Citations

Abstract Purpose: DROSHA, DGCR8, and DICER1 regulate miRNA biogenesis and are commonly mutated in cancer. Although DGCR8 and DICER1 germline pathogenic variants (GPV) cause autosomal dominant tumor predisposition, no association between DROSHA GPVs and clinical phenotypes has been reported. Experimental Design: After obtaining informed consent, sequencing was performed on germline and tumor samples from all patients. The occurrence of germline DROSHA GPVs was investigated in large pediatric and adult cancer datasets. The population prevalence of DROSHA GPVs was investigated in the UK Biobank and Geisinger DiscovEHR cohorts. Results: We describe nine children from eight families with heterozygous DROSHA GPVs and a diagnosis of pineoblastoma (n = 8) or Wilms tumor (n = 1). A somatic second hit in DROSHA was detected in all eight tumors analyzed. All pineoblastoma tumors analyzed were classified as miRNA processing–altered 1 subtype. We estimate the population prevalence of germline DROSHA loss-of-function variants to be 1:3,875 to 1:4,843 but find no evidence for increased adult cancer risk. Conclusions: This is the first report of DROSHA-related tumor predisposition. As pineoblastoma and Wilms tumor are also associated with DICER1 GPVs, our results suggest that the tissues of origin for these tumors are uniquely tolerant of general miRNA loss. The miRNA processing–altered 1 pineoblastoma subtype is associated with older age of diagnosis and better outcomes than other subtypes, suggesting DROSHA GPV status may have important clinical and prognostic significance. We suggest that genetic testing for DROSHA GPVs be considered for patients with pineoblastoma, Wilms tumor, or other DICER1-/DGCR8-related conditions and propose surveillance recommendations through research studies for individuals with DROSHA GPVs.

ABSTRACT Objectives: This evidence implementation project aimed to assess and improve compliance with evidence-based neuroimaging criteria for adult patients with suspected stroke. Introduction: Stroke is the second leading cause of mortality and severe disability, requiring timely and accurate diagnosis. Clinical guidelines recommend brain imaging within 60 minutes of hospital arrival for suspected stroke patients. This project involved hospitals in North West Anglia NHS Foundation Trust, UK, serving 850,000 people with over 800 admissions annually. Methods: The JBI Evidence Implementation Framework was used to guide this project. JBI software, the Practical Application of Clinical Evidence System (PACES), as well as JBI's Getting Research into Practice (GRiP) approach, were used to conduct the audit and implementation phases. The project followed three stages: (1) implementation planning, (2) baseline assessment and implementation, and (3) impact evaluation. Three audit criteria were used to represent best practices for diagnosing suspected stroke patients. Results: The baseline audit revealed low compliance with the first criterion, with only 2.9% (1/35) of patients receiving a CT head scan within 1 hour of admission. In the follow-up audit, compliance improved to 45.2% (14/31). The other two criteria, diagnosis by a trained health care professional and baseline ECG assessment, had already achieved 100% compliance in the baseline audit. Conclusions: Compliance with evidence-based neuroimaging criteria improved after implementing targeted educational strategies and training. The rate of CT scans conducted within 1 hour increased, although door-to-imaging times remain suboptimal compared with achievable benchmarks of ≤ 20 minutes. Ongoing education and training are crucial for sustaining high compliance and improving stroke patient outcomes. Spanish abstract: http://links.lww.com/IJEBH/A324

BACKGROUND: Catheter ablation of ventricular arrhythmias, one of the most rapidly growing procedures in cardiac electrophysiology, is associated with magnetic resonance imaging–detected brain lesions in more than half of cases. Although a retrograde aortic approach is conventional, modern tools enable entry through a transseptal approach that may avoid embolization of debris from the arterial system. We sought to test the hypothesis that a transseptal puncture would mitigate brain injury compared with a retrograde aortic approach. METHODS: The TRAVERSE trial (Transseptal Versus Retrograde Aortic Ventricular Entry to Reduce Systemic Emboli) was a multicenter randomized controlled comparative effectiveness trial. Patients with left ventricular arrhythmias undergoing catheter ablation procedures were randomly assigned to a transseptal puncture approach compared (1:1) with a retrograde aortic approach. The primary outcome was the presence of an acute brain lesion detected by magnetic resonance imaging. Secondary outcomes included clinically manifest complications, procedural efficacy, and 6-month neurocognitive assessments. RESULTS: Among the 62 patients randomly assigned to a retrograde aortic approach with postoperative brain magnetic resonance imaging, 28 (45%) exhibited an acute brain lesion compared with 19 of the 69 (28%) of those randomized to a transseptal puncture ( P =0.036). No differences in clinically manifest complications or procedural efficacy were observed. More patients in the retrograde aortic arm were categorized as having a high likelihood of cognitive impairment at 6 months (33% compared with 19% of those in the transseptal arm), but substantial loss to follow-up was present. CONCLUSIONS: Among patients undergoing left ventricular catheter ablation procedures, a transseptal approach reduced the risk of acute brain lesions by nearly half compared with a retrograde aortic approach without sacrificing safety or efficacy. Given a likely embolic pathogenesis, the brain magnetic resonance imaging findings may reflect a propensity to other organ damage; these findings may extend to other procedures requiring left ventricular entry. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03946072.

Abstract Prevention trials in oncology are some of the most important cancer clinical trials that can be designed, implemented, analyzed, and interpreted. They are pivotal in the goal of stopping the development of cancer before it starts. Prevention trials are unique in that they not only have some of the same requirements and challenges as treatment trials but also have challenges that may make their design more complex. This paper aims to discuss some types of prevention trials and highlight their most common design challenges, including large sample size requirements, slow accrual rates with long accrual duration, extended follow-up periods with trial adherence issues and missing data, endpoints that require extended follow-up or have a high level of confounding, and problems with optimizing study design. This article provides real study examples and suggestions for designing prevention clinical trials while mitigating the known issues they face.

