USP1 deubiquitinates ID proteins to preserve a mesenchymal stem cell program in osteosarcoma.

Inhibitors of DNA binding (IDs) antagonize basic-helix-loop-helix (bHLH) transcription factors to inhibit differentiation and maintain stem cell fate. ID ubiquitination and proteasomal degradation occur in differentiated tissues, but IDs in many neoplasms appear to escape degradation. We show that the deubiquitinating enzyme USP1 promotes ID protein stability and stem cell-like characteristics in osteosarcoma. USP1 bound, deubiquitinated, and thereby stabilized ID1, ID2, and ID3. A subset of primary human osteosarcomas coordinately overexpressed USP1 and ID proteins. USP1 knockdown in osteosarcoma cells precipitated ID protein destabilization, cell-cycle arrest, and osteogenic differentiation. Conversely, ectopic USP1 expression in mesenchymal stem cells stabilized ID proteins, inhibited osteoblastic differentiation, and enhanced proliferation. Consistent with USP1 functioning in normal mesenchymal stem cells, USP1-deficient mice were osteopenic. Our observations implicate USP1 in preservation of the stem cell state that characterizes osteosarcoma and identify USP1 as a target for differentiation therapy.

PlGF blockade does not inhibit angiogenesis during primary tumor growth.

It has been recently reported that treatment with an anti-placenta growth factor (PlGF) antibody inhibits metastasis and primary tumor growth. Here we show that, although anti-PlGF treatment inhibited wound healing, extravasation of B16F10 cells, and growth of a tumor engineered to overexpress the PlGF receptor (VEGFR-1), neutralization of PlGF using four novel blocking antibodies had no significant effect on tumor angiogenesis in 15 models. Also, genetic ablation of the tyrosine kinase domain of VEGFR-1 in the host did not result in growth inhibition of the anti-VEGF-A sensitive or resistant tumors tested. Furthermore, combination of anti-PlGF with anti-VEGF-A antibodies did not result in greater antitumor efficacy than anti-VEGF-A monotherapy. In conclusion, our data argue against an important role of PlGF during primary tumor growth in most models and suggest that clinical evaluation of anti-PlGF antibodies may be challenging.

TGFß attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells.

Therapeutic antibodies that block the programmed death-1 (PD-1)-programmed death-ligand 1 (PD-L1) pathway can induce robust and durable responses in patients with various cancers, including metastatic urothelial cancer. However, these responses only occur in a subset of patients. Elucidating the determinants of response and resistance is key to improving outcomes and developing new treatment strategies. Here we examined tumours from a large cohort of patients with metastatic urothelial cancer who were treated with an anti-PD-L1 agent (atezolizumab) and identified major determinants of clinical outcome. Response to treatment was associated with CD8+ T-effector cell phenotype and, to an even greater extent, high neoantigen or tumour mutation burden. Lack of response was associated with a signature of transforming growth factor ß (TGFß) signalling in fibroblasts. This occurred particularly in patients with tumours, which showed exclusion of CD8+ T cells from the tumour parenchyma that were instead found in the fibroblast- and collagen-rich peritumoural stroma; a common phenotype among patients with metastatic urothelial cancer. Using a mouse model that recapitulates this immune-excluded phenotype, we found that therapeutic co-administration of TGFß-blocking and anti-PD-L1 antibodies reduced TGFß signalling in stromal cells, facilitated T-cell penetration into the centre of tumours, and provoked vigorous anti-tumour immunity and tumour regression. Integration of these three independent biological features provides the best basis for understanding patient outcome in this setting and suggests that TGFß shapes the tumour microenvironment to restrain anti-tumour immunity by restricting T-cell infiltration.

Deep Ensembles Are Robust to Occasional Catastrophic Failures of Individual DNNs for Organs Segmentations in CT Images.

