Literature DB >> 22959913

A branching process model of ovarian cancer.

Kaveh Danesh1, Rick Durrett, Laura J Havrilesky, Evan Myers.   

Abstract

Ovarian cancer is usually diagnosed at an advanced stage, rendering the possibility of cure unlikely. To date, no cost-effective screening test has proven effective for reducing mortality. To estimate the window of opportunity for ovarian cancer screening, we develop a branching process model for ovarian cancer growth and progression accounting for three cell populations: Primary (cells in the ovary or fallopian tube), Peritoneal (viable cells in peritoneal fluid), and Metastatic (cells implanted on other intra-abdominal surfaces). Growth and migration parameters were chosen to match results of clinical studies. Using these values, our model predicts a window of opportunity of 2.9 years, indicating that one would have to screen at least every other year to be effective. The model can be used to inform future efforts in designing improved screening and treatment strategies.
Copyright © 2012 Elsevier Ltd. All rights reserved.

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Year:  2012        PMID: 22959913      PMCID: PMC3478401          DOI: 10.1016/j.jtbi.2012.08.025

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  15 in total

1.  Evolution of resistance during clonal expansion.

Authors:  Yoh Iwasa; Martin A Nowak; Franziska Michor
Journal:  Genetics       Date:  2006-04       Impact factor: 4.562

2.  Accumulation of driver and passenger mutations during tumor progression.

Authors:  Ivana Bozic; Tibor Antal; Hisashi Ohtsuki; Hannah Carter; Dewey Kim; Sining Chen; Rachel Karchin; Kenneth W Kinzler; Bert Vogelstein; Martin A Nowak
Journal:  Proc Natl Acad Sci U S A       Date:  2010-09-27       Impact factor: 11.205

3.  Evolutionary dynamics of tumor progression with random fitness values.

Authors:  Rick Durrett; Jasmine Foo; Kevin Leder; John Mayberry; Franziska Michor
Journal:  Theor Popul Biol       Date:  2010-05-19       Impact factor: 1.570

4.  Cancer statistics, 2010.

Authors:  Ahmedin Jemal; Rebecca Siegel; Jiaquan Xu; Elizabeth Ward
Journal:  CA Cancer J Clin       Date:  2010-07-07       Impact factor: 508.702

5.  Ovarian cancer development and metastasis.

Authors:  Ernst Lengyel
Journal:  Am J Pathol       Date:  2010-07-22       Impact factor: 4.307

Review 6.  SGO White Paper on ovarian cancer: etiology, screening and surveillance.

Authors:  John O Schorge; Susan C Modesitt; Robert L Coleman; David E Cohn; Noah D Kauff; Linda R Duska; Thomas J Herzog
Journal:  Gynecol Oncol       Date:  2010-08-07       Impact factor: 5.482

7.  Evolution of resistance and progression to disease during clonal expansion of cancer.

Authors:  Richard Durrett; Stephen Moseley
Journal:  Theor Popul Biol       Date:  2009-11-05       Impact factor: 1.570

8.  Reducing ovarian cancer mortality through screening: Is it possible, and can we afford it?

Authors:  Laura J Havrilesky; Gillian D Sanders; Shalini Kulasingam; Evan R Myers
Journal:  Gynecol Oncol       Date:  2008-08-21       Impact factor: 5.482

Review 9.  Ovarian cancer metastasis: integrating insights from disparate model organisms.

Authors:  Honami Naora; Denise J Montell
Journal:  Nat Rev Cancer       Date:  2005-05       Impact factor: 60.716

10.  The preclinical natural history of serous ovarian cancer: defining the target for early detection.

Authors:  Patrick O Brown; Chana Palmer
Journal:  PLoS Med       Date:  2009-07-28       Impact factor: 11.069

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  10 in total

Review 1.  Modeling Tumor Clonal Evolution for Drug Combinations Design.

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

Review 2.  Mathematical models of breast and ovarian cancers.

Authors:  Dana-Adriana Botesteanu; Stanley Lipkowitz; Jung-Min Lee; Doron Levy
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2016-06-03

3.  Evolution of pre-existing versus acquired resistance to platinum drugs and PARP inhibitors in BRCA-associated cancers.

Authors:  Kimiyo N Yamamoto; Kouji Hirota; Shunichi Takeda; Hiroshi Haeno
Journal:  PLoS One       Date:  2014-08-26       Impact factor: 3.240

4.  Spatial heterogeneity in drug concentrations can facilitate the emergence of resistance to cancer therapy.

Authors:  Feng Fu; Martin A Nowak; Sebastian Bonhoeffer
Journal:  PLoS Comput Biol       Date:  2015-03-19       Impact factor: 4.475

Review 5.  Cancer evolution: mathematical models and computational inference.

Authors:  Niko Beerenwinkel; Roland F Schwarz; Moritz Gerstung; Florian Markowetz
Journal:  Syst Biol       Date:  2014-10-07       Impact factor: 15.683

6.  Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations.

Authors:  Daphne Sun; Simona Dalin; Michael T Hemann; Douglas A Lauffenburger; Boyang Zhao
Journal:  Sci Rep       Date:  2016-11-07       Impact factor: 4.379

7.  Stochastic modelling of tyrosine kinase inhibitor rotation therapy in chronic myeloid leukaemia.

Authors:  H Jonathan G Lindström; Astrid S de Wijn; Ran Friedman
Journal:  BMC Cancer       Date:  2019-05-28       Impact factor: 4.430

8.  Predicting colorectal cancer risk from adenoma detection via a two-type branching process model.

Authors:  Brian M Lang; Jack Kuipers; Benjamin Misselwitz; Niko Beerenwinkel
Journal:  PLoS Comput Biol       Date:  2020-02-05       Impact factor: 4.475

9.  Modeling the Dynamics of High-Grade Serous Ovarian Cancer Progression for Transvaginal Ultrasound-Based Screening and Early Detection.

Authors:  Dana-Adriana Botesteanu; Jung-Min Lee; Doron Levy
Journal:  PLoS One       Date:  2016-06-03       Impact factor: 3.240

10.  Stochastic multi-scale models of competition within heterogeneous cellular populations: Simulation methods and mean-field analysis.

Authors:  Roberto de la Cruz; Pilar Guerrero; Fabian Spill; Tomás Alarcón
Journal:  J Theor Biol       Date:  2016-07-22       Impact factor: 2.691

  10 in total

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