Characterization of an oxaliplatin sensitivity predictor in a preclinical murine model of colorectal cancer.
Despite advances in contemporary chemotherapeutic strategies, long-term survival still remains elusive for patients with metastatic colorectal cancer. A better understanding of the molecular markers of drug sensitivity to match therapy with patient is needed to improve clinical outcomes. In this study, we used in vitro drug sensitivity data from the NCI-60 cell lines together with their Affymetrix microarray data to develop a gene expression signature to predict sensitivity to oxaliplatin. To validate our oxaliplatin sensitivity signature, patient-derived colorectal cancer explants (PDCCE) were developed in nonobese diabetic/severe combined immunodeficient (NOD/SCID) mice from resected human colorectal tumors. Analysis of gene expression profiles found similarities between the PDCCEs and their parental human tumors, suggesting their utility to study drug sensitivity in vivo. The oxaliplatin sensitivity signature was then validated in vivo with response data from 14 PDCCEs treated with oxaliplatin and was found to have an accuracy of 92.9% (sensitivity = 87.5%; specificity = 100%). Our findings suggest that PDCCEs can be a novel source to study drug sensitivity in colorectal cancer. Furthermore, genomic-based analysis has the potential to be incorporated into future strategies to optimize individual therapy for patients with metastatic colorectal cancer.
Duke Scholars
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Related Subject Headings
- Transplantation, Heterologous
- Prognosis
- Oxaliplatin
- Organoplatinum Compounds
- Oncology & Carcinogenesis
- Mice
- Humans
- Gene Expression Regulation, Neoplastic
- Gene Expression Profiling
- Disease Models, Animal
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Transplantation, Heterologous
- Prognosis
- Oxaliplatin
- Organoplatinum Compounds
- Oncology & Carcinogenesis
- Mice
- Humans
- Gene Expression Regulation, Neoplastic
- Gene Expression Profiling
- Disease Models, Animal