Citations

PURPOSE Pharmacologic therapies for neurofibromatosis type 1-associated plexiform neurofibromas (NF1-PNs) are limited; currently, none are US Food and Drug Administration–approved for adults. METHODS ReNeu is an open-label, multicenter, pivotal, phase IIb trial of mirdametinib in 58 adults (≥18 years of age) and 56 children (2 to 17 years of age) with NF1-PN causing significant morbidities. Patients received mirdametinib capsules or tablets for oral suspension (2 mg/m 2 twice daily, maximum 4 mg twice daily), regardless of food intake, in 3 weeks on/1 week off 28-day cycles. The primary end point was confirmed objective response rate (ORR; proportion of patients with a ≥20% reduction of target PN volume from baseline on consecutive scans during the 24-cycle treatment phase) assessed by blinded independent central review (BICR) of volumetric magnetic resonance imaging. RESULTS Twenty-four of 58 adults (41%) and 29 of 56 children (52%) had a BICR-confirmed objective response during the 24-cycle treatment phase; in addition, two adults and one child had confirmed responses during long-term follow-up. Median (range) target PN volumetric best response was –41% (−90 to 13) in adults and –42% (−91 to 48) in children. Both cohorts reported significant and clinically meaningful improvement in patient- or parent proxy-reported outcome measures of worst tumor pain severity, pain interference, and health-related quality of life (HRQOL) that began early and were sustained during treatment. The most commonly reported treatment-related adverse events were dermatitis acneiform, diarrhea, and nausea in adults and dermatitis acneiform, diarrhea, and paronychia in children. CONCLUSION In ReNeu, the largest multicenter NF1-PN trial reported to date, mirdametinib treatment demonstrated significant confirmed ORRs by BICR, deep and durable PN volume reductions, and early, sustained, and clinically meaningful improvement in pain and HRQOL. Mirdametinib was well-tolerated in adults and children.

PURPOSE The nodal burden of patients with residual isolated tumor cells (ITCs) in the sentinel lymph nodes (SLNs) after neoadjuvant chemotherapy (NAC) (ypN0i+) is unknown, and axillary management is not standardized. We investigated rates of additional positive lymph nodes (LNs) at axillary lymph node dissection (ALND) and oncologic outcomes in patients with ypN0i+ treated with and without ALND. METHODS The Oncoplastic Breast Consortium-05/ICARO cohort study (ClinicalTrials.gov identifier: NCT06464341 ) retrospectively analyzed data from patients with stage I to III breast cancer with ITCs in SLNs after NAC from 62 centers in 18 countries. The primary end point was the 3-year rate of any axillary recurrence. The rate of any invasive recurrence was the secondary end point. RESULTS In total, 583 patients were included, of whom 182 (31%) had completion ALND and 401 (69%) did not. The median age was 48 years. Most patients (74%) were clinically node-positive at diagnosis and 41% had hormone receptor–positive/human epidermal growth factor receptor 2–negative tumors. The mean number of SLNs with ITCs was 1.2. Patients treated with ALND were more likely to present with cN2/3 disease (17% v 7%, P < .001), have ITCs detected on frozen section (62% v 8%, P < .001), have lymphovascular invasion (38% v 24%, P < .001), and receive adjuvant chest wall (89% v 78%, P = .024) and nodal radiation (82% v 75%, P = .038). Additional positive nodes were found at ALND in 30% of patients, but only 5% had macrometastases. The 3-year rates of any axillary and any invasive recurrence were 2% (95% CI, 0.95 to 3.6) and 11% (95% CI, 8 to 14), respectively, with no statistical difference by type of axillary surgery. CONCLUSION The nodal burden in patients with ypN0(i+) was low, and axillary recurrence after ALND omission was rare in patients selected for this approach. These results do not support routine ALND in all patients with ypN0(i+).

