Citations

ABSTRACT. This cross-sectional descriptive study was conducted at the University of Health Sciences, Lahore, over 5 years (2016–2020) to explore the association of Panton–Valentine leucocidin (pvl) genes and staphylococcal cassette chromosome mec (SCCmec) types with hospital-acquired (HA) and community-acquired methicillin-resistant Staphylococcus aureus (MRSA). Panton–Valentine leucocidin genes in MRSA have been found in HA-MRSA strains, thereby raising the prevalence of highly virulent and resistant strains. Furthermore, the usage of staphylococcal cassette chromosome mec (SCCmec) typing is crucial for understanding the epidemiology and evolution of MRSA. The isolates of Staphylococcus aureus were acquired from clinical samples of the patients. Gram staining, catalase, coagulase, and DNAse tests were used for S. aureus confirmation. The methicillin resistance genes A and C (mecA and mecC) genes were detected to confirm MRSA by using a 30 µg cefoxitin disk. Panton–Valentine leucocidin gene identification and SCCmec typing (types I–V and XI) were conducted through polymerase chain reaction testing. Only 440 strains of MRSA were isolated, and 31.6% were positive for the pvl gene. The most prevalent SCCmec type among the MRSA isolates was type III, which was found in 42.7% of isolates. The pvl gene was detected in 74% of SCCmec types IV and V and in only 14% of SCCmec types I, II, III, and IIIA. This study revealed the dynamic adaptability of MRSA strains within healthcare settings, with the cooccurrence of the pvl gene and various SCCmec types highlighting the complexity of MRSA epidemiology. The urgent implementation of comprehensive strategies that integrate molecular surveillance and infection control measures is crucial for mitigating MRSA.

Background: Orthopaedic surgeons routinely delay surgical management of femoral neck fractures in patients taking direct oral anticoagulants (DOACs) to decrease perioperative bleeding and associated complications. However, this practice contradicts the principles of hip fracture management, as early surgery is associated with morbidity and mortality benefits. The purpose of this study was to quantify the association of DOAC use and perioperative outcomes in patients who underwent hemiarthroplasty (HA) or total hip arthroplasty (THA) for femoral neck fractures. We hypothesized that early surgical intervention on a patient taking a DOAC medication would not lead to worse perioperative outcomes. Methods: A retrospective cohort study was conducted on 2,833 patients who underwent primary THA or HA for femoral neck fractures between December 31, 2017, and January 29, 2024, across our hospital system. The patients taking a DOAC were divided into 3 groups based on the time since the last DOAC intake: 1 day, 2 days, and ≥3 days. Propensity matching was performed 1:1, accounting for age, sex, Elixhauser Comorbidity Index, preoperative chronic kidney disease stage, preoperative hemoglobin, body mass index, and hospital type. Subanalyses utilizing linear and conditional logistic regression models were performed to assess differences in outcomes between the groups that had a DOAC withheld and the control groups. Results: The mean age of all patients was 81 ± 10 years, 1,805 patients (64%) were women, and 207 patients (7%) were taking a DOAC prior to surgery. Despite comparable preoperative and postoperative hemoglobin levels between the groups that had a DOAC withheld and the control groups (all p > 0.05), the patients who had a DOAC withheld for 1 day were more likely to receive a postoperative blood transfusion (23.1% compared with 0%; p = 0.002). This difference in transfusion rate was not observed in other cohorts. There were no differences in medical complications, reoperation, discharge disposition, or mortality between the groups that had a DOAC withheld and the matched controls at any time point. Conclusions: Delaying surgical management due to DOAC medications may be unnecessary in patients undergoing arthroplasty for femoral neck fractures. Consideration should be given to adjusting transfusion triggers to reduce unwarranted blood transfusions in patients taking a DOAC. Level of Evidence: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Abstract Background: The standard first line therapy for cholangiocarcinoma (CCA), gemcitabine and cisplatin +/- immunotherapy, only induces disease control in approximately 75% of patients (KEYNOTE-966 Trial). While several candidate single genes that predict gemcitabine and cisplatin resistance have been proposed, the overall molecular landscape that leads to resistance is not well understood. Predicting response based on molecular characteristics would benefit the clinical decision-making process when selecting appropriate chemotherapy regimens for CCA patients. Methods: Fifty-five Mayo Clinic CCA patients who underwent whole transcriptome RNA sequencing were included. Forty-eight patients had TempusXR sequencing and 7 patients used institutional labs. Normalized transcript-level data was converted to KEGG pathway-level data using overrepresentation ranking. Patient oncology notes were manually reviewed for response to gemcitabine plus cisplatin therapy, with or without concurrent pembrolizumab or durvalumab. Oncologists used a mixture of formal RECIST criteria and expert opinion to determine whether the response to gemcitabine and cisplatin +/- immunotherapy was progressive disease (PD), stable disease (SD), partial response (PR), or complete response (CR). The binary outcome of PD vs SD/PR/CR based on KEGG pathway overrepresentation was modelled using a Tabular Prior-data Fitted Network (TabPFN). Validation was performed using leave-one-out cross-validation. External model testing was performed on RNA sequencing data from 11 patient-derived xenografts that were treated with gemcitabine and cisplatin and underwent ultrasound measurements of tumor size. Results: There were 34 patients with SD/PR/CR and 21 patients with PD as the best response while receiving gemcitabine and cisplatin +/- immunotherapy. TabPFN cross-validation accuracy was 0.73, recall 0.94, precision 0.71, and F1 score 0.81. External validation accuracy was 0.70, with one xenograft exhibiting mixed response. In contrast, the best trained model using a 14-gene subset previously published for gemcitabine resistance prediction in pancreatic adenocarcinoma had accuracy 0.58. Conclusions: Machine learning models like TabPFN can capture relationships in high-dimensional transcriptomic information in novel ways that enhance our ability to predict chemotherapy response. Integrating machine learning to understand the complex molecular landscape of CCA will be a valuable tool for therapeutic regimen selection. Citation Format: Ellen L. Larson, Erik Jessen, Dong-Gi Mun, Amro Abdelrahman, Jennifer Tomlinson, Danielle Carlson, Hojjat Salehinejad, Caitlin Conboy, Rory Smoot. Machine learning models of RNA expression landscapes help predict overall tumor response to chemotherapy in cholangiocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A020.

