Citations

AbstractBackgroundFerroptosis is an important iron‐dependent form of cell death in hepatocellular carcinoma (HCC). Sorafenib, a potent ferroptosis inducer, is used to treat advanced HCC but its efficacy is limited by the development of drug resistance.MethodsThe effects of DUXAP8 expression on HCC progression were evaluated by TCGA database, Kaplan‐Meier analysis, and in situ hybridization analysis. Sorafenib resistant HCC cell lines were modeled in vitro to study the regulation of DUXAP8 on ferroptosis in HCC induced by sorafenib. We used RNA pull‐down, immunofluorescence assays, acyl‐biotinyl exchange assay and mass spectrometry analysis to assess the molecular mechanism of ferroptosis regulation by DUXAP8. Syngeneic subcutaneous and orthotopic CDX models were used to assess whether DUXAP8 inhibition improves HCC in vivo.ResultsLncRNA DUXAP8, which is highly expressed in liver cancer and associated with poor prognosis, contributes to sorafenib resistance through suppression of ferroptosis. In vitro tests revealed that DUXAP8 reduced the sensitivity of HCC to sorafenib‐induced ferroptosis by acting on SLC7A11, a subunit of the amino acid antiporter system xc‐. DUXAP8 facilitates SLC7A11 palmitoylation and impedes its lysosomal degradation, thereby enhancing SLC7A11 action and suppressing ferroptosis. RNA pull‐down and immunofluorescence assays confirmed that DUXAP8 decreased membrane translocation and promoted sorting of de‐palmitoylated SLC7A11 to lysosomes by binding of DUXAP8 to SLC7A11. In addition, mass spectrometric analysis found that the Cys414 residue of SLC7A11 might be the predominant mutant site responsible for molecular masking of SLC7A11 lysosomal sorting. Further, the antitumor effect of DUXAP8 knockdown was verified in orthotopic and subcutaneous CDX models.ConclusionsOur findings suggest that a novel translational strategy combining sorafenib with DUXAP8 silencing to overcome drug resistance may improve treatment efficacy in patients with advanced HCC.

Abstract Medulloblastoma (MB) is a highly malignant childhood brain tumor comprising a collection of molecularly and clinically distinct entities. Mutations in chromatin-modifying genes distribute across subgroups and account for nearly half of all MBs. However, the molecular consequences of chromatin modifier alterations remain poorly understood. Herein, we characterize the chromatin landscape of fifty-two primary MBs representing each MB subgroup using chromatin immunoprecipitation followed by sequencing (ChIP-seq) for six histone marks and perform multi-layered integration with somatic genetics, genome-wide DNA methylation, and transcriptomic datasets extracted from the same tumors. Analysis of differential chromatin states across MB subgroups identified significant enrichment of a bivalent enhancer state (EnhBiv) in Group 4 MB. Group 4-specific EnhBivs mapped to promoters of key neurodevelopmental genes and transcription factors and was associated with increased DNA methylation and reduced expression. In accordance with higher EnhBivs coverage in Group 4 MB, we found that KDM2B, a H3K4me3 and H3K36me2 demethylase, is highly expressed in Group 4 MBs and patient-derived xenografts (PDXs). KDM2B knockout mediated by CRISPR targeting suppressed growth of both Group 3 MB cell lines in vitro and Group 4 MB PDXs in vivo but had no effect on control SHH-MB cells, suggesting that KDM2B is a novel selective dependency in MB. Knockout of KDM2B in MB cells induced profound upregulation of PRC2 targets, genes regulated by bivalent histone marks, as well as genes involved in neuronal differentiation. Further biochemical and molecular studies demonstrated that KDM2B interacts with PRC2 complex subunits and occupies gene promoters associated with Group 4-specific EnhBiv chromatin. In summary, our comprehensive characterization of the MB chromatin landscape in a large cohort of primary tumors provides novel insights into the epigenetic basis of MB and implicates KDM2B as a novel Group 4 MB dependency that should be prioritized as a therapeutic target in this subgroup.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

Our understanding of tissue-resident memory T (T RM ) cell biology has been largely developed from acute infection models in which antigen is cleared and sterilizing immunity is achieved. Less is known about T RM cells in the context of chronic antigen persistence and inflammation. We investigated factors that underlie T RM maintenance in a kidney transplantation model in which T RM cells drive rejection. In contrast to acute infection, we found that T RM cells declined markedly in the absence of cognate antigen, antigen presentation, or antigen sensing by the T cells. Depletion of graft-infiltrating dendritic cells or interruption of antigen presentation after T RM cells were established was sufficient to disrupt T RM maintenance and reduce allograft pathology. Likewise, removal of IL-15 transpresentation or of the IL-15 receptor on T cells during T RM maintenance led to a decline in T RM cells, and IL-15 receptor blockade prevented chronic rejection. Therefore, antigen and IL-15 presented by dendritic cells play nonredundant key roles in CD8 T RM cell maintenance in settings of antigen persistence and inflammation. These findings provide insights that could lead to improved treatment of chronic transplant rejection and autoimmunity.

