Real-Time Noninvasive Estimation of Intrapleural Pressure in Mechanically Ventilated Patients: a Feasibility Study

A method for real-time noninvasive estimation of intrapleural pressure in mechanically ventilated patients is proposed. The method employs a simple first-order lung mechanics model that is fitted in real-time to flow and pressure signals acquired non-invasively at the opening of the patient airways, in order to estimate lung resistance (RL), lung compliance (CL) and intrapleural pressure (Ppl) continuously in time. Estimation is achieved by minimizing the sum of squared residuals between measured and model predicted airway pressure using a modified Recursive Least Squares (RLS) approach. Particularly, two different RLS algorithms, namely the conventional RLS with Exponential Forgetting (EF-RLS) and the RLS with Vector-type Forgetting Factor (VFF-RLS), are considered in this study and their performances are first evaluated using simulated data. Simulations suggest that the conventional EFRLS algorithm is not suitable for our purposes, whereas the VFF-RLS method provides satisfactory results. The potential of the VFF-RLS based method is then proved on experimental data collected from a mechanically ventilated pig. Results show that the method provides continuous estimated lung resistance and compliance in normal physiological ranges and pleural pressure in good agreement with invasive esophageal pressure measurements

Modeling Wheezing Spells Identifies Phenotypes with Different Outcomes and Genetic Associates

Funding Information: Supported by the UK Medical Research Council (UK MRC) Programme grant MR/S025340/1 and grants G0601361 and MR/K002449/1. R.G. is in part funded through Wellcome Trust Strategic Award 108818/15/Z. The UK MRC and Wellcome (grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC (Avon Longitudinal Study of Parents and Children). MAAS (Manchester Asthma and Allergy Study) was supported by the Asthma UK Grants No 301 (1995–1998), No 362 (1998–2001), No 01/012 (2001–2004), No 04/014 (2004–2007), British Medical Association James Trust (2005), and the JP Moulton Charitable Foundation (2004–2016), the North West Lung Centre Charity (1997–current), and the UK MRC grant MR/L012693/1 (2014–2018). Acknowledgment This article is dedicated to the memory of our wonderful colleague and friend Prof. John Henderson (1958–2019), whose contribution to the understanding of the heterogeneity of childhood asthma cannot be overstated. Rainbow chasers and UNICORN riders forever.Peer reviewedPublisher PD

Therapeutic targeting of integrin αvβ6 in breast cancer

BACKGROUND: Integrin ?v?6 promotes migration, invasion, and survival of cancer cells; however, the relevance and role of ?v?6 has yet to be elucidated in breast cancer.METHODS: Protein expression of integrin subunit beta6 (?6) was measured in breast cancers by immunohistochemistry (n &gt; 2000) and ITGB6 mRNA expression measured in the Molecular Taxonomy of Breast Cancer International Consortium dataset. Overall survival was assessed using Kaplan Meier curves, and bioinformatics statistical analyses were performed (Cox proportional hazards model, Wald test, and Chi-square test of association). Using antibody (264RAD) blockade and siRNA knockdown of ?6 in breast cell lines, the role of ?v?6 in Human Epidermal Growth Factor Receptor 2 (HER2) biology (expression, proliferation, invasion, growth in vivo) was assessed by flow cytometry, MTT, Transwell invasion, proximity ligation assay, and xenografts (n ? 3), respectively. A student's t-test was used for two variables; three-plus variables used one-way analysis of variance with Bonferroni's Multiple Comparison Test. Xenograft growth was analyzed using linear mixed model analysis, followed by Wald testing and survival, analyzed using the Log-Rank test. All statistical tests were two sided.RESULTS: High expression of either the mRNA or protein for the integrin subunit ?6 was associated with very poor survival (HR = 1.60, 95% CI = 1.19 to 2.15, P = .002) and increased metastases to distant sites. Co-expression of ?6 and HER2 was associated with worse prognosis (HR = 1.97, 95% CI = 1.16 to 3.35, P = .01). Monotherapy with 264RAD or trastuzumab slowed growth of MCF-7/HER2-18 and BT-474 xenografts similarly (P &lt; .001), but combining 264RAD with trastuzumab effectively stopped tumor growth, even in trastuzumab-resistant MCF-7/HER2-18 xenografts.CONCLUSIONS: Targeting ?v?6 with 264RAD alone or in combination with trastuzumab may provide a novel therapy for treating high-risk and trastuzumab-resistant breast cancer patients.<br/

Hypoxia promotes stem cell phenotypes and poor prognosis through epigenetic regulation of DICER

