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. 2012 Dec 31:5:66.
doi: 10.1186/1755-8794-5-66.

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

Andreas Schlicker  1 , Garry Beran, Christine M Chresta, Gael McWalter, Alison Pritchard, Susie Weston, Sarah Runswick, Sara Davenport, Kerry Heathcote, Denis Alferez Castro, George Orphanides, Tim French, Lodewyk F A Wessels
Affiliations

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

Andreas Schlicker et al. BMC Med Genomics. .

Abstract

Background: Colorectal 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.

Methods: We 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.

Results: In 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.

Conclusions: The 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.

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Figures

Figure 1
Figure 1
Histology images of four samples from AZTS (20x magnification).
Figure 2
Figure 2
Overview of the workflow followed in this work (left) and the proposed iterative Nonnegative Matrix Factorization (iNMF) clustering approach (right). (A) First, we clustered a dataset consisting of 62 CRC samples using NMF based on four selected pathways. (B) Then, we applied iNMF for stratifying the samples with an unbiased selection of probe sets and (C) matched CRC cell lines (CL) to the resulting clusters. (D) By overlaying matching pharmacology data, (E) we investigated the potential for generating testable hypotheses regarding response of cell lines in different clusters.
Figure 3
Figure 3
Expression patterns of CRC subtypes defined by iNMF in different datasets: (A) Types 1 (black) and 2 (green) from iNMF iteration 1; (B) Subtypes 1.1, 1.2, and 1.3; and (C) Subtypes 2.1 and 2.2. Samples are shown in columns and probe sets contained in the subtype signatures are shown in rows.
Figure 4
Figure 4
Overview of SPEED analysis of pathway activation in (A) Types 1 and 2, (B) Subtypes 1.1, 1.2, and 1.3, and (C) Subtypes 2.1 and 2.2. The y-axes denote negative logarithm to base 10 of the activation p-value. The horizontal lines indicate the significance threshold of p-value = 0.05.
Figure 5
Figure 5
Comparison between iNMF subtypes and subtypes identified by Loboda et al. and Oh et al. Shown are samples contained in GSE2109, GSE14333, GSE17536, and GSE17537. The x- and y-axes depict the difference between average expression of signatures published by Oh et al. and Loboda et al. Lines along the axes represent the density of samples of the respective iNMF subtypes.
Figure 6
Figure 6
Expression patterns of CRC subtypes defined by iNMF in the AZTS set and different cell line panels. (A) Cell lines that are matched to Types 1 (black) and 2 (green) show a very similar expression pattern to the tumor samples. Expression in cell lines assigned to (B) the subtypes of Type 1 and (C) subtypes of Type 2 shows less similarity with expression in tumor samples.
Figure 7
Figure 7
Pharmacological response of cell lines in the AZCL panel to targeted inhibition. The y-axis denotes difference between average –log10 (EC50) of cell lines assigned to one subtype and average –log10 (EC50) of all measurements for compounds targeting the indicated protein. Positive or negative values indicate that cell lines in a cluster are more sensitive or resistant, respectively, than the overall average. Standard error of subtype means are represented as lines.
Figure 8
Figure 8
Pharmacological response of cell lines in the Sanger panel to targeted inhibition. The y-axis denotes difference between average –log10 (IC50) of cell lines assigned to one subtype and average –log10 (IC50) of all measurements for compounds targeting the indicated protein. Positive or negative values indicate that cell lines in a cluster are more sensitive or resistant, respectively, than the overall average. Standard error of subtype means are represented as lines.
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