Citations

Abstract Purpose: Our goal was to determine if the HRDefine biomarker combining methylation and mutation assessment of key homologous recombination repair genes identifies BRCA wildtype (BRCAwt) ovarian cancer (OC) with outcomes similar to BRCA mutated (BRCAm) OC within the context of a frontline PARP inhibitor trial for advanced ovarian cancer. Methods: We performed an exploratory biomarker analysis of blood and neoplastic samples from the VELIA trial, a phase 3 study evaluating the PARP inhibitor veliparib as frontline maintenance therapy in OC. We developed a highly sensitive droplet digital PCR assay for quantitative BRCA1 and RAD51C promoter methylation and assessed germline/somatic mutations using the BROCA next generation sequencing platform. Cases were grouped into: BRCAm, HRDefine positive (BRCAwt HRDefine+), and HRDefine negative (BRCAwt HRDefine-). Carcinomas were considered HRDefine+ if quantitative BRCA1 or RAD51C promoter methylation was >/= 60% or if a germline or somatic pathogenic variant (PV) was identified in the following non-BRCA HRR repair genes: RAD51C, RAD51D, PALB2, BARD1. For this analysis, the control group included the two study arms with placebo maintenance compared to the investigational arm of veliparib with chemotherapy and continued as maintenance (veliparib throughout group). HRDefine was compared to the Myriad genomic instability score (GIS) used as HRD assessment in the trial. Progression free survival (PFS) was compared using log rank tests. Results: Among 1140 VELIA participants, biomarker analysis could be performed for 487 cases. Of these, 255 (52%) were BRCAm, 60 (12%) were BRCAwt HRDefine+, and 172 (35%) were BRCAwt HRDefine-. BRCAwt HRDefine+ was driven by promoter methylation (57%), germline (40%), or somatic (3%) PVs. Median PFS (months, all arms combined) was 26 months (BRCAm), 27 months (BRCAwt HRDefine+), and 15 months (BRCAwt HRDefine-), with corresponding hazard ratios (HR) of 0.50 (95% CI: 0.39-0.65) and 0.50 (0.34-0.73) versus HRDefine-. All HRDefine+ molecular subgroups were associated with longer PFS, including RAD51C/D mutations (HR 0.44, 0.25-0.77) and promoter methylation (HR 0.60, 0.36-0.97). HRDefine positivity was significantly associated with improved PFS compared to HRDefine-/GIS low (n=60 vs n=111, median PFS 27 versus 14 months, HR 0.44, 0.30-0.65), while the HRDefine- cases with high GIS demonstrated a trend toward an intermediate PFS (n=56, median 18 months, HR 0.67, 0.45-1.01). When compared by treatment arm, median PFS was significantly longer in the veliparib throughout arm compared to control only for the BRCAm group (32 months vs 23 months, HR 0.61, 0.42-0.87), though the analysis was underpowered for the BRCAwt HRDefine+ sub-group (30 months vs 25 months, HR 0.79, 0.4-1.57). Conclusions: HRDefine identifies BRCAwt OC associated with a PFS that is like that of BRCAm cases and may improve stratification for PARP inhibitor therapy beyond current HRD assays. GIS may have some, albeit reduced, prognostic implication that is independent of HRDefine. Citation Format: Isabel V. Rodriguez, Marc Radke, Ronit Katz, Persephone Miller, Paula Ladd, Nithisha Khasnavis, Jennifer Ochoa, Melanie Dillon, Heather Lankes, Peter Ansell, Carol Aghajanian, Gini Fleming, Michael Birrer, Scott Kaufmann, Charles Leath, Kathleen Moore, Theresa Werner, Linda Van Le, Robert L. Coleman, Elizabeth M. Swisher. Application of HRDefine: a biomarker for identifying root causes of homologous recombination deficiency to a phase 3, randomized, placebo-controlled trial of veliparib in front-line treatment of advanced ovarian cancer (VELIA/GOG-3005) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr A044.

