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Improving gastric cancer preclinical studies using diverse in vitro and in vivo model systems

Cited 9 time in Web of Science Cited 9 time in Scopus
Authors
Chang, Hae Ryung; Park, Hee Seo; Ahn, Young Zoo; Nam, Seungyoon; Jung, Hae Rim; Park, Sungjin; Lee, Sang Jin; Balch, Curt; Powis, Garth; Ku, Ja-Lok; Kim, Yon Hui
Issue Date
2016-03-09
Publisher
BioMed Central
Citation
BMC Cancer, 16(1):200
Keywords
BiomarkerCell microarrayERBB2 expressionGastric cancer cell linesTargeted therapiesTrastuzumabTumor heterogeneityXenograft microarray
Description
This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Abstract
Abstract

Background
Biomarker-driven targeted therapy, the practice of tailoring patients treatment to the expression/activity levels of disease-specific genes/proteins, remains challenging. For example, while the anti-ERBB2 monoclonal antibody, trastuzumab, was first developed using well-characterized, diverse in vitro breast cancer models (and is now a standard adjuvant therapy for ERBB2-positive breast cancer patients), trastuzumab approval for ERBB2-positive gastric cancer was largely based on preclinical studies of a single cell line, NCI-N87. Ensuing clinical trials revealed only modest patient efficacy, and many ERBB2-positive gastric cancer (GC) patients failed to respond at all (i.e., were inherently recalcitrant), or succumbed to acquired resistance.


Method
To assess mechanisms underlying GC insensitivity to ERBB2 therapies, we established a diverse panel of GC cells, differing in ERBB2 expression levels, for comprehensive in vitro and in vivo characterization. For higher throughput assays of ERBB2 DNA and protein levels, we compared the concordance of various laboratory quantification methods, including those of in vitro and in vivo genetic anomalies (FISH and SISH) and xenograft protein expression (Western blot vs. IHC), of both cell and xenograft (tissue-sectioned) microarrays.


Results
The biomarker assessment methods strongly agreed, as did correlation between RNA and protein expression. However, although ERBB2 genomic anomalies showed good in vitro vs. in vivo correlation, we observed striking differences in protein expression between cultured cells and mouse xenografts (even within the same GC cell type). Via our unique pathway analysis, we delineated a signaling network, in addition to specific pathways/biological processes, emanating from the ERBB2 signaling cascade, as a potential useful target of clinical treatment. Integrated analysis of public data from gastric tumors revealed frequent (10 – 20 %) amplification of the genes NFKBIE, PTK2, and PIK3CA, each of which resides in an ERBB2-derived subpathway network.


Conclusion
Our comprehensive bioinformatics analyses of highly heterogeneous cancer cells, combined with tumor omics profiles, can optimally characterize the expression patterns and activity of specific tumor biomarkers. Subsequent in vitro and in vivo validation, of specific disease biomarkers (using multiple methodologies), can improve prediction of patient stratification according to drug response or nonresponse.
Language
English
URI
http://hdl.handle.net/10371/100535
DOI
https://doi.org/10.1186/s12885-016-2232-2
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College of Medicine/School of Medicine (의과대학/대학원)Program in Cancer Biology (협동과정-종양생물학전공)Journal Papers (저널논문_협동과정-종양생물학전공)
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