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Campone, M.

Publications and source records attributed to Campone, M..

2 recordsLinked to original sources

Targeting of MCL-1 in breast cancer associated fibroblasts reverses their myofibroblastic phenotype and pro-invasive properties

Cancer associated fibroblasts (CAF) are a major cellular component of epithelial tumors. In breast cancers in particular these stromal cells have numerous tumorigenic effects in part due to their acquisition of a myofibroblastic phenotype. Breast CAFs (bCAFS) typically express MCL-1. We show here that targeting this regulator of mitochondrial integrity using a specific BH-3 mimetic promotes fragmentation of these organelles without inducing cell death. MCL-1 antagonism in primary bCAFs directly derived from human samples mitigates myofibroblastic features and decreases expression of genes involved in actomyosin organization and contractility, associated with a cytoplasmic retention of the transcriptional regulator, Yes-Associated Protein (YAP). Such treatment decreases bCAFs ability to promote cancer cells invasion in 3D co-culture assays. These effects are counteracted by an inhibitor of the mitochondrial fission protein DRP-1, which interacts with MCL-1 upon BH3 mimetic treatment. Our findings underscore the usefulness of targeting MCL-1 in breast cancer ecosystems, not only to favor death of cancer cells but also to counteract the tumorigenic activation of fibroblasts with which they co-evolve. The authors declare no conflict of interest.

cancer biology

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