Abstract In this work, we seek to characterize the learned feature space of cancer drug response prediction models in a drug-blind setting and quantify the limits of their generalizability. Drug-blind prediction failure describes the inability of models to predict cell line response to drugs in the test set that are not present during training. The experiments performed in this study utilize a two-arm multilayer perceptron model where embeddings for drugs and cell-lines are calculated separately and then concatenated to predict response. Drug structure is represented by Morgan fingerprint while cell lines utilize gene expression values. We first examine the connection between learned drug features and cell lines by permuting responses within both cell lines and drugs during model training. When permuting response values within cell lines, model performance was entirely depleted, but permutation of responses within drugs resulted in only a 10-15% decrease in performance. This displays the bulk of model performance is due to learned distributions of response for each drug. We also determine the impact of dataset size on drug-blind performance. Increasing the cell line examples per drug did not improve performance, but drug-blind performance exhibited higher variance than mixed set testing. From these experiments, we hypothesized that drug-blind performance was a function of the set of drugs in the training set. We trained a set of 208 models with varying training set and constant test set and fit an elastic net model using drugs in the training set as features and performance of each model as the target. We were able to accurately predict the drug-blind performance of a model, measured in Pearson correlation, based on the drugs in the training set with a mean absolute error of 0.049. We then trained a set of 1641 models where all drugs in the dataset were present in the test set but only 50% of unique drugs were present during training. Hierarchical clustering on the coefficients of an elastic net model fit to each unique drug’s performance across all models shows drugs cluster into groups that correspond to specific mechanisms of action. By examining the relationship between drug embeddings during model training, we see that decreasing validation loss corresponds to reinforcement of mechanistic relationships of drugs not captured by raw Morgan fingerprints. Finally, we show that training on a dataset confined to a single mechanism of action significantly improves overall mixed set performance on those drugs against training on the entire set of drugs. In this study, we identify that drug-blind performance in current large pharmacogenomic datasets is limited to generalization within drug mechanism of action classes. Therefore, global drug-blind prediction benchmarking is a poor indicator of model generalization as the data itself creates these limits, not model architecture. As more data is collected on novel anti-cancer compounds, we hope that the results presented here create a foundation on which to measure progress in cancer drug generalization. Citation Format: William G. Herbert, Paul A. Jensen, Nicholas Chia, Marina RS. Walther-Antonio. Monotherapy cancer drug-blind response prediction is limited to intraclass generalization [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A005.

