Abstract 4135: Identification of potential responders to chemotherapy against colorectal cancer by predictor genes and activated pathways using Random Forests analysis
Authors: Midorikawa Yutaka , Tsuji Shingo , Takahashi Takao , Yagi Koichi , Takayama Tadatoshi , Yoshida Kazuhiro , Sugiyama Yasuyuki , Aburatani Hiroyuki 
Published: August 2014

Abstract
Combinations of oxaliplatin combined with various schedules of antimetabolite 5- fluorouracil and leucovorin (FOLFOX) have been the first-line treatment for unresectable colorectal cancer (CRC) and survival benefits for the patients undergoing FOLFOX therapy were demonstrated. That is, the median duration of survival for the group treated with FOLFOX has been reported to be about 20 months, compared with 8 months for the best supportive care group. However, the rate of effect is not so high and has been reported to be approximately 50%. Molecular characterization using gene expression profiling will undoubtedly improve the prediction of treatment responses and ultimately the clinical outcome of cancer patients, which has never been accomplished by conventional clinicopathological classification. The aim of this study was to identify responders for FOLFOX therapy by applying Random Forests (RF) algorithm to gene expression data. Sixty-six unresectable colorectal cancer (CRC) patients were included, which were divided into training and test sets. Evaluated by computed tomography after 4 courses of FOLFOX therapy, 25 patients were diagnosed as responders and 25 patients as non-responders, respectively, in 50 training samples. Using RF, 22 of 25 responders (88.0% sensitivity) and 24 of 25 non-responders (96.0% specificity) were correctly classified, with an accuracy of 92.0% in out-of-bag cross-validation, and predictor genes were identified for FOLFOX therapy. Based on the predictor genes, proximities could clearly classify tumor samples into three subtypes, responders, non-responders, and outliers. After getting 5 outliers out of 50 training samples, RF was performed again with identification of predictor genes, such as RDX, SMURF2, and PEG10. In order to validate these predictor genes, we applied these decision tree analyses to independent 16 test samples, and achieved 75.9% sensitivity, 88.9% specificity, and 84.6% of classification accuracy. Based on these predictor genes, we performed Gene Set Enrichment Analysis and found activated pathways such as ‘hematopoietic stem cell genes’ pathway (including CIRBP and NPEPPS). Furthermore, we investigated survival benefit of the patients who were diagnosed as responders for FOLFOX therapy by RF algorithm and found the survival distribution for unresectable CRC outcome. Taken together, RF on gene expression data for unresectable CRC patients could effectively stratify responders for FOLFOX therapy with high accuracy rate, and pharmacogenomics in anticancer therapy is definitely the first step for personalized therapy.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4135. doi:10.1158/1538-7445.AM2011-4135