The discovery of I-BRD9, a selective cell active chemical probe for bromodomain containing protein 9 inhibition

Acetylation of histone lysine residues is one of the most well-studied post-translational modifications of chromatin, selectively recognized by bromodomain “reader” modules. Inhibitors of the bromodomain and extra terminal domain (BET) family of bromodomains have shown profound anticancer and anti-inflammatory properties, generating much interest in targeting other bromodomain-containing proteins for disease treatment. Herein, we report the discovery of I-BRD9, the first selective cellular chemical probe for bromodomain-containing protein 9 (BRD9). I-BRD9 was identified through structure-based design, leading to greater than 700-fold selectivity over the BET family and 200-fold over the highly homologous bromodomain-containing protein 7 (BRD7). I-BRD9 was used to identify genes regulated by BRD9 in Kasumi-1 cells involved in oncology and immune response pathways and to the best of our knowledge, represents the first selective tool compound available to elucidate the cellular phenotype of BRD9 bromodomain inhibition

Molecular Design, Functional Characterization and Structural Basis of a Protein Inhibitor Against the HIV-1 Pathogenicity Factor Nef

Increased spread of HIV-1 and rapid emergence of drug resistance warrants development of novel antiviral strategies. Nef, a critical viral pathogenicity factor that interacts with host cell factors but lacks enzymatic activity, is not targeted by current antiviral measures. Here we inhibit Nef function by simultaneously blocking several highly conserved protein interaction surfaces. This strategy, referred to as “wrapping Nef”, is based on structure-function analyses that led to the identification of four target sites: (i) SH3 domain interaction, (ii) interference with protein transport processes, (iii) CD4 binding and (iv) targeting to lipid membranes. Screening combinations of Nef-interacting domains, we developed a series of small Nef interacting proteins (NIs) composed of an SH3 domain optimized for binding to Nef, fused to a sequence motif of the CD4 cytoplasmic tail and combined with a prenylation signal for membrane association. NIs bind to Nef in the low nM affinity range, associate with Nef in human cells and specifically interfere with key biological activities of Nef. Structure determination of the Nef-inhibitor complex reveals the molecular basis for binding specificity. These results establish Nef-NI interfaces as promising leads for the development of potent Nef inhibitors

Activation of JNK Triggers Release of Brd4 from Mitotic Chromosomes and Mediates Protection from Drug-Induced Mitotic Stress

Some anti-cancer drugs, including those that alter microtubule dynamics target mitotic cells and induce apoptosis in some cell types. However, such drugs elicit protective responses in other cell types allowing cells to escape from drug-induced mitotic inhibition. Cells with a faulty protective mechanism undergo defective mitosis, leading to genome instability. Brd4 is a double bromodomain protein that remains on chromosomes during mitosis. However, Brd4 is released from mitotic chromosomes when cells are exposed to anti-mitotic drugs including nocodazole. Neither the mechanisms, nor the biological significance of drug-induced Brd4 release has been fully understood. We found that deletion of the internal C-terminal region abolished nocodazole induced Brd4 release from mouse P19 cells. Furthermore, cells expressing truncated Brd4, unable to dissociate from chromosomes were blocked from mitotic progression and failed to complete cell division. We also found that pharmacological and peptide inhibitors of the c-jun-N-terminal kinases (JNK) pathway, but not inhibitors of other MAP kinases, prevented release of Brd4 from chromosomes. The JNK inhibitor that blocked Brd4 release also blocked mitotic progression. Further supporting the role of JNK in Brd4 release, JNK2–/– embryonic fibroblasts were defective in Brd4 release and sustained greater inhibition of cell growth after nocodazole treatment. In sum, activation of JNK pathway triggers release of Brd4 from chromosomes upon nocodazole treatment, which mediates a protective response designed to minimize drug-induced mitotic stress

Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.Comment: 16 pages, 11 tables, 10 figures, MICCA

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.</p

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future