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B. May

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    OS02 - Emerging Technologies - Block Chain / Genomics (ID 3)

    • Event: e-Health 2018 Virtual Meeting
    • Type: Oral Session
    • Track: Clinical Delivery
    • Presentations: 1
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      OS02.03 - Genomics Data and Cancer: Risk Prevention to Diagnosis and Treatment (ID 42)

      B. May, CGI; Victoria/CA

      • Abstract
      • Slides

      Purpose/Objectives: The purpose of this presentation is to provide an overview of the current state of genomics data use and to highlight the associated successes and limitations. The applications addressed will encompass the pursuit to provide effective and personalized methods of cancer prevention, diagnosis, and treatment. Furthermore, technical considerations that would allow improved data sharing and analysis will be presented.

      Methodology/Approach: Current literature and clinical standards have been assessed to illustrate initiatives taken to catalogue the gene-level data pertaining to: the DNA-related factors correlated with increased rates of cancer development, the genetic fingerprints of observed cancer types, and the expected efficacy of different chemotherapy regimens. Furthermore, a comparative analysis on the efficacy of these initiatives when implemented are compared with standard treatment protocols. Lastly, the barriers inhibiting the large-scale implementation of individualized genomic data in personalized medicine will be addressed.

      Finding/Results: Personalized medicine has become an appealing facet of cancer management strategies for its perceived suitability in handling a disease with a unique genetic fingerprint between individuals. However, its implementation has been piecewise and in need of improved coordination because of the vast array of technological factors and related clinical directions. Genetic markers that indicate an individual’s predisposition to cancer have been identified in many cell types but large-scale intervention by means of changes to health care standards have been slow to act on such data. Major barriers to this include the cost of genome sequencing and the immense amount of information that needs to be maintained and analyzed. In spite of this, the long-term relative savings in health care could be significant. Many oncologists currently use xenografts to gather patient-specific genomic data of cancerous cells; however, this does not always involve full genome sequencing. Although this provides a more personalized approach in designing treatment protocols, it fails to encapsulate fully many important factors at the genomic level. Full genome sequencing could provide more detailed evidence regarding prognosis and which drugs would be most effective for individual patients. Lastly, given the sheer number of different genetic presentations of cancer, a large collaborative network of researchers and clinicians would be required to provide an information library that adequately spans the disease’s genetic manifestations. Reliable data storage, access, and analysis at this scale presents concerns over the near-future feasibility of personalized genomic medicine.

      Conclusion/Implications/Recommendations: While the benefits of personalized medicine have been addressed in many healthcare contexts, full-scale data at the genomic level has yet to be included from institutions outside of academic contexts. Technological concerns regarding data access and analysis across practitioners and researchers highlight the need to develop specialized data sharing methods and techniques.

      140 Character Summary: There is a wealth of knowledge related to cancer available at the genomic level but means of data sharing and analysis are needed to allow the benefits to be realized.

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