Citations

ABSTRACT The increasing prevalence of carbapenem-resistant Klebsiella pneumoniae represents a serious global health challenge. Rapid and accurate detection methods are essential to inform early and correct use of antimicrobial therapy to idealize patient outcomes and limit the spread of resistant isolates. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been evaluated for resistance detection, but its clinical application remains limited. In this study, MALDI-TOF MS was integrated with machine learning (ML) to improve detection of Klebsiella pneumoniae carbapenemase (KPC) among K. pneumoniae isolates harboring KPC carbapenemase. Ensemble learning strategies were applied to 435 clinical isolates from different geographic locations (Spain, United States, South America, and Asia), with validation performed using external data sets. By constructing two peak matrices and applying four ML algorithms, as well as combinations of two and three in ensemble models, 92 different classifiers were tested. Ensemble combinations increased the specificity of classifiers to over 95%, while sensitivity reached 72%, being significantly higher than that of the MALDI Biotyper KPC module. Low sensitivity may be affected by technical variability during spectral acquisition. The first annotated MALDI-TOF MS spectrum for K. pneumoniae by in silico prediction of protein masses was developed to enable peak identification, including peaks potentially related to antimicrobial resistance. Overall, the results of this study show that combining MALDI-TOF MS with ensemble learning can enhance KPC detection performance. IMPORTANCE Rapid and accurate detection of Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae informs early and correct use of antimicrobial therapy to idealize patient outcomes and limit the spread of antimicrobial resistance. In this study, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and ensemble machine learning were employed to detect KPC-producing isolates, using diverse isolate collections, obtaining excellent specificity. An annotated K. pneumoniae MALDI-TOF MS profile is provided with peak annotation, providing a resource for peak analysis.

Abstract Idecabtagene vicleucel (ide-cel) is a chimeric antigen receptor (CAR) T-cell therapy approved for relapsed and refractory multiple myeloma (RRMM). Out-of-specification (OOS) CAR T cells not meeting criteria for commercial release are considered an investigational product; the expanded access protocol (EAP) allows release of OOS product for administration. The manufacturing analysis of capability, reliability, and timely delivery of ide-cel in the United States over >3 years since its approval included patients with RRMM who underwent leukapheresis for ide-cel treatment after ≥2 prior lines of therapy between 23 February 2021 and 1 November 2024. Additionally, the BB2121-EAP-001 study evaluated safety and efficacy of OOS ide-cel and included patients who had undergone leukapheresis for commercial ide-cel treatment not meeting commercial release specifications, most commonly due to high CD137 activation or low-infused dose. Primary end point was safety. Secondary end points were overall response rate (ORR) and complete response rate (CRR). At data cutoff (1 November 2024), the manufacturing success rate of ide-cel improved from 95.3% in 2021 to 96.8% in 2024. Turnaround time also improved from 31 days in 2021 to 24 days in 2024. In BB2121-EAP-001, 68 (72.3%) patients experienced ≥1 cytokine release syndrome event (mostly grade 1/2) and 15 (16.0%) had neurological toxicities (2 grade 3, 13 grade 1/2). ORR was 75.4% (95% confidence interval [CI], 63.1-85.2), and CRR was 35.4% (95% CI, 23.9-48.2). The manufacturing success of ide-cel is reliable and consistently high, and when OOS ide-cel products emerged, they demonstrated consistent clinical efficacy and safety. The BB2121-EAP-001 study was registered at www.clinicaltrials.gov as NCT04771078.

