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Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

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Published on: February 27, 2015

[Gene expression profiling in cancer research].

Stefan Michiels¹, S Koscielny, Thomas Boulet

¹Unité de biostatistiques et d'épidémiologie, Institut Gustave Roussy, 39, rue Camille-Desmoulins, 94807 Villejuif.

Bulletin Du Cancer

|December 7, 2007

Summary

Gene expression profiling uses microarrays to classify cancer patients. This study highlights methodological challenges in analyzing this complex gene expression data, emphasizing the need for rigorous evaluation.

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Area of Science:

Genomics and Bioinformatics
Cancer Research
Translational Medicine

Context:

Gene expression profiling is a powerful tool in cancer research.
Microarray technology allows simultaneous measurement of thousands of genes in tumor samples.
Studies face challenges due to a small number of patients and a large number of variables.

Purpose:

To classify cancer patients using gene expression data.
To explore unsupervised and supervised classification methods.
To analyze published breast cancer microarray studies to assess experimental power and identify interpretation/validity issues.

Summary:

Gene expression profiling via microarrays aids cancer patient classification.
Unsupervised classification identifies disease subtypes; supervised classification finds molecular signatures.
Analysis of breast cancer data reveals significant methodological challenges in interpretation and validity.

Impact:

Highlights critical methodological problems in analyzing high-dimensional gene expression data.
Underscores the need for rigorous evaluation of microarray technology in cancer research.
Informs future studies on the reliable application of gene expression profiling for clinical decision-making.