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Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
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Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures

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Identifying and targeting cancer-specific metabolism with network-based drug target prediction

Maria Pires Pacheco1, Tamara Bintener1, Dominik Ternes1

  • 1Life Sciences Research Unit, University of Luxembourg, Esch-Alzette, Luxembourg.

Ebiomedicine
|May 26, 2019
PubMed
Abstract

Insights

This study introduces rFASTCORMICS to model cancer metabolism, identifying new drugs for colorectal cancer (CRC). The workflow accurately predicts drug targets and potential new therapies for CRC.

Area of Science:

  • Computational Biology
  • Metabolic Engineering
  • Oncology

Background:

  • Cancer cells exhibit metabolic rewiring to support high proliferation rates.
  • Targeting cancer-specific metabolism can protect healthy tissues.
  • Understanding metabolic alterations is crucial for cancer therapy.

Purpose of the Study:

  • To develop an efficient workflow (rFASTCORMICS) for reconstructing high-resolution metabolic models from RNA-seq data.
  • To capture metabolic rewiring strategies in cancer cells.
  • To identify novel drug repurposing candidates for colorectal cancer (CRC).

Main Methods:

  • Developed the rFASTCORMICS workflow for RNA-seq data analysis.
  • Generated 10,005 metabolic models using The Cancer Genome Atlas (TCGA) dataset.
  • Applied a drug repurposing workflow based on rFASTCORMICS to CRC.

Main Results:

  • Identified cancer-specific essential genes enriched for known drug targets.
  • Discovered that alternative metabolic pathways not essential for proliferation are often shut down in tumors.
  • Experimentally validated naftifine, ketoconazole, and mimosine as potential new drugs for CRC.

Conclusions:

  • The rFASTCORMICS workflow successfully reconstructs metabolic models from RNA-seq data.
  • The workflow accurately predicted drug targets and novel therapeutic agents for colorectal cancer.
  • This approach offers a powerful tool for cancer drug discovery and personalized medicine.
Keywords:
CancerDrug repurposingMachine learningMetabolic modelling

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