Cell Painting-Based Tool for the Risk Assessment of Mammary Carcinogens and Endocrine Disruptors

Breast cancer is the most common cancer in women worldwide and chemicals disrupting estrogen or progesterone signaling are recognized as potential risk factors. However, chemicals that alter the mammary gland (MG) development and function remain understudied, highlighting the need for additional research in this area. To address this gap, we investigate the relevance of using high-content imaging assays and more specifically, Cell Painting technology to measure cell morphology perturbation caused by chemical exposure and identify morphological features that could characterize mammary carcinogens (MC) risk factors. Using a dataset of MC and non-mammary carcinogens (Non-MC) with Cell Painting profiles from the JUMP-CP dataset, we retrieved 51 compounds: 28 MC, 23 non-genotoxic Non-MC. We characterized the morphological data by non-linear dimensionality reduction (UMAP) and hierarchical clustering. We, then, developed a Guilt-By-Association (GBA) framework comparing multiple configurations of similarity metrics, risk-score aggregation approaches and features representations. Morphological profiles clustered by mechanism of action rather than carcinogenicity label: genotoxic MC produced strong perturbations in endoplasmic reticulum, mitochondria, nucleus, and RNA compartments, whereas hormonally active compounds were indistinguishable from controls, reflecting the lack of functional steroid hormone receptors in the cell line used. Our best GBA configuration achieved an AUC-ROC of 0.630 and an AUC-PR of 0.696. Applied prospectively to endocrine disruptors chemicals, it prioritized clofentezine, 3-methylpyrazole, resorcinol, 2-tert-butyl-4-methoxyphenol, and thiabendazole as candidates for confirmatory testing. This study provides a transparent, interpretable tool for prioritizing chemicals in mammary carcinogenicity assessment, highlights limitations and clarifies where Cell Painting datasets must be complemented by hormone-sensitive models.