1. While gene expression data is widely available describing mRNA levels in different cancer cells lines, the molecular regulatory mechanisms responsible for these changes are still poorly understood. Here we developed a rationale approach to infer regulatory mechanisms governing changes in gene expression by integrating datasets of protein/DNA interactions, protein- protein interactions and kinase-substrate interactions collected from prior biological knowledge. We first utilize data obtained from genome-wide ChIP-on-chip and ChIP-Seq experiments to connect mRNA expression levels of the NCI-60 cancer cell lines to the transcription factors most likely regulating them. These identified transcription factors are then “connected”, using known protein-protein interactions, to form cancer specific sub-networks. Within these sub-networks we assess the enrichment for protein kinase substrates to infer the protein kinases likely regulating these complexes. Finally, using quantitative comparison of the up and down regulated genes for each cancer cell line, and genes affected by FDA approved drugs applied to cancer cells, we predict the mechanisms of action of these drugs. Following this path, from changes in gene expression to transcription factors to protein kinases we can provide a more thorough understanding of the regulatory mechanisms behind the observed mRNA levels in the NCI-60 cancer cell lines and other cancer cells. This approach proposes mechanisms of action for drugs. Wet lab experimental validation of this approach is still necessary, it can be done using single drugs or combinations of them. ChEA Genes2Networks KEA This research was supported by NIH Grant No. 5P50GM071558 Regulatory Signatures of Cancer Cell Lines Inferred from Expression Data Jayanth (Jay) Krishnan 1,2, Avi Ma’ayan 2 1 Mahopac High School, Mahopac, NY 10541 1 Mahopac High School, Mahopac, NY 10541 2 Systems Biology Center New York and Department of Pharmacology and Systems 2 2 Systems Biology Center New York and Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York NY Regulatory Signatures of Cancer Cell Lines Inferred from Expression Data Jayanth (Jay) Krishnan 1,2, Avi Ma’ayan 2 1 Mahopac High School, Mahopac, NY 10541 1 Mahopac High School, Mahopac, NY 10541 2 Systems Biology Center New York and Department of Pharmacology and Systems 2 2 Systems Biology Center New York and Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York NY Abstract Acknowledgements Introduction Workflow The NCI-60 database provides mRNA profiles from microarray experiments of 60 commonly studies cancer cell lines Although analyzing these mRNA values is a reliable method to measure the mRNA level of many genes within a cell, this method offers little clues about how cells are regulated While mRNA profiles indicates changes caused by cancer, understanding the underlying regulatory mechanisms disregulated in different cancers will bring us closer to therapeutics In this project we aim to identify the transcription factors, protein complexes and protein kinases responsible for the aberrant expression of genes in the various types of cancer cell lines Differentially expressed gene lists from the various NCI-60 Cancer Cell Lines are used as input. Over expressed and under expressed genes are identified for specific cancer cell lines The following algorithm was implemented: The NCI-60 database was parsed and 18,133 unique genes were identified The population mean for the expression of each of the genes across all the 60 cancer cell lines was calculated The sample mean and sigma for each (gene, cancer cell line) pair was calculated The two-sided T-test statistic was applied for each (gene, cancer cell line) pair. Whether the gene was over expressed or under expressed was calculated by checking whether the test statistic exceeded a critical T score or was a less than a critical T score determined based on a particular P value. A list of genes which are over/under expressed for multiple cancer cell lines was developed Analyzing the mRNA profile from the NCI-60 database Statistical Methods: ChEA, Genes2Networks, and KEA are all web-based tools developed at the Ma’ayan lab to allow users to predict which transcription factors, protein sub- networks, and protein kinases are most correlated with their inputted seed list By using the identified up and down regulated genes for each cancer cell line as an input for ChEA; the top ranked transcription factors (based on p-value from Fisher’s Exact Test) that most likely influence the input seed list are given as the output Future Research Future research involves further analyzing other cancer datasets Cluster analysis will be done to groups transcription factors or kinases that were identified Additionally, by combining such data with data collected for drug perturbation of these cells, we may be able to suggest which drugs can reverse the observed changes Genes2Networks The transcription factor output for each cancer cell line from ChEA is used as an input to Genes2Networks Genes2Networks connects lists of transcription factors with other protein intermediates from mammalian protein interactions databases KEA The unique protein sub-networks outputted by Genes2Networks can then be inputted into KEA which identifies protein kinases most likely regulating the proteins from the subnetwork using the Fisher’s Exact Test. At this stage top regulating transcription factors, protein sub-networks and kinases have been identified for each of the NCI-60 cancer cell lines An integrated matrix can now be created in order to holistically compare the data by displaying the top regulating elements and their putative effects on the different cell lines Example of Process Microarray Analyze mRNA profile from NCI -60 database by using statistical techniques to compute over/under expressed genes Identify protein sub-networks that “connect” the transcription factors through additional proteins Wet lab experimental validation Future Research Identify protein sub-networks that “connect” the transcription factors through additional proteins Wet lab experimental validation Top ranked protein kinases most likely regulating the protein sub-networks Top 222 over expressed genes for cancer cell line MDA_N (melanoma) With gene input, ChEA identified the top ranked transcription factors Genes2Networks output of protein sub- networks when top 10 transcription factors from ChEA were given as an input Top ranked kinase proteins identified from KEA