Citations

Background: Accurate characterization of tumor stiffness and adhesion is crucial when surgically resecting meningiomas. Currently, such determinations are difficult to ascertain through standard preoperative imaging diagnostics and clinical experience. Herein, we explored magnetic resonance elastography (MRE) as a means of assessing these parameters and thereby improving neurosurgical resections. Methods: This prospective study of 193 patients with meningiomas involved three distinct patient subsets (cohort 1, 88 [2017–2021]; cohort 2, 55 [2022–2023]; cohort 3, 50 [2023–2024]. We analyzed preoperatively obtained MRE data on meningioma stiffness (cohorts 1 and 2) and adhesion (cohort 2) for accuracy, while matching subjects with or without MRE studies (cohort 3, 1:1 ratio) to define the role of preoperative MRE in improving operative outcomes. Results: Areas under the curve (AUCs) for MRE in gauging meningioma stiffness and adhesion were 0.87 and 0.89, respectively. Both parameters significantly surpassed empiric predictions of stiffness and adhesion rendered by surgeons and radiologists, based on routine magnetic resonance imaging (MRI). MRE-determined stiffness measures independently correlated with prolonged resection duration, whereas MRE-assessed adhesion showed independent associations with increased blood loss, longer duration of tumor resection, and higher risk of pial injury. Ultimately, results based on cohort 3 demonstrated that MRE has great potential in this setting to improve neurosurgical procedures by significantly reducing intraoperative bleeding, resection duration, and risks of pial injury. Conclusion: MRE is a promising, noninvasive tool for accurately assessing meningioma stiffness and adhesion, standing to improve the precision and safety of needed surgical intervention.

To improve synthetic CT (sCT) generation from cone-beam CT (CBCT) in radiotherapy, we propose a multiscale segmentation-guided diffusion framework. The proposed model integrates anatomical priors across multiple spatial resolutions through a segmentation mask pyramid and introduces a scale-specific loss function to guide learning at each level. When evaluated on the SynthRAD2023 brain dataset, our model achieves a mean absolute error (MAE) of 61.82 HU, a peak signal-to-noise ratio (PSNR) of 32.05 dB, and a structural similarity index (SSIM) of 0.90, outperforming baseline models. These results suggest that multiscale anatomical guidance can improve the fidelity and anatomical consistency of sCT images, thus facilitating high-quality CBCT-to-CT translation in radiotherapy applications.

Abstract Background: Acute myeloid leukemia (AML) with TP53 alteration (TP53alt), including deletion (TP53del) detected by cytogenetics (CG) (karyotype, FISH, or genomic microarray) and/or TP53 mutation (TP53mut) identified by next-generation sequencing, is associated with poor prognosis. However, a subset of patients may achieve long-term survival after allogeneic hematopoietic cell transplantation (allo-HCT). Patient-, disease-, and transplant-related variables may inform risk stratification and improve treatment decision-making in this high-risk population. We used the CIBMTR registry to investigate outcomes of patients with TP53alt AML undergoing allo-HCT. Methods: We included adult patients with 2017 ELN adverse-risk AML who underwent first allo-HCT between 2013–2019 and had available CG and molecular data. TP53del was centrally reviewed by expert cytogeneticist. Patients were stratified into three TP53alt groups: Group 1 (TP53mut+/ TP53del+), Group 2 (TP53mut −/ TP53del+), and Group 3 (TP53mut+/ TP53del −). The control group comprised patients with 2017 ELN adverse-risk AML negative for both TP53mut and TP53del. Patients from embargoed centers, lacking consent or comprehensive research data, or missing CG and molecular information were excluded. Results: Of 6,474 patients with adverse-risk AML, 703 patients with TP53alt met inclusion criteria: Group 1 (n=203), Group 2 (n=271), Group 3 (n=229). The control group included 2,763 patients. Median age was 58 years (44% ≥60); 44% were female; 16% had Karnofsky score <90, and 52% had HCT-CI ≥3. Secondary AML was more frequent in TP53alt groups (32%, 27%, 27%) versus control (21%) (p<.01). Complex and monosomal karyotypes were most common in Group 1 (61%), followed by Group 2 (54%), Group 3 (31%), and control (6%), p<.01. More patients in Groups 1 and 2 (28% each) underwent HCT in primary induction failure/relapse (PIF/Rel) versus Group 3 (19%) and control (19%), p<.01. Median time from diagnosis to transplant was 5 months (1-331). Donor type was matched related in 23%, matched or mismatched unrelated in 50%, haploidentical in 18%, and umbilical cord blood in 7%. Conditioning intensity was myeloablative (MA) in 51%; reduced-intensity (RIC) in 36% , and 13% non-MA (NMA). Post-transplant cyclophosphamide (PTCy) was used in 27%; and in vivo T-cell depletion (ATG or alemtuzumab) in 26%. After median follow-up of 66 months (3-130), 5-yr OS was 18% (Group 1), 18% (Group 2), 35% (Group 3) vs 46% in controls (p<0.001). 5-yr LFS was 14%, 15%, and 28%, respectively, vs 38% (p<0.001). 5-yr CIR was 69%, 66%, and 52% vs 43% (p<0.001). Transplant-related mortality (TRM) was similar across groups (1-year: 12–16%). On multivariate analysis, compared with controls, TP53del (with or without TP53mut) was independently associated with higher relapse risk: Group 1 HR 1.97, Group 2 HR 1.91, Group 3 HR 1.45 (all p<0.001). These findings translated into inferior OS (HR 1.94, 1.87, and 1.45, respectively, p<0.001). PIF/Rel and CR2+ were also associated with worse survival (HR 2.58 (p<0.001) and 1.18 (p<0.001, respectively). No significant difference was observed between MA and RIC for relapse, while non-myeloablative regimens (HR 1.65; p<0.001) were associated with higher CIR. In vivo T-cell depletion increased relapse risk (HR 1.22; p=0.004). TRM was not influenced by TP53alt status; it was impacted by age, KPS, time from diagnosis to HCT, and GVHD prophylaxis regiment (non-PTCy). Conclusions: This large, registry-based study with long term median follow-up showed that among patients with adverse-risk AML, TP53alt is a strong independent predictor of poor post-HCT outcomes in AML, particularly when TP53del is present. Patients with TP53del , with or without concurrent TP53mut, experienced the worst outcomes, followed by those with isolated TP53mut. Despite the limitations of registry data including lack of information on timing of TP53mut, variant allele frequency (VAF), number of mutations, or accurate information on measurable residual disease (MRD), these findings reinforce the adverse prognostic impact of TP53alt and support its inclusion in risk stratification frameworks. While some patients with TP53alt —particularly those with isolated mutations—may benefit from allo-HCT, relapse remains the dominant cause of failure. Future studies incorporating TP53 functional status, VAF, clonal architecture, specific CG, MRD, and novel transplant approaches are warranted.

