SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets

Jing Guo1, Hui Liu2, Jie Zheng3

  • 1School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore.

Nucleic Acids Research
|October 31, 2015
PubMed

Insights

Synthetic lethality (SL) exploits gene interactions to selectively kill cancer cells. A new database, SynLethDB, consolidates SL gene pairs and aids in developing targeted cancer therapies with reduced side effects.

Area of Science:

  • Genetics
  • Genomics
  • Bioinformatics

Background:

  • Synthetic lethality (SL) describes genetic interactions where simultaneous gene perturbations cause cell death, offering a targeted cancer therapy approach.
  • SL exploits cancer-specific genetic differences to selectively eliminate malignant cells while sparing normal tissues, potentially reducing chemotherapy side effects.
  • Existing methods for identifying SL gene pairs are fragmented, necessitating a centralized resource.

Purpose of the Study:

  • To introduce SynLethDB, a comprehensive database for synthetic lethality (SL) gene pairs and associated knowledge.
  • To integrate SL data from diverse sources including experimental assays, databases, computational predictions, and text mining.
  • To provide analytical tools for assessing the druggability and cancer cell sensitivity of SL partners.

Main Methods:

  • Collected and curated SL gene pairs from human and four model organisms (mouse, fruit fly, worm, yeast).
  • Developed a confidence scoring system integrating multiple evidence sources for each SL pair.
  • Integrated large-scale genomic, gene expression, and drug sensitivity data for over 1000 cancer cell lines.
  • Implemented functionalities for data retrieval, orthology search, gene set enrichment analysis, and a user-friendly web interface.

Main Results:

  • SynLethDB provides a unified repository of experimentally validated and computationally predicted SL gene pairs.
  • A confidence score quantifies the reliability of each identified SL pair.
  • The database includes analytical modules to predict cancer cell response to drugs targeting SL partners.
  • SynLethDB supports advanced data mining and interpretation through its integrated functionalities.

Conclusions:

  • SynLethDB serves as a valuable resource for researchers and the pharmaceutical industry in the field of synthetic lethality.
  • The database facilitates the discovery and development of novel, targeted cancer therapies.
  • SynLethDB's integrated data and analytical tools empower deeper insights into SL interactions and their therapeutic potential.

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