Citations

Abstract Introduction: Src Family Kinases (SFKs) have a crucial role in tumor proliferation, survival, and metastasis. NXP900 is a highly selective SFK inhibitor a novel mechanism of action that locks SFKs in a closed, inhibited conformation, preventing interaction with substrate proteins, unlike other SFK inhibitors like dasatinib. A phase 1 study of NXP900 in patients with advanced solid tumors was recently initiated. Our preclinical studies showed therapeutic efficacy of NXP900 in cholangiocarcinoma (CCA) through decreased YAP, RAF-MAP and PI3K-AKT signaling. Here, we examined mechanisms of sensitivity and resistance to SFK inhibition. Methods: Secondary resistance was explored by generating a NXP900-resistant human cell line (HuCCT1-R) via prolonged exposure to NXP900, followed by multiomic profiling. Primary resistance was studied in vivo using nine heterogeneous patient-derived xenograft (PDX) tumors implanted in the flanks of NOD/SCID mice. Tumor-bearing mice were treated with vehicle or 40 mg/kg NXP900 daily for up to 4 weeks. To identify factors associated with response, a multiomic approach was used, integrating RNA-Seq, global proteomics, and phosphoproteomics. A hierarchical-all-against-all algorithm (HallA) assessed associations between multiomic features and drug response in tumor growth. Kinase enrichment analysis (KEA3) identified features linked to sensitivity or resistance. Results: HuCCT1-R cells showed increased pTyr abundancy of TNK2, a protein known to increase PIP3 levels leading to AKT activation, and of DYRK3 and LAMTOR1, both of which can sustain AKT phosphorylation via mTORC, indicating acquired resistance through bypassing inhibited PI3K-AKT signaling to rescue AKT. NXP900 reduced tumor growth rates in 5 of 9 PDX models. Kinases associated with sensitivity in the PDX models included SFKs (SRC, LYN, and FYN), and AKT, PTK2, PRKCZ, EGFR, SYK, and GSK3B, suggesting involvement of the PI3K-AKT pathway in responsiveness to NXP900. Based on tumor inhibition responses in the tested models, we estimated NXP900 response for our additional PDX models, predicting 39% (11/28) of patient tumors may respond. Conclusion: This study identified molecular markers linked to response to SFK inhibition, which could be utilized for patient selection for NXP900 treatment. Further studies are validating these findings and exploring combination therapies to address resistance mechanisms. Citation Format: Hendrien Kuipers, Danielle M. Carlson, Jack W. Sample, Erik Jessen, Dong-Gi Mun, Aushinie M. Abeynayake, Jennifer L. Tomlinson, Amro M. Abdelrahman, Nathan W. Werneburg, Binbin Li, Mitesh J. Borad, Mark J. Truty, Sumera I. Ilyas, Gregory J. Gores, Rory L. Smoot. Multiomic profiling identified biomarkers of response to selective Src family kinase inhibition in preclinical models of cholangiocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5595.

Abstract Background The lupus anticoagulant (LAC) is an important laboratory criterion in the diagnosis of antiphospholipid antibody syndrome. LAC testing at the Mayo Clinic Special Coagulation Laboratory includes up to 13 tests, which are interpreted by trained physicians to identify the presence of LAC and rule out anticoagulant interferences. This feasibility study explored the use of two deep neural network (DNN) architectures for multilabel classification of LAC profiles as a first step toward automating interpretation. Methods Seven thousand two hundred and two retrospective cases were randomly split (64:16:20) for training, validation, and test, respectively. LAC positivity by dilute Russell's viper venom time (LAC-DRVVT) and activated partial thromboplastin time (LAC-APTT) and the presence of warfarin (WAR) and heparin (HEP) were adjudicated by one expert. DNN architectures included: single-output DNNs using domain-knowledge input feature selection and a single-column multioutput DNN using all 13 inputs. Results Domain-knowledge-naïve multioutput DNN achieved similar or improved performance for all 4 label prediction tasks compared with domain knowledge optimized DNNs: F1 scores of 0.977 vs 0.968 for LAC-DRVVT, 0.954 vs 0.945 for LAC-APTT, 0.961 vs 0.957 for HEP, and 0.995 vs 0.977 for WAR, respectively. Conclusions The comparable performance of the 4 domain knowledge optimized DNNs and the multioutput DNN using all 13 input features suggests that the DNN may learn feature importance or mapping to a task without explicit input selection. Given its relative simplicity and versatility, the multioutput DNN is the preferred choice for implementation in a clinical laboratory to standardize LAC diagnosis.

