Improving Quality of Life Cluster


Precision Health Imaging and Genetics (PHIG)

One of the promises of precision medicine is that genomic data may better inform diagnosis and individualized treatment of patients with potential to develop specific disease states. Advances in multimodal medical imaging (computed tomography, magnetic resonance imaging) have become central to modern medicine and individualized therapy. While the genomics provide information about long-term traits of an individual, the medical imaging profiles the individual’s current state. The confluence of genomics and medical imaging hence will provide the necessary substrate to make precision health a reality. Researchers in this focus group work on multimodal imaging and genetics to drive clinical application of knowledge extracted from this aggregate data. Central to this proposal is the prediction of individuals’ health outcome based on quantitative characterization of imaging phenotypes using advanced imaging and large-scale genomic data.

Pharmaceutical Data Sciences

Despite innovative drug discovery and implementation of evidence-based treatment guidelines, sub-optimal medication response rates, in the range of 30% to 60%, are still observed in clinical practice for many conditions (e.g., depression, schizophrenia, rheumatoid arthritis, cardiovascular diseases, cystic fibrosis, and pain management, to name a few). Unfortunately, clinicians lack effective tools to predict drug responses and adverse drug reactions. Pharmacogenomics, including the millions of genes located within our microbiomes, has tremendous potential to improve treatment success and prevent unnecessary drug toxicity. A wealth of pharmacogenomics data is emerging; however, the best evidence is not effectively implemented in the clinical setting. To bridge this gap between the advances of pharmacogenomics discovery and clinical practice, and yield optimal outcomes for patients, the Skaggs School of Pharmacy and Pharmaceutical Sciences is creating a program at UCSD with informatics and computational innovations to collect, link, store, and analyze pharmacogenomics with related data (e.g., multi-omic, pharmacokinetic/dynamic, metabolomic, and other phenotypic information), and serve as a global resource center to integrate pharmacogenomics information into clinical decision-making tools. Pharmacogenomics information is inherently “big data”, sourced from whole genome sequencing or single gene analysis linking to related data sources. Informatics platforms to link these data with phenotypes for effective clinical translations do not exist. Collaborating with HDSI, we plan to design informatics tools, equipment, and projects to collect, link, store, and analyze pharmacogenomics and related data, facilitating and stimulating research that results in new clinical decision-making tools.

Cancer Cytogenomics and Immunogenomics

In the ‘early’ days, tumors were examined by looking at chromosomes using fluorescence microscopy, staining directly for DNA, but also using fluorescent hybridization to probe specific markers. The approach was quite successful identifying genetic aberrations that are the hallmark of cancer. However, these approaches were limited in the number of genes that could be probed. In recent years, next generation sequencing has become the technology of choice, and almost completely replaced older cytogenetic studies. While NGS is very precise in getting genomic variation down to nucleotides, and copy number changes, it can be quite misleading wrt the spatial localization of amplified oncogenes (Turner, 2017, Nature). We plan to collaborate with scientists to combine Cytogenetics (Fluorescent images of chromosomes and proteins), and next generation sequencing (Genomics) data, and develop computational techniques to improve our understanding of large chromosomal and extra-chromosomal lesions and their role in cancer. The four year goal of the Center for Cytogenetics is to engineer new technologies and use them to gather data from hundreds of cells from thousands of patients from all cancer subtypes, and correlate that data with treatment outcome and other phenotypic variables from the patients. This Petabyte scale data will be in the form of quantifiable statistics for each patient through the course of their tumor progression, and will constitute an invaluable resource for developing new cancer therapies.

Sustainable Development

Is it possible to raise the standard of living and quality of life for all (economic and human capital), while at the same time safeguarding natural resources (natural capital) for future generations? A scientifically-based sustainable development agenda requires a deep understanding of the short- and long- term dynamics of and feedbacks between human and natural systems. We integrate a wide variety of environmental and societal data to understand these complex couplings and build testable models for future planetary health.

  • Chaitan Baru San Diego Supercomputer Center
  • Eli Berman Economics
  • Jennifer Burney Global Policy & Strategy
  • Richard Carson Economics
  • Teevrat Garg School of Global Policy & Strategy
  • Gordon Hanson Global Policy & Strategy and Economics
  • Grace Kuo Clinical Pharmacy and Family Medicine & Public Health
  • Brett Stalbaum Visual Arts
  • Kamala Visweswaran Ethnic Studies
  • Pinar Yoldas Visual Arts

Cancer Biology

Systems Medicine

Medical and Population Genomics

Neurogenetics, Cell Biology and Imaging Data Processing

Computational Ophthalmology and Virtual Histology