CDKN1C/p57kip2 Is a Candidate Tumor Suppressor Gene in Human Breast Cancer

BACKGROUND. CDKN1C (also known as p57KIP2) is a cyclin-dependent kinase inhibitor previously implicated in several types of human cancer. Its family members (CDKN1A/p21CIP1 and B/p27KIP1) have been implicated in breast cancer, but information about CDKN1C's role is limited. We hypothesized that decreased CDKN1C may be involved in human breast carcinogenesis in vivo. METHODS. We determined rates of allele imbalance or loss of heterozygosity (AI/LOH) in CDKN1C, using an intronic polymorphism, and in the surrounding 11p15.5 region in 82 breast cancers. We examined the CDKN1C mRNA level in 10 cancers using quantitative real-time PCR (qPCR), and the CDKN1C protein level in 20 cancers using immunohistochemistry (IHC). All samples were obtained using laser microdissection. Data were analyzed using standard statistical tests. RESULTS. AI/LOH at 11p15.5 occurred in 28/73 (38%) informative cancers, but CDKN1C itself underwent AI/LOH in only 3/16 (19%) cancers (p = ns). In contrast, CDKN1C mRNA levels were reduced in 9/10 (90%) cancers (p < 0.0001), ranging from 2–60% of paired normal epithelium. Similarly, CDKN1C protein staining was seen in 19/20 (95%) cases' normal epithelium but in only 7/14 (50%) cases' CIS (p < 0.004) and 5/18 (28%) cases' IC (p < 0.00003). The reduction appears primarily due to loss of CDKN1C expression from myoepithelial layer cells, which stained intensely in 17/20 (85%) normal lobules, but in 0/14 (0%) CIS (p < 0.00001). In contrast, luminal cells displayed less intense, focal staining fairly consistently across histologies. Decreased CDKN1C was not clearly associated with tumor grade, histology, ER, PR or HER2 status. CONCLUSION. CDKN1C is expressed in normal epithelium of most breast cancer cases, mainly in the myothepithelial layer. This expression decreases, at both the mRNA and protein level, in the large majority of breast cancers, and does not appear to be mediated by AI/LOH at the gene. Thus, CDKN1C may be a breast cancer tumor suppressor.Department of Defense Breast Cancer Research Program (DAMD 17-99-1-9573); National Institutes of Health PHS (CA081078); LaPann Fun

Balancing carrots and sticks in REDD+: implications for social safeguards

Reducing carbon emissions through avoided deforestation and forest degradation and enhancement of carbon stocks (REDD+) is key to mitigating global climate change. The aim of REDD+ social safeguards is to ensure that REDD+ does not harm, and actually benefits, local people. To be eligible for results-based compensation through REDD+, countries should develop national-level safeguard information systems to monitor and report on the impacts of REDD+. Although safeguards represent a key step for promoting social responsibility in REDD+, they are challenging to operationalize and monitor. We analyzed the impacts of different types of REDD+ interventions (incentives vs. disincentives) on key safeguard-relevant indicators, i.e., tenure security, participation, and subjective well-being, as well as on reported forest clearing. We used household-level data collected in Brazil, Peru, Cameroon, Tanzania, Indonesia, and Vietnam from approximately 4000 households in 130 villages at two points in time (2010-2012 and 2013-2014). Our findings highlight a decrease in perceived tenure security and overall perceived well-being over time for households exposed to disincentives alone, with the addition of incentives helping to alleviate negative effects on well-being. In Brazil, although disincentives were associated with reduced reported forest clearing by smallholders, they were the intervention that most negatively affected perceived well-being, highlighting a clear trade-off between carbon and noncarbon benefits. Globally, although households exposed to REDD+ interventions were generally aware of local REDD+ initiatives, meaningful participation in initiative design and implementation lagged behind. Our analysis contributes to a relatively small literature that seeks to operationalize REDD+ social safeguards empirically and to evaluate the impacts of REDD+ interventions on local people and forests

