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'Big data' approaches for novel anti-cancer drug discovery

Graeme Benstead-Hume1, Sarah K Wooller1, Frances M G Pearl1

  • 1a Bioinformatics Group, School of Life Sciences , University of Sussex , Brighton , United Kingdom.

Expert Opinion on Drug Discovery
|May 3, 2017
PubMed
Abstract

Insights

Big data analysis is revolutionizing cancer research by identifying key genes and pathways driving tumor growth. This approach accelerates the discovery of novel drug targets for more effective cancer therapies.

Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Improved cancer therapies remain a critical unmet medical need.
  • Advances in platform technologies and biological 'big data' offer new opportunities for cancer research.
  • Systematic identification of tumorigenesis drivers can lead to novel therapeutic interventions.

Purpose of the Study:

  • To discuss current approaches utilizing 'big data' for cancer driver identification.
  • To review the use of big data in discovering novel drug targets.
  • To provide an overview of data repositories and tools in cancer drug discovery.

Main Methods:

  • Analysis of genomic sequencing data.
  • Pathway data analysis.
  • Multi-platform data integration.
  • Identification of genetic interactions (e.g., synthetic lethality).
  • Cell line data utilization.

Main Results:

  • Big data approaches enable systematic identification of cancer drivers.
  • These methods facilitate the discovery of novel therapeutic targets.
  • Various data repositories and tools are available for cancer drug discovery.

Conclusions:

  • Genomic-event-based targeted therapies will shape future cancer treatment protocols.
  • Developing a broad range of targeted drugs is essential for personalized cancer care.
  • Big data analytics are pivotal in advancing precision oncology.

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