Objective Identifying eligible patients for precision oncology clinical trials is challenging, particularly for rare molecular subpopulations. To address this challenge, A2 Biotherapeutics developed BASECAMP-1 ( NCT04981119 ), a non-interventional master screening study to identify patients eligible for interventional studies of logic-gated Tmod chimeric antigen receptor T-cell therapies. Eligible patients for these interventional trials have an advanced solid malignancy and are germline human leucocyte antigen (HLA)-A*02 heterozygous, with tumour-associated HLA-A loss of heterozygosity (LOH). HLA-A LOH occurs in ~16% of advanced solid malignancies; therefore, an efficient screening strategy is required. This report describes BASECAMP-1; compares the efficiency of two screening methods; and discusses the broader advantages of BASECAMP-1 beyond efficient enrolment. Methods and analysis Patients are identified for BASECAMP-1 using two approaches. In the traditional approach, common for clinical trials, investigators consent and screen all patients who might be good candidates for cell therapy trials, with no prior knowledge of patient HLA-A type or LOH status. To further optimise our approach, we co-developed with Tempus AI (Tempus) the bioinformatic programme Aware, which identifies potentially eligible patients with tumour-associated HLA-A*02 LOH within a clinico-genomic database that includes linked genomic and transcriptomic sequencing and clinical data collected during routine care. Results Over 42 months of using a traditional approach to identify eligible patients, 1918 patients at 13 study sites were consented and screened for BASECAMP-1; of these, 30 patients with tumour-associated HLA-A*02 LOH were enrolled (~0.7 participants per month). Over the last 30 months of that same period, Tempus Aware screening was implemented and 55 patients with tumour-associated HLA-A*02 LOH were enrolled (~1.8 participants per month). The bioinformatic approach identified more patients than the traditional approach and used sequencing results produced as part of the standard clinical tumour sequencing workflow, reducing resource use and study staff burden. Additional advantages of using a screening study, such as BASECAMP-1, include manufacturing efficiencies and collection of a large dataset of molecular and clinical parameters that can be used to supplement trial analyses. Conclusions The BASECAMP-1 study demonstrates a clinico-genomic screening approach can more efficiently identify patients for precision oncology trials. Furthermore, precision oncology can be enhanced through collaborative data-sharing. Trial registration number NCT04981119 .

Importance Adjuvant chemotherapy regimens may be administered after pancreatic resection for adenocarcinoma arising from intraductal papillary mucinous neoplasms (A-IPMNs), although the evidence supporting their use is limited. Objective To evaluate the survival benefit associated with contemporary adjuvant chemotherapy regimens after resection in A-IPMNs between 2017 and 2023. Design, Setting, and Participants This retrospective cohort study was an international, multicenter study with 69 participating centers across Europe, North America, South America, and the Asia-Pacific region. Patients undergoing resection for A-IPMNs were included. Data were analyzed from May to August 2025. Intervention Contemporary adjuvant chemotherapy regimens, such as gemcitabine-capecitabine (GemCap); 5-fluorouracil, leucovorin, oxaliplatin, and irinotecan (FOLFIRINOX), modified FOLFIRINOX (mFOLFIRINOX), and S-1. Main Outcome and Measure Overall survival (months). Results Among 1321 patients (median [IQR] age 70 [63 to 76] years; 713 males [54.0%] and 608 females [46.0%]), 781 patients (59.1%) received adjuvant chemotherapy, while in 181 patients (13.7%) it was omitted due to poor patient fitness. Of patients who received adjuvant chemotherapy, 568 patients (72.6%) received contemporary regimens, including GemCap (232 patients [29.7%]), FOLFIRINOX (176 patients [22.5%]), mFOLFIRINOX (71 patients [9.1%]), and S-1 (70 patients [9.0%]). The median (IQR) follow-up for the cohort was 64.2 (40.4 to 85.0) months, and the median overall survival was 73.8 months (95% CI, 66.4 to 81.9 months). After 90-day landmark analysis and exclusion of patients ineligible for chemotherapy, adjuvant chemotherapy vs no adjuvant chemotherapy (propensity score–matched populations, 243:243 patients) was not associated with improved overall survival (median, 82.3 months; 95% CI, 78.2 months to not applicable [NA] vs not reached; 95% CI, 75.3 months to NA; P = .58). Contemporary regimens vs no adjuvant chemotherapy (propensity score–matched populations, 309:309 patients) was not associated with longer survival, and a mean survival benefit greater than 4.2 months over 5 years was excluded (difference in restricted mean survival, 1.26 months; 95%, −1.72 to 4.24 months). Treatment outcomes did not vary by chemotherapy regimen or disease characteristic (eg, N stage or carbohydrate antigen 19-9 level). Conclusions and Relevance In this study, contemporary adjuvant chemotherapy was not associated with improved overall survival in A-IPMNs, and a randomized clinical trial is indicated.

