Title: Sequence, structure and network methods to uncover cancer genes
Abstract: A major aim of cancer genomics is to pinpoint which somatically mutated genes are involved in tumor initiation and progression. This is a difficult task, as numerous somatic mutations are typically observed in each cancer genome, only a subset of which are cancer-relevant, and very few genes are found to be somatically mutated across large numbers of individuals. In this talk, I will overview three methods my group has introduced for identifying cancer genes. First, I will present a framework for uncovering cancer genes, differential mutation analysis, that compares the mutational profiles of genes across cancer genomes with their natural germline variation across healthy individuals. Next, I will show how to leverage per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are altered across (i.e., “cover”) a large fraction of individuals. Finally, I will demonstrate that cancer genes can be discovered by identifying genes whose interaction interfaces are enriched in somatic mutations. Overall, these methods recapitulate known cancer driver genes, and discover novel, and sometimes rarely-mutated, genes with likely roles in cancer.

Biography: Mona Singh obtained her AB and SM degrees at Harvard University, and her PhD at MIT, all three in Computer Science. She did postdoctoral work at the Whitehead Institute for Biomedical Research. She has been on the faculty at Princeton since 1999, and currently she is Professor of Computer Science in the Department of Computer Science and the Lewis-Sigler Institute for Integrative Genomics. Her group works broadly in computational molecular biology, as well as its interface with machine learning and algorithms. Her group is especially interested in developing methods for predicting and characterizing protein specificity, interactions, and networks. She is currently a Methods Editor at PLOS Computational Biology, has been program committee chair for several major computational biology conferences, including ISMB (2010), WABI (2010), ACM-BCB (2012), and RECOMB (2016), and has been Chair of the NIH Modeling and Analysis of Biological Systems Study Section (2012-2014). She received the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2001, and is an ISCB Fellow.

Title: Huge cohorts, genomics, and clinical data to personalize medicine [download presentation]
Abstract: Precision medicine offers the promise of improved diagnosis and for more effective, patient-specific therapies. Typically, such studies have been pursued using research cohorts. At Vanderbilt, we have linked de-identified electronic health records (EHRs), to a DNA repository, called BioVU, which has nearly 250,000 samples. Through BioVU and a NHGRI-funded network using EHRs for discovery, the Electronic Medical Records and Genomics (eMERGE) network, we have used clinical data of genomic basis of disease and drug response using real-world clinical data. The EHR also enables the inverse experiment – starting with a genotype and discovering all the phenotypes with which it is associated – a phenome-wide association study. By looking for clusters of diseases and symptoms through phenotype risk scores, we find unrecognized genetic variants associated with common disease. The era of huge international cohorts such as the UK Biobank, Million Veteran Program, and the newly started All of Us Research Program will make millions of individuals available with dense molecular and phenotypic data. All of Us launched May 6, 2018 and will engage one million diverse individuals across the US who will contribute data and also receive results back.

Biography: Dr. Joshua Denny is Professor of Biomedical Informatics and Medicine, Director of the Center for Precision Medicine and Vice President of Personalized Medicine at Vanderbilt University Medical Center. He is a Fellow of the American College of Medical Informatics and a member of the National Academy of Medicine. He has substantial experience in the design, development, and implementation of electronic health record (EHR) data mining algorithms and was the primary author of several natural language processing systems to support phenotype extraction algorithms for genomic research projects, including development of the phenome-wide association study (PheWAS) method. He is principal investigator (PI) of nodes in the Electronic Medical Records and Genomics (eMERGE) Network, Pharmacogenomics Research Network (PGRN), and the Implementing Genomics into Practice (IGNITE) Network. Dr. Denny is PI of the Data and Research Center of the Precision Medicine Initiative All of Us Research Program (previously called the Precision Medicine Initiative Cohort Program), which will eventually enroll at least 1 million Americans in an effort to understand the genetic, environmental, and behavioral factors that influence human health and disease. To date, he has led >100 genome-wide and candidate gene association studies using EHR data linked to genetic data. Dr. Denny serves on a number of mentoring committees and has trained >30 postdoctoral and predoctoral trainees.

Title: The Art of Connectivity Mapping [download presentation]
Abstract: Motivation: The powerful idea of the Connectivity Mapping proposes the creation of a library of drug induced gene expression signatures. Such a resource can facilitate finding small molecules to mimic or reverse disease signatures, identifying drug targets, discovering the mechanisms of action for novel small molecules, elucidating off-target effect mechanisms, and directing cellular differentiation and reprogramming. A related concept is Gene Set Enrichment Analysis.
Problem statement: In my presentation I will discuss how these two transformative ideas can be expanded in various creative ways to unify knowledge representation in system biology.
Approach: I will demonstrate how expanded Connectivity Mapping and Gene Set Enrichment Analyses combined with Machine Learning can enable imputing and illuminating new biological and pharmacological knowledge.

Biography: The Ma’ayan laboratory applies computational methods to study the complexity of regulatory networks in mammalian cells. We develop software tools to study how molecular intracellular regulatory networks control cellular processes such as differentiation, proliferation, and apoptosis. Our main focus is in developing methods that link changes in genome-wide gene and protein expression to transcriptional regulators and cell signaling pathways. So far we published several popular web-based software tools and databases that enable biologists to perform enrichment analyses and build networks using their own data. By employing the tools we have developed, we work closely with experimental biologists on projects that utilize high-throughput experiments to understand cell regulation at a global scale. So far we published over 130 peer-reviewed articles where several of them are in top-tier journals and are highly cited. In addition, we have mentored over 80 trainees, including postdoctoral fellows, graduate and medical students, undergraduates and high school students. We also contribute to educational and outreach activities of these initiatives including teaching two MOOCs on the Coursera platform. In the past four years we have served as an integral part of the BD2K-LINCS Data Coordination and Integration Center (DCIC), and the Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (KMC-IDG) project, where we are centrally involved in the development of computational methods, databases and web-based software tools for LINCS, IDG and BD2K. Recently, we also became involved with the construction and design of the NIH Data Commons pilot project.