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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Published on: May 17, 2019

Predicting breast cancer chemotherapeutic response using a novel tool for microarray data analysis.

Jie Cheng¹, Joel Greshock, Jeffery Painter

¹Quantitative Sciences, GlaxoSmithKline, Collegeville, PA 19426, USA. jie.j.cheng@gsk.com

Journal of Integrative Bioinformatics

|August 4, 2012

Summary

A new tool identifies key genes for predicting breast cancer chemotherapy response. It found the estrogen receptor (ER) gene is crucial, but effective prediction is limited to a specific patient subgroup.

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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

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Published on: July 25, 2020

Area of Science:

Bioinformatics
Genomics
Cancer Research

Background:

Microarray data analysis is crucial for identifying predictive biomarkers in cancer.
Existing methods may not optimally balance statistical significance and biological relevance.
Predicting response to preoperative chemotherapy in breast cancer remains a challenge.

Purpose of the Study:

To develop and validate a novel tool for parsimonious discovery of highly predictive genes from microarray data.
To identify gene signatures that predict patient response to preoperative chemotherapy for breast cancer.
To discover clinically relevant predictive markers for specific breast cancer subgroups.

Main Methods:

Developed a novel microarray data analysis tool optimizing the trade-off between fold change and t-test p-value via cross-validation.
Implemented a recursive gene discovery and removal procedure.
Applied the tool to a public breast cancer dataset to predict chemotherapeutic response.
Validated discovered predictive markers on a blinded dataset.

Main Results:

The estrogen receptor (ER) gene was identified as the most significant predictor of chemotherapeutic response in the overall patient population.
No gene signatures provided substantial clinical benefit for the entire patient cohort.
A clinically homogenous subgroup (ER-negative, PR-negative, HER2-negative) with predictable chemotherapy response was identified.
Predictive markers for this subgroup showed successful validation.

Conclusions:

The developed tool effectively identifies parsimonious sets of highly predictive genes.
Estrogen receptor status is a primary determinant of preoperative chemotherapy response in breast cancer.
Targeted analysis within specific patient subgroups is essential for accurate predictive biomarker discovery.
The findings highlight the potential for personalized treatment strategies based on molecular profiling.