Background: Sinonasal malignancies (SNM) are rare cancers associated with poor prognosis and impaired quality of life (QOL). The 22-question Sino-Nasal Outcome Test (SNOT-22) has been used to assess QOL in patients with SNM. Objective: To determine the minimal clinically important difference (MCID) for the SNOT-22 in patients with SNM. Methods: About 264 patients with SNM were drawn from a prospective, longitudinal, multi-institutional study. SNOT-22 scores were collected at pre-treatment baseline and follow-up through 5 years post-treatment. Cronbach’s alpha was used to assess the internal consistency of the SNOT-22. Distribution-based approaches were used to estimate the MCID. Multivariable regression identified factors associated with SNOT-22 improvement exceeding the determined MCID. Results: Cronbach’s alpha was >.85. The estimated MCIDs were: 1.44 (Cohen’s effect size), 1.32 (standard error of the mean), 10.45 (1/2 of baseline standard deviation), and 3.67 (minimum detectable change). The mean MCID across methods was 4.22. Multivariable regression demonstrated higher odds of clinically meaningful improvement from baseline in patients with early-stage disease (odds ratio (OR) 3.510, P = .035), no neck irradiation (OR 11.050, P = .014), and neoadjuvant therapy (OR 16.667, P = .047). Conclusions: The SNOT-22 MCID was estimated as 4.22 in a large multi-center cohort of patients with SNM, suggesting that relatively small QOL changes are clinically relevant. When applying the MCID to the current cohort, advanced stage, treatment of neck disease, and not requiring neoadjuvant therapy were predictive of worse QOL.

Aim To determine whether there is a difference in the time to complete a rapid number naming task during the interictal state compared to prodrome, migraine headache, postdrome phases of migraine and non-migraine headache, and characterize non-headache symptoms experienced during these phases. Methods This prospective longitudinal cohort study evaluated 30 adults with migraine with or without aura who completed diary entries and a timed rapid number naming task at least three times daily for up to four months from October 2020 to July 2022 at a tertiary headache center. Diary entries included self-report of current migraine phase (having a migraine headache, non-migraine headache, prodrome, postdrome, or interictal baseline) and detailed symptoms experienced. The outcome of interest for the timed rapid number naming task was the time to complete the task with zero errors. Results Compared to the interictal baseline, the time with zero errors for the rapid number naming task was significantly slower during the prodrome (median (Q1, Q3) 1.0 s slower (0.4, 2.9), p -value 0.010), migraine headache (median (Q1, Q3) 3.8 s slower (2.6, 7.5), p -value <0.001), and postdrome (median (Q1, Q3) 0.9 s slower (0.2, 2.1), p -value 0.011) phases of migraine, and non-migraine headache attacks (median (Q1, Q3) 1.2 s slower (0.1, 3.1), p -value 0.006). For those with migraine with aura (N = 15), the time with zero errors was significantly slower during all phases compared to interictal phase. For those with migraine without aura (N = 15), the time with zero errors was significantly slower only during the migraine headache, but not other phases compared to the interictal phase. The top 5 most reported non-headache symptoms during any phase of migraine and non-migraine headache attacks included fatigue, neck stiffness, light sensitivity, blurred vision/vision changes, and difficulty concentrating. Conclusions Among the overall participant cohort, the time with zero errors to complete a rapid number naming task was significantly slower during all ictal phases compared to interictal phase. This was also the case in the migraine with aura cohort but in the migraine without aura cohort there was only a significant difference during the migraine headache phase versus interictal phase. There is overlap in non-headache symptoms between the prodrome, migraine headache, postdrome, and non-migraine headache phases, which may make distinguishing between these phases difficult. Combining subjective data, such as symptom diaries, with objective measures, such as rapid number naming tasks, may allow for more refined assessment of migraine phases and distinguishing migraine from non-migraine headache attacks.