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MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

Predicting chemosensitivity using drug perturbed gene dynamics.

Joshua D Mannheimer^1,2, Ashok Prasad^1,3, Daniel L Gustafson^4,5,6,7

¹School of Biomedical Engineering, Colorado State University, Fort Collins, CO, USA.

BMC Bioinformatics

|January 8, 2021

Summary

This summary is machine-generated.

Drug-perturbed gene expression signatures accurately predict cancer drug sensitivity in computational models. However, these signatures are not effective when applied to unperturbed gene expression data, highlighting the need for dynamic expression data in precision medicine.

Keywords:

Cancer Chemotherapy Drug response Genomics models Machine learning NCI60

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Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

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Published on: February 6, 2015

Area of Science:

Computational biology
Genomics
Precision medicine

Background:

Precision medicine leverages data-driven computational methods for disease diagnosis, prognosis, and treatment.
Oncology research focuses on developing algorithms and biomarkers for clinical applications, including predicting drug sensitivity using omics data.
Web-based tools like the NCI Transcriptional Pharmacodynamic Workbench explore drug-perturbed gene expression as biomarkers.

Purpose of the Study:

To investigate the influence of drug-perturbed gene dynamics on computational models for predicting in vitro drug sensitivity.
To evaluate the predictive power of gene signatures derived from drug-perturbed gene expression.
To assess the utility of gene interaction networks in identifying cancer-relevant genes.

Main Methods:

Developed computational models to predict in vitro drug sensitivity for 15 drugs using the NCI60 cell line panel.
Analyzed gene expression profiles after 24-hour drug exposure at high concentrations.
Created 100-gene signatures and evaluated their performance across different gene expression profiles and networks.

Main Results:

Gene expression profiles from 24-hour high-concentration drug exposure yielded the most accurate predictive models.
Model performance significantly decreased when gene signatures from perturbed expression were applied to unperturbed expression data.
Gene interaction networks derived from signatures exhibited distinct topologies, aiding in the selection of cancer-relevant genes.

Conclusions:

Perturbed gene signatures are predictive of drug response but cannot be applied to unperturbed gene expression.
Additional drug-perturbed gene expression data can enhance predictive model accuracy.
Computational methods combined with perturbed gene expression data can uncover critical drug-disease relationships.