Abstract Introduction: Radiotherapy (RT)-induced acute esophagitis (AE) is a common side effect in lung cancer patients receiving RT, which can significantly impact their quality of life. This highlights the need for a predictive model that can estimate AE risk in advance using pretreatment imaging data. However, collecting sufficient data for model development is often resource-intensive and costly. Additionally, acquiring a homogeneous training dataset (e.g., from a single center or modality) is not always feasible. This study aimed to develop an automated artificial intelligence-based 3D vision foundation model (VFM) combining standard-of-care planning computed tomography (pCT) and planned radiation dose maps to predict grade II or higher AE. Materials and methods: This study included 246 patients with non-small cell lung cancer who underwent either image-guided radiation treatment (IMRT) or proton-beam radiation therapy. The endpoint was grade two or higher AE (33% positive and 67% negative). The IMRT group consisted of 182 patients from a single center, whereas the proton therapy group included 64 patients from the Proton Collaborative Group trial from 11 different institutions. For each patient, pCT, dose maps, and radiotherapy segmentations were available. The VFM was created using a transformer pretrained using a large number of unlabeled volumetric 3D CTs from patients with varied diseases through a self-supervised learning approach, which extracts useful features directly from images. Next, the pretrained encoder was combined with fully connected classification layers and fine-tuned using stratified 5-fold cross-validation with 20 patients set aside for testing. We developed two models: a CT only and CT + dose model. Model performance was assessed using the area under the curve (AUC), specificity, and sensitivity. Results: In cross-validation, adding dose information to CT (CT+Dose) improved model performance, increasing AUC from 0.72 ± 0.08 to 0.76 ± 0.10 and specificity from 0.82 ± 0.09 to 0.87 ± 0.04, while sensitivity remained the same. On the independent test set, CT+Dose showed a marked improvement over CT only (AUC: 0.82 vs. 0.59; sensitivity: 0.57 vs. 0.14), with a slight decrease in specificity (0.77 vs. 1.00). Conclusion: A VFM model combining CT and radiation dose showed the capability to predict AE more accurately with higher specificity. Further studies on larger cohorts of testing patients are planned to assess model generalization. Citation Format: Chloe Min Seo Choi, Jue Jiang, Nikhil Mankuzhy, John Chang, Jing Zeng, Carlos Vargas, James Urbanic, Isabella Choi, Mark Mcdonald, James Gray, Joseph Deasy, Maria Thor, Charles Simone, Harini Veeraraghavan. Prediction of radiotherapy-Induced esophagitis in non-small cell lung cancer using a 3D vision foundation model [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B022.

CLL is the most prevalent adult leukemia in Western countries, characterized by the accumulation of monoclonal B lymphocytes. Over the past decade, the therapeutic landscape for CLL has undergone significant transformations, primarily due to the introduction of targeted small molecular therapies like BTK inhibitors and BCL-2 inhibitors, that have improved patient outcomes drastically. Despite significant advances, long-term disease management remains challenging for patients with double-refractory CLL, where responses with subsequent therapies are short-lived. Resistance to these therapies can arise through several mechanisms like kinase-altering BTK mutations, alterations in the BCL-2 pathway, and adaptations within the tumor microenvironment, necessitating the exploration of new therapeutic options. This review provides an in-depth overview of the promising novel treatment approaches under investigation in CLL, focusing on advanced cellular therapies (CAR T-cell therapy), T-cell engagers, new monoclonal antibodies, and various next-generation small molecule inhibitors including BTK degraders, PI3K inhibitors, MALT1 inhibitors, c-MYC inhibitors, CDK9 inhibitors, and agents targeting angiogenesis and DNA damage repair. In this review, we will discuss the novel therapeutic targets and agents as well as ongoing trials, emphasizing the potential of these treatments to overcome resistance and meet the unmet needs of patients, particularly those with double-refractory CLL.

ImportanceThe Ophthalmic Knowledge Assessment Program (OKAP) has been widely used to determine readiness to take the high-stakes Written Qualifying Examination (WQE). However, it is unclear how well OKAP performance can predict WQE outcomes.ObjectiveTo examine the association between the OKAP and WQE nationally.Design, Setting, and ParticipantsThis was a retrospective cohort study conducted from 2017 to 2022. This national data analysis included residents from ophthalmology residency programs in the US. Study data were analyzed June 2023 to December 2024.ExposuresOKAP and WQE data.Main Outcomes and MeasuresThe national OKAP examination data and the WQE data were matched. Candidates who had complete data for the OKAP during postgraduate year (PGY) 2, 3, and 4 and completed the WQE were included in the study. Two-sample t tests, logistic regression analysis, receiver operating characteristic curves, and confusion matrix were used to examine the association of various test scores (PGY-2 OKAP, PGY-3 OKAP, PGY-4 OKAP, minimum OKAP, maximum OKAP, average OKAP) as predictors for WQE outcomes (pass or fail). Multiple logistic regression analyses were also used to assess the association of gender, time since graduation, program size, and WQE pass/fail outcomes.ResultsA total of 1597 residents (907 male [56.8%]) from 117 residency programs were included in this study. First-time pass rate of the WQE was 89% (1418 of 1597 residents). OKAP scores, particularly average OKAP, maximum OKAP, and PGY-4 OKAP scores (all had area under the curve = 0.88), were the most useful predictor of WQE outcomes. PGY-4 OKAP scores were used for the rest of the analysis for simplicity. A PGY-4 OKAP scaled score of 550 corresponded to 94% of candidates passing the WQE. Using PGY-4 OKAP scores to predict passing/failing, the WQE had 90.7% accuracy. Gender (odds ratio [OR], 1.50; 95% CI, 1.01-2.26; P = .048), program size (OR, 1.55; 95% CI, 0.98-2.41; P = .06), and time since residency graduation (OR, 1.00; 95% CI, 1.00-1.00; P = .14) were not included in the logistic regression model investigating the association of PGY-4 OKAP scores with the likelihood of passing the WQE.Conclusions and RelevanceResults suggest that OKAP examination scores were associated with passing the WQE after adjusting for program size, gender, and time since residency graduation. Further analyses may be warranted to predict failing the WQE so that potential interventions can be attempted.