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.

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 Uterine leiomyosarcoma (ULMS) is an orphan disease that frequently recurs and metastasizes, with patients undergoing multiple lines of chemotherapy due to lack of effective therapeutic targets. To address this gap, we used single-cell RNA sequencing and spatial transcriptomic analysis to comprehensively profile ULMS. We uncovered multiple states of tumor cells, including tumor cells with mesenchyme-like features, ischemic tumor cells defined by a MYC program, inflammatory tumor cells with active interferon signaling, and stem cell-like hormone receptor-positive cells. The inferred spatial correlates of these tumor cell states demonstrated unique localization patterns. By correlating these signatures to bulk RNA sequencing data, we demonstrate the relevance of these findings to clinical outcomes. Finally, using the single-cell integration and drug response prediction algorithm (scIDUC), we propose drug predictions that may target specific tumor states. Our findings suggest new avenues for further exploration of individualized and multifaceted therapeutic strategies to treat ULMS.

Receptor activator of nuclear factor kappa B ligand (RANKL) is an essential cytokine that induces osteoclastic differentiation by monocyte-macrophage lineage precursors. Here, we showed that in addition to its conventional action, RANKL controls vascular permeability in the bone marrow, where it facilitates the mobilization of hematopoietic monocytic cells, including osteoclast precursors, and resultantly regulates bone metabolism. RANK, a cognate receptor for RANKL, is abundantly expressed in sinusoidal endothelial cells and controls vascular permeability by regulating the expression patterns of intercellular adhesion molecule 1 and vascular cell adhesion molecule 1. High RANKL expression was detected in perivascular C–X–C motif chemokine ligand 12-abundant reticular (CAR) stromal cells. Specific deletion of RANKL expression in CAR cells abrogated the vascular leakage, suggesting that perivascular RANKL is responsible for controlling permeability. In summary, our study revealed a role for RANK/RANKL signaling as a gatekeeper of bone marrow sinusoids in vivo.

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.

Neuroendocrine carcinomas of the breast are a rare subtype of invasive breast carcinomas, and those with morphologic and immunophenotypic features supporting Merkel cell carcinoma (MCC) are even more rare. Accurate diagnosis of these entities is important for clinical decision-making regarding the selection of optimal treatments. We report a patient withMCC in the deep breast parenchyma of a 52-year-old woman, without evidence of a cutaneous component. This tumor exhibited characteristic morphologic and immunophenotypic features of MCC, including sheets of high grade, hyperchromatic tumor cells, with perinuclear dot-like staining of keratin 20. To our knowledge, this is the sixth report of parenchymal breast MCC, and due to its rarity and aggressive course, we reviewed the literature, including prior experience on its diagnosis and management.

Abstract Chronic widespread pain (CWP) remains challenging due to its heterogeneous causes and complex mechanisms. A total of 2920 plasma proteins are analyzed from 29,254 UK Biobank participants. A total of 256 proteins are identified as cross‐sectionally correlated with CWP. A simple (top 10 proteins) and comprehensive (all significant proteins) proteomic‐based score (ProtS) is created for CWP diagnosis, both outperforming and improving the existing clinical score (area under the curve, AUC: 0.801, 0.723, and 0.791 alone, and 0.856 and 0.880 in combination). In addition, the protein score predicted 13‐years risk of pain‐related traits over the body, including pain onset, progression, and intensity; Moreover, it has stronger associations with nociplastic pain and fibromyalgia compared to nociceptive and neuropathic pain, implying a unique protein signature of different pain mechanisms. Finally, among 434 candidate proteins prioritized in the observational analysis, 18 are corroborated with causal relevance by Mendelian randomization, and importantly, four (CA14, DPEP1, LGALS3, and TNF) showed potential as novel drug targets repurposed for treating CWP.