Balancing carrots and sticks in REDD+: implications for social safeguards

Reducing carbon emissions through avoided deforestation and forest degradation and enhancement of carbon stocks (REDD+) is key to mitigating global climate change. The aim of REDD+ social safeguards is to ensure that REDD+ does not harm, and actually benefits, local people. To be eligible for results-based compensation through REDD+, countries should develop national-level safeguard information systems to monitor and report on the impacts of REDD+. Although safeguards represent a key step for promoting social responsibility in REDD+, they are challenging to operationalize and monitor. We analyzed the impacts of different types of REDD+ interventions (incentives vs. disincentives) on key safeguard-relevant indicators, i.e., tenure security, participation, and subjective well-being, as well as on reported forest clearing. We used household-level data collected in Brazil, Peru, Cameroon, Tanzania, Indonesia, and Vietnam from approximately 4000 households in 130 villages at two points in time (2010-2012 and 2013-2014). Our findings highlight a decrease in perceived tenure security and overall perceived well-being over time for households exposed to disincentives alone, with the addition of incentives helping to alleviate negative effects on well-being. In Brazil, although disincentives were associated with reduced reported forest clearing by smallholders, they were the intervention that most negatively affected perceived well-being, highlighting a clear trade-off between carbon and noncarbon benefits. Globally, although households exposed to REDD+ interventions were generally aware of local REDD+ initiatives, meaningful participation in initiative design and implementation lagged behind. Our analysis contributes to a relatively small literature that seeks to operationalize REDD+ social safeguards empirically and to evaluate the impacts of REDD+ interventions on local people and forests

Circulating microRNAs Reveal Time Course of Organ Injury in a Porcine Model of Acetaminophen-Induced Acute Liver Failure

Acute liver failure is a rare but catastrophic condition which can progress rapidly to multi-organ failure. Studies investigating the onset of individual organ injury such as the liver, kidneys and brain during the evolution of acute liver failure, are lacking. MicroRNAs are short, non-coding strands of RNA that are released into the circulation following tissue injury. In this study, we have characterised the release of both global microRNA and specific microRNA species into the plasma using a porcine model of acetaminophen-induced acute liver failure. Pigs were induced to acute liver failure with oral acetaminophen over 19h±2h and death occurred 13h±3h thereafter. Global microRNA concentrations increased 4h prior to acute liver failure in plasma (P<0.0001) but not in isolated exosomes, and were associated with increasing plasma levels of the damage-associated molecular pattern molecule, genomic DNA (P<0.0001). MiR122 increased around the time of onset of acute liver failure (P<0.0001) and was associated with increasing international normalised ratio (P<0.0001). MiR192 increased 8h after acute liver failure (P<0.0001) and was associated with increasing creatinine (P<0.0001). The increase in miR124-1 occurred concurrent with the pre-terminal increase in intracranial pressure (P<0.0001) and was associated with decreasing cerebral perfusion pressure (P<0.002)

The Mid-infrared Instrument for JWST and Its In-flight Performance

The Mid-Infrared Instrument (MIRI) extends the reach of the James Webb Space Telescope (JWST) to 28.5 μm. It provides subarcsecond-resolution imaging, high sensitivity coronagraphy, and spectroscopy at resolutions of λ/Δλ ∼ 100-3500, with the high-resolution mode employing an integral field unit to provide spatial data cubes. The resulting broad suite of capabilities will enable huge advances in studies over this wavelength range. This overview describes the history of acquiring this capability for JWST. It discusses the basic attributes of the instrument optics, the detector arrays, and the cryocooler that keeps everything at approximately 7 K. It gives a short description of the data pipeline and of the instrument performance demonstrated during JWST commissioning. The bottom line is that the telescope and MIRI are both operating to the standards set by pre-launch predictions, and all of the MIRI capabilities are operating at, or even a bit better than, the level that had been expected. The paper is also designed to act as a roadmap to more detailed papers on different aspects of MIRI

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE).

