Notch signaling is a conserved signaling pathway that participates in many aspects of mammary gland development and homeostasis, and has extensively been associated with breast tumorigenesis. Here, to unravel the as yet debated role of Notch3 in breast cancer development, we investigated its expression in human breast cancer samples and effects of its loss in mice. Notch3 expression was very weak in breast cancer cells and was associated with good patient prognosis. Interestingly, its expression was very strong in stromal cells of these patients, though this had no prognostic value. Mechanistically, we demonstrated that Notch3 prevents tumor initiation via HeyL-mediated inhibition of Mybl2, an important regulator of cell cycle. In the mammary glands of Notch3-deficient mice, we observed accelerated tumor initiation and proliferation in a MMTV-Neu model. Notch3-null tumors were enriched in Mybl2 mRNA signature and protein expression. Hence, our study reinforces the anti-tumoral role of Notch3 in breast tumorigenesis.

The presence of genomic mutations in cancer can be associated with a response to a targeted therapy. Therefore, it has become a crucial information for giving more efficient treatments to every patient. Detection of mutation is routinely made by DNA-sequencing diagnostic tests. Recent developments showed promising results for tumoral mutational status prediction using new deep learning based methods on histopathological images. However, it is still unknown whether these methods can be useful aside from sequencing methods for efficient population diagnosis. Here, we use a standard prediction pipeline for the detection of clinically relevant genomic alterations in breast, lung and colon cancer. We propose 3 diagnostic strategies using deep learning methods as first-line diagnostic tools. We show that these methods help reduce DNA sequencing by up to 34.6% with a high sensitivity (95%). In a context of limited resources, these methods increase sensitivity up to 75% at a 30% capacity of DNA sequencing tests, up to 85.7% at a 50% capacity, and up to 92.3% at a 70% capacity. These methods can also be used to prioritize patients with a positive predictive value up to 86.7% in the 10% patient most at risk of being mutated.

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