Breast cancer can be subdivided into a number of subtypes. Six major subtypes, previously identified and documented, are considered particularly useful for prognosis and treatment strategy. These subtypes respond differently to chemotherapy and hormone treatments. Currently, doctors only test for a handful of molecular signatures and over 40% of those patients’ cancers do not fit into those categories. Cell lines are often used in research for pre-clinical models, as they mirror many of the molecular characteristics of tumors. Cell lines are used to study cancer in a lab without human or animal subject involvement, modeling interactions between the sample and various drugs and therapeutics. Breast cancer cell lines mirror breast cancer in a number of ways, such as the cellular and molecular characteristics.
This project was inspired by Daemon et al., 2013, “Modeling precision treatment of breast cancer”, which focuses on over 70 different Breast Cancer cell lines and over 90 different therapeutic agents. The project included SNP Array (a type of microarray), RNA-seq (which looks at the whole transcriptome), exome-seq (exome capture, which looks at all of the expressed genes at a given point in time), genome-wide methylation (epigenics), and as well as integrating a number of algorithmic methods to identify molecular features,using advanced machine learning algorithms.The Biassociation algorithm was used to integrate a number of different omics data types, including RNA expression, cell mutations, and drugs to find relationships and better understand how medications affect the breast cancer cells.This work was able to develop predictive drug response signatures and this research can be built upon with future clinical models. One issue with this study is a cell panel does not capture features such as tumor microenvironment, which is critical to understanding tumors.
Biassociation/P-clustering Preliminary Results:
P-clustering gave modules of co-associated features (drug response, expression, mutations and etc).
Network of Integrations for Drug Response