"Developing Personalized Treatment Strategies for Breast Cancer"
, 499 DSL,
Breast cancer is a very heterogeneous disease. The development of high-throughput genomics technologies (e.g., microarrays and next generation sequencing) has enabled personalized cancer treatment strategies using genomic information of the patients. In this talk, I will present two of our recent works on developing personalized treatment strategies for breast cancer. In the first study, we have used high-throughput gene expression data together with clinical information for 1079 breast cancer patients to build models to predict responses to commonly used chemotherapy regimens. A small number of genes were selected by our method as biomarkers for chemotherapy response prediction. Our new approach, called PRES (Personalized REgimen Selection), resulted in a remarkable improvement of the pCR rates in an independent validation setting using multiple independent public datasets. In the second study, we developed a sequential biclustering approach and applied it to study the immune evasion mechanisms of breast cancer using RNA-seq data from 1065 breast cancer patients obtained from the Cancer Genome Atlas (TCGA). We have identified several different sub-groups of breast cancer patients with characteristic immune evasion mechanisms. This study not only provides guidance for personalized immunotherapy decisions for breast cancer patients, but also sheds light on developing combination immunotherapies for breast cancer.