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Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning.

Rassanee Bissanum1, Sitthichok Chaichulee1, Rawikant Kamolphiwong1

  • 1Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.

Journal of Personalized Medicine
|September 28, 2021
PubMed
Summary

Researchers developed a machine learning model to classify triple negative breast cancer (TNBC) into four subtypes, improving personalized treatment strategies for this challenging cancer. The SVM algorithm achieved high accuracy in identifying BLIA, BLIS, MES, and LAR subtypes.

Keywords:
TNBC subtypegene expression profilemachine learningmicroarray

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Triple negative breast cancer (TNBC) is a heterogeneous disease lacking targeted therapies.
  • Current TNBC subtyping methods are not standardized, hindering effective treatment.
  • TNBC has been previously classified into four subtypes: BLIA, BLIS, MES, and LAR based on gene expression.

Purpose of the Study:

  • To define gene signatures for each TNBC subtype.
  • To develop a machine learning-based classification method for TNBC subtyping.
  • To enable accurate and consistent classification for personalized treatment and prognosis.

Main Methods:

  • Downloaded gene expression microarray data from the Gene Expression Omnibus database.
  • Identified differentially expressed genes (DEGs) unique to each TNBC subtype.
  • Trained and validated seven machine learning models, focusing on the Support Vector Machine (SVM) algorithm.

Main Results:

  • A training set of 719 DEGs was established.
  • The SVM algorithm achieved high classification accuracy (95-98.8%) and AUC (0.99-1.00) for the four TNBC subtypes.
  • Validation on 334 unknown samples showed successful prediction of subtypes, with some samples not fitting existing categories.

Conclusions:

  • The developed gene set and SVM model are suitable for accurate TNBC subtyping.
  • The SVM algorithm effectively classifies TNBC into four distinct subtypes.
  • Accurate TNBC subtyping is crucial for advancing personalized medicine and improving patient outcomes.