The goal of precision medicine, as laid out by the National Academy of Sciences, “is to embed our scientific enterprise into the normal course of clinical care.” As a result, clinical discovery reaches beyond clinical trials and research using human samples and data. It also involves incorporating trial and research results, electronic health records and other types of personal health data into the clinic. Those findings are then shared with other clinicians, researchers, policy makers and technology developers through the knowledge network.

By analyzing clinical information from a large number of healthy participants and patients with a given condition and combining that data with genetics, treatment response and other information, we can identify patterns of health, disease and treatment that might otherwise be invisible. In this way, precision medicine translates research discoveries into clinical practice. In turn, clinical discovery plays an essential role in developing and making resources available to researchers.

One invaluable tool for clinical discovery is a biobank, a repository of tissues and accompanying data that can be used for research purposes. UCSF biobanks are vital to obtaining molecular and genomic data, but they are only fully useful if they have consistent, accessible and high-quality samples. To that end, the UCSF Clinical and Translational Science Institute is devising consistent and reliable approaches for consent, tissue collection and use, as well as creating a virtual biorepository that links all biobanks throughout UCSF.

Digital health goes hand in hand with clinical data collection. Wearable devices and clinical instruments that can record patient information accurately can provide a plethora of new information. Electronic health records are key to incorporating clinical information – including diagnostic tests, demographics and genomic data – and sharing information between clinicians, researchers and patients.

The relationship between clinical discovery and basic discovery is a dynamic one. Next-generation sequencing (NGS), for example, was developed in the basic discovery setting, but is now the underlying ‘omic technology behind many clinical precision medicine projects, including a UCSF investigation of the use of NGS in rapid identification of infectious diseases. Similarly, patterns revealed by clinical trial data and biobank specimens can inform new research questions.

Current Projects

  • Next-generation sequencing diagnosis of infectious disease
    UCSF’s Charles Chiu’s demonstration project will validate the use of a next-generation sequencing test for patients presenting with encephalitis, meningitis, sepsis or pneumonia. The test is one of two demonstration projects funded by the California Initiative to Advance Precision Medicine.
  • UCSF Biobanking and Biospecimen Initiative
    UCSF’s Clinical and Translational Science Institute is developing infrastructure for consent, acquisition and management of human biospecimens. These specimens will be coupled with electronic health records and become part of an annotated, searchable web-based biospecimen database that includes more than 150 legacy biobanks across campus. The Biobanking and Biospecimen Science Symposium (February 18, 2016) was livestreamed and may be viewed here.
  • The Center for Maternal-Fetal Precision Medicine
    This trans-disciplinary program is designed to improve understanding and treatment of patients with congenital anomalies and pregnancy complications. The program aims to integrate existing demographic, epidemiological, social, environmental, clinical and biomarker data in an effort to predict risk of adverse outcomes and response to treatment. By doing so, the program will create stronger bridges between basic research and clinical applications, and improve maternal, fetal and neonatal care.
  • MS Bioscreen
    MS Bioscreen is an iPad app that allows patients and practitioners to see an individual’s data in comparison to others. In pilot studies of MS Bioscreen, giving patients access to this visualized data helped them understand why they were following a given course of treatment and increased compliance to treatment plans.