Citations

Abstract Background Etentamig is a second-generation B-cell maturation antigen (BCMA) x CD3 bispecific antibody that has shown promising efficacy and a favorable safety profile in heavily pretreated patients with relapsed/refractory multiple myeloma (RRMM). Etentamig possesses a unique combination of 2 high-affinity BCMA binding domains, coupled to a low-affinity CD3 binding domain that have been shown to reduce the negative impact of soluble BCMA (sBCMA), decrease risk for cytokine release syndrome (CRS), and drive sustained T-cell activation with reduced immune exhaustion in preclinical models of MM (Blood 2023;142 [suppl 1]:4666). Here, we describe correlative biomarker analyses from Phase 1 (NCT03933735) and Phase 1b (NCT05650632) clinical trials of etentamig in RRMM designed to assess the impact of sBCMA on clinical response and pharmacodynamic changes in proinflammatory cytokines, and T-cell redistribution, activation, proliferation, and exhaustion. Methods Peripheral blood and serum samples from patients with RRMM enrolled in Phase 1 and Phase 1b open-label studies who received etentamig monotherapy were collected at baseline, on treatment, and at disease progression. The Phase 1b study consisted of dose-optimization (DO) and dose-expansion (DE) phases. During DO, patients received a single step-up dose (SUD) of intravenous etentamig (2 or 4 mg) on Cycle (C) 1 Day (D) 1, followed by the target dose of 60 mg on D4. In DE, based on DO findings, patients received the recommended etentamig SUD (2 mg) on C1D1, followed by 60 mg on D4 with modified dexamethasone predosing. Samples were analyzed by flow cytometry for immune cell populations, Luminex® for cytokines, and electrochemiluminescence for sBCMA. Peripheral blood mononuclear cells and bone marrow mononuclear cells (BMMC) at baseline and on-treatment were subjected to single-cell Cellular Indexing of Transcriptomes and Epitopes sequencing (scCITEseq) and single-cell T-cell receptors (scTCR) / B-cell receptors clonality analyses. Results While baseline serum sBCMA levels did not correlate with clinical response to etentamig at the selected dose level of 60 mg every 4 weeks (Q4W) with 2 mg SUD, a reduction in sBCMA levels over time was associated with best International Myeloma Working Group response. Etentamig treatment led to a rapid and transient increase of proinflammatory cytokines and promoted T-cell redistribution, activation, and proliferation. Maximum reduction in peak levels of CRS-related cytokines, including (interleukin [IL]-6, IL-8, IL-10, tumor necrosis factor–a), were observed in DE compared with DO and 60 mg Q4W without SUD. Peak induction levels of proinflammatory cytokines during C1 correlated with occurrence of CRS (≥Grade [G] 1) as well as clinical response across dose levels. A modified dexamethasone premedication schedule of 2 mg/60 mg Q4W cohort for C1 resulted in significantly reduced baseline levels of IL-6 and key macrophage-associated cytokine/chemokines (eg, monocyte chemoattractant protein-1 and macrophage inflammatory protein-3 beta). Incidence of CRS was lowest in the 2 mg/60 mg Q4W cohort (26% G1, 4% G2, 0% ≥G3). Higher T-cell to myeloma cell ratio in BMMCs at baseline correlated with better response to etentamig. scCITEseq and scTCR analysis revealed that at baseline, responders also had greater CD8+ T-cell cytotoxic activities, increased TCR clonality, and elevated inflammatory signatures in T-cells from both peripheral blood and bone marrow. Etentamig at a dosage of 60 mg Q4W resulted in significant clonal expansion of CD8+ effector T-cells and enhanced gene expression signatures related to activation and cytotoxicity in both CD8+ T effector and natural killer cells. Along treatment cycles, responders maintained their T-cell fitness, high clonality, and low T-cell exhaustion levels. Frequency of baseline CD4+ or CD8+ T-cell exhaustion (programmed cell death receptor 1+/ T cell immunoglobulin and mucin-domain containing-3 +) did not impact clinical response at optimal dose level of 2 mg/60 mg Q4W etentamig. Conclusions Responses to etentamig were independent of baseline sBCMA levels and resulted in rapid and transient proinflammatory cytokine production that promoted robust T-cell redistribution, activation, and proliferation indicating that the optimal dose of etentamig (2 mg/60 mg Q4W) maximizes its clinical potential as a convenient, safe, and effective therapy for MM.

