Citations

Pediatric sarcomas are a heterogeneous group of rare mesodermal malignancies. These cancers, which affect children from infancy through adolescence and young adulthood, are in general challenging to treat with currently available therapies. Biologically, many are characterized by quiet genomes, fusion oncoproteins, immune “cold” microenvironments, and vast epigenetic deregulation that contributes to diverse and complex mechanistic drivers. Multifaceted advancements in research strategies, including high-throughput screening, new model systems, surfaceome profiling, and study of oncogenic fusion condensates have led to new opportunities for understanding the biology of pediatric sarcomas. To continue to make progress for these difficult to treat cancers, it will be critical to continue to improve access to bioinformatic data, approach patient care using innovative clinical trial frameworks, and foster interdisciplinary partnerships among medicinal chemists, scientists, clinicians, advocates, and industry partners.

Importance: Chronic liver disease requires sustained engagement with the health system, which co-occurring social and functional risks may disrupt. Whether these risks form reproducible patterns with different mortality associations is uncertain. Objective: To identify social and functional risk patterns among US adults with chronic liver disease and evaluate their associations with all-cause mortality. Design, Setting, and Participants: Nationally representative cohort study of National Health Interview Survey sample adults interviewed from 2011 through 2018 and linked to the National Death Index through 31 December 2019. Participants were aged 18 years or older, eligible for mortality linkage, reported a chronic liver condition, and had no baseline liver cancer. Exposures: Latent class membership derived from 7 binary indicators: poverty, food insecurity, uninsurance, cost-related delayed care, no usual place of care, single-adult household status, and activity limitation. A complete 0- to 7-item count was a secondary exposure. Main Outcomes and Measures: All-cause mortality. Survey-weighted Cox models estimated hazard ratios (HRs) with sequential demographic, clinical, and liver-specific adjustment. Prespecified sensitivity analyses used 20 multiple imputed data sets, sampling-weighted pseudo-likelihood latent class models, complete-case re-estimation, a fixed 5-year horizon, and class-by-age interaction testing. Results: Among 3407 adults (weighted mean age, 54.0 years; 49.4% female), 529 deaths occurred over 17,297.6 person-years (median follow-up, 5.0 years [IQR, 3.0–7.0]). Four classes were retained: low social and access burden (weighted prevalence, 63.1%); functional limitation with financial strain (13.2%); poverty, single-adult household, and functional limitation (15.0%); and uninsurance with major access barriers (8.7%). Compared with the low-burden class, the functional-limitation/financial-strain class had higher adjusted mortality (HR, 1.95; 95% CI, 1.34–2.82; p < 0.001); the other 2 classes did not differ significantly. Sampling-weighted latent class analysis after multiple imputation reproduced 97.2% of modal assignments, and the association persisted with attenuation (HR, 1.55; 95% CI, 1.08–2.22; p = 0.02). Each additional risk was associated with higher mortality (exact count: HR, 1.24 [95% CI, 1.13–1.36]; imputed count: HR, 1.19 [95% CI, 1.10–1.29]). The class-by-age interaction was not significant (p = 0.41). Conclusions and Relevance: Among US adults with self-reported chronic liver disease, social and functional adversity was both cumulative and patterned. A profile combining functional limitation, food insecurity, and cost-related delayed care had the clearest independent mortality association despite largely retained insurance coverage. These profiles are not a validated clinical score, but the findings argue against reliance on any single indicator, particularly insurance status, as a marker of vulnerability.

Abstract Although sequencing costs have steadily decreased with advances in technology, they remain high for large scale studies. The design of traditional individual-disease sequencing studies is either case only or cases with relatively few controls, resulting in potential loss of statistical power for discovery of disease associated genes. Here we show that for a given number of sequenced cases, a large control sample size is critical to maximize power for rare variant burden analysis. Furthermore, we have developed an end-to-end workflow based tool (CoCoRV-nf) to facilitate the use of external biobank sequence resources as controls. The modules include consistent variant QC, variant annotation, ancestry population prediction, and gene based burden analysis using summary genotype information, and combined analysis from multiple independent results. The tool supports exomes and genomes from gnomAD and All of Us as controls with preprocessed datasets. We apply the tool in two rare neurological diseases: amyotrophic lateral sclerosis and neuroblastoma. For each disease, two case cohorts are paired with gnomAD and All of Us data, respectively, followed by a combined analysis. Not only did we recapture known genes, but also, we identified new candidate genes for both diseases. By leveraging multiple large external biobank sequence data, we demonstrate the feasibility of using our tool to maximize statistical power to identify new disease predisposition genes.

