Literature DB >> 24853547

Mechanisms of resistance to intermittent androgen deprivation in patients with prostate cancer identified by a novel computational method.

Jason D Morken1, Aaron Packer2, Rebecca A Everett2, John D Nagy3, Yang Kuang2.   

Abstract

For progressive prostate cancer, intermittent androgen deprivation (IAD) is one of the most common and effective treatments. Although this treatment is usually initially effective at regressing tumors, most patients eventually develop castration-resistant prostate cancer (CRPC), for which there is no effective treatment and is generally fatal. Although several biologic mechanisms leading to CRPC development and their relative frequencies have been identified, it is difficult to determine which mechanisms of resistance are developing in a given patient. Personalized therapy that identifies and targets specific mechanisms of resistance developing in individual patients is likely one of the most promising methods of future cancer therapy. Prostate-specific antigen (PSA) is a biomarker for monitoring tumor progression. We incorporated a cell death rate (CDR) function into a previous dynamical PSA model that was highly accurate at fitting clinical PSA data for 7 patients. The mechanism of action of IAD is largely induction of apoptosis, and each mechanism of resistance varies in its CDR dynamics. Thus, we analyze the CDR levels and their time-dependent oscillations to identify mechanisms of resistance to IAD developing in individual patients. ©2014 American Association for Cancer Research.

Entities:  

Mesh:

Substances:

Year:  2014        PMID: 24853547     DOI: 10.1158/0008-5472.CAN-13-3162

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


  13 in total

Review 1.  Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers.

Authors:  Robert Clarke; John J Tyson; Ming Tan; William T Baumann; Lu Jin; Jianhua Xuan; Yue Wang
Journal:  Endocr Relat Cancer       Date:  2019-06       Impact factor: 5.678

2.  Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome.

Authors:  Guillermo Lorenzo; Víctor M Pérez-García; Alfonso Mariño; Luis A Pérez-Romasanta; Alessandro Reali; Hector Gomez
Journal:  J R Soc Interface       Date:  2019-08-14       Impact factor: 4.118

3.  Multidrug Cancer Therapy in Metastatic Castrate-Resistant Prostate Cancer: An Evolution-Based Strategy.

Authors:  Jeffrey B West; Mina N Dinh; Joel S Brown; Jingsong Zhang; Alexander R Anderson; Robert A Gatenby
Journal:  Clin Cancer Res       Date:  2019-04-16       Impact factor: 12.531

4.  Improving treatment strategies for patients with metastatic castrate resistant prostate cancer through personalized computational modeling.

Authors:  Jill Gallaher; Leah M Cook; Shilpa Gupta; Arturo Araujo; Jasreman Dhillon; Jong Y Park; Jacob G Scott; Julio Pow-Sang; David Basanta; Conor C Lynch
Journal:  Clin Exp Metastasis       Date:  2014-08-31       Impact factor: 5.150

5.  Tissue-scale, personalized modeling and simulation of prostate cancer growth.

Authors:  Guillermo Lorenzo; Michael A Scott; Kevin Tew; Thomas J R Hughes; Yongjie Jessica Zhang; Lei Liu; Guillermo Vilanova; Hector Gomez
Journal:  Proc Natl Acad Sci U S A       Date:  2016-11-16       Impact factor: 11.205

Review 6.  Executable cancer models: successes and challenges.

Authors:  Matthew A Clarke; Jasmin Fisher
Journal:  Nat Rev Cancer       Date:  2020-04-27       Impact factor: 69.800

7.  Intermittent Androgen Suppression: Estimating Parameters for Individual Patients Based on Initial PSA Data in Response to Androgen Deprivation Therapy.

Authors:  Yoshito Hirata; Kai Morino; Koichiro Akakura; Celestia S Higano; Nicholas Bruchovsky; Teresa Gambol; Susan Hall; Gouhei Tanaka; Kazuyuki Aihara
Journal:  PLoS One       Date:  2015-06-24       Impact factor: 3.240

8.  Prediction of treatment efficacy for prostate cancer using a mathematical model.

Authors:  Huiming Peng; Weiling Zhao; Hua Tan; Zhiwei Ji; Jingsong Li; King Li; Xiaobo Zhou
Journal:  Sci Rep       Date:  2016-02-12       Impact factor: 4.379

Review 9.  A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors.

Authors:  Anyue Yin; Dirk Jan A R Moes; Johan G C van Hasselt; Jesse J Swen; Henk-Jan Guchelaar
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2019-08-09

Review 10.  Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology.

Authors:  Anum S Kazerouni; Manasa Gadde; Andrea Gardner; David A Hormuth; Angela M Jarrett; Kaitlyn E Johnson; Ernesto A B F Lima; Guillermo Lorenzo; Caleb Phillips; Amy Brock; Thomas E Yankeelov
Journal:  iScience       Date:  2020-11-13
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.