Abstract Although structures of vitrified supramolecular complexes have been determined at near-atomic resolution, elucidating in situ molecular structure in living cells remains a major challenge. Here, we apply a novel but simple liquid-cell technique, developed previously for real-time imaging of the dynamics at a liquid-gas interface, to image wet biological samples. With extra scattering from the liquid phase, the transmission electron micrographs show amplitude contrast comparable to that in negatively stained samples. Single-molecule domains are resolved in the protein complex GroEL imaged in buffer solution at room temperature. Moreover, various stages of virus cell entry, which are transient events with very few structural information to date, are also captured. Morphological details are reconstructed using the technique of individual particle electron tomography. These results demonstrate that this approach can be a valuable yet cost-effective technique complementary to other microscopy techniques for addressing important biological questions at the molecular level.

Purpose of review There have been major advances in our understanding of molecular pathogenesis of myeloid neoplasms, which prompt the updates in the classification of myeloid neoplasms in the fifth edition of World Health Organization Classification (WHO-5) and the new International Consensus Classification (ICC). The purpose of this review is to provide an overview of these two classification systems for myeloid neoplasms. Recent findings The definition, classification, and diagnostic criteria in many myeloid entities have been refined in WHO-5 and ICC with improved understanding of morphology and integration of new genetic findings. Particularly, molecular and cytogenetic studies have been increasingly incorporated into the classification, risk stratification, and selection of therapy of myeloid neoplasms. Overall, despite some revisions and discrepancies between WHO-5 and ICC, the major categories of myeloid neoplasms remain the same. Further validation studies are warranted to fine-tune and, ideally, integrate these two classifications. Summary Integration of clinical information, laboratory parameters, morphologic features, and cytogenetic and molecular studies is essential for the classification of myeloid neoplasms, as recommended by both WHO-5 and ICC.

AbstractThis report reviews the image acquisition and reconstruction characteristics of C‐arm Cone Beam Computed Tomography (C‐arm CBCT) systems and provides guidance on quality control of C‐arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image‐Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report.Section 1 provides an introduction to C‐arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C‐arm fluoroscopy quality control (QC) in relation to 3D C‐arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C‐arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C‐arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free‐in‐air and in‐phantom approaches to evaluating radiation dose indices.The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6.

Abstract Durable objective response rate (ORR) remains a meaningful endpoint in recurrent cancer; however, the target ORR for single-arm recurrent glioblastoma trials has not been based on historic information or tied to patient outcomes. The current study reviewed 68 treatment arms comprising 4793 patients in past trials in recurrent glioblastoma in order to judiciously define target ORRs for use in recurrent glioblastoma trials. ORR was estimated at 6.1% [95% CI 4.23; 8.76%] for cytotoxic chemothera + pies (ORR = 7.59% for lomustine, 7.57% for temozolomide, 0.64% for irinotecan, and 5.32% for other agents), 3.37% for biologic agents, 7.97% for (select) immunotherapies, and 26.8% for anti-angiogenic agents. ORRs were significantly correlated with median overall survival (mOS) across chemotherapy (R2= 0.4078, P < .0001), biologics (R2= 0.4003, P = .0003), and immunotherapy trials (R2= 0.8994, P < .0001), but not anti-angiogenic agents (R2= 0, P = .8937). Pooling data from chemotherapy, biologics, and immunotherapy trials, a meta-analysis indicated a strong correlation between ORR and mOS (R2= 0.3900, P < .0001; mOS [weeks] = 1.4xORR + 24.8). Assuming an ineffective cytotoxic (control) therapy has ORR = 7.6%, the average ORR for lomustine and temozolomide trials, a sample size of ≥40 patients with target ORR>25% is needed to demonstrate statistical significance compared to control with a high level of confidence (P < .01) and adequate power (>80%). Given this historic data and potential biases in patient selection, we recommend that well-controlled, single-arm phase II studies in recurrent glioblastoma should have a target ORR >25% (which translates to a median OS of approximately 15 months) and a sample size of ≥40 patients, in order to convincingly demonstrate antitumor activity. Crucially, this response needs to have sufficient durability, which was not addressed in the current study.