Robust Identification of Target Genes and Outliers in Triple-negative Breast Cancer Data

TL;DR

This study applies robust statistical methods to identify key genes and outliers in triple-negative breast cancer data, revealing potential new biomarkers and emphasizing the importance of robustness in high-dimensional clinical omics analysis.

Contribution

The paper introduces a robust sparse logistic regression approach for gene selection and outlier detection in TNBC, uncovering novel biomarkers and demonstrating the effectiveness of robust methods in clinical data analysis.

Findings

Identified 36 relevant genes, with 14 being novel potential biomarkers.

Detected statistical outliers that may correspond to misdiagnosed patients.

Showed significant differences in gene networks between TNBC and non-TNBC.

Abstract

Correct classification of breast cancer sub-types is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer (TNBC) which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma (BRCA) transcriptomic data publicly available from The Cancer Genome Atlas (TCGA) data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail in the presence of these outliers, 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 TNBC, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for TNBC. 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 (DDC) outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between TNBC and non-TNBC data. The individual role of FOXA1 in TNBC and non-TNBC, and the strong FOXA1-AGR2 connection in TNBC stand out. Not only will our results contribute to the breast cancer/TNBC understanding and ultimately its management, they also show that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data.