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Rapid Assembly of Multi-Gene Constructs using Modular Golden Gate Cloning
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FUNGI: FUsioN Gene Integration toolset.

Alejandra Cervera1,2, Heidi Rausio3, Tiia Kähkönen3

  • 1Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland.

Bioinformatics (Oxford, England)
|March 27, 2021
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Summary

The FUNGI toolset identifies and prioritizes cancer fusion genes, aiding in biomarker discovery and drug targeting. Applied to ovarian cancer, it validated 10 of 11 detected fusion genes, many impacting the PI3K-AKT pathway.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Fusion genes are crucial cancer biomarkers and drug targets.
  • Genomic instability complicates the identification of relevant fusion events.
  • The FUsionN Gene Identification toolset (FUNGI) was developed to address these challenges.

Purpose of the Study:

  • To develop and validate a computational tool for identifying and prioritizing fusion genes.
  • To apply the toolset to a real-world cancer dataset for biomarker discovery.
  • To investigate the potential role of identified fusion genes in treatment resistance.

Main Methods:

  • FUNGI utilizes an ensemble of fusion detection algorithms.
  • Prioritization and visualization modules are integrated within FUNGI.
  • The toolset was applied to an ovarian cancer dataset comprising 107 tumor samples from 36 patients.

Main Results:

  • FUNGI successfully detected and prioritized 11 fusion genes.
  • Ten out of the 11 detected fusion genes were experimentally validated.
  • A significant number of identified fusion genes were found to affect the PI3K-AKT pathway.

Conclusions:

  • FUNGI is an effective tool for revealing and prioritizing clinically relevant fusion genes.
  • The identified fusion genes in ovarian cancer may play a role in therapeutic resistance.
  • Further research into the PI3K-AKT pathway alterations driven by these fusions is warranted.