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.

Multi-arm U-Net with dense input and skip connectivity for T2 lesion segmentation in clinical trials of multiple sclerosis.

T2 lesion quantification plays a crucial role in monitoring disease progression and evaluating treatment response in multiple sclerosis (MS). We developed a 3D, multi-arm U-Net for T2 lesion segmentation, which was trained on a large, multicenter clinical trial dataset of relapsing MS. We investigated its generalization to other relapsing and primary progressive MS clinical trial datasets, and to an external dataset from the MICCAI 2016 MS lesion segmentation challenge. Additionally, we assessed the model's ability to reproduce the separation of T2 lesion volumes between treatment and control arms; and the association of baseline T2 lesion volumes with clinical disability scores compared with manual lesion annotations. The trained model achieved a mean dice coefficient of ≥ 0.66 and a lesion detection sensitivity of ≥ 0.72 across the internal test datasets. On the external test dataset, the model achieved a mean dice coefficient of 0.62, which is comparable to 0.59 from the best model in the challenge, and a lesion detection sensitivity of 0.68. Lesion detection performance was reduced for smaller lesions (≤ 30 µL, 3-10 voxels). The model successfully maintained the separation of the longitudinal changes in T2 lesion volumes between the treatment and control arms. Such tools could facilitate semi-automated MS lesion quantification; and reduce rater burden in clinical trials.

Automated segmentation of lungs and lung tumors in mouse micro-CT scans.

Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions.

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.

Deformation-based morphometry identifies deep brain structures protected by ocrelizumab.

BACKGROUND: Despite advancements in treatments for multiple sclerosis, insidious disease progression remains an area of unmet medical need, for which atrophy-based biomarkers may help better characterize the progressive biology. METHODS: We developed and applied a method of longitudinal deformation-based morphometry to provide voxel-level assessments of brain volume changes and identified brain regions that were significantly impacted by disease-modifying therapy. RESULTS: Using brain MRI data from two identically designed pivotal trials of relapsing multiple sclerosis (total N = 1483), we identified multiple deep brain regions, including the thalamus and brainstem, where volume loss over time was reduced by ocrelizumab (p < 0.05), a humanized anti-CD20 + monoclonal antibody approved for the treatment of multiple sclerosis. Additionally, identified brainstem shrinkage, as well as brain ventricle expansion, was associated with a greater risk for confirmed disability progression (p < 0.05). CONCLUSIONS: The identification of deep brain structures has a strong implication for developing new biomarkers of brain atrophy reduction to advance drug development for multiple sclerosis, which has an increasing focus on targeting the progressive biology.

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.

Trem2 restrains the enhancement of tau accumulation and neurodegeneration by ß-amyloid pathology.

Loss-of-function TREM2 mutations strongly increase Alzheimer's disease (AD) risk. Trem2 deletion has revealed protective Trem2 functions in preclinical models of ß-amyloidosis, a prominent feature of pre-diagnosis AD stages. How TREM2 influences later AD stages characterized by tau-mediated neurodegeneration is unclear. To understand Trem2 function in the context of both ß-amyloid and tau pathologies, we examined Trem2 deficiency in the pR5-183 mouse model expressing mutant tau alone or in TauPS2APP mice, in which ß-amyloid pathology exacerbates tau pathology and neurodegeneration. Single-cell RNA sequencing in these models revealed robust disease-associated microglia (DAM) activation in TauPS2APP mice that was amyloid-dependent and Trem2-dependent. In the presence of ß-amyloid pathology, Trem2 deletion further exacerbated tau accumulation and spreading and promoted brain atrophy. Without ß-amyloid pathology, Trem2 deletion did not affect these processes. Therefore, TREM2 may slow AD progression and reduce tau-driven neurodegeneration by restricting the degree to which ß-amyloid facilitates the spreading of pathogenic tau.

A TRPA1 inhibitor suppresses neurogenic inflammation and airway contraction for asthma treatment.