Deep neural networks (DNNs) have recently showed remarkable performance in various computer vision tasks, including classification and segmentation of medical images. Deep ensembles (an aggregated prediction of multiple DNNs) were shown to improve a DNN's performance in various classification tasks. Here we explore how deep ensembles perform in the image segmentation task, in particular, organ segmentations in CT (Computed Tomography) images. Ensembles of V-Nets were trained to segment multiple organs using several in-house and publicly available clinical studies. The ensembles segmentations were tested on images from a different set of studies, and the effects of ensemble size as well as other ensemble parameters were explored for various organs. Compared to single models, Deep Ensembles significantly improved the average segmentation accuracy, especially for those organs where the accuracy was lower. More importantly, Deep Ensembles strongly reduced occasional "catastrophic" segmentation failures characteristic of single models and variability of the segmentation accuracy from image to image. To quantify this we defined the "high risk images": images for which at least one model produced an outlier metric (performed in the lower 5% percentile). These images comprised about 12% of the test images across all organs. Ensembles performed without outliers for 68%-100% of the "high risk images" depending on the performance metric used.

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials.

Background Deep learning-based segmentation could facilitate rapid and reproducible T1 lesion load assessments, which is crucial for disease management in multiple sclerosis (MS). T1 unenhancing and contrast-enhancing lesions in MS are those that enhance or do not enhance after administration of a gadolinium-based contrast agent at T1-weighted MRI. Purpose To develop deep learning models for automated assessment of T1 unenhancing and contrast-enhancing lesions; to investigate if joint training improved performance; to reproduce a known ocrelizumab treatment response; and to evaluate the association of baseline T1-weighted imaging metrics with clinical outcomes in relapsing MS clinical trials. Materials and Methods Joint and individual deep learning models (U-Nets) were developed retrospectively on multimodal MRI data sets from large multicenter OPERA trials of relapsing MS (August 2011 to May 2015). The joint model included cross-network connections and a combined loss function. Models were trained on OPERA I data sets with three-fold cross-validation. OPERA II data sets were the internal test set. Dice coefficients, lesion true-positive and false-positive rates, and areas under the receiver operating characteristic curve (AUCs) were used to evaluate model performance. Association of baseline imaging metrics with clinical outcomes was assessed with Cox proportional hazards models. Results A total of 796 patients (3030 visits; mean age, 37 years ± 9; 521 women) from the OPERA II trial were evaluated. The joint model achieved a mean Dice coefficient of 0.77 and 0.74, lesion true-positive rate of 0.88 and 0.86, and lesion false-positive rate of 0.04 and 0.19 for T1 contrast-enhancing and T1 unenhancing lesion segmentation, respectively. Joint training improved performance for smaller T1 contrast-enhancing lesions (≤0.06 mL; individual training AUC: 0.75; joint training AUC: 0.87; P < .001). A significant ocrelizumab treatment effect (P < .001) was seen in reducing the mean number of T1 contrast-enhancing lesions at 24, 48, and 96 weeks (manual assessment at 24 weeks: 10 lesions in 366 patients with ocrelizumab, 141 lesions in 355 patients with interferon, 93% reduction; manual assessment at 48 weeks: six lesions in 355 patients with ocrelizumab, 150 lesions in 317 patients with interferon, 96% reduction; manual assessment at 96 weeks: five lesions in 340 patients with ocrelizumab, 157 lesions in 294 patients with interferon, 97% reduction; joint model assessment at 24 weeks: 19 lesions in 365 patients with ocrelizumab, 128 lesions in 354 patients with interferon, 86% reduction; joint model assessment at 48 weeks: 14 lesions in 355 patients with ocrelizumab, 121 lesions in 317 patients with interferon, 90% reduction; joint model assessment at 96 weeks: 10 lesions in 340 patients with ocrelizumab, 144 lesions in 294 patients with interferon, 94% reduction) and the mean number of new T1 unenhancing lesions across all follow-up examinations (manual assessment: 504 lesions in 1060 visits for ocrelizumab-treated patients, 1438 lesions in 965 visits for interferon-treated patients, 68% reduction; joint model assessment: 205 lesions in 1053 visits for ocrelizumab-treated patients, 661 lesions in 957 visits for interferon-treated patients, 78% reduction). Baseline T1 unenhancing total lesion volume was associated with clinical outcomes (manual hazard ratio [HR]: 1.12, P = .02; joint model HR: 1.11, P = .03). Conclusion Joint architecture and training improved segmentation of MRI T1 contrast-enhancing multiple sclerosis lesions, and both deep learning models had sufficiently high performance to detect an ocrelizumab treatment response consistent with manual assessments. ClinicalTrials.gov: NCT01247324 and NCT01412333 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Talbott in this issue.

