Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. 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. In this retrospective study, we use a standard prediction pipeline based on a convolutional neural network for the detection of cancer driver genomic alterations in The Cancer Genome Atlas (TCGA) breast (BRCA, n = 719), lung (LUAD, n = 541) and colon (COAD, n = 459) cancer datasets. We propose 3 diagnostic strategies using deep learning methods as first-line diagnostic tools. Focusing on cancer driver genes such as KRAS, EGFR or TP53, we show that these methods help reduce DNA sequencing by up to 49.9% with a high sensitivity (95%). In a context of limited resources, these methods increase sensitivity up to 69.8% at a 30% capacity of DNA sequencing tests, up to 85.1% at a 50% capacity, and up to 91.8% at a 70% capacity. These methods can also be used to prioritize patients with a positive predictive value up to 90.6% in the 10% patient most at risk of being mutated. Limitations of this study include the lack of external validation on non-TCGA data, dependence on prevalence of mutations in datasets, and use of a standard DL method on a limited dataset. Future studies using state-of-the-art methods and larger datasets are needed for better evaluation and clinical implementation.

Notch3 regulates Mybl2 via HeyL to limit proliferation and tumor initiation in breast cancer.

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.