Abstract CDK12 is frequently deleted and/or mutated in human cancers, and CDK12 inactivation is associated with aggressive phenotypes, including the tendency to metastasis. In this study, we showed that CDK12 deficiency induced intronic polyadenylation (IPA) usage in BRD4 to upregulate expression of the bromodomain-containing protein 4 (BRD4) short isoform (BRD4S). BRD4S demonstrated an enhanced capacity to form phase separation despite lacking the intrinsically disordered region located in the C-terminal of full-length BRD4. Oligomerization of BRD4S mediated phase separation through a base-interacting structural domain. CUT&Tag and RNA sequencing analyses revealed that BRD4S preferentially bound to the promoters of TGFβ signaling genes, including TGFB2 and LTBP1, driving their expression. Consistently, BRD4S drove cancer cell dissemination in vitro and metastasis in vivo, which could be blocked by BET or TGFβ signaling pathway inhibitors. Overall, this study identifies IPA usage at the BRD4 gene locus induced by CDK12 deficiency as a mechanism responsible for BRD4S genesis, which represents an important mechanism driving cancer metastasis and a potential target for therapeutic intervention in CDK12-deficient cancers. Significance: The CDK12–BRD4S–TGFβ signaling axis represents a key regulatory module that promotes metastatic progression and can be targeted by BET or TGFβ pathway inhibitors for treating CDK12-deficient cancers.

Glioblastoma (GBM) is an aggressive brain tumor that often progresses despite resection and treatment. Timely and continuous assessment of GBM progression is critical to expedite secondary surgery or enrollment in clinical trials. However, current progression detection requires costly and specialized MRI examinations, which, in the absence of new symptoms or signs, are usually scheduled every 2–3 months. Here, we hypothesized that changes in daily activity are associated with GBM growth and disease progression. We found that wheel-running activity in GBM-bearing mice declined as tumors grew and preceded weight loss and circadian breakdown by over a week. Temozolomide treatment in the morning, but not evening, significantly reduced tumor size and restored daily locomotion in mice. In a pilot study of 6 patients with GBM wearing an actigraphy watch, wrist movement provided a feasible and continuous longitudinal indicator of daily activity with 1-minute resolution. After tumor resection and radiation, daily activity declined in 2 patients 19 and 55 days before detection of progression by MRI but did not change for the 4 patients with stable disease. These results suggest that daily activity tracking using wearable devices may serve as a real-time indicator and potential monitoring tool for GBM progression and treatment efficacy.

With the rapid development of artificial intelligence (AI), various deep learning (DL) methods have been introduced into radiation oncology. Among them, the generation of synthetic Computed Tomography (sCT) images has attracted increasing attention, as it supports different clinical scenarios, from image-guided adaptive radiotherapy (IGART) to the simulation-free workflow. This review provides a comprehensive overview of recent studies on DL-based sCT synthesis in radiotherapy from multiple imaging modalities, including Cone-Beam CT (CBCT), Magnetic Resonance Imaging (MRI), and diagnostic CT, and discusses their clinical applications in CBCT-based online adaptive radiotherapy, MRI-guided radiotherapy, and simulation-free workflows. We also examine the architectures of representative DL models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) and summarize emerging training strategies. Finally, we discuss current challenges of clinical translation of DL algorithms into clinical practice and suggest potential directions for future research. Overall, this paper highlights the potential of AI-driven sCT generation to advance treatment planning by reducing imaging burden, improving dose accuracy, and accelerating workflow efficiency, thus ultimately improving the treatment outcome of patient care.

Abstract Description Cholangiocarcinoma (CCA) is an aggressive epithelial malignancy of the bile duct, with rising global incidence and poor prognosis. Current treatments achieve only 20-30% response rates in advanced cases, highlighting the need for novel therapeutic approaches. The tumor microenvironment (TME) has emerged as a critical focus for innovative therapies. Sulfatase 2 (SULF2) is a heparan sulfate (HS)-editing enzyme that alters HS chain affinity for growth factors and receptors, modulating heparan sulfate proteoglycans (HSPG) in cell signaling pathways. Specific sulfatases in HSPGs have been implicated as therapeutic targets in CCA. This study investigates the potential of anti-SULF2 antibodies to modulate immune cells within the TME using a C57BL6 syngeneic mouse model. Anti-SULF2-treated mice were compared to untreated controls, with tumors analyzed via CyTOF. Treatment resulted in reduced immunosuppressive cells and increased anti-tumor immune cells, suggesting that SULF2 inhibition shifts the TME toward an anti-tumorigenic state. These findings highlight SULF2 as a promising target for therapeutic intervention. Future studies will explore spatial transcriptomic and proteomics to elucidate the interplay between SULF2 expression in the TME makeup. We will also explore the molecular mechanisms of SULF2 suppression and its influence on cytokine signaling pathways within the TME. Funding Sources Supported by CTSA Grant Number TL1 TR002380 from the National Center for Advancing Translational Science (NCATS). Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