Heart and bladder detection and segmentation on FDG PET/CT by deep learning.

PURPOSE: Positron emission tomography (PET)/ computed tomography (CT) has been extensively used to quantify metabolically active tumors in various oncology indications. However, FDG-PET/CT often encounters false positives in tumor detection due to 18fluorodeoxyglucose (FDG) accumulation from the heart and bladder that often exhibit similar FDG uptake as tumors. Thus, it is necessary to eliminate this source of physiological noise. Major challenges for this task include: (1) large inter-patient variability in the appearance for the heart and bladder. (2) The size and shape of bladder or heart may appear different on PET and CT. (3) Tumors can be very close or connected to the heart or bladder. APPROACH: A deep learning based approach is proposed to segment the heart and bladder on whole body PET/CT automatically. Two 3D U-Nets were developed separately to segment the heart and bladder, where each network receives the PET and CT as a multi-modal input. Data sets were obtained from retrospective clinical trials and include 575 PET/CT for heart segmentation and 538 for bladder segmentation. RESULTS: The models were evaluated on a test set from an independent trial and achieved a Dice Similarity Coefficient (DSC) of 0.96 for heart segmentation and 0.95 for bladder segmentation, Average Surface Distance (ASD) of 0.44 mm on heart and 0.90 mm on bladder. CONCLUSIONS: This methodology could be a valuable component to the FDG-PET/CT data processing chain by removing FDG physiological noise associated with heart and/or bladder accumulation prior to image analysis by manual, semi- or automated tumor analysis methods.

Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET/CT Using Cascaded 2D and 3D Convolutional Neural Networks.

18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) is commonly used in clinical practice and clinical drug development to identify and quantify metabolically active tumors. Manual or computer-assisted tumor segmentation in FDG-PET images is a common way to assess tumor burden, such approaches are both labor intensive and may suffer from high inter-reader variability. We propose an end-to-end method leveraging 2D and 3D convolutional neural networks to rapidly identify and segment tumors and to extract metabolic information in eyes to thighs (whole body) FDG-PET/CT scans. The developed architecture is computationally efficient and devised to accommodate the size of whole-body scans, the extreme imbalance between tumor burden and the volume of healthy tissue, and the heterogeneous nature of the input images. Our dataset consists of a total of 3664 eyes to thighs FDG-PET/CT scans, from multi-site clinical trials in patients with non-Hodgkin's lymphoma (NHL) and advanced non-small cell lung cancer (NSCLC). Tumors were segmented and reviewed by board-certified radiologists. We report a mean 3D Dice score of 88.6% on an NHL hold-out set of 1124 scans and a 93% sensitivity on 274 NSCLC hold-out scans. The method is a potential tool for radiologists to rapidly assess eyes to thighs FDG-avid tumor burden.

Micro-CT imaging and structural analysis of glomeruli in a model of Adriamycin-induced nephropathy.

Glomeruli number and size are important for determining the pathogenesis of glomerular disease, chronic kidney disease, and hypertension. Moreover, renal injury can occur in specific cortical layers and alter glomerular spatial distribution. In this study, we present a comprehensive structural analysis of glomeruli in a model of Adriamycin (doxorubicin) nephropathy. Glomeruli are imaged (micro-CT at 10 × 10 × 10 µm3) in kidney specimens from C57Bl/6 mouse cohorts: control treated with saline ( n = 9) and Adriamycin treated with 20 mg/kg Adriamycin ( n = 7). Several indices were examined, including glomerular number, glomerular volume, glomerular volume heterogeneity, and spatial density at each glomerulus and in each cortical layer (superficial, midcortical, and juxtamedullary). In the Adriamycin-treated animals, glomerular number decreased significantly in the left kidney [control: 8,298 ± 221, Adriamycin: 6,781 ± 630 (mean ± SE)] and right kidney (control: 7,317 ± 367, Adriamycin: 5,522 ± 508), and glomerular volume heterogeneity increased significantly in the left kidney (control: 0.642 ± 0.015, Adriamycin: 0.786 ± 0.018) and right kidney (control: 0.739 ± 0.016, Adriamycin: 0.937 ± 0.023). Glomerular spatial density was not affected. Glomerular volume heterogeneity increased significantly in the superficial and midcortical layers of the Adriamycin cohort. Adriamycin did not affect glomerular volume or density metrics in the juxtamedullary region, suggesting a compensatory mechanism of juxtamedullary glomeruli to injury in the outer cortical layers. Left/right asymmetry was observed in kidney size and various glomeruli metrics. The methods presented here can be used to evaluate renal disease models with subtle changes in glomerular endowment locally or across the entire kidney, and they provide an imaging tool to investigate diverse interventions and therapeutic drugs.