Background Neoadjuvant treatment has become standard for patients with high-risk operable stage III melanoma, but the optimal regimen is unknown. Targeted therapy approaches yield high pathological response rates, while immunotherapy regimens show favorable recurrence-free survival (RFS). NeoACTIVATE was designed to address whether a neoadjuvant combination of both targeted therapy and immunotherapy might leverage the benefits of each. Methods We tested neoadjuvant treatment with 12 weeks of vemurafenib, cobimetinib, and atezolizumab for patients with BRAF-mutated (BRAFm) melanoma (cohort A) and cobimetinib and atezolizumab for patients with BRAF-wild-type (BRAFwt) melanoma (cohort B), regimens which we have shown generate a substantial major pathological response. After therapeutic lymph node dissection, patients received 24 weeks of adjuvant atezolizumab. Here, we report survival outcomes and their association with biomarkers assayed among the gut microbiome and peripheral blood immune subsets. Results With 49 months median follow-up, the median RFS was not reached for cohort A and was 40.8 months for cohort B. At 24 months after operation, 2 of 14 cohort A patients and 4 of 13 cohort B patients had experienced distant relapse. Key findings from correlative analyses included diversity, taxonomic and functional metagenomic gut microbiome signals associated with distant metastasis-free survival at 2 years. Notably, we observed a strong correlation between low microbial arginine biosynthesis (required for T-cell activation and effector function) and early distant recurrence (p=0.0005), which correlated with taxonomic differential abundance findings. Peripheral blood immune monitoring revealed increased double-positive (CD4+CD8+) T cells in patients with early recurrence. Conclusions Neoadjuvant treatment with cobimetinib and atezolizumab±vemurafenib was associated with a low rate of distant metastasis in patients with high-risk stage III melanoma. Freedom from early distant metastasis was highly associated with taxonomic differences in gut microbiome structure and with functional pathway alterations known to modulate T cell immunity. Identification of predictive biomarkers will permit optimization of neoadjuvant therapy regimens for individual patients. Trial registration number NCT03554083.

Diffuse large B cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma, and the activated B cell–like subtype (ABC-DLBCL) is associated with particularly poor outcome. Many ABC-DLBCLs harbor gain-of-function mutations that cause inappropriate assembly of the CARMA1-BCL10-MALT1 (CBM) signalosome, a cytoplasmic complex that drives downstream NF-κB signaling. MALT1 is the effector protein of the CBM signalosome such that its recruitment to the signalosome via interaction with BCL10 allows it to exert both protease and scaffolding activities that together synergize in driving NF-κB. Here, we demonstrate that a molecular groove located between two adjacent immunoglobulin-like domains within MALT1 represents a binding pocket for BCL10. Leveraging this discovery, we performed an in silico screen to identify small molecules that dock within this MALT1 groove and act as BCL10-MALT1 protein-protein interaction (PPI) inhibitors. We report the identification of M1i-124 as a first-in-class compound that blocks BCL10-MALT1 interaction, abrogates MALT1 scaffolding and protease activities, promotes degradation of BCL10 and MALT1 proteins, and specifically targets ABC-DLBCLs characterized by dysregulated MALT1. Our findings demonstrate that small-molecule inhibitors of BCL10-MALT1 interaction can function as potent agents to block MALT1 signaling in selected lymphomas, and provide a road map for clinical development of a new class of precision-medicine therapeutics.

Abstract Hereditary tumor predisposition syndromes pose a challenge for early detection and timely treatment of tumors. In von Hippel–Lindau disease, desirable personalized surveillance programs are lacking due to insufficient data on genotype-specific risk profiles of individual mutations. To describe neoplastic risk profiles for carriers of pathogenic and likely pathogenic VHL germline mutations, our observational study recruited 1,350 participants from 40 centers worldwide. 432 different VHL germline mutations were observed, with p.Asn78Ser, p.Arg161Ter, p.Arg161Gln, p.Arg167Gln, p.Arg167Trp and p.Tyr98His being the six most frequent, occurring in a total of 493 carriers (36.5%) and in ≥30 patients each. Age-related penetrance risks for retinal hemangioblastoma, central nervous system hemangioblastoma, renal cell carcinoma, pancreatic neuroendocrine tumors and pheochromocytoma/paraganglioma in carriers of the most frequent VHL mutations were assessed. In addition, the number of organs affected, the frequency of surgery and the outcome are reported. Pairwise comparisons of the age-dependent tumor penetrance of these six mutations showed that 47 out of 90 pairs were significantly different. The most significant associations were found in p.Tyr98His (n = 19), followed by p.Arg161Ter (n = 10). All pairwise comparisons of mutations affecting different codons showed at least one significant (P < 0.05) difference, except for p.Asn78Ser vs p.Arg161Ter. Thus, tumor risk varied by VHL mutation type and location, but did not differ between the truncating mutation p.Arg161Ter and the missense mutation p.Asn78Ser. Our study demonstrates the importance of mutation-specific phenotype prediction. With appropriate validation, the data have important implications for risk assessment and decision making in tumor prevention for carriers of the respective VHL mutations.

