Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

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

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

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Objectively Measured Sedentary Behavior, Physical Activity, and Cardiometabolic Risk in Hispanic Youth: Hispanic Community Health Study/Study of Latino Youth

Time spent in moderate-to-vigorous physical activity (MVPA), but not in sedentary behavior (SB), is related to cardiometabolic risk among non-Hispanic white youth. Examine associations of SB and MVPA with cardiometabolic risk factors among Hispanic/Latino youth. Cross-sectional analysis. Four US communities. Hispanic/Latino youth (N = 1,426) ages 8 to 16 years. Associations of MVPA and SB, measured using 7-day accelerometer data (independent variables), with markers of glucose and lipid metabolism, inflammation, and endothelial function (dependent variables), were assessed in multivariable linear regression models while adjusting for sociodemographic characteristics and accelerometer wear time. Additional models controlled for obesity measures. SB comprised a mean (SD) of 75% (13%) of accelerometer wear time; mean (SD) time of MVPA was 35 min/d (22 min/d). Deleterious levels of high-density lipoprotein-cholesterol (HDL-C), triglycerides, insulin resistance, C-reactive protein, and plasminogen activator inhibitor-1 were associated with lower levels of MVPA and higher levels of SB (all P < 0.05). Associations of MVPA with log-transformed triglyceride concentrations (β per 15-min/d increment, -0.039; SE, 0.018; P = 0.037) and SB with HDL-C (β per 30-min/d increment, -0.63; SE, 0.26; P = 0.018), but not those with other markers, remained significant after adjusting for MVPA or SB and further adjustment for body mass index and waist circumference. Higher SB tertiles were associated with lower soluble receptor for advanced glycation end products in fully adjusted models (P for trend = 0.037). Physiological precursors of diabetes and cardiovascular disease were associated with MVPA and SB among US Hispanic/Latino youth, a group that bears a disproportionate burden of metabolic disorders

Structure and integration of specialty palliative care in three NCI-designated cancer centers: a mixed methods case study

Abstract Introduction Early access to specialty palliative care is associated with better quality of life, less intensive end-of-life treatment and improved outcomes for patients with advanced cancer. However, significant variation exists in implementation and integration of palliative care. This study compares the organizational, sociocultural, and clinical factors that support or hinder palliative care integration across three U.S. cancer centers using an in-depth mixed methods case study design and proposes a middle range theory to further characterize specialty palliative care integration. Methods Mixed methods data collection included document review, semi-structured interviews, direct clinical observation, and context data related to site characteristics and patient demographics. A mixed inductive and deductive approach and triangulation was used to analyze and compare sites’ palliative care delivery models, organizational structures, social norms, and clinician beliefs and practices. Results Sites included an urban center in the Midwest and two in the Southeast. Data included 62 clinician and 27 leader interviews, observations of 410 inpatient and outpatient encounters and seven non-encounter-based meetings, and multiple documents. Two sites had high levels of “favorable” organizational influences for specialty palliative care integration, including screening, policies, and other structures facilitating integration of specialty palliative care into advanced cancer care. The third site lacked formal organizational policies and structures for specialty palliative care, had a small specialty palliative care team, espoused an organizational identity linked to treatment innovation, and demonstrated strong social norms for oncologist primacy in decision making. This combination led to low levels of specialty palliative care integration and greater reliance on individual clinicians to initiate palliative care. Conclusion Integration of specialty palliative care services in advanced cancer care was associated with a complex interaction of organization-level factors, social norms, and individual clinician orientation. The resulting middle range theory suggests that formal structures and policies for specialty palliative care combined with supportive social norms are associated with greater palliative care integration in advanced cancer care, and less influence of individual clinician preferences or tendencies to continue treatment. These results suggest multi-faceted efforts at different levels, including social norms, may be needed to improve specialty palliative care integration for advanced cancer patients