Precision Medicine

“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.

Current Projects

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.

Genomics in big data analytics / cloud computing pipelines 

ICBI develops and applies computational pipelines for analysis of molecular profiling data from high-throughput genome wide technologies such as Next Generation Sequencing, gene and microRNA expression, DNA copy number, proteomics, metabolomics, viroinformatics and metagenomics as well as Immuno-Oncology ICBI’s Dr. Yuriy Gusev, Dr. Matthew McCoy and Krithika Bhuvaneshwar are involved in these efforts. Some of our pipelines/software packages developed include viGEN and CINdex.

The research information technology group at ICBI develops innovative scientific software to enable translational research. Our projects include muti-omics data analysis, vaccine safety research, clinical data analysis, high definition data visualization, natural language processing, and mobile application development. We have a total of 60 github repositories. More about ICBI’s Open Science efforts is available here.