Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines

Abstract

BackgroundColorectal cancer (CRC) is a heterogeneous and biologically poorly understood disease. To tailor CRC treatment, it is essential to first model this heterogeneity by defining subtypes of patients with homogeneous biological and clinical characteristics and second match these subtypes to cell lines for which extensive pharmacological data is available, thus linking targeted therapies to patients most likely to respond to treatment.MethodsWe applied a new unsupervised, iterative approach to stratify CRC tumor samples into subtypes based on genome-wide mRNA expression data. By applying this stratification to several CRC cell line panels and integrating pharmacological response data, we generated hypotheses regarding the targeted treatment of different subtypes.ResultsIn agreement with earlier studies, the two dominant CRC subtypes are highly correlated with a gene expression signature of epithelial-mesenchymal-transition (EMT). Notably, further dividing these two subtypes using iNMF (iterative Non-negative Matrix Factorization) revealed five subtypes that exhibit activation of specific signaling pathways, and show significant differences in clinical and molecular characteristics. Importantly, we were able to validate the stratification on independent, published datasets comprising over 1600 samples. Application of this stratification to four CRC cell line panels comprising 74 different cell lines, showed that the tumor subtypes are well represented in available CRC cell line panels. Pharmacological response data for targeted inhibitors of SRC, WNT, GSK3b, aurora kinase, PI3 kinase, and mTOR, showed significant differences in sensitivity across cell lines assigned to different subtypes. Importantly, some of these differences in sensitivity were in concordance with high expression of the targets or activation of the corresponding pathways in primary tumor samples of the same subtype.ConclusionsThe stratification presented here is robust, captures important features of CRC, and offers valuable insight into functional differences between CRC subtypes. By matching the identified subtypes to cell line panels that have been pharmacologically characterized, it opens up new possibilities for the development and application of targeted therapies for defined CRC patient sub-populations.

Colorectal Cancer Cell Lines Are Representative Models of the Main Molecular Subtypes of Primary Cancer

“…Within these molecular subtypes, although some differences were evident, cell lines showed globally similar genetic alterations to primary cancers, including genome-wide mutation, DNA copy number, and driver gene mutation profiles. Accordingly, gene expression profiles of colorectal cancer cell lines have previously been shown to broadly represent those of primary tumors (46). Our genomic data significantly expand on cancer cell line characterization efforts by the major cancer genome is the least squares fit on the log-log scale and the small vertical lines are the 99% prediction intervals.…”

SIRT1/PGC1α-Dependent Increase in Oxidative Phosphorylation Supports Chemotherapy Resistance of Colon Cancer

“…"Omics" technologies are nowadays increasingly used to determine the (epi-)genetic underpinnings of drug resistance (2,3). In colon cancer, this has resulted in the identification of gene signatures that can identify intrinsically drug-resistant tumor subtypes (4)(5)(6)(7). In the current study, we use such technologies to study how drug resistance develops in colon cancer patients that have been treated with chemotherapy.…”

Colorectal Cancer Heterogeneity and Targeted Therapy: A Case for Molecular Disease Subtypes

“…A potential approach to this problem is the introduction of a comprehensive molecular classification of colorectal cancer that integrates many aspects that contribute to cancer heterogeneity and predicts the efficacy of targeted agents. Recently, several groups have reported on such a taxonomy of colorectal cancer using gene expression data to identify distinct subtypes of this disease (4)(5)(6)(7)(8)(9)(10). Indeed, in some cases, these subtypes differ in response to anti-EGFR therapy (4,10).…”