New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Novel Molecular Markers of Malignancy in Histologically Normal and Benign Breast

To detect the molecular changes of malignancy in histologically normal breast (HNB) tissues, we recently developed a novel 117-gene-malignancy-signature. Here we report validation of our leading malignancy-risk-genes, topoisomerase-2-alpha (TOP2A), minichromosome-maintenance-protein-2 (MCM2) and “budding-uninhibited-by-benzimidazoles-1-homolog-beta” (BUB1B) at the protein level. Using our 117-gene malignancy-signature, we classified 18 fresh-frozen HNB tissues from 18 adult female breast cancer patients into HNB-tissues with low-grade (HNB-LGMA; ) and high-grade molecular abnormality (HNB-HGMA; ). Archival sections of additional HNB tissues from these patients, and invasive ductal carcinoma (IDC) tissues from six other patients were immunostained for these biomarkers. TOP2A/MCM2 expression was assessed as staining index (%) and BUB1B expression as H-scores (0–300). Increasing TOP2A, MCM2, and BUB1B protein expression from HNB-LGMA to HNB-HGMA tissues to IDCs validated our microarray-based molecular classification of HNB tissues by immunohistochemistry. We also demonstrated an increasing expression of TOP2A protein on an independent test set of HNB/benign/reductionmammoplasties, atypical-ductal-hyperplasia with and without synchronous breast cancer, DCIS and IDC tissues using a custom tissue microarray (TMA). In conclusion, TOP2A, MCM2, and BUB1B proteins are potential molecular biomarkers of malignancy in histologically normal and benign breast tissues. Larger-scale clinical validation studies are needed to further evaluate the clinical utility of these molecular biomarkers

Novel Molecular Markers of Malignancy in Histologically Normal and Benign Breast

To detect the molecular changes of malignancy in histologically normal breast (HNB) tissues, we recently developed a novel 117-gene-malignancy-signature. Here we report validation of our leading malignancy-risk-genes, topoisomerase-2-alpha (TOP2A), minichromosome-maintenance-protein-2 (MCM2) and “budding-uninhibited-by-benzimidazoles-1-homolog-beta” (BUB1B) at the protein level. Using our 117-gene malignancy-signature, we classified 18 fresh-frozen HNB tissues from 18 adult female breast cancer patients into HNB-tissues with low-grade (HNB-LGMA; =9) and high-grade molecular abnormality (HNB-HGMA; =9). Archival sections of additional HNB tissues from these patients, and invasive ductal carcinoma (IDC) tissues from six other patients were immunostained for these biomarkers. TOP2A/MCM2 expression was assessed as staining index (%) and BUB1B expression as H-scores (0–300). Increasing TOP2A, MCM2, and BUB1B protein expression from HNB-LGMA to HNB-HGMA tissues to IDCs validated our microarray-based molecular classification of HNB tissues by immunohistochemistry. We also demonstrated an increasing expression of TOP2A protein on an independent test set of HNB/benign/reductionmammoplasties, atypical-ductal-hyperplasia with and without synchronous breast cancer, DCIS and IDC tissues using a custom tissue microarray (TMA). In conclusion, TOP2A, MCM2, and BUB1B proteins are potential molecular biomarkers of malignancy in histologically normal and benign breast tissues. Larger-scale clinical validation studies are needed to further evaluate the clinical utility of these molecular biomarkers

Novel Molecular Markers of Malignancy in Histologically Normal and Benign Breast

To detect the molecular changes of malignancy in histologically normal breast (HNB) tissues, we recently developed a novel 117-gene-malignancy-signature. Here we report validation of our leading malignancy-risk-genes, topoisomerase-2-alpha (TOP2A), minichromosome-maintenance-protein-2 (MCM2) and “budding-uninhibited-by-benzimidazoles-1-homolog-beta” (BUB1B) at the protein level. Using our 117-gene malignancy-signature, we classified 18 fresh-frozen HNB tissues from 18 adult female breast cancer patients into HNB-tissues with low-grade (HNB-LGMA; ) and high-grade molecular abnormality (HNB-HGMA; ). Archival sections of additional HNB tissues from these patients, and invasive ductal carcinoma (IDC) tissues from six other patients were immunostained for these biomarkers. TOP2A/MCM2 expression was assessed as staining index (%) and BUB1B expression as H-scores (0–300). Increasing TOP2A, MCM2, and BUB1B protein expression from HNB-LGMA to HNB-HGMA tissues to IDCs validated our microarray-based molecular classification of HNB tissues by immunohistochemistry. We also demonstrated an increasing expression of TOP2A protein on an independent test set of HNB/benign/reductionmammoplasties, atypical-ductal-hyperplasia with and without synchronous breast cancer, DCIS and IDC tissues using a custom tissue microarray (TMA). In conclusion, TOP2A, MCM2, and BUB1B proteins are potential molecular biomarkers of malignancy in histologically normal and benign breast tissues. Larger-scale clinical validation studies are needed to further evaluate the clinical utility of these molecular biomarkers

Diagnostic digital cytopathology: Are we ready yet?

Background: The cytology literature relating to diagnostic accuracy using whole slide imaging is scarce. We studied the diagnostic concordance between glass and digital slides among diagnosticians with different profiles to assess the readiness of adopting digital cytology in routine practice. Materials and Methods: This cohort consisted of 22 de-identified previously screened and diagnosed cases, including non-gynecological and gynecological slides using standard preparations. Glass slides were digitalized using Aperio ScanScope XT (×20 and ×40). Cytopathologists with (3) and without (3) digital experience, cytotechnologists (4) and senior pathology residents (2) diagnosed the digital slides independently first and recorded the results. Glass slides were read and recorded separately 1-3 days later. Accuracy of diagnosis, time to diagnosis and diagnostician′s profile were analyzed. Results: Among 22 case pairs and four study groups, correct diagnosis (93% vs. 86%) was established using glass versus digital slides. Both methods more (>95%) accurately diagnosed positive cases than negatives. Cytopathologists with no digital experience were the most accurate in digital diagnosis, even the senior members. Cytotechnologists had the fastest diagnosis time (3 min/digital vs. 1.7 min/glass), but not the best accuracy. Digital time was 1.5 min longer than glass-slide time/per case for cytopathologists and cytotechnologists. Senior pathology residents were slower and less accurate with both methods. Cytopathologists with digital experience ranked 2 nd fastest in time, yet last in accuracy for digital slides. Conclusions: There was good overall diagnostic agreement between the digital whole-slide images and glass slides. Although glass slide diagnosis was more accurate and faster, the results of technologists and pathologists with no digital cytology experience suggest that solid diagnostic ability is a strong indicator for readiness of digital adoption

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease