Modern advances in personalized medicine have used technology to characterize a patient's fundamental biology, in terms of DNA, RNA, or protein. This can be used to classify a disease (such as breast cancer subtype) or to characterize important details of the patient (such as genes related to drug response for a particular treatment). These techniques can also be used in research on diseases such as cancer and genetic diseases.
The variety of cancer mutations means that effective diagnosis and treatment of cancer must take into account a high degree of complexity. By sequencing individual cancer genomes, researchers and physicians may develop more targeted medical solutions. Cancer is the second major cause of mortality in the United States and targeted cancer therapies are bringing about an exponential increase in effectiveness over traditional cancer therapies.
Breast Cancer has many different mutations. It 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 includedSNP 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.