MicroRNAs are small regulatory RNAs that post-transcriptionally control gene expression. Reduced expression of DICER, the enzyme involved in microRNA processing, is frequently observed in cancer and is associated with poor clinical outcome in various malignancies. Yet the underlying mechanisms are not well understood. Here, we identify tumor hypoxia as a regulator of DICER expression in large cohorts of breast cancer patients. We show that DICER expression is suppressed by hypoxia through an epigenetic mechanism that involves inhibition of oxygen-dependent H3K27me3 demethylases KDM6A/B and results in silencing of the DICER promoter. Subsequently, reduced miRNA processing leads to derepression of the miR-200 target ZEB1, stimulates the epithelial to mesenchymal transition and ultimately results in the acquisition of stem cell phenotypes in human mammary epithelial cells. Our study uncovers a previously unknown relationship between oxygen-sensitive epigenetic regulators, miRNA biogenesis and tumor stem cell phenotypes that may underlie poor outcome in breast cancer

The integrin αvβ6 drives pancreatic cancer through diverse mechanisms and represents an effective target for therapy

Pancreatic ductal adenocarcinoma (PDAC) has a five‐year survival rate of &lt;4% and desperately needs novel effective therapeutics. Integrin αvβ6 has been linked with poor prognosis in cancer but its potential as a target in PDAC remains unclear. We report that transcriptional expression analysis revealed high levels of β6 mRNA correlated strongly with significantly poorer survival (n=491 cases, p= 3.17x10‐8). In two separate cohorts we showed that over 80% of PDAC expressed αvβ6 protein and that paired metastases retained αvβ6 expression. In vitro, integrin αvβ6 promoted PDAC cell growth, survival, migration and invasion. Treatment of both αvβ6‐positive human PDAC xenografts and transgenic mice bearing αvβ6‐positive PDAC with the αvβ6 blocking antibody 264RAD, combined with gemcitabine, significantly reduced tumour growth (p&lt;0.0001) and increased survival (Log‐rank test, p&lt;0.05). Antibody therapy was associated with suppression of both tumour cell activity (suppression of pErk growth signals, increased apoptosis seen as activated Caspase 3) and suppression of the pro‐tumourigenic microenvironment (suppression of TGFβ signalling, fewer αSMA‐positive myofibroblasts, decreased blood vessel density). These data show that αvβ6 promotes PDAC growth through both tumour cell and tumour microenvironment mechanisms and represents a valuable target for PDAC therapy

Identifying molecular features that distinguish fluvastatin-sensitive breast tumor cells

Statins, routinely used to treat hypercholesterolemia, selectively induce apoptosis in some tumor cells by inhibiting the mevalonate pathway. Recent clinical studies suggest that a subset of breast tumors is particularly susceptible to lipophilic statins, such as fluvastatin. To quickly advance statins as effective anticancer agents for breast cancer treatment, it is critical to identify the molecular features defining this sensitive subset. We have therefore characterized fluvastatin sensitivity by MTT assay in a panel of 19 breast cell lines that reflect the molecular diversity of breast cancer, and have evaluated the association of sensitivity with several clinicopathological and molecular features. A wide range of fluvastatin sensitivity was observed across breast tumor cell lines, with fluvastatin triggering cell death in a subset of sensitive cell lines. Fluvastatin sensitivity was associated with an estrogen receptor alpha (ERa)-negative, basal-like tumor subtype, features that can be scored with routine and/or strong preclinical diagnostics. To ascertain additional candidate sensitivity-associated molecular features, we mined publicly available gene expression datasets, identifying genes encoding regulators of mevalonate production, nonsterol lipid homeostasis, and global cellular metabolism, including the oncogene MYC. Further exploration of this data allowed us to generate a 10-gene mRNA abundance signature predictive of fluvastatin sensitivity, which showed preliminary validation in an independent set of breast tumor cell lines. Here, we have therefore identified several candidate predictors of sensitivity to fluvastatin treatment in breast cancer, which warrant further preclinical and clinical evaluation.Fil: Goard, Carolyn A.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Chan Seng Yue, Michelle . University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Mullen, Peter J.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; CanadáFil: Quiroga, Ariel Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina. University of Alberta; CanadáFil: Wasylishen, Amanda R.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Clendening, James W.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; CanadáFil: Sendorek, Dorota H. S.. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Haider, Syed. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Lehner, Richard. University of Alberta; CanadáFil: Boutros, Paul C.. University Of Toronto; Canadá. Ontario Institute of Cancer Research. Informatics and Biocomputing Platform; CanadáFil: Penn, Linda Z.. University Health Network. Princess Margaret Cancer Centre. Ontario Cancer Institute and Campbell Family Institute for Breast Cancer Research; Canadá. University Of Toronto; Canad

Distinguishing wheezing phenotypes from infancy to adolescence : A pooled analysis of five birth cohorts

The authors thank all the families who took part in this study; the midwives for their help in recruiting them; and the MAAS, IOW, Ashford, SEATON, and ALSPAC teams, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.Peer reviewedPublisher PD

Spirometric phenotypes from early childhood to young adulthood : a Chronic Airway Disease Early Stratification study

Acknowledgements Cohort-specific acknowledgements are presented in the supplementary material. We also acknowledge collaboration with the EXPANSE consortium (funded by the EU H2020 programme, grant number 874627). We thank Elise Heuvelin, European Respiratory Society, Lausanne, Switzerland, for her assistance on the current project.Peer reviewedPublisher PD

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA