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.

Clinical translation of ferumoxytol-based vessel size imaging (VSI): Feasibility in a phase I oncology clinical trial population.

PURPOSE: To assess the feasibility of acquiring vessel size imaging (VSI) metrics using ferumoxytol injections and stock pulse sequences in a multicenter Phase I trial of a novel therapy in patients with advanced metastatic disease. METHODS: Scans were acquired before, immediately after, and 48 h after injection, at screening and after 2 weeks of treatment. ΔR2 , ΔR2*, vessel density (Q), and relative vascular volume fractions (VVF) were estimated in both normal tissue and tumor, and compared with model-derived theoretical and experimental estimates based on preclinical murine xenograft data. RESULTS: R2 and R2* relaxation rates were still significantly elevated in tumors and liver 48 h after ferumoxytol injection; liver values returned to baseline by week 2. Q was relatively insensitive to changes in ΔR2*, indicating lack of dependence on contrast agent concentration. Variability in Q was higher among human tumors compared with xenografts and was mostly driven by ΔR2 . Relative VVFs were higher in human tumors compared with xenografts, while values in muscle were similar between species. CONCLUSION: Clinical ferumoxytol-based VSI is feasible using standard MRI techniques in a multicenter study of patients with lesions outside of the brain. Ferumoxytol accumulation in the liver does not preclude measurement of VSI parameters in liver metastases. Magn Reson Med 77:814-825, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Metabolic response of prostate cancer to nicotinamide phophoribosyltransferase inhibition in a hyperpolarized MR/PET compatible bioreactor.

BACKGROUND: Metabolic shifts in disease are of great interest for the development of novel therapeutics. In cancer treatment, these therapies exploit the metabolic phenotype associated with oncogenesis and cancer progression. One recent strategy involves the depletion of the cofactors needed to maintain the high rate of glycolysis seen with the Warburg effect. Specifically, blocking nicotinamide adenine dinucleotide (NAD) biosynthesis via nicotinamide phosphoribosyltransferase (NAMPT) inhibition depletes cancer cells of the NAD needed for glycolysis. To characterize this metabolic phenotype in vivo and describe changes in flux with treatment, non-invasive biomarkers are necessary. One such biomarker is hyperpolarized (HP) [1-(13) C] pyruvate, a clinically translatable probe that allows real-time assessment of metabolism. METHODS: We therefore developed a cell perfusion system compatible with HP magnetic resonance (MR) and positron emission tomography (PET) to develop translatable biomarkers of response to NAMPT inhibition in reduced volume cell cultures. RESULTS: Using this platform, we observed a reduction in pyruvate flux through lactate dehydrogenase with NAMPT inhibition in prostate cancer cells, and showed that both HP lactate and 2-[(18) F] fluoro-2-deoxy-D-glucose (FDG) can be used as biomarkers for treatment response of such targeted agents. Moreover, we observed dynamic flux changes whereby HP pyruvate was re-routed to alanine, providing both positive and negative indicators of treatment response. CONCLUSIONS: This study demonstrated the feasibility of a MR/PET compatible bioreactor approach to efficiently explore cell and tissue metabolism, the understanding of which is critical for developing clinically translatable biomarkers of disease states and responses to therapeutics.

Mixed-effects modeling of clinical DCE-MRI data: application to colorectal liver metastases treated with bevacizumab.

PURPOSE: Most dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data are evaluated for individual patients with cohorts analyzed to detect significant changes from baseline values, repeating the process at each posttreatment timepoint. Our study aimed to develop a statistically valid model for the complete time course of DCE-MRI data in a patient cohort. MATERIALS AND METHODS: Data from 10 patients with colorectal cancer liver metastases were analyzed, including two baseline scans and four post-bevacizumab scans. Apparent changes in tumor median K(trans) were adjusted for changes in observed enhancing tumor fraction (EnF) by multiplying K(trans) by EnF (KEnF). A mixed-effects model (MEM) was defined to describe the KEnF time course for all patients simultaneously by assuming a three-parameter indirect response model with model parameters lognormally distributed across patients. RESULTS: The typical cohort time course showed a KEnF reduction to 59% of baseline at 24 hours, returning to 65% of baseline values by day 12. Interpatient variability of model parameters ranged from 11% to 307%. CONCLUSION: The MEM approach has potential for comparing responses at a group level in clinical trials with different doses, schedules, or combination regimens. Furthermore, the KEnF biomarker successfully resolved confounds in interpreting K(trans) arising from therapy induced changes in the volume of enhancing tumor.

Automated Lugano Metabolic Response Assessment in 18F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on 18F-Fluorodeoxyglucose-Positron Emission Tomography.

PURPOSE: Artificial intelligence can reduce the time used by physicians on radiological assessments. For 18F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic. METHODS: Here, we present a deep learning-based algorithm for fully automated treatment response assessments according to the Lugano 2014 classification. The proposed four-stage method, trained on a multicountry clinical trial (ClinicalTrials.gov identifier: NCT01287741) and tested in three independent multicenter and multicountry test sets on different non-Hodgkin lymphoma subtypes and different lines of treatment (ClinicalTrials.gov identifiers: NCT02257567, NCT02500407, 20% holdout in ClinicalTrials.gov identifier: NCT01287741), outputs the detected lesions at baseline and follow-up to enable focused radiologist review. RESULTS: The method's response assessment achieved high agreement with the adjudicated radiologic responses (eg, agreement for overall response assessment of 93%, 87%, and 85% in ClinicalTrials.gov identifiers: NCT01287741, NCT02500407, and NCT02257567, respectively) similar to inter-radiologist agreement and was strongly prognostic of outcomes with a trend toward higher accuracy for death risk than adjudicated radiologic responses (hazard ratio for end of treatment by-model CMR of 0.123, 0.054, and 0.205 in ClinicalTrials.gov identifiers: NCT01287741, NCT02500407, and NCT02257567, compared with, respectively, 0.226, 0.292, and 0.272 for CMR by the adjudicated responses). Furthermore, a radiologist review of the algorithm's assessments was conducted. The radiologist median review time was 1.38 minutes/assessment, and no statistically significant differences were observed in the level of agreement of the radiologist with the model's response compared with the level of agreement of the radiologist with the adjudicated responses. CONCLUSION: These results suggest that the proposed method can be incorporated into radiologic response assessment workflows in cancer imaging for significant time savings and with performance similar to trained medical experts.

Chondroitin sulfate synthase 1 (Chsy1) is required for bone development and digit patterning.

Joint and skeletal development is highly regulated by extracellular matrix (ECM) proteoglycans, of which chondroitin sulfate proteoglycans (CSPGs) are a major class. Despite the requirement of joint CSPGs for skeletal flexibility and structure, relatively little is understood regarding their role in establishing joint positioning or in modulating signaling and cell behavior during joint formation. Chondroitin sulfate synthase 1 (Chsy1) is one of a family of enzymes that catalyze the extension of chondroitin and dermatan sulfate glycosaminoglycans. Recently, human syndromic brachydactylies have been described to have loss-of-function mutations at the CHSY1 locus. In concordance with these observations, we demonstrate that mice lacking Chsy1, though viable, display chondrodysplasia and decreased bone density. Notably, Chsy1(-/-) mice show a profound limb patterning defect in which orthogonally shifted ectopic joints form in the distal digits. Associated with the digit-patterning defect is a shift in cell orientation and an imbalance in chondroitin sulfation. Our results place Chsy1 as an essential regulator of joint patterning and provide a mouse model of human brachydactylies caused by mutations in CHSY1.

A novel inhibitor of the alternative pathway of complement reverses inflammation and bone destruction in experimental arthritis.

Complement is an important component of the innate and adaptive immune response, yet complement split products generated through activation of each of the three complement pathways (classical, alternative, and lectin) can cause inflammation and tissue destruction. Previous studies have shown that complement activation through the alternative, but not classical, pathway is required to initiate antibody-induced arthritis in mice, but it is unclear if the alternative pathway (AP) plays a role in established disease. Previously, we have shown that human complement receptor of the immunoglobulin superfamily (CRIg) is a selective inhibitor of the AP of complement. Here, we present the crystal structure of murine CRIg and, using mutants, provide evidence that the structural requirements for inhibition of the AP are conserved in human and mouse. A soluble form of CRIg reversed inflammation and bone loss in two experimental models of arthritis by inhibiting the AP of complement in the joint. Our data indicate that the AP of complement is not only required for disease induction, but also disease progression. The extracellular domain of CRIg thus provides a novel tool to study the effects of inhibiting the AP of complement in established disease and constitutes a promising therapeutic with selectivity for a single complement pathway.

Hedgehog signaling regulates the generation of ameloblast progenitors in the continuously growing mouse incisor.

In many organ systems such as the skin, gastrointestinal tract and hematopoietic system, homeostasis is dependent on the continuous generation of differentiated progeny from stem cells. The rodent incisor, unlike human teeth, grows throughout the life of the animal and provides a prime example of an organ that rapidly deteriorates if newly differentiated cells cease to form from adult stem cells. Hedgehog (Hh) signaling has been proposed to regulate self-renewal, survival, proliferation and/or differentiation of stem cells in several systems, but to date there is little evidence supporting a role for Hh signaling in adult stem cells. We used in vivo genetic lineage tracing to identify Hh-responsive stem cells in the mouse incisor and we show that sonic hedgehog (SHH), which is produced by the differentiating progeny of the stem cells, signals to several regions of the incisor. Using a hedgehog pathway inhibitor (HPI), we demonstrate that Hh signaling is not required for stem cell survival but is essential for the generation of ameloblasts, one of the major differentiated cell types in the tooth, from the stem cells. These results therefore reveal the existence of a positive-feedback loop in which differentiating progeny produce the signal that in turn allows them to be generated from stem cells.

Specific Btk inhibition suppresses B cell- and myeloid cell-mediated arthritis.

Bruton's tyrosine kinase (Btk) is a therapeutic target for rheumatoid arthritis, but the cellular and molecular mechanisms by which Btk mediates inflammation are poorly understood. Here we describe the discovery of CGI1746, a small-molecule Btk inhibitor chemotype with a new binding mode that stabilizes an inactive nonphosphorylated enzyme conformation. CGI1746 has exquisite selectivity for Btk and inhibits both auto- and transphosphorylation steps necessary for enzyme activation. Using CGI1746, we demonstrate that Btk regulates inflammatory arthritis by two distinct mechanisms. CGI1746 blocks B cell receptor-dependent B cell proliferation and in prophylactic regimens reduces autoantibody levels in collagen-induced arthritis. In macrophages, Btk inhibition abolishes FcγRIII-induced TNFα, IL-1ß and IL-6 production. Accordingly, in myeloid- and FcγR-dependent autoantibody-induced arthritis, CGI1746 decreases cytokine levels within joints and ameliorates disease. These results provide new understanding of the function of Btk in both B cell- or myeloid cell-driven disease processes and provide a compelling rationale for targeting Btk in rheumatoid arthritis.

Anti-VEGF antibody therapy does not promote metastasis in genetically engineered mouse tumour models.

Resistance to anti-angiogenic therapy can occur via several potential mechanisms. Unexpectedly, recent studies showed that short-term inhibition of either VEGF or VEGFR enhanced tumour invasiveness and metastatic spread in preclinical models. In an effort to evaluate the translational relevance of these findings, we examined the consequences of long-term anti-VEGF monoclonal antibody therapy in several well-validated genetically engineered mouse tumour models of either neuroendocrine or epithelial origin. Anti-VEGF therapy decreased tumour burden and increased overall survival, either as a single agent or in combination with chemotherapy, in all four models examined. Importantly, neither short- nor long-term exposure to anti-VEGF therapy altered the incidence of metastasis in any of these autochthonous models, consistent with retrospective analyses of clinical trials. In contrast, we observed that sunitinib treatment recapitulated previously reported effects on tumour invasiveness and metastasis in a pancreatic neuroendocrine tumour (PNET) model. Consistent with these results, sunitinib treatment resulted in an up-regulation of the hypoxia marker GLUT1 in PNETs, whereas anti-VEGF did not. These results indicate that anti-VEGF mediates anti-tumour effects and therapeutic benefits without a paradoxical increase in metastasis. Moreover, these data underscore the concept that drugs targeting VEGF ligands and receptors may affect tumour metastasis in a context-dependent manner and are mechanistically distinct from one another.