Developing a novel method for predicting tumor responses to drug combination based on pathway expression signatures
Authors: Maria Villarroel, Aik Choon Tan, Ignacio Garrido-Laguna, Belen Rubio-Viqueira, N.V RajeshKummar, Antonio Jimeno, Manuel Hidalgo
Journal: Clinical Cancer Research (2008)

B51    Purpose:  Treatment response rate in pancreatic cancer is poor. Novel therapeutics offer a second option in some cases, however, the response rates are still minimal. Conversely, combination therapy may provide a better response, but the challenge remains in identifying (i) which drugs to be combined and (ii) who should receive the drug combination. With the aim to improve drug combination prediction and to enrich patient response selection to the combination, we developed a reliable computational method for the prediction of tumor response on drug combination based on pathway expression signature. To demonstrate the feasibility of this method, we compared two compounds, CI-1040 and CCI-779 (Temsirolimus), which target Ras downstream signaling pathways, MEK-ERK signaling cascade and PI3K/AKT/mTOR signaling pathway, respectively, in a patient derived pancreatic tumor xenograft model (PancXenoBank). We hypothesized that a case will respond to this drug combination if it has intermediate response to both drugs based on pathway expression signature prediction.  Methods:  Sixteen pancreatic cancer xenograft were treated with CI-1040 (150mg/Kg); nineteen were treated with Temsirolimus (20mg/Kg) in monotherapy; relative tumor growth inhibition (TGI) was calculated on day 28. Baseline gene expression profiles for these xenografts were obtained by Affymetrix human gene chip HG_U133 Plus2.0. For each monotherapy, we obtained a pathway expression sensitivity signature by comparing the sensitive and resistant cases using gene set enrichment analysis (GSEA). To predict whether a case is sensitive to the drug combination, we computed the correlation score of this new case with each individual pathway signatures. Based on this correlation score, the method predicts a case as either sensitive or resistant for this drug combination.  Results:  We defined a case as sensitive, intermediate or resistant to a drug treatment as TGI   30%, respectively. Based on this criterion, there were two cases sensitive to CI-1040 (Panc219:-13% and Panc98:-21%) and three cases sensitive to CCI-779 (Panc219:-37%, Panc266:-19% and Panc198:-15%) monotherapy. Using the computational method, we predicted three intermediate cases (Panc281, Panc185 and Panc163) in both drugs as sensitive to the combination of CI-1040 and CCI-779. The method also predicted Panc219 and Panc294 as sensitive and resistant to the combination, respectively. To prospectively validate these predictions, we treated these cases with the drug combination for 28 days. The TGI values for Panc219:-59%, Panc281:-36%, Panc185:-22%, Panc163:-17% and Panc294:37%. Notably, the computational method correctly predicted the response on this drug combination for these cases.  Conclusion:  We demonstrated the usefulness and prospectively validated our computational model in predicting cases respond to CI-1040 + CCI-779 combination based on pathway expression signature. This method is easily applicable to other drug combinations and can be a useful tool in selecting patients’ response to combination therapy.