Literature DB >> 22593194

Genetically engineered mouse models: closing the gap between preclinical data and trial outcomes.

Mallika Singh1, Christopher L Murriel, Leisa Johnson.   

Abstract

The high failure rate of late-stage human clinical trials, particularly in oncology, predicates the need for improved translation of preclinical data from mouse tumor models into clinical predictions. Genetically engineered mouse models (GEMM) may fulfill this need, because they mimic spontaneous and autochthonous disease progression. Using oncogenic Kras-driven GEMMs of lung and pancreatic adenocarcinoma, we recently showed that these models can closely phenocopy human therapeutic responses to standard-of-care treatment regimens. Here we review the successful preclinical application of such GEMMs, as well as the potential for discovering predictive biomarkers and gaining mechanistic insights into clinical outcomes and drug resistance in human cancers. ©2012 AACR

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Year:  2012        PMID: 22593194     DOI: 10.1158/0008-5472.CAN-11-2786

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


  52 in total

Review 1.  Preclinical mouse cancer models: a maze of opportunities and challenges.

Authors:  Chi-Ping Day; Glenn Merlino; Terry Van Dyke
Journal:  Cell       Date:  2015-09-24       Impact factor: 41.582

Review 2.  New cast for a new era: preclinical cancer drug development revisited.

Authors:  Grit S Herter-Sprie; Andrew L Kung; Kwok-Kin Wong
Journal:  J Clin Invest       Date:  2013-09-03       Impact factor: 14.808

Review 3.  Establishing the Impact of Vascular Damage on Tumor Response to High-Dose Radiation Therapy.

Authors:  Katherine D Castle; David G Kirsch
Journal:  Cancer Res       Date:  2019-08-19       Impact factor: 12.701

4.  Effective utilization and appropriate selection of genetically engineered mouse models for translational integration of mouse and human trials.

Authors:  Cory Abate-Shen; Pier Paolo Pandolfi
Journal:  Cold Spring Harb Protoc       Date:  2013-11-01

5.  Translational value of mouse models in oncology drug development.

Authors:  Stephen E Gould; Melissa R Junttila; Frederic J de Sauvage
Journal:  Nat Med       Date:  2015-05       Impact factor: 53.440

6.  Impact of treatment response metrics on photodynamic therapy planning and outcomes in a three-dimensional model of ovarian cancer.

Authors:  Sriram Anbil; Imran Rizvi; Jonathan P Celli; Nermina Alagic; Brian W Pogue; Tayyaba Hasan
Journal:  J Biomed Opt       Date:  2013-09       Impact factor: 3.170

7.  Co-clinical trials demonstrate superiority of crizotinib to chemotherapy in ALK-rearranged non-small cell lung cancer and predict strategies to overcome resistance.

Authors:  Zhao Chen; Esra Akbay; Oliver Mikse; Tanya Tupper; Katherine Cheng; Yuchuan Wang; Xiaohong Tan; Abigail Altabef; Sue-Ann Woo; Liang Chen; Jacob B Reibel; Pasi A Janne; Norman E Sharpless; Jeffrey A Engelman; Geoffrey I Shapiro; Andrew L Kung; Kwok-Kin Wong
Journal:  Clin Cancer Res       Date:  2013-12-10       Impact factor: 12.531

Review 8.  Interstitial calcinosis in renal papillae of genetically engineered mouse models: relation to Randall's plaques.

Authors:  Xue-Ru Wu
Journal:  Urolithiasis       Date:  2014-08-06       Impact factor: 3.436

Review 9.  Mouse models for studying angiogenesis and lymphangiogenesis in cancer.

Authors:  Lauri Eklund; Maija Bry; Kari Alitalo
Journal:  Mol Oncol       Date:  2013-03-05       Impact factor: 6.603

Review 10.  Mouse models for lung cancer.

Authors:  Min-chul Kwon; Anton Berns
Journal:  Mol Oncol       Date:  2013-02-19       Impact factor: 6.603

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