Progesterone receptor membrane component 1 regulates lipid homeostasis and drives oncogenic signaling resulting in breast cancer progression.

BACKGROUND: PGRMC1 (progesterone receptor membrane component 1) is a highly conserved heme binding protein, which is overexpressed especially in hormone receptor-positive breast cancer and plays an important role in breast carcinogenesis. Nevertheless, little is known about the mechanisms by which PGRMC1 drives tumor progression. The aim of our study was to investigate the involvement of PGRMC1 in cholesterol metabolism to detect new mechanisms by which PGRMC1 can increase lipid metabolism and alter cancer-related signaling pathways leading to breast cancer progression. METHODS: The effect of PGRMC1 overexpression and silencing on cellular proliferation was examined in vitro and in a xenograft mouse model. Next, we investigated the interaction of PGRMC1 with enzymes involved in the cholesterol synthesis pathway such as CYP51, FDFT1, and SCD1. Further, the impact of PGRMC1 expression on lipid levels and expression of enzymes involved in lipid homeostasis was examined. Additionally, we assessed the role of PGRMC1 in key cancer-related signaling pathways including EGFR/HER2 and ERα signaling. RESULTS: Overexpression of PGRMC1 resulted in significantly enhanced proliferation. PGRMC1 interacted with key enzymes of the cholesterol synthesis pathway, alters the expression of proteins, and results in increased lipid levels. PGRMC1 also influenced lipid raft formation leading to altered expression of growth receptors in membranes of breast cancer cells. Analysis of activation of proteins revealed facilitated ERα and EGFR activation and downstream signaling dependent on PGRMC1 overexpression in hormone receptor-positive breast cancer cells. Depletion of cholesterol and fatty acids induced by statins reversed this growth benefit. CONCLUSION: PGRMC1 may mediate proliferation and progression of breast cancer cells potentially by altering lipid metabolism and by activating key oncogenic signaling pathways, such as ERα expression and activation, as well as EGFR signaling. Our present study underlines the potential of PGRMC1 as a target for anti-cancer therapy.

Home and workforce reintegration one year after thrombectomy in acute stroke patients.

INTRODUCTION: Endovascular thrombectomy (EVT) combined with intravenous thrombolysis is the current standard treatment for acute large-vessel occlusion stroke. Beyond clear clinical benefits in the acute and post-acute phases, comprehensive evaluations of long-term outcomes, including home and workforce reintegration, remain limited. This study aimed to assess home and workforce reintegration 1 year post-EVT in a cohort of acute stroke patients and explore their association with health-related quality of life (HRQoL). PATIENTS AND METHODS: We conducted a prospective observational study of 404 patients undergoing EVT at a tertiary university medical center between October 2019 and December 2021. Patients' functional outcomes were evaluated using the modified Rankin Scale (mRS), and HRQoL was assessed via the European Quality of Life Five Dimension Scale (EQ-5D). Data on occupational and living status were collected through standardized telephone interviews at 3- and 12-months post-treatment. RESULTS: Of 357 patients with 12-month follow-up data, 33.6% had a favorable outcome (mRS 0-2). Among stroke survivors, the rate of home reintegration without nursing care was 42.1%, and workforce reintegration among previously employed patients was 43.3% at 12 months. Both outcomes were significantly associated with improved HRQoL. Lower neurological deficits and younger age were predictive of successful home and workforce reintegration. DISCUSSION AND CONCLUSION: One year post-EVT, approximately 40%-50% of acute stroke patients successfully reintegrate into home and work settings. These findings underscore the need for ongoing support tailored to improving long-term reintegration and quality of life for stroke survivors. DATA ACCESS STATEMENT: The data supporting the findings of the study are available from the corresponding author upon reasonable request and in accordance to European data privacy obligations.

Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion.

OBJECTIVE: Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency. METHODS: Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0-2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key features were identified through backward feature selection. RESULTS: We included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89-0.91). INTERPRETATION: Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.

Multiparametric Circulating Tumor Cell Analysis to Select Targeted Therapies for Breast Cancer Patients.

BACKGROUND: The analysis of liquid biopsies, e.g., circulating tumor cells (CTCs) is an appealing diagnostic concept for targeted therapy selection. In this proof-of-concept study, we aimed to perform multiparametric analyses of CTCs to select targeted therapies for metastatic breast cancer patients. METHODS: First, CTCs of five metastatic breast cancer patients were analyzed by whole exome sequencing (WES). Based on the results, one patient was selected and monitored by longitudinal and multiparametric liquid biopsy analyses over more than three years, including WES, RNA profiling, and in vitro drug testing of CTCs. RESULTS: Mutations addressable by targeted therapies were detected in all patients, including mutations that were not detected in biopsies of the primary tumor. For the index patient, the clonal evolution of the tumor cells was retraced and resistance mechanisms were identified. The AKT1 E17K mutation was uncovered as the driver of the metastatic process. Drug testing on the patient's CTCs confirmed the efficacy of drugs targeting the AKT1 pathway. During a targeted therapy chosen based on the CTC characterization and including the mTOR inhibitor everolimus, CTC numbers dropped by 97.3% and the disease remained stable as determined by computer tomography/magnetic resonance imaging. CONCLUSION: These results illustrate the strength of a multiparametric CTC analysis to choose and validate targeted therapies to optimize cancer treatment in the future. Furthermore, from a scientific point of view, such studies promote the understanding of the biology of CTCs during different treatment regimens.