This Article Full Text (Online First [PDF]) Supplementary Data Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Ochs, M. F. Articles by Godwin, A. K. PubMed PubMed Citation Articles by Ochs, M. F. Articles by Godwin, A. K.

Detection of Treatment-Induced Changes in Signaling Pathways in Gastrointestinal Stromal Tumors Using Transcriptomic Data

¹ Division of Oncology Biostatistics and Bioinformatics, Johns Hopkins University, Baltimore, Maryland; ² Women's Cancer Program and Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania; ³ Division of Human Health and Medical Science, Kochi University, Kochi, Japan; and ⁴ Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire

Requests for reprints: Michael F. Ochs, Johns Hopkins University, 550 North Broadway, Suite 1103, Baltimore, MD 21205. Phone: 410-955-8830; Fax: 410-955-0859; E-mail: mfo{at}jhu.edu.

Cell signaling plays a central role in the etiology of cancer. Numerous therapeutics in use or under development target signaling proteins; however, off-target effects often limit assignment of positive clinical response to the intended target. As direct measurements of signaling protein activity are not generally feasible during treatment, there is a need for more powerful methods to determine if therapeutics inhibit their targets and when off-target effects occur. We have used the Bayesian Decomposition algorithm and data on transcriptional regulation to create a novel methodology, Differential Expression for Signaling Determination (DESIDE), for inferring signaling activity from microarray measurements. We applied DESIDE to deduce signaling activity in gastrointestinal stromal tumor cell lines treated with the targeted therapeutic imatinib mesylate (Gleevec). We detected the expected reduced activity in the KIT pathway, as well as unexpected changes in the p53 pathway. Pursuing these findings, we have determined that imatinib-induced DNA damage is responsible for the increased activity of p53, identifying a novel off-target activity for this drug. We then used DESIDE on data from resected, post-imatinib treatment tumor samples and identified a pattern in these tumors similar to that at late time points in the cell lines, and this pattern correlated with initial clinical response. The pattern showed increased activity of ETS domain-containing protein Elk-1 and signal transducers and activators of transcription 3 transcription factors, which are associated with the growth of side population cells. DESIDE infers the global reprogramming of signaling networks during treatment, permitting treatment modification that leverages ongoing drug development efforts, which is crucial for personalized medicine. [Cancer Res 2009;69(23):OF1–8]

Key Words: Bayesian analysis • Pathway modeling