MicroRNA-570 targets the HSP chaperone network, increases proteotoxic stress and inhibits mammary tumor cell migration.

The dynamic network of chaperone interactions known as the chaperome contributes significantly to the proteotoxic cell response and the malignant phenotype. To bypass the inherent redundancy in the network, we have used a microRNA (mir) approach to target multiple members of the chaperome simultaneously. We identified a potent microRNA, miR-570 that could bind the 3'untranslated regions of multiple HSP mRNAs and inhibit HSP synthesis. Transfection of cells with this miR species reduced expression of multiple HSPs, inhibited the heat shock response and reduced tumor cell growth while acted additively in combination with cytotoxic drugs. As overexpression of miR-570 elicited tumor suppressive effects, we inferred that this miR could play a potential role in inhibiting tumorigenesis and cancer cell growth. In accordance with this hypothesis, we determined a significant role for miR-570 in regulating markers of mammary tumor progression, including cell motility and invasion. Our data provide a proof of the principle that the tumor chaperome can be targeted by microRNAs suggesting a potential therapeutic avenue towards cancer therapy.

Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study.

AIM: Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a late, incurable stage. We sought to determine whether individuals at high risk of developing PDAC could be identified early using routinely collected data. METHODS: Electronic health record (EHR) databases from two independent hospitals in Boston, Massachusetts, providing inpatient, outpatient, and emergency care, from 1979 through 2017, were used with case-control matching. PDAC cases were selected using International Classification of Diseases 9/10 codes and validated with tumour registries. A data-driven feature selection approach was used to develop neural networks and L2-regularised logistic regression (LR) models on training data (594 cases, 100,787 controls) and compared with a published model based on hand-selected diagnoses ('baseline'). Model performance was validated on an external database (408 cases, 160,185 controls). Three prediction lead times (180, 270 and 365 days) were considered. RESULTS: The LR model had the best performance, with an area under the curve (AUC) of 0.71 (confidence interval [CI]: 0.67-0.76) for the training set, and AUC 0.68 (CI: 0.65-0.71) for the validation set, 365 days before diagnosis. Data-driven feature selection improved results over 'baseline' (AUC = 0.55; CI: 0.52-0.58). The LR model flags 2692 (CI 2592-2791) of 156,485 as high risk, 365 days in advance, identifying 25 (CI: 16-36) cancer patients. Risk stratification showed that the high-risk group presented a cancer rate 3 to 5 times the prevalence in our data set. CONCLUSION: A simple EHR model, based on diagnoses, can identify high-risk individuals for PDAC up to one year in advance. This inexpensive, systematic approach may serve as the first sieve for selection of individuals for PDAC screening programs.

Genotoxic stress induces Sca-1-expressing metastatic mammary cancer cells.

We describe a cell damage-induced phenotype in mammary carcinoma cells involving acquisition of enhanced migratory and metastatic properties. Induction of this state by radiation required increased activity of the Ptgs2 gene product cyclooxygenase 2 (Cox2), secretion of its bioactive lipid product prostaglandin E2 (PGE2), and the activity of the PGE2 receptor EP4. Although largely transient, decaying to low levels in a few days to a week, this phenotype was cumulative with damage and levels of cell markers Sca-1 and ALDH1 increased with treatment dose. The Sca-1+ , metastatic phenotype was inhibited by both Cox2 inhibitors and PGE2 receptor antagonists, suggesting novel approaches to radiosensitization.