Objective Drug-induced long QT syndrome (diLQTS) is an adverse reaction from over 150 FDA-approved medications, posing the risk of triggering torsades de pointes and sudden death. While common genetic variants may modestly impact QT interval individually, their collective effect can significantly amplify risk of diLQTS. Consequently, this study aimed to validate a polygenic risk score (PRS) for diLQTS previously proposed by Strauss et al. Methods A retrospective cohort study was conducted utilizing patients from the Michigan Genomics Initiative prescribed 27 high-risk QT-prolonging drugs and an ECG during the prescription. The primary outcome was marked prolongation of the QTc interval (either >60 ms change from baseline or >500 ms absolute value) during treatment with a high-risk QT-prolonging drug. Results The primary outcome occurred in 12.0% of n = 6070 self-reported White, 12.4% of 558 African American, and 8.2% of 110 Asian patients. The PRS significantly associated with diLQTS in White patients [adjusted odds ratio = 1.44 (95% CI: 1.09–1.89); P = 0.009]. However the study lacked sufficient statistical power to confirm the PRS as a risk factor in African Americans [adjusted odds ratio = 2.18 (95% CI: 0.98–5.49); P = 0.073] and Asians [adjusted odds ratio = 3.21 (95% CI: 0.69–16.87); P = 0.139] due to smaller sample sizes in these groups. Conclusion The previously published PRS for diLQTS was validated in a large, real-world cohort, demonstrating its potential as a tool for identifying high-risk patients. Incorporating this PRS into routine clinical practice could enable proactive measures to prevent life-threatening diLQTS.

Abstract Background Transcatheter aortic valve replacement (TAVR) continues to be increasingly performed. While machine learning has demonstrated utility in predicting adverse outcomes in various cardiovascular procedures, its effectiveness in predicting in-hospital mortality in TAVR remains uncertain. Purpose We aimed to examine the accuracy of machine learning in predicting in-hospital mortality in TAVR. Methods We searched PubMed and Cochrane databases up to February 2024 for observational studies assessing the diagnostic accuracy of machine learning for predicting in-hospital mortality in TAVR. Extracted data included study characteristics, patient demographics, type of predictive model used, and area under the receiver operating characteristic curves (AUC). Predictive accuracy was assessed by pooling AUC of machine-learning models across studies. AUC values ranging from 0.80 to 0.90, 0.70 to 0.80, 0.60 to 0.70, and 0.50 to 0.60 corresponded to excellent, good, fair, and poor discrimination ability, respectively (1). We used a random effects model to estimate the overall effect size with a 95% confidence interval (CI). Heterogeneity was evaluated with the I2 test.We conducted subgroup analyses based on the type of machine-learning model utilized to assess for any potential differences in predictive accuracy. Results A total of three studies were identified, comprising a study population of 70,714 patients. Across studies, eight machine learning models were utilized, consisting of three artificial neural network models, one logistic regression model, one naive Bayes model, two random forest models, and one support vector machine model. Pooled AUC showed excellent accuracy of machine learning in predicting in-hospital mortality in TAVR (0.92; 95% CI 0.90 - 0.95). Subgroup analyses revealed similar yet excellent accuracy across all types of machine learning models utilized: artificial neural network (0.92; 95% CI 0.86-0.99), logistic regression (0.92; 95% CI 0.89-0.95), naive Bayes (0.90; 95% CI 0.88-0.92), random forest (0.94; 95% CI 0.87-1.00), and support vector machine (0.94; 95% CI 0.91-0.96). Conclusion Our study suggests that utilizing machine learning can accurately predict in-hospital mortality in TAVR. Operators of TAVR procedures may benefit from incorporating machine learning prediction models in their clinical practice. Further exploration of machine learning on outcomes in TAVR is needed to verify our findings.

AbstractIntroductionAcute myeloid leukemia (AML) remains one of the deadliest hematopoietic malignancies. A better understanding of the molecular biology governing AML may lead to improved risk stratification and facilitate the development of novel therapies. Proteins are responsible for much of the biology of cells. Several studies have examined the global proteome in bulk mononuclear cells (MNCs) from AML specimens, which are comprised a heterogenous population of cells at various stages of differentiation.MethodsGiven the potential impact of the nonleukemic cells on protein expression profiles, we applied an integrative proteogenomic approach utilizing next‐generation sequencing and mass spectrometry‐based proteomics to identify novel protein biomarkers in unsorted MNCs and viable leukemic blasts (VLBs) isolated from blood and bone marrow specimens obtained at the time of AML diagnosis.ResultsWe identified significant differences in protein expression between VLBs and MNCs. Subsequent studies (N = 27) focused on proteomic profiling of VLBs that identified novel candidate biomarkers associated with mutational genotypes and clinical outcome, some of which were recapitulated in an independent cohort of patients. Using mass spectrometry, we also detected mutated protein products, some of which were predicted via in silico analyses to be potential neoantigens amenable to adoptive immunotherapy. As previously described, analyses comparing transcript and protein expression showed an overall modest correlation between mRNA and protein dataset, but enriching for genes associated with mutations significantly improved the protein–RNA correlation.ConclusionTogether, the results provide insight into the biology of VLBs and demonstrate the gains derived from examining the proteome in addition to genome and transcriptome.