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1.  Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines 
BMC Medical Genomics  2012;5:66.
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.
doi:10.1186/1755-8794-5-66
PMCID: PMC3543849  PMID: 23272949
Colorectal cancer; Tumor subtyping; Cell lines; Targeted therapy
2.  Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244) 
Cancer research  2010;70(6):2264-2273.
Selumetinib (AZD6244, ARRY-142886) is a selective, non–ATP-competitive inhibitor of mitogen-activated protein/extracellular signal–regulated kinase kinase (MEK)-1/2. The range of antitumor activity seen preclinically and in patients highlights the importance of identifying determinants of response to this drug. In large tumor cell panels of diverse lineage, we show that MEK inhibitor response does not have an absolute correlation with mutational or phospho-protein markers of BRAF/MEK, RAS, or phosphoinositide 3-kinase (PI3K) activity. We aimed to enhance predictivity by measuring pathway output through coregulated gene networks displaying differential mRNA expression exclusive to resistant cell subsets and correlated to mutational or dynamic pathway activity. We discovered an 18-gene signature enabling measurement of MEK functional output independent of tumor genotype. Where the MEK pathway is activated but the cells remain resistant to selumetinib, we identified a 13-gene signature that implicates the existence of compensatory signaling from RAS effectors other than PI3K. The ability of these signatures to stratify samples according to functional activation of MEK and/or selumetinib sensitivity was shown in multiple independent melanoma, colon, breast, and lung tumor cell lines and in xenograft models. Furthermore, we were able to measure these signatures in fixed archival melanoma tumor samples using a single RT-qPCR–based test and found intergene correlations and associations with genetic markers of pathway activity to be preserved. These signatures offer useful tools for the study of MEK biology and clinical application of MEK inhibitors, and the novel approaches taken may benefit other targeted therapies.
doi:10.1158/0008-5472.CAN-09-1577
PMCID: PMC3166660  PMID: 20215513
3.  Automatic extraction of angiogenesis bioprocess from text 
Bioinformatics  2011;27(19):2730-2737.
Motivation: Understanding key biological processes (bioprocesses) and their relationships with constituent biological entities and pharmaceutical agents is crucial for drug design and discovery. One way to harvest such information is searching the literature. However, bioprocesses are difficult to capture because they may occur in text in a variety of textual expressions. Moreover, a bioprocess is often composed of a series of bioevents, where a bioevent denotes changes to one or a group of cells involved in the bioprocess. Such bioevents are often used to refer to bioprocesses in text, which current techniques, relying solely on specialized lexicons, struggle to find.
Results: This article presents a range of methods for finding bioprocess terms and events. To facilitate the study, we built a gold standard corpus in which terms and events related to angiogenesis, a key biological process of the growth of new blood vessels, were annotated. Statistics of the annotated corpus revealed that over 36% of the text expressions that referred to angiogenesis appeared as events. The proposed methods respectively employed domain-specific vocabularies, a manually annotated corpus and unstructured domain-specific documents. Evaluation results showed that, while a supervised machine-learning model yielded the best precision, recall and F1 scores, the other methods achieved reasonable performance and less cost to develop.
Availability: The angiogenesis vocabularies, gold standard corpus, annotation guidelines and software described in this article are available at http://text0.mib.man.ac.uk/~mbassxw2/angiogenesis/
Contact: xinglong.wang@gmail.com
doi:10.1093/bioinformatics/btr460
PMCID: PMC3179660  PMID: 21821664
4.  Identification of common predictive markers of in vitro response to the MEK inhibitor selumetinib (AZD6244; ARRY-142886) in human breast cancer and non-small cell lung cancer cell lines 
Molecular cancer therapeutics  2010;9(7):1985-1994.
Selumetinib (AZD6244; ARRY-142886) is a tight-binding, uncompetitive inhibitor of MEK1/2 currently in clinical development. We evaluated the effects of selumetinib in 31 human breast cancer cell lines and 43 human non-small cell lung cancer (NSCLC) cell lines to identify characteristics correlating with in vitro sensitivity to MEK inhibition. IC50 less than 1µM (considered sensitive) was seen in 5 of 31 breast cancer cell lines and 15 of 43 NSCLC cell lines, with a correlation between sensitivity and raf mutations in breast cancer cell lines (p= 0.022) and ras mutations in NSCLC cell lines (p= 0.045). Evaluation of 27 of the NSCLC cell lines with Western blots demonstrated no clear association between MEK and PI3K pathway activation and sensitivity to MEK inhibition. Baseline gene expression profiles were generated for each cell line using Agilent gene expression arrays to identify additional predictive markers. Genes associated with differential sensitivity to selumetinib were seen in both histologies, including a small number of genes in which differential expression was common to both histologies. In total, these results suggest that clinical trials of selumetinib in breast cancer and NSCLC might select patients whose tumors harbor raf and ras mutations respectively.
doi:10.1158/1535-7163.MCT-10-0037
PMCID: PMC2939826  PMID: 20587667
AZD6244; MEK; Breast cancer; Lung Cancer

Results 1-4 (4)