Background and Aims: Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB) prevent fibrosis progression in a preclinical model of steatotic liver disease. Our objective was to assess the impact of ACEi/ARB use on clinical outcomes in patients with metabolic dysfunction–associated steatotic liver diseases. Approach and Results: Using TriNetX, a nationwide database, we identified all patients with metabolic dysfunction–associated steatotic liver diseases from January 1, 2011, to December 31, 2019. Using a target trial emulation framework, ACEi/ARB users were matched with calcium channel blocker (CCB) users using propensity score matching (PSM). Patients were followed up to 10 years after the index date. Cox proportional hazards regression was used to determine the risk of mortality, major adverse liver outcomes, major adverse cardiac events, and incident cancers. Of the 35,988 eligible patients, 28,423 were ACEi/ARB users, and 7565 were CCB users. After PSM, 7238 pairs were well-balanced. ACEi/ARB use was associated with a significantly decreased mortality risk (HR: 0.59, 95% CI: 0.51–0.68). ACEi/ARB was associated with a significantly reduced risk of developing major adverse liver outcomes (HR: 0.70, 95% CI: 0.61–0.80), including ascites (HR: 0.78, 95% CI: 0.63–0.98) and HE (HR: 0.67, 95% CI: 0.57–0.78). ACEi/ARB use was also associated with a lower risk of major adverse cardiac events (HR: 0.82, 95% CI: 0.76–0.90) but not incident cancer (HR: 0.97, 95% CI: 0.86-1.10) compared with CCB. Conclusions: ACEi/ARB use in patients with metabolic dysfunction–associated steatotic liver diseases was associated with a reduced risk of mortality, major adverse liver outcomes, and major adverse cardiac events compared with CCB use. A large prospective study is needed for external validation.

OBJECTIVES: The goal of this scoping review was to review some of the challenges hospitals faced in dealing with the shortage of ventilators during the COVID-19 pandemic and the solutions they were able to implement or suggested. By highlighting these problems and solutions, we hope this review can catalyze further discussions about how to better prepare for future pandemics and medical supply shortages. DATA SOURCES: A comprehensive search strategy using identifying key words was applied to several different databases to procure relevant literature. STUDY SELECTION: Four thousand two hundred fifty-nine studies were found in the initial search. Inclusion and exclusion criteria were created and applied to screen studies. Included studies focused on the supply and distribution of ventilators during the COVID-19 pandemic. In the case where reviewers disagreed about whether a study should be included, a third reviewer acted as a tie-breaker. DATA EXTRACTION: Thirty-three studies were included for final data extraction. Two independent reviewers collected various data points from these studies, including the main challenges discussed by the authors, the level of ventilator shortage being addressed, whether ventilator sharing was discussed, and the limitations of the study. DATA SYNTHESIS: A third reviewer compared the collected data and decided on the results. CONCLUSIONS: Some of the common solutions for the ventilator shortage discussed included augmenting overall ventilator supply through increased production, transporting ventilators between hospitals, ventilator sharing, designing new ventilators, and repurposing other resources to help address shortages of supplies.

