Literature DB >> 25349424

Timing and heterogeneity of mutations associated with drug resistance in metastatic cancers.

Ivana Bozic1, Martin A Nowak2.   

Abstract

Targeted therapies provide an exciting new approach to combat human cancer. The immediate effect is a dramatic reduction in disease burden, but in most cases, the tumor returns as a consequence of resistance. Various mechanisms for the evolution of resistance have been implicated, including mutation of target genes and activation of other drivers. There is increasing evidence that the reason for failure of many targeted treatments is a small preexisting subpopulation of resistant cells; however, little is known about the genetic composition of this resistant subpopulation. Using the novel approach of ordering the resistant subclones according to their time of appearance, here we describe the full spectrum of resistance mutations present in a metastatic lesion. We calculate the expected and median number of cells in each resistant subclone. Surprisingly, the ratio of the medians of successive resistant clones is independent of any parameter in our model; for example, the median of the second clone divided by the median of the first is √2-1. We find that most radiographically detectable lesions harbor at least 10 resistant subclones. Our predictions are in agreement with clinical data on the relative sizes of resistant subclones obtained from liquid biopsies of colorectal cancer patients treated with epidermal growth factor receptor (EGFR) blockade. Our theory quantifies the genetic heterogeneity of resistance that exists before treatment and provides information to design treatment strategies that aim to control resistance.

Entities:  

Keywords:  cancer; drug resistance; heterogeneity; mathematical biology

Mesh:

Substances:

Year:  2014        PMID: 25349424      PMCID: PMC4234551          DOI: 10.1073/pnas.1412075111

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  28 in total

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Journal:  N Engl J Med       Date:  2010-06-24       Impact factor: 91.245

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Authors:  Cristina R Antonescu; Peter Besmer; Tianhua Guo; Knarik Arkun; Glory Hom; Beata Koryotowski; Margaret A Leversha; Philip D Jeffrey; Diann Desantis; Samuel Singer; Murray F Brennan; Robert G Maki; Ronald P DeMatteo
Journal:  Clin Cancer Res       Date:  2005-06-01       Impact factor: 12.531

5.  Drug resistance in cancer: principles of emergence and prevention.

Authors:  Natalia L Komarova; Dominik Wodarz
Journal:  Proc Natl Acad Sci U S A       Date:  2005-06-24       Impact factor: 11.205

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Journal:  N Engl J Med       Date:  2012-02-23       Impact factor: 91.245

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Authors:  Chetan Bettegowda; Mark Sausen; Rebecca J Leary; Isaac Kinde; Yuxuan Wang; Nishant Agrawal; Bjarne R Bartlett; Hao Wang; Brandon Luber; Rhoda M Alani; Emmanuel S Antonarakis; Nilofer S Azad; Alberto Bardelli; Henry Brem; John L Cameron; Clarence C Lee; Leslie A Fecher; Gary L Gallia; Peter Gibbs; Dung Le; Robert L Giuntoli; Michael Goggins; Michael D Hogarty; Matthias Holdhoff; Seung-Mo Hong; Yuchen Jiao; Hartmut H Juhl; Jenny J Kim; Giulia Siravegna; Daniel A Laheru; Calogero Lauricella; Michael Lim; Evan J Lipson; Suely Kazue Nagahashi Marie; George J Netto; Kelly S Oliner; Alessandro Olivi; Louise Olsson; Gregory J Riggins; Andrea Sartore-Bianchi; Kerstin Schmidt; le-Ming Shih; Sueli Mieko Oba-Shinjo; Salvatore Siena; Dan Theodorescu; Jeanne Tie; Timothy T Harkins; Silvio Veronese; Tian-Li Wang; Jon D Weingart; Christopher L Wolfgang; Laura D Wood; Dongmei Xing; Ralph H Hruban; Jian Wu; Peter J Allen; C Max Schmidt; Michael A Choti; Victor E Velculescu; Kenneth W Kinzler; Bert Vogelstein; Nickolas Papadopoulos; Luis A Diaz
Journal:  Sci Transl Med       Date:  2014-02-19       Impact factor: 17.956

Review 8.  Targeted cancer therapy.

Authors:  Charles Sawyers
Journal:  Nature       Date:  2004-11-18       Impact factor: 49.962

9.  The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers.

Authors:  Luis A Diaz; Richard T Williams; Jian Wu; Isaac Kinde; J Randolph Hecht; Jordan Berlin; Benjamin Allen; Ivana Bozic; Johannes G Reiter; Martin A Nowak; Kenneth W Kinzler; Kelly S Oliner; Bert Vogelstein
Journal:  Nature       Date:  2012-06-28       Impact factor: 49.962

10.  Bcr-Abl kinase domain mutations, drug resistance, and the road to a cure for chronic myeloid leukemia.

Authors:  Thomas O'Hare; Christopher A Eide; Michael W N Deininger
Journal:  Blood       Date:  2007-05-11       Impact factor: 22.113

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  41 in total

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2.  Modeling the Subclonal Evolution of Cancer Cell Populations.

Authors:  Diego Chowell; James Napier; Rohan Gupta; Karen S Anderson; Carlo C Maley; Melissa A Wilson Sayres
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Review 3.  Non-coding RNA networks in cancer.

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4.  Immunological ignorance is an enabling feature of the oligo-clonal T cell response to melanoma neoantigens.

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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.  Universal Asymptotic Clone Size Distribution for General Population Growth.

Authors:  Michael D Nicholson; Tibor Antal
Journal:  Bull Math Biol       Date:  2016-10-20       Impact factor: 1.758

Review 7.  The challenges of tumor genetic diversity.

Authors:  Edmund A Mroz; James W Rocco
Journal:  Cancer       Date:  2016-11-08       Impact factor: 6.860

Review 8.  Modeling Tumor Clonal Evolution for Drug Combinations Design.

Authors:  Boyang Zhao; Michael T Hemann; Douglas A Lauffenburger
Journal:  Trends Cancer       Date:  2016-03

Review 9.  The mathematics of cancer: integrating quantitative models.

Authors:  Philipp M Altrock; Lin L Liu; Franziska Michor
Journal:  Nat Rev Cancer       Date:  2015-12       Impact factor: 60.716

10.  A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity.

Authors:  Bartlomiej Waclaw; Ivana Bozic; Meredith E Pittman; Ralph H Hruban; Bert Vogelstein; Martin A Nowak
Journal:  Nature       Date:  2015-08-26       Impact factor: 49.962

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