PURPOSE: Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. EXPERIMENTAL DESIGN: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. RESULTS: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. CONCLUSIONS: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.Core funding for this project was provided by the National Institutes of Health (R01-CA172404, PI: S.J. Ramus; and R01-CA168758, PIs: J.A. Doherty and M.A.Rossing), the Canadian Institutes for Health Research (Proof-of-Principle I program, PIs: D.G.Huntsman and M.S. Anglesio), the United States Department of Defense Ovarian Cancer Research Program (OC110433, PI: D.D. Bowtell). A. Talhouk is funded through a Michael Smith Foundation for Health Research Scholar Award. M.S. Anglesio is funded through a Michael Smith Foundation for Health Research Scholar Award and the Janet D. Cottrelle Foundation Scholars program managed by the BC Cancer Foundation. J. George was partially supported by the NIH/National Cancer Institute award number P30CA034196. C. Wang was a Career Enhancement Awardee of the Mayo Clinic SPORE in Ovarian Cancer (P50 CA136393). D.G. Huntsman receives support from the Dr. Chew Wei Memorial Professorship in Gynecologic Oncology, and the Canada Research Chairs program (Research Chair in Molecular and Genomic Pathology). M. Widschwendter receives funding from the European Union’s Horizon 2020 European Research Council Programme, H2020 BRCA-ERC under Grant Agreement No. 742432 as well as the charity, The Eve Appeal (https://eveappeal.org.uk/), and support of the National Institute for Health Research (NIHR) and the University College London Hospitals (UCLH) Biomedical Research Centre. G.E. Konecny is supported by the Miriam and Sheldon Adelson Medical Research Foundation. B.Y. Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124. H.R. Harris is 20 supported by the NIH/National Cancer Institute award number K22 CA193860. OVCARE (including the VAN study) receives support through the BC Cancer Foundation and The VGH+UBC Hospital Foundation (authors AT, BG, DGH, and MSA). The AOV study is supported by the Canadian Institutes of Health Research (MOP86727). The Gynaecological Oncology Biobank at Westmead, a member of the Australasian Biospecimen Network-Oncology group, was funded by the National Health and Medical Research Council Enabling Grants ID 310670 & ID 628903 and the Cancer Institute NSW Grants ID 12/RIG/1-17 & 15/RIG/1-16. The Australian Ovarian Cancer Study Group was supported by the U.S. Army Medical Research and Materiel Command under DAMD17-01-1-0729, The Cancer Council Victoria, Queensland Cancer Fund, The Cancer Council New South Wales, The Cancer Council South Australia, The Cancer Council Tasmania and The Cancer Foundation of Western Australia (Multi-State Applications 191, 211 and 182) and the National Health and Medical Research Council of Australia (NHMRC; ID199600; ID400413 and ID400281). BriTROC-1 was funded by Ovarian Cancer Action (to IAM and JDB, grant number 006) and supported by Cancer Research UK (grant numbers A15973, A15601, A18072, A17197, A19274 and A19694) and the National Institute for Health Research Cambridge and Imperial Biomedical Research Centres. Samples from the Mayo Clinic were collected and provided with support of P50 CA136393 (E.L.G., G.L.K, S.H.K, M.E.S.)

Rare and low-frequency coding variants alter human adult height

Height is a highly heritable, classic polygenic trait with ~700 common associated variants identified so far through genome - wide association studies . Here , we report 83 height - associated coding variants with lower minor allele frequenc ies ( range of 0.1 - 4.8% ) and effects of up to 2 16 cm /allele ( e.g. in IHH , STC2 , AR and CRISPLD2 ) , >10 times the average effect of common variants . In functional follow - up studies, rare height - increasing alleles of STC2 (+1 - 2 cm/allele) compromise d proteolytic inhibition of PAPP - A and increased cleavage of IGFBP - 4 in vitro , resulting in higher bioavailability of insulin - like growth factors . The se 83 height - associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates ( e.g. ADAMTS3, IL11RA, NOX4 ) and pathways ( e.g . proteoglycan/ glycosaminoglycan synthesis ) involved in growth . Our results demonstrate that sufficiently large sample sizes can uncover rare and low - frequency variants of moderate to large effect associated with polygenic human phenotypes , and that these variants implicate relevant genes and pathways

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14