Joint Prediction of Adjuvant Therapy Response and Time-to-Response for Cancer Patients Using the Personalized-DrugRank Method

BackgroundThe ideal of personalized medicine is to support the clinical decision process towards the right drug for the right patient at the right time, by using, among other diagnostic tools, molecular biomarkers that are specifically dependent on the patient status and on the therapeutic options. Several challenges must be overcome to realize this vision. Patients present a wide spectrum of genetic variability even before developing diseases, and disease like cancer add an extra layer of mutations, while only a very small fraction of such variants have diagnostic or prognostic value. Moreover it is also challenging to predict how the patient will respond to a specific drug based on the patients omic profiling, since any drug introduces further perturbations in the biochemical model. MethodsIn this paper we propose the method Personalized-DrugRank for joint prediction of therapy response and time-to-response for cancer patients undergoing pharmacological therapy after surgery. The method is based on personalizing the DrugMerge methodology for drug repositioning in order to extract a few synthetic indices useful as input to ML prediction tools. In particular the proposed methodology is a novel and principled approach to merging independent patient-specific transcriptomic data with drug perturbation data from cell line assays. One of the key novel features of our approach over the state of the art is the joint prediction of the response of the patient to therapy along with an estimate of the time-to-response (i.e the prediction of the time needed for the therapy to succeed or fail). FindingsWe tested our methodology on data from the TCGA (The Cancer Genome Atlas) Program for three cancer types (Breast, Stomach and Colorectal cancer), 10 pharmacological regimens and 13 homogeneous cohorts. For the therapy response prediction task we developed models that attain an average AUC performance 0.749, average pvalue 0.030, average accuracy 0.809 with balanced Positive and Negative Predicting Values. For the time-to-event prediction task we developed regression models for the 13 homogeneous cohorts that attain an average (geometric) Concordance Index performance 0.782 (max 0.904, min 0.651) with average log likelihood pvalue 0.004, improving in nine cohorts over 13 upon models based only on clinical parameters having average Concordance Index 0.678 and average p-value 0.006. Interestingly, we attain statistical significant results even with quite small therapy-homogeneous cohorts (ranging from a minimum of 7 patients to a maximum of 32). ConclusionsThe ability of predicting with high accuracy the response of a cancer patient to a chosen pharmacological therapeutic regimen along with an estimate of the time-to-response helps adapting the clinical decision process to the specific patient profile, thus increasing the likelihood of providing correct and timely therapeutic decisions.