Abstract Computer vision now enables deep learning models to link histopathologic features to molecular features in a quantitative manner not previously achievable by human pathologists. However, human interpretability is often limited. We trained a deep neural network to predict gene expression from digital whole slide images (WSIs) using 848 central nervous system (CNS) tumors with paired WSI and RNA-sequencing data. We used inferred RNA expression levels as a surrogate for protein expression of the CNS tumor lineage markers GFAP, OLIG2, and SSTR2, as well as the proliferation marker MKI67 (Ki67). We established thresholds for categorizing the inferred expression levels as positive or negative based on levels observed in cross-validation testing in select tumor types to approximate routine clinical interpretation of immunohistochemical (IHC) staining. We tested the sensitivity and specificity of our ‘virtual’ IHC on an independent, multi-institutional cohort of over 2,000 CNS tumors with objective diagnostic labels derived from DNA methylation-based tumor classification. As a dichotomous variable, inferred GFAP expression showed a sensitivity of 74% and a specificity of 99% as a glial marker in a cohort of gliomas and meningiomas. In the same cohort, OLIG2 showed a sensitivity of 75% and a specificity of 97% as a glial marker, while SSTR2 showed a sensitivity of 83% and a specificity of 97% as a marker of meningioma. In a cohort of ependymomas and gliomas, virtual IHC for OLIG2 showed a sensitivity of 75% and a specificity of 82% in distinguishing ependymomas from gliomas. Finally, in a cohort of gliomas, inferred expression of MKI67 demonstrated a trend consistent with what would be expected by actual IHC, with increasing MKI67 expression with increasing tumor grade. Our model provides the basis for a human interpretable and clinically applicable deep neural network to aid human pathologists in the diagnosis and grading of CNS tumors.

Abstract Molecular testing has become critical components in the diagnostic classification of the central nervous system (CNS) tumors. However, these methods require substantial resources, limiting accessibility for many patients. Recent advances in artificial intelligence (AI) and computer vision empower deep learning models to infer molecular features from histopathology images, which may often be more readily available than molecular testing, to classify CNS tumors. We trained pan-CNS tumor models to predict DNA methylation and gene expression from whole-slide images (WSIs). These molecular predictions were then used in a hierarchical machine learning framework, termed Neuropath-AI, to predict nine broad tumor families and 52 tumor types using a large diverse cohort of 5,715 histopathology samples, of which 2,988 had paired DNA methylation profiling and 848 had paired RNA-sequencing. We evaluated Neuropath-AI on another large independent multi-institutional cohort of 5,516 CNS tumors, for which Neuropath-AI predicted tumor class with an associated confidence score. Neuropath-AI designated 46% of test samples as predictable with high-confidence, which it then accuracy classified in 97% of samples. It made moderate-confidence (and above) predictions for 87% of test samples, for which it achieved a diagnostic accuracy of 80% for the top-1 prediction and 86% accuracy for a top-2 predictions. A comparison with human neuropathologists showed that Neuropath-AI is comparable at classifying tumors. A second test showed that human neuropathologists were more accurate when the Neuropath-AI classifier results were provided to them, testifying to the potential translational benefit of integrating AI classification tools into the pathologist’s workflow. Our model provides the basis for a clinically applicable deep learning assistant to improve human efficiency and accuracy of CNS tumor diagnosis. The model will be made publicly available and can be readily implemented to assist human pathologists in clinical workflows, in further expanded prospective studies.

Abstract Eosinophils play an important role in mediating itch and inflammation in dermatitis. The role of the eosinophil granule protein eosinophil peroxidase (EPX) in mediating inflammation and itch was tested in a dermatitis mouse model. Mice were sensitized to trimellitic anhydride (TMA) and subsequently challenged chronically on the ear to establish dermatitis. Loss of EPX (in EPX−/− mice) or blocking EPX with the drug resorcinol significantly reduced dermatitis in mice exposed to TMA. Resorcinol also reduced levels of thymic stromal lymphopoietin protein (TSLP) in skin. Further studies showed that EPX increased different cytokines in keratinocytes in cell culture via 2 distinct mechanisms. EPX-induced TSLP expression requires lysophosphatidic acid signaling while EPX-induced expression of TNF-α, CSF2, CSF3, and IL1a required IL-1 signaling. We also showed that blocking IL-1 reduced inflammation in skin following TMA exposure in mice. Thus, EPX is an important mediator of inflammation and itch, that are mediated via at least 2 pathways. This suggests that both EPX and its signaling pathways may provide novel therapeutic strategies in dermatitis.

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

ABSTRACT Antimicrobial‐resistant bacteria, a growing worldwide concern, reduce the effectiveness of antibiotics against a wide range of microbial infections. Various bacterial species have quickly developed antibiotic resistance since the first mention of penicillin resistance in 1947. A rise in mortality, more extended hospital stays, more healthcare expenditures, and morbidity are all brought about by these bacteria that are resistant to antibiotics. To develop resistance, bacteria may undergo genetic changes, engage in horizontal gene transfer, produce β‐lactamase, activate efflux pumps, form biofilms, and alter their metabolism to become less susceptible to drugs. Environmental factors and sublethal antibiotic exposure exacerbate resistance, particularly in cases of persistent infections caused by biofilms. This tendency is prompted by the overuse of antibiotics in both human and veterinary medicine, as well as inadequate infection control measures and environmental pollution. This review presents an extensive survey of antimicrobial resistance across bacterial taxa, with a focus on the physiological and genetic processes underlying this phenomenon. It delves into the current therapeutic landscape and showcases cutting‐edge methods—such as artificial intelligence‐driven antibiotic discovery and resistance prediction—to inform the development of next‐generation antibiotics and containment systems.