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Related Experiment Video

Updated: Jun 25, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Knowledge driven decomposition of tumor expression profiles.

Martin H van Vliet¹, Lodewyk F A Wessels, Marcel J T Reinders

¹Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. M.H.vanVliet@TUDelft.nl

BMC Bioinformatics

|February 12, 2009

Summary

This summary is machine-generated.

This study introduces a new linear model for tumor expression profile decomposition using knowledge-driven components. The lasso-based method links gene perturbations to cancer hallmarks and reveals molecular patterns in breast cancer subtypes.

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Area of Science:

Computational Biology
Genomics
Cancer Research

Background:

Tumors arise from multiple oncogenic events reflected in gene expression.
Current data-driven methods decompose tumor profiles but lack direct knowledge incorporation.
Interpreting data-driven components often relies on post-hoc gene set comparisons.

Purpose of the Study:

To develop a novel linear model for decomposing tumor expression profiles.
To integrate prior biological knowledge directly into the decomposition process.
To analyze human breast cancer samples and identify molecular patterns linked to clinical parameters.

Main Methods:

A knowledge-driven linear decomposition model was developed.
The model was solved using constrained linear least squares with a lasso-based solution.
The method was validated using single gene perturbation data and applied to human breast cancer expression profiles.

Main Results:

The lasso-based decomposition effectively linked gene perturbation data to mouse data.
Decomposition of breast cancer samples revealed links to cancer hallmarks and clinical parameters.
Analysis of weight shrinkage patterns identified consensus molecular patterns within clinical subgroups.

Conclusions:

The lasso-based constrained least squares decomposition offers a stable, knowledge-driven alternative to data-driven methods.
This approach provides enhanced molecular characterization of breast cancer subtypes.
The method yields new insights into tumor biology by linking expression profiles to clinical data.