Breast Tumor Susceptibility to Chemotherapy via Support Vector Machines

Abstract

Support vector machines (SVMs), utilizing RNA signature measurements, were used to generate a classi er to distinguish breast cancer patients that are partial-responders to chemotherapy treatment, from patients that are nonresponders. Partial responders are patients whose tumors were reduced by at least 50%. A stand-alone linear-programmingbased SVM algorithm was used to separate the partial-responders from the nonresponders. A novel aspect of the classi cation approach utilized here is that each patient is represented by multiple points (replicates) in the 25-dimensional input space of RNA signature measurements. Replicates for all patients except those for one patient, were used as a training set. The average of the replicates for the patient left out was then used to test the leave one out correctness (looc). The looc for a group of 35 patients, with 9 partial-responders and 26 nonresponders was 94.2%, in an input space of 5 RNA measurements extracted from an original space of 25 RNA measurements.