Optical coherence tomography-based contact indentation for diaphragm mechanics in a mouse model of transforming growth factor alpha induced lung disease

Funding provided by the National Health and Medical Research Council (NHMRC) of Australia (1027218). P.N. and K.W. are supported by NHMRC Fellowships (1045824, 1090888). P.W. was supported by the William and Marlene Schrader Postgraduate Scholarship, The University of Western Australia, and C.A. by an NHMRC Preterm Infants CRE top-up scholarship.This study tested the utility of optical coherence tomography (OCT)-based indentation to assess mechanical properties of respiratory tissues in disease. Using OCT-based indentation, the elastic modulus of mouse diaphragm was measured from changes in diaphragm thickness in response to an applied force provided by an indenter. We used a transgenic mouse model of chronic lung disease induced by the overexpression of transforming growth factor-alpha (TGF-α), established by the presence of pleural and peribronchial fibrosis and impaired lung mechanics determined by the forced oscillation technique and plethysmography. Diaphragm elastic modulus assessed by OCT-based indentation was reduced by TGF-α at both left and right lateral locations (p < 0.05). Diaphragm elastic modulus at left and right lateral locations were correlated within mice (r = 0.67, p < 0.01) suggesting that measurements were representative of tissue beyond the indenter field. Co-localised images of diaphragm after TGF-α overexpression revealed a layered fibrotic appearance. Maximum diaphragm force in conventional organ bath studies was also reduced by TGF-α overexpression (p < 0.01). Results show that OCT-based indentation provided clear delineation of diseased diaphragm, and together with organ bath assessment, provides new evidence suggesting that TGF-α overexpression produces impairment in diaphragm function and, therefore, an increase in the work of breathing in chronic lung disease.Publisher PDFPeer reviewe

A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

BACKGROUND: As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. METHODS: In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN) algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. RESULTS: We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM) algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. CONCLUSION: Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets

Trends in non-metastatic prostate cancer management in the Northern and Yorkshire region of England, 2000–2006

Background: Our objective was to analyse variation in non-metastatic prostate cancer management in the Northern and Yorkshire region of England. Methods: We included 21 334 men aged ⩾55, diagnosed between 2000 and 2006. Principal treatment received was categorised into radical prostatectomy (11%), brachytherapy (2%), external beam radiotherapy (16%), hormone therapy (42%) and no treatment (29%). Results: The odds ratio (OR) for receiving a radical prostatectomy was 1.53 in 2006 compared with 2000 (95% CI 1.26–1.86), whereas the OR for receiving hormone therapy was 0.57 (0.51–0.64). Age was strongly associated with treatment received; radical treatments were significantly less likely in men aged ⩾75 compared with men aged 55–64 years, whereas the odds of receiving hormone therapy or no treatment were significantly higher in the older age group. The OR for receiving radical prostatectomy, brachytherapy or external beam radiotherapy were all significantly lower in the most deprived areas when compared with the most affluent (0.64 (0.55–0.75), 0.32 (0.22–0.47) and 0.83 (0.74–0.94), respectively) whereas the OR for receiving hormone therapy was 1.56 (1.42–1.71). Conclusions: This study highlights the variation and inequalities that exist in the management of non-metastatic prostate cancer in the Northern and Yorkshire region of England

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

A fast radio burst localized at detection to a galactic disk using very long baseline interferometry

Fast radio bursts (FRBs) are millisecond-duration, luminous radio transients of extragalactic origin. These events have been used to trace the baryonic structure of the Universe using their dispersion measure (DM) assuming that the contribution from host galaxies can be reliably estimated. However, contributions from the immediate environment of an FRB may dominate the observed DM, thus making redshift estimates challenging without a robust host galaxy association. Furthermore, while at least one Galactic burst has been associated with a magnetar, other localized FRBs argue against magnetars as the sole progenitor model. Precise localization within the host galaxy can discriminate between progenitor models, a major goal of the field. Until now, localizations on this spatial scale have only been carried out in follow-up observations of repeating sources. Here we demonstrate the localization of FRB 20210603A with very long baseline interferometry (VLBI) on two baselines, using data collected only at the time of detection. We localize the burst to SDSS J004105.82+211331.9, an edge-on galaxy at $z\approx 0.177$, and detect recent star formation in the kiloparsec-scale vicinity of the burst. The edge-on inclination of the host galaxy allows for a unique comparison between the line of sight towards the FRB and lines of sight towards known Galactic pulsars. The DM, Faraday rotation measure (RM), and scattering suggest a progenitor coincident with the host galactic plane, strengthening the link between the environment of FRB 20210603A and the disk of its host galaxy. Single-pulse VLBI localizations of FRBs to within their host galaxies, following the one presented here, will further constrain the origins and host environments of one-off FRBs.Comment: 40 pages, 13 figures, submitted. Fixed typo in abstrac