Abstract PARP inhibitors (PARPis) are now a standard of care for high-grade serous ovarian cancer (HGSOC), but resistance to these drugs poses a significant concern, necessitating the exploration of novel treatment strategies. Here we investigated the mechanism underlying enhanced PARPi activity in HGSOC cells following treatment with brigatinib—a second-generation anaplastic lymphoma kinase (ALK) inhibitor approved for treating lung cancer. Our previous work has shown that brigatinib is a dual inhibitor of tyrosine kinases FAK and EPHA2 with subsequent additive reduction in downstream PI3K/AKT and MAPK/ERK cell survival signaling. Suppression of these pathways conferred PARPi sensitivity to HGSOC cell lines and patient-derived xenograft (PDX) models, particularly in those with high FAK and EPHA2. To dissect the mechanism linking the FAK/EPHA2 – AKT/ERK pathway to PARPi synergy, we focused on proteomics approaches which revealed that FRA1—an Activator Protein 1 (AP-1) transcription factor known to be downstream of AKT/ERK—and its targets were decreased by brigatinib treatment. In subsequent phosphoproteomics approaches and cell-based confirmatory studies, a phosphorylation crucial for FRA1 stability was found to be disrupted, leading to reduction in total FRA1 protein levels and synergy with PARPis. To explore how FRA1 mediates response to PARPis, immunoblotting analysis revealed that PARPis increased the abundance of FRA1 in HGSOC cells at early timepoints, indicating potential involvement in an adaptive survival response. Follow-up pharmacologic and gene silencing techniques demonstrated that brigatinib still reduces FRA1 in PARPi-treated cells, and FRA1 depletion induces apoptosis when combined with PARPi to levels comparable to brigatinib treatment. Overall, our studies unveil a beneficial ALK-independent effect of brigatinib that may offer new avenues for considering its repurposing as a HGSOC therapy through disruption of a PARPi-induced adaptive survival mechanism. Citation Format: Julie R. Duffield, Xiaonan Hou, Benjamin W. Wilson, Anjali Prasad, Iman K. McKeon-Makki, Amelia M. Huehls, Xinyan Wu, Cristina Correia, Melissa C. Larson, Fergus J. Couch, Ann L. Oberg, Scott H. Kaufmann, Larry M. Karnitz, Arun Kanakkanthara. A PARP-inhibitor-induced early adaptive survival response is tackled through depletion of FRA1 by brigatinib in high-grade serous ovarian carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr A024.

Artificial intelligence (AI) is transforming health care and has implications for palliative care (PC) and serious illness communication (SIC). This article integrates interdisciplinary and interprofessional expertise, providing key tips for PC clinicians to best leverage AI-driven tools to enhance PC practices, care efficiency, and patient- and family-centered outcomes. AI-driven tools range from predictive analytics to improve intervention delivery to AI-assisted communication and documentation of goals of care. Beyond these potential benefits are important ethical, logistical, and equity considerations that must be carefully addressed.

ABSTRACT Cellular senescence is an irreversible form of cell‐cycle arrest caused by excessive stress or damage. While various biomarkers of cellular senescence have been proposed, there are currently no universal, stand‐alone indicators of this condition. The field largely relies on the combined detection of multiple biomarkers to differentiate senescent cells from non‐senescent cells. Here we introduce a new approach: unbiased cell culture selections to identify senescent cell‐specific folded DNA aptamers from vast libraries of trillions of random 80‐mer DNAs. Senescent mouse adult fibroblasts and their non‐senescent counterparts were employed for selection. We demonstrate aptamer specificity for senescent mouse cells in culture, identify a form of fibronectin as the molecular target of two selected aptamers, show increased aptamer staining in naturally aged mouse tissues, and demonstrate decreased aptamer staining when p16 expressing cells are removed in a transgenic INK‐ATTAC mouse model. This work demonstrates the value of unbiased cell‐based selections to identify new senescence‐specific DNA reagents.