Abstract Background While biologic and small molecule therapies have expanded treatment options for moderate to severe ulcerative colitis (UC), prior research revealed that only 26% of patients in routine practice would have qualified for pivotal randomized controlled trials (RCTs) due to stringent eligibility criteria. This study aimed to evaluate contemporary clinical trial eligibility for UC patients in a tertiary care setting based on more recently approved therapies. Methods We performed a retrospective cohort study of adult patients with moderate to severe UC (total Mayo score ≥6) seen between January 2022 and February 2023. Inclusion and exclusion criteria from seven phase III RCTs conducted between 2012 and 2022 (eg, VARSITY, UNIFI, OCTAVE, LUCENT) were applied to determine patient eligibility and identify reasons for ineligibility. Results Of 150 patients (mean age 44.9 years; 55.7% male), only 42.7% were eligible for at least one RCT. The most common exclusion factor was current or prior medication use, followed by anemia, dysplasia, and disease extent. Trial eligibility did not significantly differ by age, gender, or disease duration. Conclusions Despite modest improvements since 2012, over half of real-world UC patients remain ineligible for current RCTs. Expanding inclusion criteria, especially regarding prior medication exposure, and adopting more inclusive trial designs may enhance external validity. Addressing disparities in trial access and participation remains essential to ensuring equitable representation and improving clinical applicability of RCT findings.

Transfers are high-risk events, and outside hospital admissions lack standardized processes to ensure timely delivery of essential clinical information. We piloted a novel, standardized process for outside admissions to a single academic medical center’s resident- and hospital medicine-attending-based medicine services. Following literature review, Epic UserWeb review, and frontline physician input, a workflow and templated electronic health record accept note were developed to centralize outside admission documentation. Completed by physicians during real-time communication with outside physicians, the template captures key clinical data and incorporates a communication checklist. Physician education was delivered electronically and in person. Subjective attending and resident perceptions were assessed via electronic prepost surveys administered 1 week before and 90 days after implementation. Secondarily, exploratory patient clinical outcomes were assessed through 90-day prepost manual retrospective chart review. Survey responses were obtained from 60/38 residents and 12/7 attendings (pre/post). Documentation sufficiency on patient arrival was directionally promising among residents (21.7% vs 39.5%, P = 0.062). Satisfaction with the admission process and perceived patient safety improved significantly among residents and attendings ( P ≤ 0.036). Most respondents rated the note as useful or very useful (84% residents; 86% attendings), and attendings reported reduced time managing outside admissions. The intervention was associated with exploratory, nonsignificant reductions in rapid response team activation, intensive care unit transfer, 30-day readmission, mortality, and length of stay compared with preintervention patients. A standardized documentation and communication process improved physician satisfaction and perceived patient safety, with promising exploratory patient outcomes. Further study is warranted to assess impact at scale.

Marketing authorizations for oncology drugs are often based on single‐arm trials (SATs), and real‐world data‐based external comparator cohorts (ECCs) are increasingly explored to contextualize results. In retrospective studies, comparators may be identified by time of diagnosis but included based on time of relapse; such sampling strategies can introduce bias, potentially leading to erroneous efficacy conclusions. Simulated diffuse large B‐cell lymphoma (DLBCL) cohorts were generated using published time‐to‐relapse and post‐relapse survival distributions. Three ECC sampling strategies were evaluated: naïve (screening and inclusion periods coincide), delayed (inclusion period extends beyond screening), and displacement (screening and inclusion periods shifted relative to each other). Different sampling strategies caused significant changes in the time‐to‐relapse distribution in the ECC and, by proxy, the estimated 2‐year post‐relapse overall survival (22.5–52.2%). Naïve sampling consistently resulted in oversampling of early relapses, though bias was reduced with extended inclusion periods; in hypothetical trials, it produced inflated Type I errors (10.5–84.9%) despite α = 0.025. Delayed sampling reduced bias when the inclusion period was sufficiently extended to capture late relapses, yielding unbiased estimates under long delays (Type I error of 2.5% at a 6‐year delay). Displacement sampling depended strongly on the degree of shift: moderate shifts minimized bias, whereas long displacements introduced substantial distortion (Type I error of 2.7% and 0.0% at displacements of 2 and 5, respectively). Real‐world data‐based ECCs in relapsed/refractory oncology are susceptible to selection bias from sampling strategies, particularly through distortions in time‐to‐relapse, leading to erroneous efficacy conclusions; similar bias may occur in non‐oncology settings where time‐to‐treatment initiation is prognostic.