they overwhelm less efficient non-mutator pathways to cancer.
Copyright © 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20934514     DOI: 10.1016/j.semcancer.2010.10.004

Source DB:  PubMed          Journal:  Semin Cancer Biol        ISSN: 1044-579X            Impact factor:   15.707


  10 in total

1.  Point mutation instability (PIN) mutator phenotype as model for true back mutations seen in hereditary tyrosinemia type 1 - a hypothesis.

Authors:  Etresia van Dyk; Pieter J Pretorius
Journal:  J Inherit Metab Dis       Date:  2011-10-15       Impact factor: 4.982

2.  Hepatitis B Virus DNA Integration Occurs Early in the Viral Life Cycle in an In Vitro Infection Model via Sodium Taurocholate Cotransporting Polypeptide-Dependent Uptake of Enveloped Virus Particles.

Authors:  Thomas Tu; Magdalena A Budzinska; Florian W R Vondran; Nicholas A Shackel; Stephan Urban
Journal:  J Virol       Date:  2018-05-14       Impact factor: 5.103

3.  Phase I Imaging and Pharmacodynamic Trial of CS-1008 in Patients With Metastatic Colorectal Cancer.

Authors:  Marika Ciprotti; Niall C Tebbutt; Fook-Thean Lee; Sze-Ting Lee; Hui K Gan; David C McKee; Graeme J O'Keefe; Sylvia J Gong; Geoffrey Chong; Wendie Hopkins; Bridget Chappell; Fiona E Scott; Martin W Brechbiel; Archie N Tse; Mendel Jansen; Manabu Matsumura; Masakatsu Kotsuma; Rira Watanabe; Ralph Venhaus; Robert A Beckman; Jonathan Greenberg; Andrew M Scott
Journal:  J Clin Oncol       Date:  2015-06-29       Impact factor: 44.544

4.  Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer.

Authors:  Robert A Beckman; Gunter S Schemmann; Chen-Hsiang Yeang
Journal:  Proc Natl Acad Sci U S A       Date:  2012-08-13       Impact factor: 11.205

Review 5.  Evolutionary dynamics and significance of multiple subclonal mutations in cancer.

Authors:  Robert A Beckman; Lawrence A Loeb
Journal:  DNA Repair (Amst)       Date:  2017-06-09

6.  Cancer genomic research at the crossroads: realizing the changing genetic landscape as intratumoral spatial and temporal heterogeneity becomes a confounding factor.

Authors:  Shengwen Calvin Li; Lisa May Ling Tachiki; Mustafa H Kabeer; Brent A Dethlefs; Michael J Anthony; William G Loudon
Journal:  Cancer Cell Int       Date:  2014-11-12       Impact factor: 5.722

7.  Long range personalized cancer treatment strategies incorporating evolutionary dynamics.

Authors:  Chen-Hsiang Yeang; Robert A Beckman
Journal:  Biol Direct       Date:  2016-10-22       Impact factor: 4.540

8.  Modelling the evolution of genetic instability during tumour progression.

Authors:  Ruchira S Datta; Alice Gutteridge; Charles Swanton; Carlo C Maley; Trevor A Graham
Journal:  Evol Appl       Date:  2012-11-26       Impact factor: 5.183

9.  New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs.

Authors:  Robert A Beckman; Cong Chen
Journal:  Chin J Cancer       Date:  2013-03-15

Review 10.  How Should Cancer Models Be Constructed?

Authors:  Robert A Beckman; Irina Kareva; Frederick R Adler
Journal:  Cancer Control       Date:  2020 Jan-Dec       Impact factor: 3.302

  10 in total

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