Breast Tumor Susceptibility to Chemotherapy Via Support Vector Machines

Abstract

Support vector machine (SVM) classification together with alternative DNA splicing techniques were used to generate a classifier for breast cancer patients that are partial-responders to chemotherapy treatment. Partial responders are patients whose tumors were reduced by at least 50%. A stand-alone linear-programming-based SVM algorithm was used to separate the partial-responders from the nonresponders. A novel aspect of the classification approach utilized here is that each patient is represented by multiple points (replicates) in the 25-dimensional input space of DNA splice 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 88.6%, in an input space of 5 gene expressions extracted from an original space of 25 gene expression transcripts.