PSB'14 Cancer Panomics Session

Background

Precision medicine promises to transform cancer treatment in the next decade through the use of high-throughput sequencing and other technologies to identify telltale molecular aberrations that in turn suggest therapeutic vulnerabilities of each patient’s tumor. Beyond generalized cancer therapies that are indiscriminate in nature, we aim to address the “panomics” of cancer – the complex combination of patient-specific characteristics that drive the development of each person’s tumor and response to therapy. The American Society of Clinical Oncology has used this definition of cancer panomics in their vision document: Shaping the Future of Oncology: Envisioning Cancer Care in 2030. The realization of this vision will require new infrastructure and computational methods to integrate this data effectively and query it in real-time for therapy and/or clinical trial selection for each patient.

What is Cancer Panomics & why it matters?

Precision oncology will entail obtaining somatic mutation, transcript and methylation profiles of a patient’s tumor and matched normal tissue from high-throughput sequence data. A major challenge for bioinformatics is to then combine this information with prior biological knowledge such as pathway information, protein-protein interactions, and biomedical literature, with pertinent outcome or other diagnostic data obtained from the patient’s medical record, and the database of available clinical trials. The computational challenges of such integrative data analysis are significant and demand a rethinking of how data is processed and aggregated from the raw molecular/sequence data level to the therapeutic options report, to make this process more efficient, accurate, and enlightening for clinicians, researchers and patients.

We aim to explore the computational needs to enable precision oncology through cancer panomics, and to encourage computational biologists in academia and industry to come together to tackle the hard problems to implement such a vision. We seek original contributions that discuss new methods or algorithms/infrastructure able to integrate two or more “omics” and/or clinical data types aimed to enable precision medicine in individual cancer patients (as opposed to pattern finding algorithms for cross-sectional studies). Submission could also formally pose a novel problem in cancer integrative analysis that the community will need to address, but more often will describe algorithms, models, and original solutions to a specific data integration analysis problem in cancer.

• Integrative analysis of high-throughput "omics" data from related samples or data types
• Pathway disruption analysis by combining data from different "omics" sources in single patients
• Joint analysis of "omics" data, literature, clinical trial data, and medical records
• Data structures & systems to enable big-data integrative analysis in patients

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  Søren Brunak, Ph.D.

  Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark
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  Francisco M. De La Vega, D.Sc.

  Real Time Genomics, Inc., San Bruno, CA
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  Gunnar Rätsch, Ph.D.

  Memorial Sloan-Kettering Cancer Center, NY, NY
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  Joshua M. Stuart, Ph.D.

  Center for Biomolecular Science and Engineering. University of California Santa Cruz.