Background and Aims: Immunotherapy has emerged as an effective treatment for advanced HCC. We aimed to investigate the real-world effectiveness of immunotherapy compared to lenvatinib in HCC. Approach and Results: From the TriNetX database, we used a target trial emulation framework and identified patients with HCC who received first-line treatment with immunotherapy (atezolizumab/bevacizumab or tremelimumab/durvalumab) or lenvatinib between or between August 2018 and December 2023. OS was compared using Kaplan-Meier analysis and Cox proportional hazards regression. After propensity score matching, 1203 patients were included in each group. Immunotherapy was associated with improved OS versus lenvatinib (median survival: 545 vs. 425 d; HR: 0.86, 95% CI: 0.76–0.97). Regarding treatment type, atezolizumab plus bevacizumab showed improved survival compared to lenvatinib (n=1070 in each group; HR: 0.87, 95% CI: 0.77–0.99), while the point estimate favored durvalumab plus tremelimumab versus lenvatinib (HR: 0.81, 95% CI: 0.59–1.12), though this difference was not statistically significant, likely due to small sample size. Regarding etiology, immunotherapy had improved OS compared to lenvatinib in viral hepatitis (n=510 in each group; HR: 0.74, 95% CI: 0.61–0.89) and alcohol-associated liver disease (n=190 in each group; HR: 0.65, 95% CI: 0.49–0.87), but not in metabolic dysfunction–associated steatotic liver diseases (n=156 in each group; HR: 0.96, 95% CI: 0.70–1.31). Conclusions: In this real-world analysis, immunotherapy was associated with improved OS compared to lenvatinib in advanced HCC, with consistent benefit across most subgroups. These findings support the use of immunotherapy as a first-line treatment for advanced HCC.

Abstract This retrospective analysis aimed to provide evidence-based risk stratification of TP53-mutated (TP53mut) myeloid neoplasms (MNs). Of 580 MNs harboring TP53mut with variant allele frequency (VAF) ≥2%, 219 (37.8%), 194 (33.4%), 92 (15.9%), and 75 (12.9%) were classified as acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) with low blasts (MDS-LB), MDS with excess blasts (MDS-EB)-2, and -EB1 according to the revised fourth edition of the World Health Organization (WHO) classification, respectively. Hierarchical analysis identified the following 4 risk groups with distinct survival: (1) MDS-LB, (2) MDS-EB1/EB2/AML VAF <10%, (3) MDS-EB1/EB2 VAF ≥10%, and (4) AML VAF ≥10%. We next evaluated the impact of allelic status, VAF, and complex karyotype (CK). In our cohort, the significance of biallelic status was limited to MDS with <5% blasts and not for blasts 5% to 9%, as proposed by the International Consensus Classification (ICC), or 5% to 19%, as proposed by the fifth edition of the WHO (WHO-5). MDS-EB1 and -EB2 with VAF ≥10% had comparable survival (9.6 vs 7.2 months; P = .12), regardless of allelic status. Contrary to the ICC proposal, MDS-EB1/EB2 with VAF <10% and CK had poor survival compared with those without CK, comparable to MDS-EB1/EB2 with VAF ≥10% (5.6 vs 26.2 vs 6.3 months; P = .003). Survival of TP53mut AML was poor (median 3.9 months) regardless of allelic/CK status. Thus, using ICC or WHO-5 may underestimate prognosis of MDS with blasts 5% to 19% and 5% to 9%, respectively. Collectively, the hierarchical model acknowledges poor survival of 91.9% TP53mut MDS and AML compared with 36.5% and 80.7% by WHO-5 and ICC, respectively.

ABSTRACTIntroductionVon Willebrand factor (VWF) multimer analysis is essential for diagnosing and classifying von Willebrand disease (VWD) but requires expert interpretation and is subject to inter‐rater variability. We developed an automated image analysis pipeline using deep learning to improve the reproducibility and efficiency of VWF multimer pattern classification.MethodsWe trained a YOLOv8 deep learning model on 514 gel images (6168 labeled instances) to classify VWF multimer patterns into 12 classes. The model was validated on 192 images (2304 instances) and tested on an independent set of 94 images (1128 instances). Images underwent preprocessing, including histogram equalization, contrast enhancement, and gamma correction. Two expert raters provided ground truth classifications.ResultsThe model achieved 91% accuracy compared to Expert 1 (macro‐averaged precision = 0.851, recall = 0.757, F1‐score = 0.786) and 87% accuracy compared to Expert 2 (macro‐averaged precision = 0.653, recall = 0.653, F1‐score = 0.641). Inter‐rater agreement was very high between experts (κ = 0.883), with strong agreement between the model and Expert 1 (κ = 0.845) and good agreement with Expert 2 (κ = 0.773). The model performed exceptionally well on common patterns (F1 > 0.93) but showed lower performance on rare subtypes.ConclusionAutomated VWF multimer analysis using deep learning demonstrates high accuracy in pattern classification and could standardize the interpretation of VWF multimer patterns. While not replacing expert analysis, this approach could improve the efficiency of expert human review, potentially streamlining laboratory workflow and expanding access to VWF multimer testing.