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Estrogen and progesterone receptor (ER, PR) signaling control breast development and impinge on breast carcinogenesis. ER is an established driver of ER + disease but the role of the PR, itself an ER target gene, is debated. We assess the issue in clinically relevant settings by a genetic approach and inject ER + breast cancer cell lines and patient-derived tumor cells to the milk ducts of immunocompromised mice. Such ER + xenografts were exposed to physiologically relevant levels of 17-β-estradiol (E2) and progesterone (P4). We find that independently both premenopausal E2 and P4 levels increase tumor growth and combined treatment enhances metastatic spread. The proliferative responses are patient-specific with MYC and androgen receptor (AR) signatures determining P4 response. PR is required for tumor growth in patient samples and sufficient to drive tumor growth and metastasis in ER signaling ablated tumor cells. Our findings suggest that endocrine therapy may need to be personalized, and that abrogating PR expression can be a therapeutic option.

In the context of hormonal contraception and hormone replacement therapy (HRT), many women are exposed to exogenous hormones. Current use of hormonal contraception with combined ethinyl estradiol and different progestins bestows a breast cancer relative risk (RR) of 1.2-while combined HRT has a RR of 2. Although these exposures present an important public health issue, little is known about the effects of individual progestins on the breast and other tissues. Increasing availability of large scale biobanks, high throughput analyses and data management tools enable ever expanding, sophisticated population studies. In order to address the impact of distinct progestins on various health indicators, it is desirable to accurately quantify progestins in clinical samples. Here we have developed and validated a high resolution liquid chromatography mass spectrometry (LC-MS) targeted method for the simultaneous quantification of 11 synthetic progestins widely used in oral contraceptives, gestodene, levonorgestrel, etonogestrel, chlormadinone acetate, cyproterone acetate, drospirenone, desacetyl norgestimate, medroxyprogesterone acetate, norethindrone, dienogest, nomegestrol acetate, and 4 endogenous steroid hormones, progesterone, testosterone, androstenedione, and cortisol in blood samples. This highly specific quantitative analysis with high resolution Orbitrap technology detects and quantifies 15 compounds using their internal standard counterparts in a single 12 min LC-MS run. Sensitivity is attained by the use of the instrument in targeted selected ion monitoring mode. Lower limit of quantitation ranges from 2.4 pg/ml for drospirenone to 78.1 pg/ml for chlormadinone acetate. The method provides comprehensive progestin panel measurements with as little as 50 pi of murine or human plasma. (C) 2019 The Authors. Published by Elsevier B.V.

Glioblastoma is the most common primary brain tumor in adults; with a survival rate of 12 months from diagnosis. However, a small subgroup of patients, termed long-term survivors (LTS), has a survival rate longer then 12–14 months. There is thus increasing interest in the identification of molecular signatures predicting glioblastoma prognosis and in how to improve the therapeutic approach. Here, we report miR-340 as prognostic tumor-suppressor microRNA for glioblastoma. We analyzed microRNA expression in > 500 glioblastoma patients and found that although miR-340 is strongly down-regulated in glioblastoma overall, it is up-regulated in LTS patients compared to short-term survivors (STS). Indeed, miR-340 expression predicted better prognosis in glioblastoma patients. Coherently, overexpression of miR-340 in glioblastoma cells was found to produce a tumor-suppressive activity. We identified NRAS mRNA as a critical, direct target of miR-340: in fact, miR-340 negatively influenced multiple aspects of glioblastoma tumorigenesis by down-regulating NRAS and downstream AKT and ERK pathways. Thus, we demonstrate that expression of miR-340 in glioblastoma is responsible for a strong tumor-suppressive effect in LTS patients by down-regulating NRAS. miR-340 may thus represent a novel marker for glioblastoma diagnosis and prognosis, and may be developed into a tool to improve treatment of glioblastoma.

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier.