Detection of BRCA1, BRCA2, and ATM Alterations in Matched Tumor Tissue and Circulating Tumor DNA in Patients with Prostate Cancer Screened in PROfound

Circulating tumor DNA; Prostate cancerADN tumoral circulante; Cáncer de próstataADN tumoral circulant; Càncer de pròstataPurpose: Not all patients with metastatic castration-resistant prostate cancer (mCRPC) have sufficient tumor tissue available for multigene molecular testing. Furthermore, samples may fail because of difficulties within the testing procedure. Optimization of screening techniques may reduce failure rates; however, a need remains for additional testing methods to detect cancers with alterations in homologous recombination repair genes. We evaluated the utility of plasma-derived circulating tumor DNA (ctDNA) in identifying deleterious BRCA1, BRCA2 (BRCA), and ATM alterations in screened patients with mCRPC from the phase III PROfound study. Patients and Methods: Tumor tissue samples were sequenced prospectively at Foundation Medicine, Inc. (FMI) using an investigational next-generation sequencing (NGS) assay based on FoundationOne®CDx to inform trial eligibility. Matched ctDNA samples were retrospectively sequenced at FMI, using an investigational assay based on FoundationOne®Liquid CDx. Results: 81% (503/619) of ctDNA samples yielded an NGS result, of which 491 had a tumor tissue result. BRCA and ATM status in tissue compared with ctDNA showed 81% positive percentage agreement and 92% negative percentage agreement, using tissue as reference. At variant-subtype level, using tissue as reference, concordance was high for nonsense (93%), splice (87%), and frameshift (86%) alterations but lower for large rearrangements (63%) and homozygous deletions (27%), with low ctDNA fraction being a limiting factor. Conclusions: We demonstrate that ctDNA can greatly complement tissue testing in identifying patients with mCRPC and BRCA or ATM alterations who are potentially suitable for receiving targeted PARP inhibitor treatments, particularly patients with no or insufficient tissue for genomic analyses.This study was funded by AstraZeneca and is part of an alliance between AstraZeneca and Merck Sharp & Dohme Corp

Spatial aspects of MRSA epidemiology:A case study using stochastic simulation, kernel estimation and SaTScan

The identification of disease clusters in space or space-time is of vital importance for public health policy and action. In the case of methicillin-resistant Staphylococcus aureus (MRSA), it is particularly important to distinguish between community and health care-associated infections, and to identify reservoirs of infection. 832 cases of MRSA in the West Midlands (UK) were tested for clustering and evidence of community transmission, after being geo-located to the centroids of UK unit postcodes (postal areas roughly equivalent to Zip+4 zip code areas). An age-stratified analysis was also carried out at the coarser spatial resolution of UK Census Output Areas. Stochastic simulation and kernel density estimation were combined to identify significant local clusters of MRSA (p<0.025), which were supported by SaTScan spatial and spatio-temporal scan. In order to investigate local sampling effort, a spatial 'random labelling' approach was used, with MRSA as cases and MSSA (methicillin-sensitive S. aureus) as controls. Heavy sampling in general was a response to MRSA outbreaks, which in turn appeared to be associated with medical care environments. The significance of clusters identified by kernel estimation was independently supported by information on the locations and client groups of nursing homes, and by preliminary molecular typing of isolates. In the absence of occupational/ lifestyle data on patients, the assumption was made that an individual's location and consequent risk is adequately represented by their residential postcode. The problems of this assumption are discussed, with recommendations for future data collection

A UK survey of COVID‐19 related social support closures and their effects on older people, people with dementia, and carers

Abstract Objectives The aim of this national survey was to explore the impact of COVID‐19 public health measures on access to social support services and the effects of closures of services on the mental well‐being of older people and those affected by dementia. Methods A UK‐wide online and telephone survey was conducted with older adults, people with dementia, and carers between April and May 2020.The survey captured demographic and postcode data, social support service usage before and after COVID‐19 public health measures, current quality of life, depression, and anxiety. Multiple linear regression analysis was used to explore the relationship between social support service variations and anxiety and well‐being. Results 569 participants completed the survey (61 people with dementia, 285 unpaid carers, and 223 older adults). Paired samples t‐tests and X2‐tests showed that the mean hour of weekly social support service usage and the number of people having accessed various services was significantly reduced post COVID‐19. Multiple regression analyses showed that higher variations in social support service hours significantly predicted increased levels of anxiety in people with dementia and older adults, and lower levels of mental well‐being in unpaid carers and older adults. Conclusions Being unable to access social support services due to COVID contributed to worse quality of life and anxiety in those affected by dementia and older adults across the UK. Social support services need to be enabled to continue providing support in adapted formats, especially in light of continued public health restrictions for the foreseeable future. This article is protected by copyright. All rights reserved

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

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

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

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

Displacement encoding with stimulated echoes enables the identification of infarct transmurality early postmyocardial infarction

Background Segmental extent of infarction assessed by late gadolinium enhancement (LGE) imaging early post‐ST‐segment elevation myocardial infarction (STEMI) has utility in predicting left ventricular functional recovery. Hypothesis: We hypothesized that segmental circumferential strain with displacement encoding with stimulated echoes (DENSE) would be a stronger predictor of infarct transmurality than feature‐tracking strain, and noninferior to extracellular volume fraction (ECV). Study Type: Prospective. Population: Fifty participants (mean ± SD, 59 ± 9 years, 40 [80%] male) underwent cardiac MRI on day 1 post‐STEMI. Field‐Strength/Sequences: 1.5T/cine, DENSE , T1 mapping, ECV , LGE. Assessment Two observers assessed segmental percentage LGE extent, presence of microvascular obstruction (MVO), circumferential and radial strain with DENSE and feature‐tracking, T1 relaxation times, and ECV. Statistical Tests: Normality was tested using the Shapiro–Wilk test. Skewed distributions were analyzed utilizing Mann–Whitney or Kruskal–Wallis tests and normal distributed data using independent t ‐tests. Diagnostic cutoff values were identified using the Youden index. The difference in area under the curve was compared using the z‐statistic. Results: Segmental circumferential strain with DENSE was associated with the extent of infarction ≥50% (AUC [95% CI], cutoff value = 0.9 [0.8, 0.9], −10%) similar to ECV (AUC = 0.8 [0.8, 0.9], 37%) (P = 0.117) and superior to feature‐tracking circumferential strain (AUC = 0.7[0.7, 0.8], −19%) (P  &lt; 0.05). For the detection of segmental infarction ≥75%, circumferential strain with DENSE (AUC = 0.9 [0.8, 0.9], −10%) was noninferior to ECV (AUC = 0.8 [0.7, 0.9], 42%) (P = 0.132) and superior to feature‐tracking (AUC = 0.7 [0.7, 0.8], −13%) (P  &lt; 0.05). For MVO detection, circumferential strain with DENSE (AUC = 0.8 [0.8, 0.9], −12%) was superior to ECV (AUC = 0.8 [0.7, 0.8] 34%) (P  &lt; 0.05) and feature‐tracking (AUC = 0.7 [0.6, 0.7] −21%) (P  &lt; 0.05). Data Conclusion: Circumferential strain with DENSE is a functional measure of infarct severity and may remove the need for gadolinium contrast agents in some circumstances. Level of Evidence: 2 Technical Efficacy Stage:

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15–17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype–phenotype map than previously anticipated