Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches - Archive ouverte HAL Access content directly
Journal Articles Computers in Biology and Medicine Year : 2020

Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches

(1, 2) , (3, 4, 1, 2, 5) , (1, 2) , (1, 2) , (4) , (4) , (4) , (1, 2) , (6, 1, 2) , (6, 3, 1, 2) , (6, 3, 1, 2, 7)
1
2
3
4
5
6
7

Abstract

Triple-negative breast cancer (TNBC) heterogeneity represents one of the main obstacles to precision medicine for this disease. Recent concordant transcriptomics studies have shown that TNBC could be divided into at least three subtypes with potential therapeutic implications. Although a few studies have been conducted to predict TNBC subtype using transcriptomics data, the subtyping was partially sensitive and limited by batch effect and dependence on a given dataset, which may penalize the switch to routine diagnostic testing. Therefore, we sought to build an absolute predictor (i.e., intra-patient diagnosis) based on machine learning algorithms with a limited number of probes. To that end, we started by introducing probe binary comparison for each patient (indicators). We based the predictive analysis on this transformed data. Probe selection was first involved combining both filter and wrapper methods for variable selection using cross-validation. We tested three prediction models (random forest, gradient boosting [GB], and extreme gradient boosting) using this optimal subset of indicators as inputs. Nested cross-validation consistently allowed us to choose the best model. The results showed that the fifty selected indicators highlighted the biological characteristics associated with each TNBC subtype. The GB based on this subset of indicators performs better than other models.
Embargoed file
Embargoed file
Ne sera jamais visible

Dates and versions

inserm-03349338 , version 1 (20-09-2021)

Identifiers

Cite

Fadoua Ben Azzouz, Bertrand Michel, Hamza Lasla, Wilfried Gouraud, Anne-Flore François, et al.. Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches. Computers in Biology and Medicine, 2020, 129, pp.104171. ⟨10.1016/j.compbiomed.2020.104171⟩. ⟨inserm-03349338⟩
24 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More