ABSTRACT Achromobacter species are Gram-negative bacilli that predominantly affect individuals with cystic fibrosis or those with immunocompromising conditions. Clinical and microbiologic data on Achromobacter species are limited due to the rarity of these organisms and challenges with species-level identification. We conducted a 10-year retrospective analysis (1 January 2013 to 14 March 2023) of all bloodstream and non-bloodstream Achromobacter isolates identified at three tertiary-care Mayo Clinic locations (Minnesota, Arizona, and Florida). Additionally, we examined clinical characteristics of adult patients with Achromobacter bloodstream isolates. A total of 1,598 Achromobacter isolates, encompassing 1,545 non-bloodstream isolates and 53 bloodstream isolates, were identified. The most frequently identified species was Achromobacter xylosoxidans , though species-level identification was not possible for many isolates. Among adult patients with Achromobacter bloodstream infection, the mean age was 58.3 years (standard deviation ± 16.7), 73.1% had a central venous catheter, and 59.6% were immunocompromised. All-cause mortality was 10.2% at 30 days and 18.4% at 90 days. Bloodstream isolates exhibited greater than 90% susceptibility to meropenem, piperacillin-tazobactam, and trimethoprim-sulfamethoxazole. The respiratory tract was the most common source of non-bloodstream isolates (57.5%). Non-bloodstream isolates showed high susceptibility to imipenem (94.6%), piperacillin-tazobactam (92.8%), trimethoprim-sulfamethoxazole (92.1%), and meropenem (87.5%) while demonstrating significant resistance to fluoroquinolones and aminoglycosides. Our findings support the use of piperacillin-tazobactam, carbapenems, or trimethoprim-sulfamethoxazole as initial therapy for Achromobacter infections. Further research is needed to better define the clinical and microbiologic characteristics of Achromobacter infections. IMPORTANCE Achromobacter is a rare but important genus of bacteria that tends to cause infection in those with cystic fibrosis, recurrent healthcare exposures, and/or an immunocompromising condition. There is limited data on the clinical profile of Achromobacter infections as well as optimal antibiotic selection for affected patients. We conducted a retrospective study to improve understanding of the microbiologic and clinical characteristics of Achromobacter . Our study consists of a 10-year survey of all Achromobacter isolates processed by three Mayo Clinic tertiary-care centers in Minnesota, Florida, and Arizona from 2013 to 2023. We report multiple findings, including the clinical characteristics of patients with Achromobacter bloodstream infection, the number of different Achromobacter species identified, and the sources of isolates not obtained from blood cultures. We additionally present antimicrobial susceptibility results for Achromobacter isolates. Our study provides guidance to clinicians treating Achromobacter infections and, it is hoped, will facilitate ongoing study of Achromobacter .

BACKGROUND AND OBJECTIVES: Flow-diverting stents (FDSs) have improved treatment options for intracranial aneurysms. Although FDS have proven successful in treating aneurysms that would previously have required complex microsurgical or endovascular repairs, treatment failures remain a concern. In this multicenter study, we aim to analyze the predictors of residual aneurysmal filling at early angiographic follow-up. METHODS: A retrospective analysis of a prospectively maintained neuroendovascular database at 14 high-volume US centers was conducted for all patients who underwent FDS placement for intracranial aneurysms between 2011 and 2019. Aneurysms were graded on the O'Kelly-Marotta scale (OKM). RESULTS: A total of 2277 patients underwent FDS embolization for intracranial aneurysms during the study period. The patient's mean age was 55 (SD ± 13.5) years, and 82% were female. The median maximal aneurysm diameter was 6.7 mm (IQR 4.10-11 mm). Complete aneurysm obliteration (OKM grade 4) occurred in 1109 patients (68%), and 146 (9%) were OKM grade 3, 188 (12%) were OKM grade 2, and 174 (11%) were OKM grade 1. In the adjusted multivariate analysis, individuals in the 65-74 years and 75+ years age groups had significantly increased risk of residual aneurysm filling at 6-month follow-up compared with the 18-44 years age group (odds ratio [OR] 1.92 (95% CI: 1.33-2.78) and 2.92 (95% CI: 1.79-4.77), respectively). Aneurysms arising from the superior cerebellar artery, the basilar artery, and the carotid terminus were more likely to have a residual filling (OR 12.6, 95% CI: 1.39–14.7; OR 4.27, 95% CI: 1.39–14.7; OR 3.16, 95% CI: 1.24–8.06, respectively). CONCLUSION: As the utilization of FDS for intracranial aneurysms becomes more widespread, the efficacy of aneurysm obliteration needs to be assessed. Our results indicate that several patient-dependent factors, such as age, aneurysm location, and size, predict the likelihood of residual aneurysm filling 6 months post-treatment.