Abstract Interpreting cancer clinical trial results often depends on addressing issues of multiplicity. When testing multiple hypotheses, unreliable findings can occur by chance due to the inflation of the type I error rate, the probability of mistakenly rejecting the null hypothesis when the null hypothesis is true. In this setting, researchers may often set the type I error rate (or the alpha level) low to limit false positive findings and the interpretation of a causal relationship where none exists. Conversely, overly conservative type I error control may result in declaring findings, that do not meet multiplicity-adjusted alpha levels, as false when they are actually true, reducing opportunities for new discovery. This presentation focuses on multiplicity adjustment in the context of clinical trials conducted within the NCI’s Community Oncology Research Program (NCORP). Because federally sponsored trials often require long-term participation from patients and represent a substantial investment by taxpayers, striking the right balance between optimizing what is learned from these trials, while avoiding false positive results, should be a priority.

Abstract Examining treatment effects in subgroups of patients defined by demographic, genetic, or clinical characteristics is increasingly of interest given the pursuit of personalized medicine and the importance of representation and equity in treatment decisions. The magnitude or even the direction of the treatment effect may vary across subgroups, and these differential treatment effects could have clinical implications. Subgroup analyses require caution in their interpretation, however, because of the high probability of a false-positive or false-negative conclusion. We outline study design and analysis considerations for responsibly investigating and reporting differential treatment effects across subgroups in oncology trials, with examples from the National Cancer Institute’s National Clinical Trials Network and Community Oncology Research Program. Recommendations include ensuring appropriate representation of patients from subgroups of interest, recognizing power and multiplicity limitations, and treating exploratory subgroup analyses as hypothesis generating rather than practice changing.

Abstract The intracluster correlation coefficient (ICC) measures the correlation of observations within clusters and is a key parameter for power and sample size calculations for cluster randomized trials (CRTs). To facilitate the design of future CRTs within the National Cancer Institute Community Oncology Research Program (NCORP), all studies from the NCORP website were reviewed to identify completed CRTs. ICCs for primary and secondary outcomes (when available) were ascertained from these trials and summarized in this article as a resource for future trial development. Although ICCs are relatively small for many outcome cluster combinations, that is not always the case, so consideration should always be given to the specific outcome of interest, trial design, and type of cluster when estimating an ICC to facilitate trial development.

Abstract Cancer care delivery research trials conducted within the National Cancer Institute (NCI) Community Oncology Research Program (NCORP) routinely implement interventions at the practice or provider level, necessitating the use of cluster randomized controlled trials (cRCTs). The intervention delivery requires cluster-level randomization instead of participant-level, affecting sample size calculation and statistical analyses to incorporate correlation between participants within a practice. Practical challenges exist in the conduct of these cRCTs due to unique trial network infrastructures, including the possibility of unequal participant accrual totals and rates and staggered study initiation by clusters, potentially with differences between randomized arms. Execution of cRCT designs can be complex, ie, if some clusters do not accrue participants, unintended cluster-level crossover occurs, how best to identify appropriate cluster-level stratification, timing of randomization, and multilevel eligibility criteria considerations. This article shares lessons learned with potential mitigation strategies from 3 NCORP cRCTs.

In the introduction to his 1970 textbook BIOCHEMISTRY, Albert Lehninger wrote “Living things are composed of lifeless molecules [...]

Background: Fecal microbiota transplantation (FMT) is a successful therapy for Clostridioides difficile infection (CDI). FMT from overweight donors is speculated to influence the recipient’s body mass index (BMI) after administration for CDI. Objectives: We investigated changes in the recipient’s BMI after FMT in relation to the donor’s BMI. Design: We conducted a retrospective cohort study involving patients who underwent FMT for recurrent CDI at Mayo Clinic between 2012 and 2019. Methods: We analyzed demographic and donor data for patients undergoing FMT at Mayo Clinic (2012–2019). Recipient BMI (pre- and post-FMT) and donor BMI were extracted from medical records. Mixed-effects linear regression was used to evaluate the impact of donor BMI, donor BMI category, recipient baseline BMI, time before and after FMT, and interactions between these variables on overall BMI change and BMI change per month. Kaplan–Meier curves were used to assess BMI changes (⩾5 units) based on the last recorded post-FMT BMI. Results: We analyzed data from 401 patients with recorded BMI measurements before and after FMT. The median age of the recipients at the time of FMT was 59.1 years (interquartile range (IQR): 40.5–70.1 years), with 61.6% being female. The median BMI for recipients prior to FMT was 26.7 kg/m² (IQR: 22.7–31.6 kg/m²), while the median BMI of the donors was 24.5 kg/m2 (IQR: 23.9–27.5 kg/m2). Stool from donors with a normal BMI was used for 58.2% of recipients, while 41.8% received stool from pre-obese donors. Donor BMI data were missing for 3.2% of recipients. Donor BMI was not significantly associated with changes in recipient BMI; for each 1-unit increase in donor BMI, a 0.01-unit monthly increase was observed (95% confidence interval: −0.0003, 0.02; p = 0.11). The log-rank test for BMI increases (⩾+5) and decreases (⩽−5) revealed no significant differences among the donor BMI groups (Chi-squared = 4.4, p = 0.1 for increases, Chi-squared = 2, p = 0.4 for decreases). Conclusion: The lack of impact of donor BMI on BMI changes post-FMT suggests that these changes are more dependent on the recipient’s metabolic profile. Prospective, controlled trials are required to analyze these results more comprehensively.