Muscle specific kinase (MuSK) activation preserves neuromuscular junctions in the diaphragm but is not sufficient to provide a functional benefit in the SOD1G93A mouse model of ALS.

Amyotrophic lateral sclerosis (ALS), a neurodegenerative disease affecting motor neurons, is characterized by rapid decline of motor function and ultimately respiratory failure. As motor neuron death occurs late in the disease, therapeutics that prevent the initial disassembly of the neuromuscular junction may offer optimal functional benefit and delay disease progression. To test this hypothesis, we treated the SOD1G93A mouse model of ALS with an agonist antibody to muscle specific kinase (MuSK), a receptor tyrosine kinase required for the formation and maintenance of the neuromuscular junction. Chronic MuSK antibody treatment fully preserved innervation of the neuromuscular junction when compared with control-treated mice; however, no preservation of diaphragm function, motor neurons, or survival benefit was detected. These data show that anatomical preservation of neuromuscular junctions in the diaphragm via MuSK activation does not correlate with functional benefit in SOD1G93A mice, suggesting caution in employing MuSK activation as a therapeutic strategy for ALS patients.

Bruton's Tyrosine Kinase Small Molecule Inhibitors Induce a Distinct Pancreatic Toxicity in Rats.

Bruton's tyrosine kinase (BTK) is a member of the Tec family of cytoplasmic tyrosine kinases involved in B-cell and myeloid cell signaling. Small molecule inhibitors of BTK are being investigated for treatment of several hematologic cancers and autoimmune diseases. GDC-0853 ((S)-2-(3'-(hydroxymethyl)-1-methyl-5-((5-(2-methyl-4-(oxetan-3-yl)piperazin-1-yl)pyridin-2-yl)amino)-6-oxo-1,6-dihydro-[3,4'-bipyridin]-2'-yl)-7,7-dimethyl-3,4,7,8-tetrahydro-2H-cyclopenta[4,5]pyrrolo[1,2-a]pyrazin-1(6H)-one) is a selective and reversible oral small-molecule BTK inhibitor in development for the treatment of rheumatoid arthritis and systemic lupus erythematosus. In Sprague-Dawley (SD) rats, administration of GDC-0853 and other structurally diverse BTK inhibitors for 7 days or longer caused pancreatic lesions consisting of multifocal islet-centered hemorrhage, inflammation, fibrosis, and pigment-laden macrophages with adjacent lobular exocrine acinar cell atrophy, degeneration, and inflammation. Similar findings were not observed in mice or dogs at much higher exposures. Hemorrhage in the peri-islet vasculature emerged between four and seven daily doses of GDC-0853 and was histologically similar to spontaneously occurring changes in aging SD rats. This suggests that GDC-0853 could exacerbate a background finding in younger animals. Glucose homeostasis was dysregulated following a glucose challenge; however, this occurred only after 28 days of administration and was not directly associated with onset or severity of pancreatic lesions. There were no changes in other common serum biomarkers assessing endocrine and exocrine pancreatic function. Additionally, these lesions were not readily detectable via Doppler ultrasound, computed tomography, or magnetic resonance imaging. Our results indicate that pancreatic lesions in rats are likely a class effect of BTK inhibitors, which may exacerbate an islet-centered pathology that is unlikely to be relevant to humans.