Predicting Pathologic Complete Response Using Clinical and Microarray Data in the Presence of a Dominating Class

• Activity Number: 545
• Type: Contributed
• Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Jonathon Abernethy* and Jie Yang
• Companies: University of Illinois at Chicago and University of Illinois at Chicago
• Keywords: Precision Medicine ; Cross-Validation ; Breast Cancer ; SVM ; DLDA

Abstract

Pathologic complete response (PCR) is defined as the complete removal of residual invasive disease in the breast at the completion of chemotherapy. Breast cancer patients who achieve PCR often have a better chance of avoiding disease recurrence. PCR is relatively rare, occurring in around 20% percent of patients. If the goal is to predict PCR when considering chemotherapy, the case where non-achievers form a dominating class in a data set must be considered. In this paper, we used clinical and micro array data to predict PCR status using a sample of 271 training cases and 64 testing cases. Roughly 81% of the patients in this data set do not achieve PCR. Modeling without adjustment for the dominating class will lead to a top classification accuracy of 83% by over representing non-achievers (25% PPV, 96% NPV). We adjust for the dominant class using three approaches: random removal of PCR negative data, adding a modification to the standard DLDA, and using a modified loss function. Using these approaches we significantly increase the PPV to 75%, while still keeping a NPV of 71%.