Literature DB >> 22552286

The continuum model of selection in human tumors: general paradigm or niche product?

Simon Leedham1, Ian Tomlinson.   

Abstract

Berger and colleagues recently proposed a continuum model of how somatic mutations cause tumors to grow, thus supplementing the established binary models, such as oncogene activation and "two hits" at tumor suppressor loci. In the basic continuum model, decreases or increases in gene function, short of full inactivation or activation, impact linearly on cancer development. An extension, called the fail-safe model, envisaged an optimum level of gene derangement for tumor growth, but proposed that the cell gained protection from tumorigenesis because additional mutations caused excessive derangement. Most of the evidence in support of the continuum model came from Pten mutant mice rather than humans. In this article, we assess the validity and applicability of the continuum and fail-safe models. We suggest that the latter is of limited use: In part, it restates the existing "just right" of optimum intermediate gene derangement in tumorigenesis, and in part it is inherently implausible that a cell should avoid becoming cancerous only when it is some way down the road to that state. In contrast, the basic continuum model is a very useful addition to the other genetic models of tumorigenesis, especially in certain scenarios. Fittingly for a quantitative model, we propose that the continuum model is most likely to apply where multiple, cancer-promoting mutations have relatively small, additive effects, either through the well-established case of additive germline predisposition alleles or in a largely hypothetical situation where cancers may have acquired several somatic "mini-driver" mutations, each with weaker effects than classical tumor suppressors or fully activated oncogenes. ©2012 AACR.

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Year:  2012        PMID: 22552286     DOI: 10.1158/0008-5472.CAN-12-1052

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  5 in total

1.  Comparison of algorithms for the detection of cancer drivers at subgene resolution.

Authors:  Eduard Porta-Pardo; Atanas Kamburov; David Tamborero; Tirso Pons; Daniela Grases; Alfonso Valencia; Nuria Lopez-Bigas; Gad Getz; Adam Godzik
Journal:  Nat Methods       Date:  2017-07-17       Impact factor: 28.547

Review 2.  Etiologic field effect: reappraisal of the field effect concept in cancer predisposition and progression.

Authors:  Paul Lochhead; Andrew T Chan; Reiko Nishihara; Charles S Fuchs; Andrew H Beck; Edward Giovannucci; Shuji Ogino
Journal:  Mod Pathol       Date:  2014-06-13       Impact factor: 7.842

3.  The Immune Contexture Associates with the Genomic Landscape in Lung Adenomatous Premalignancy.

Authors:  Kostyantyn Krysan; Linh M Tran; Brandon S Grimes; Gregory A Fishbein; Atsuko Seki; Brian K Gardner; Tonya C Walser; Ramin Salehi-Rad; Jane Yanagawa; Jay M Lee; Sherven Sharma; Denise R Aberle; Arum E Spira; David A Elashoff; William D Wallace; Michael C Fishbein; Steven M Dubinett
Journal:  Cancer Res       Date:  2019-05-29       Impact factor: 12.701

4.  Finding driver mutations in cancer: Elucidating the role of background mutational processes.

Authors:  Anna-Leigh Brown; Minghui Li; Alexander Goncearenco; Anna R Panchenko
Journal:  PLoS Comput Biol       Date:  2019-04-29       Impact factor: 4.475

Review 5.  Computational Approaches to Prioritize Cancer Driver Missense Mutations.

Authors:  Feiyang Zhao; Lei Zheng; Alexander Goncearenco; Anna R Panchenko; Minghui Li
Journal:  Int J Mol Sci       Date:  2018-07-20       Impact factor: 5.923

  5 in total

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