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Over the last decade, there has been a significant rise in the use of high throughput structural genomic data to guide cancer scientific discovery and its clinical translation. Investigators and molecular tumor boards leverage these genetic data (DNA variants, copy number variants, translocations and fusions) to define patient eligibility and to target patient specific therapeutic interventions. Despite this progress, the number of actionable genetic lesions remains small (a few hundred) and our knowledge of how such variants influence cell function and therapeutic sensitivity remains limited. Moreover, each patient and each tumor environment represents a different context in which the cancer cell develops and survives, with effects on cellular molecular states and profiles. Integrating a wider range of data types (e.g., gene and protein expression, metabolic, epigenetic) provides a depth of functional information to better elucidate what drives cancer etiology and progression, how to detect it, and importantly how to most effectively treat it.

The SFG program has 36 members drawn from 12 departments across UCSD schools. Members of the SFG program provide international leadership in defining novel approaches to the characterization of targetable alterations, molecular signatures, and networks. The increasing breadth of expertise and interdisciplinary nature of SFG (experimental, computational, and translational) position it well to continue to lead the field: SFG will continue pursuing innovative solutions to the challenge of integrating large scale datasets to define candidate clinical tools and therapeutic targets for direct benefit to patients in the MCC catchment area, and beyond.


Program Goals

SFG seeks to acquire and use the full range of structural and functional genomics data and to relate them to biological and cancer phenotypes with the overall goal of leveraging new knowledge and data to generate hypotheses for validation and translation to support the diagnosis and treatment of cancer.

The specific aims of the SFG program are as follows:

  1. Computational Discovery Sciences: Develop innovative, integrative computational genomic methods that synthesize multi-omic patient and clinical data to drive fundamental and translational cancer research and to disseminate these tools and methods to the cancer research community.
  2. Analyze and integrate genetic, transcriptomic, epigenetic, proteomic, immunologic, and metabolomic data to elucidate the underlying biological pathways and mechanisms of cancer development and progression.
  3. Define context dependent, functional states of tumor cells and understand the dynamics of response or resistance in order to identify novel diagnostic, prognostic, and therapeutic strategies.