Robust identification of target genes and outliers in triple-negative breast cancer data

Statistical Methods in Medical Research
Pieter SegaertPeter J Rousseeuw

Abstract

Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail to identify outliers due to their heavy influence, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60% have been previously reported as biologically relevant to triple-negative breast cancer, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for triple-negative breast cancer. Out of these, JAM3, SFT2D2, and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells outlier detection technique. A comparison of gene networks on the selected genes showed sign...Continue Reading

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Citations

Sep 1, 2021·Clinical Breast Cancer·Carolina Leão de MoraesWaldemar Naves do Amaral
Jan 25, 2022·Statistical Methods in Medical Research·Antje JenschNicole Radde

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Methods Mentioned

BETA
biopsies
surgical resection
RNA-Seq

Software Mentioned

R
glmnet
LASSO
Ensemble genome browser CDS

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