“Transforming Health Through Accurate Understanding of Genes, Environment and Lifestyle”
Precision medicine (PM) is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. While significant advances in precision medicine have been made for select cancers, the practice is not currently in use for most diseases. Many efforts are underway to help make precision medicine the norm rather than the exception.
Governments, hospitals, the pharmaceutical section and the technology industry are making large investments and plans to develop targeted therapies, earlier screening for diseases and smarter monitoring and adjustment of treatments.
Cancer molecular profiling and support for MedStar tumor board – For a number of years, physicians within MedStar Health and Georgetown University have been performing molecular profiling (MP) of the tumors of cancer patients through the outsourcing of samples to vendors such as Caris and Foundation Medicine. MP finds molecular alterations in tumors, including DNA mutations and gene or protein expression changes, with the goal of recommending targeted therapies. ICBI is working closely with molecular pathology at Georgetown University Hospital, Lombardi comprehensive cancer center and Ruesch GI cancer center to acquire, process and report on molecular profiling data on MedStar cancer patients from molecular diagnostic labs. We are generating results on marker correlation within and across patient groups as well as integrating outcome data from EHRs.
Patient-specific networks for recommending targeted therapies – In many cases MP may be used when a cancer patient has few or no treatment options left. In that case, an individual may receive an off-label therapy that is prescribed for their alteration in another tumor type. Our goal is to expand the range of options of targeted therapies for cancer patients who undergo molecular profiling by developing CDGnet (Cancer-Drug-Gene network), a user-friendly, evidence-based approach that accounts for the cross-talk within and between pathways in cancer and is personalized for the individual patient. Our current prototype, which uses the shiny framework with an R backend, is available at: This work is funded through an R21 grant from the NCI/ITCR program (PI Boca).
Biocuration of chemopredictive markers – The selection of personalized cancer therapy based upon a patient’s molecular profile requires an enormous amount of data wrangling to collect, review, analyze and integrate molecular, clinical, patient-specific history and pharmacological data. We are developing data wrangling approaches including Natural Language Processing (NLP) to retrieve, structure, and curate information from personalized-therapy related publications and clinical trials data. Once curated, the structured data can be used to generate novel scientific hypotheses, design new studies, obtain a better understanding of biological mechanisms of disease, perform meta-analyses, and create clinical decision support systems. Clinical researchers interested in using public data for information on personalized medicine are often reduced to trying various keyword combinations using multiple resources or a web search engine and browsing through numerous hits hoping for studies with relevant results. The main goal of this project is to build a centralized, publicly available resource using NLP tools to extract, standardize, and organize relevant molecular information and treatment options from personalized medicine related publications and clinical studies. Given the proliferation of biomarker research and the lack of efficient approaches for searching and displaying relevant literature this research would address both technical challenges to big data wrangling and the need to enhance understanding of drug efficacy through outcomes research. Our efforts support the paradigm shift from focusing on choosing drugs based on diseases to choosing drugs based on biomarker status for a particular disease or, in some cases, based solely on molecular biomarkers.
Patient outcome data collection and analysis – ICBI is collaborating with COTA (Cancer Outcomes Tracking and Analysis) to collect and organize patient outcomes from cancer patients seen at MedStar hospitals with the goal of improving outcomes for patients. COTA is a cloud-based program, which collects select oncological case level data in order to provide three unique real time functions – cancer sorting based on clinical and molecular signatures, track outcomes including progression free survival, overall survival and cost, and reporting for clinical and research needs.
ClinGen – We are collaborating with NHGRI, ACMG and a number of other academic and industry organizations to help determine which genetic variants are most relevant to patient care by harnessing both research data and the data from the hundreds of thousands of clinical genetics tests being performed each year, as well as supporting expert curation of these data. We are specifically involved in the Somatic Workgroup with a mission of ensuring the appropriate annotation and interpretation of cancer somatic variants for clinical applications and development of practice guidelines.
Barriers to implementation of Precision Medicine – Precision medicine promises to use screening and diagnostic tests to reduce harms and improve outcomes; the challenge is knowing when and how to apply these tests so that they represent a cost-effective use of resources and are reimbursed by both public and private health insurance payers. The majority of personalized tests are introduced into practice with incomplete knowledge about their impact on healthcare costs or their ability to improve health compared to standard care. We are working with Georgetown’s McCourt school of policy, Massive Data initiative and the Health policy institute to use an innovative big data analysis framework that brings together quantitative science and legal analytics to help understand the impact of provider, patient, and system factors on the prioritization of competing strategies for the delivery of personalized cancer care in the community. Our ultimate goal is to develop a framework that can be used to identify barriers to implementation of precision medicine at each institution and enable proponents to address these barriers within their own organizations.