Congratulations to Professor Sheida Nabavi for receiving a NIH Pathway to Independence Award for her project entitled: Novel Integrative Method to Detect Biomarkers of Breast Cancer Resistance. As stated in her abstract, “Triple-negative breast cancer (TNBC) is defined by the lack of expression of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 and is characteristically an aggressive cancer, especially in a metastatic setting. Approximately 15-20% of all breast cancers are TNBC. In spite of recent improvements in TNBC treatment, the lack of known specific therapeutic targets and the heterogeneous response to chemotherapy make it difficult to attack TNBC and obtain a consistent outcome and meaningful benefit.” According to Dr. Nabavi, “Many triple-negative breast cancer patients are not responding to the treatment; and there is no clinical practical way to identify in which individuals’ chemotherapy will be effective to avoid unnecessary toxicity and cost of healthcare. Recent advances in technology make it possible to generate high throughput genomics data at low cost. As a result many cancer studies have generated genomics sequencing data. Also, large consortia such as The Cancer Genome Atlas and International Cancer Genome Consortium provide vast collections of genomics data from thousands of human tumors. However, analyzing cancer genomics data is very challenging, mainly due to data heterogeneity that limits the use of conventional methods.”
The objective of this study is to develop a computational framework, based on signal processing and machine learning techniques, for identifying response candidate biomarkers in TNBC more accurately and efficiently from next-generation sequencing data. In this study, Dr. Nabavi and her team will develop a novel sequence-based copy number variation (CNV) detection tool, using signal processing techniques; and a novel supervised integrative analysis tool, based on Bayesian network analysis which integrates CNV, point mutation and gene expression data.