Private: Speakers: Subha Madhavan, PhD

Subha Madhavan, PhD

 

Chief Data Scientist
Georgetown University Medical Center

 

Dr. Madhavan is PI and Director of the Innovation Center for Biomedical Informatics (ICBI), Chief Data Scientist at the Georgetown University Medical Center and Associate Professor of Oncology. Her current research focuses on machine learning approaches for data-driven decision making across the continuum that spans molecular sciences to clinical sciences. She is a world-class leader in data science, clinical informatics and health IT who is responsible for several biomedical informatics efforts including the Georgetown Database of Cancer (G-DOC) platform, a resource for both researchers and clinicians to realize the goals of personalized medicine. Her team won the Service to America (SAMMIES) Award in early 2000’s for building a novel brain tumor database that connected biomarker data with clinical outcomes for the first time to drive discovery for this deadly disease.

She has led the development of honest broker services for access to over 4 million patient records from 10 MedStar Health hospitals, Howard University Hospital and VA-DC to create a living laboratory for clinical translational research in the Greater Washington area. She was co-PI of the FDA Center for Excellence in Regulatory Science and Innovation (CERSI) and led projects that applied data science methods to Pharmacogenomics and vaccine safety data. Recently, her team is running a data science challenge on biomarker discovery in brain tumors (based on the database from the SAMMIES award) for PrecisionFDA. She is a fellow of the American College of Medical Informaticians (ACMI) and has contributed to novel bioinformatics findings in research articles published in journals such as Nature, Nature Scientific Data, Clinical Pharmacology and Therapeutics, Bioinformatics, Clinical Cancer Research, Frontiers in Oncology, Cancer Informatics, and Molecular Cancer Research (MCR).

She is the Chair of the scientific committee of the Georgetown annual symposium on Health Informatics and Data Science which attracts >300 participants annually from Pharma/Biotech, Federal Government, Health IT companies, Professional societies and academic organizations. Recognizing the need for skilled data scientists in life sciences, especially in Pharma/Biotech and healthcare industries, Dr. Madhavan and her team have launched a new graduate degree program in Health Informatics and Data Science (https://healthinformatics.georgetown.edu/).

Virtual Molecular Tumor Boards – A Global Sharing of Genomic Expertise in Clinical Practice

The cancer research community is on the precipice of transformational advancements in understanding tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been stymied by siloed efforts to meaningfully collect, interpret and aggregate disparate data types from multiple high-throughput modalities into clinical care processes. Many informatics efforts focused on genomic interpretation resources for neoplasms are underway to support case review and treatment planning. Molecular Tumor Boards (MTBs) are a collaborative effort of multi-disciplinary cancer experts equipped with sophisticated genomic interpretation tools to aid in the delivery of accurate and timely clinical interpretations of complex genomic results for each case, within an institution or hospital network. Virtual Molecular Tumor Boards (VMTBs) provide an online forum for collaborative governance, provenance and information sharing between experts around the world. Knowledge sharing in VMTBs and constant communication with guideline developing organizations can lead to progress evidenced by data harmonization across multiple resources, crowd-sourced and expert-curated genomic assertions, and a more informed and explainable usage of artificial intelligence in such settings. Advances in cancer genomics interpretation aid in better patient and disease classification, more streamlined identification of relevant literature, and a more thorough review of available treatments and predicted patient outcomes.