ABSTRACTEpidermolysis bullosa (EB) is a rare genetic disorder requiring intensive and costly care, often diagnosed in infancy when families are unprepared for the financial burden. We analyzed 160 U.S.‐based crowdfunding campaigns for living EB patients, extracting data on demographics and financial needs. 33.8% of patients requested financial support for wound care supplies, 36.9% of patients requested support for travel costs, and 9.4% of patients requested support with special clothing or bedding. These findings highlight persistent, unresolved financial needs for EB‐related care.

Background: Estimating operative time with low accuracy and precision leads to suboptimal scheduling of available operating rooms (ORs) and waste of healthcare resources. We aimed to develop a predictive machine learning (ML) model for operative time using our institutional data to improve the scheduling of neurosurgical ORs, minimize waste, and maximize resource utilization. Methods: We developed a predictive model using our institution’s multicenter registry, which included diverse neurosurgical cases across three tertiary hospitals. We applied multiple ML techniques, including linear regression, support vector regression, deep neural networks, and extreme gradient boosting regression (XGBR). The best model was combined with the first fit decreasing bin-packing algorithm to create a practical OR scheduling system based on prior data. Results: The XGBR model exhibited the best performance, with lower mean absolute errors (MAEs) (root mean squared error: 52.24, MAE: 37.25) and higher R2 (0.76) values than other models. Implementing this model in a simulated OR scheduling scenario using institutional data, we successfully scheduled 30 random procedures with minimal residual time, such that the model ran out of procedures for the week with 86 and 171 min remaining in the cranial and spinal ORs, respectively, demonstrating potential efficiency gains. The cranial procedures were scheduled over 4 days and spinal procedures over 6 days, optimizing available OR time. Conclusion: Integrating ML models into operative time prediction can significantly improve the accuracy and efficiency of surgical schedules. This approach minimizes wasted OR time and enhances resource allocation, potentially improving patient outcomes and satisfaction by reducing waiting times.

Ventricular tachycardia (VT) is a life-threatening arrhythmia that requires both relatively rapid and accurate detection in intensive care units (ICUs). Continuous monitoring systems play a crucial role in detecting them. However, previous studies have reported that nearly 9 out of 10 arrhythmia alarms in ICUs tend to be false positives, which usually transpire to a well-documented phenomenon called “alarm fatigue” that leads to desensitization, delayed responses and increased cognitive burden on healthcare providers. We developed a deep learning based, one-dimensional convolutional neural network (1D-CNN) to classify VT alarms using multiple raw waveform inputs, including two electrocardiogram (ECG) leads, photoplethysmogram (PPG) and arterial blood pressure (ABP) signals. The model was trained using the publicly available VTaC Arrhythmia Benchmark Dataset. Ten-second waveform segments preceding each alarm were pre-processed and used to train the model to correctly classify the VT alarm. On the test set, the model achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.901, overall accuracy of 83.22%, F1-score of 73.3%, sensitivity of 77.53%, specificity of 85.63%, and positive predictive value (PPV) of 69.57%. The model successfully detected over three-quarters of them while significantly reducing false positive rates for the detection of VT. This study demonstrates that a deep learning based 1D-CNN model using short segments of raw waveform data can achieve robust performance in distinguishing true and false VT alarms.