Lasla, H. | bioRxiv Science

bioRxiv2025

Wnt signaling promotes inflammation and EMT-associated gene expression in mesenchymal TNBC

Aberrant activation of the Wnt signaling pathway in triple-negative breast cancer (TNBC) is linked to treatment resistance and recurrence, yet its role in tumoral heterogeneity remains unclear. We developed Wnt reporter cell lines from two mesenchymal TNBC models (MDA-MB-231 and MDA-MB-436) using a Tcf/Lef-eGFP vector and observed intra- and inter-tumoral variation in Wnt activity. Paired Wnt-positive and Wnt-negative TNBC cell lines were established and profiled by RNA-Seq. Integrative analyses revealed that Wnt-positive cells consistently upregulate genes involved in epithelial-to- mesenchymal transition, inflammation (e.g., IL6/JAK/STAT3, TNF via NF-{kappa}B), and extracellular matrix remodeling. A 55-gene Wnt signature common to both nutrient conditions captured these features. Wnt-related gene sets were also enriched in the Mesenchymal-like Immune-Altered subtype of 699 primary TNBC tumors. These findings highlight the role of basal Wnt activity in driving pro-tumorigenic transcriptional programs in TNBC and provide new insight into its contribution to subtype-specific disease features.

bioinformatics↗

bioRxiv2020

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

Triple-negative breast cancer (TNBC) heterogeneity represents one of the main impediment to precision medicine for this disease. Recent concordant transcriptomics studies have shown that TNBC could be splitted into at least three subtypes with potential therapeutic implications. Although, a few studies have been done to predict TNBC subtype by means of transcriptomics data, subtyping was partially sensitive and limited by batch effect and dependence to 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 algorithm with a limited number of probes. To this end, we started by introducing probe binary comparison for each patient (indicators). We based predictive analysis on this transformed data. Probe selection was first performed by combining both filter and wrapper methods for variable selection using cross validation. We thus tested three prediction models (random forest, gradient boosting [GB] and extreme gradient boosting) using this optimal subset of indicators as inputs. Nested cross-validation allowed us to consistently choose the best model. Results showed that the 50 selected indicators highlighted biological characteristics associated with each TNBC subtype. The GB based on this subset of indicators has better performances as compared to the other models.

bioinformatics↗