Cell Lines: multi-omics network of associations to model precision treatment

Modern advances in personalized medicine have used technology to characterize a patient’s fundamental biology, in terms of DNA, RNA, and protein. This can be used to classify a disease (such as breast cancer subtype) or to characterize important details of the patient’s disease(such as genes related to drug response for a particular treatment). These techniques can also be used in research for diseases such as cancer and genetic diseases.


The high variety of cancer mutations for each individual patient 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 a growing treatment type for many cancers as it brings an exponential increase in effectiveness over traditional cancer therapies.


Breast cancer can arise from many different types of mutations. As a result of these 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 one subtype. Cell lines are often used first in research for pre-clinical models, as they mirror many of the molecular characteristics of tumors, and are a less complicated model than a human.Cell lines are used to study cancer in a lab without human or animal subject involvement and are utilized to  model interactions between cell types and various drugs and therapeutics.


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 that discovers variations in the genome), 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 (study of epigenetic alterations), and as well as integrating a number of algorithmic methods to identify molecular features,using advanced machine learning algorithms.


The TBioInfo platform has a number of advanced machine learning algorithms including,  Biassociation algorithm, which was used to integrate a number of different omics data types.This includes RNA expression, cell mutations, and drug effectiveness 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.