Literature DB >> 25305336

Lessons from patient-derived xenografts for better in vitro modeling of human cancer.

Stephen Yiu Chuen Choi1, Dong Lin2, Peter W Gout3, Colin C Collins4, Yong Xu5, Yuzhuo Wang6.   

Abstract

The development of novel cancer therapeutics is often plagued by discrepancies between drug efficacies obtained in preclinical studies and outcomes of clinical trials. The inconsistencies can be attributed to a lack of clinical relevance of the cancer models used for drug testing. While commonly used in vitro culture systems are advantageous for addressing specific experimental questions, they are often gross, fidelity-lacking simplifications that largely ignore the heterogeneity of cancers as well as the complexity of the tumor microenvironment. Factors such as tumor architecture, interactions among cancer cells and between cancer and stromal cells, and an acidic tumor microenvironment are critical characteristics observed in patient-derived cancer xenograft models and in the clinic. By mimicking these crucial in vivo characteristics through use of 3D cultures, co-culture systems and acidic culture conditions, an in vitro cancer model/microenvironment that is more physiologically relevant may be engineered to produce results more readily applicable to the clinic.
Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Acidic culture conditions; Acidic tumor microenvironment; Cancer-associated fibroblasts; Cancer–stromal interactions; Extracellular matrix; Patient-derived xenografts; Regulatory immune cells; Tumor heterogeneity

Mesh:

Substances:

Year:  2014        PMID: 25305336     DOI: 10.1016/j.addr.2014.09.009

Source DB:  PubMed          Journal:  Adv Drug Deliv Rev        ISSN: 0169-409X            Impact factor:   15.470


  61 in total

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2.  Sacrificial Bioprinting of a Mammary Ductal Carcinoma Model.

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3.  Transitions from mono- to co- to tri-culture uniquely affect gene expression in breast cancer, stromal, and immune compartments.

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Review 4.  Predictive approaches for drug combination discovery in cancer.

Authors:  Seyed Ali Madani Tonekaboni; Laleh Soltan Ghoraie; Venkata Satya Kumar Manem; Benjamin Haibe-Kains
Journal:  Brief Bioinform       Date:  2018-03-01       Impact factor: 11.622

5.  Patient-derived xenograft in zebrafish embryos: a new platform for translational research in neuroendocrine tumors.

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Journal:  Endocrine       Date:  2016-08-02       Impact factor: 3.633

Review 6.  Small-Molecule Screens: A Gateway to Cancer Therapeutic Agents with Case Studies of Food and Drug Administration-Approved Drugs.

Authors:  Nathan P Coussens; John C Braisted; Tyler Peryea; G Sitta Sittampalam; Anton Simeonov; Matthew D Hall
Journal:  Pharmacol Rev       Date:  2017-10       Impact factor: 25.468

7.  Characterization of patient-derived tumor xenografts (PDXs) as models for estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers.

Authors:  Noriko Kanaya; George Somlo; Jun Wu; Paul Frankel; Masaya Kai; Xueli Liu; Shang Victoria Wu; Duc Nguyen; Nymph Chan; Meng-Yin Hsieh; Michele Kirschenbaum; Laura Kruper; Courtney Vito; Behnam Badie; John H Yim; Yuan Yuan; Arti Hurria; Chu Peiguo; Joanne Mortimer; Shiuan Chen
Journal:  J Steroid Biochem Mol Biol       Date:  2016-05-03       Impact factor: 4.292

8.  Transcriptional changes associated with in vivo growth of muscle-invasive bladder cancer cell lines in nude mice.

Authors:  Swathi Ramakrishnan; Wendy Huss; Barbara Foster; Joyce Ohm; Jianmin Wang; Gissou Azabdaftari; Kevin H Eng; Anna Woloszynska-Read
Journal:  Am J Clin Exp Urol       Date:  2018-06-15

9.  Chemotherapeutic dosing implicated by pharmacodynamic modeling of in vitro cytotoxic data: a case study of paclitaxel.

Authors:  Hua He; Yanguang Cao
Journal:  J Pharmacokinet Pharmacodyn       Date:  2017-08-31       Impact factor: 2.745

Review 10.  Tumour and normal tissue radiobiology in mouse models: how close are mice to mini-humans?

Authors:  Bridget F Koontz; Frank Verhaegen; Dirk De Ruysscher
Journal:  Br J Radiol       Date:  2016-09-26       Impact factor: 3.039

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