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ObjectiveTo describe the participation of racial and ethnic minority groups (REMGs) in gynecologic oncology trials.MethodsGynecologic oncology studies registered on ClinicalTrials.gov between 2007 and 2020 were identified. Trials with published results were analyzed based on reporting of race/ethnicity in relation to disease site and trial characteristics. Expected enrollment by race/ethnicity was calculated and compared to actual enrollment, adjusted for 2010 US Census population data.Results2146 gynecologic oncology trials were identified. Of published trials (n = 252), 99 (39.3%) reported race/ethnicity data. Recent trials were more likely to report these data (36% from 2007 to 2009; 51% 2013-2015; and 53% from 2016 to 2018, p = 0.01). Of all trials, ovarian cancer trials were least likely to report race/ethnicity data (32.1% vs 39.3%, p = 0.011). Population-adjusted under-enrollment for Blacks was 7-fold in ovarian cancer, Latinx 10-fold for ovarian and 6-fold in uterine cancer trials, Asians 2.5-fold in uterine cancer trials, and American Indian and Alaska Native individuals 6-fold in ovarian trials. Trials for most disease sites have enrolled more REMGs in recent years - REMGs made up 19.6% of trial participants in 2007-2009 compared to 38.1% in 2016-2018 (p

BackgroundFemale underrepresentation in oncology clinical trials can result in outcome disparities. We evaluated female participant representation in US oncology trials by intervention type, cancer site, and funding.Materials and methodsData were extracted from the publicly available Aggregate Analysis of ClinicalTrials.gov database. Initially, 270,172 studies were identified. Following the exclusion of trials using Medical Subject Heading terms, manual review, those with incomplete status, non-US location, sex-specific organ cancers, or lacking participant sex data, 1650 trials consisting of 240,776 participants remained. The primary outcome was participation to prevalence ratio (PPR): percent females among trial participants divided by percent females in the disease population per US Surveillance, Epidemiology, and End Results Program data. PPRs of 0.8-1.2 reflect proportional female representation.ResultsFemales represented 46.9% of participants (95% CI, 45.4-48.4); mean PPR for all trials was 0.912. Females were underrepresented in surgical (PPR 0.74) and other invasive (PPR 0.69) oncology trials. Among cancer sites, females were underrepresented in bladder (odds ratio [OR] 0.48, 95% CI 0.26-0.91, P = .02), head/neck (OR 0.44, 95% CI 0.29-0.68, P < .01), stomach (OR 0.40, 95% CI 0.23-0.70, P < .01), and esophageal (OR 0.40 95% CI 0.22-0.74, P < .01) trials. Hematologic (OR 1.78, 95% CI 1.09-1.82, P < .01) and pancreatic (OR 2.18, 95% CI 1.46-3.26, P < .01) trials had higher odds of proportional female representation. Industry-funded trials had greater odds of proportional female representation (OR 1.41, 95% CI 1.09-1.82, P = .01) than US government and academic-funded trials.ConclusionsStakeholders should look to hematologic, pancreatic, and industry-funded cancer trials as exemplars of female participant representation and consider female representation when interpreting trial results.

Previous research has found that functional connectivity (FC) can accurately predict the identity of a subject performing a task and the type of task being performed. These results are replicated using a large data set collected at the Ohio State University Center for Cognitive and Behavioral Brain Imaging. This work introduces a novel perspective on task and subject identity prediction: blood-oxygen-level-dependent variability (BV). Conceptually, BV is a region-specific measure based on the variance within each brain region. BV is simple to compute, interpret, and visualize. This work shows that both FC and BV are predictive of task and subject, even across scanning sessions separated by multiple years. Subject differences rather than task differences account for the majority of changes in BV and FC. Similar to results in FC, BV is reduced during cognitive tasks relative to rest.

Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification-amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two-Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure-Function Linkage Database, SFLD) self-identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self-identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well-curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP-identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F-measure and performance analysis on the enolase search results and comparison to GEMMA and SCI-PHY demonstrate that TuLIP avoids the over-division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results.