Despite the development of effective therapies, a substantial proportion of asthmatics continue to have uncontrolled symptoms, airflow limitation, and exacerbations. Transient receptor potential cation channel member A1 (TRPA1) agonists are elevated in human asthmatic airways, and in rodents, TRPA1 is involved in the induction of airway inflammation and hyperreactivity. Here, the discovery and early clinical development of GDC-0334, a highly potent, selective, and orally bioavailable TRPA1 antagonist, is described. GDC-0334 inhibited TRPA1 function on airway smooth muscle and sensory neurons, decreasing edema, dermal blood flow (DBF), cough, and allergic airway inflammation in several preclinical species. In a healthy volunteer Phase 1 study, treatment with GDC-0334 reduced TRPA1 agonist-induced DBF, pain, and itch, demonstrating GDC-0334 target engagement in humans. These data provide therapeutic rationale for evaluating TRPA1 inhibition as a clinical therapy for asthma.

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.

Function of CSF1 and IL34 in Macrophage Homeostasis, Inflammation, and Cancer.

Colony-stimulating factor 1 (CSF1) and interleukin 34 (IL34) signal via the CSF1 receptor to regulate macrophage differentiation. Studies in IL34- or CSF1-deficient mice have revealed that IL34 function is limited to the central nervous system and skin during development. However, the roles of IL34 and CSF1 at homeostasis or in the context of inflammatory diseases or cancer in wild-type mice have not been clarified in vivo. By neutralizing CSF1 and/or IL34 in adult mice, we identified that they play important roles in macrophage differentiation, specifically in steady-state microglia, Langerhans cells, and kidney macrophages. In several inflammatory models, neutralization of both CSF1 and IL34 contributed to maximal disease protection. However, in a myeloid cell-rich tumor model, CSF1 but not IL34 was required for tumor-associated macrophage accumulation and immune homeostasis. Analysis of human inflammatory conditions reveals IL34 upregulation that may account for the protection requirement of IL34 blockade. Furthermore, evaluation of IL34 and CSF1 blockade treatment during Listeria infection reveals no substantial safety concerns. Thus, IL34 and CSF1 play non-redundant roles in macrophage differentiation, and therapeutic intervention targeting IL34 and/or CSF1 may provide an effective treatment in macrophage-driven immune-pathologies.

VCAM-1 Density and Tumor Perfusion Predict T-cell Infiltration and Treatment Response in Preclinical Models.

Cancer immunotherapies have demonstrated durable responses in a range of different cancers. However, only a subset of patients responds to these therapies. We set out to test if non-invasive imaging of tumor perfusion and vascular inflammation may be able to explain differences in T-cell infiltration in pre-clinical tumor models, relevant for treatment outcomes. Tumor perfusion and vascular cell adhesion molecule (VCAM-1) density were quantified using magnetic resonance imaging (MRI) and correlated with infiltration of adoptively transferred and endogenous T-cells. MRI biomarkers were evaluated for their ability to detect tumor rejection 3 days after T-cell transfer. Baseline levels of these markers were used to assess their ability to predict PD-L1 treatment response. We found correlations between MRI-derived VCAM-1 density and infiltration of endogenous or adoptively transferred T-cells in some preclinical tumor models. Blocking T-cell binding to endothelial cell adhesion molecules (VCAM-1/ICAM) prevented T-cell mediated tumor rejection. Tumor rejection could be detected 3 days after adoptive T-cell transfer prior to tumor volume changes by monitoring the extracellular extravascular volume fraction. Imaging tumor perfusion and VCAM-1 density before treatment initiation was able to predict the response of MC38 tumors to PD-L1 blockade. These results indicate that MRI based assessment of tumor perfusion and VCAM-1 density can inform about the permissibility of the tumor vasculature for T-cell infiltration which may explain some of the observed variance in treatment response for cancer immunotherapies.

Complement C3 Is Activated in Human AD Brain and Is Required for Neurodegeneration in Mouse Models of Amyloidosis and Tauopathy.

Complement pathway overactivation can lead to neuronal damage in various neurological diseases. Although Alzheimer's disease (AD) is characterized by ß-amyloid plaques and tau tangles, previous work examining complement has largely focused on amyloidosis models. We find that glial cells show increased expression of classical complement components and the central component C3 in mouse models of amyloidosis (PS2APP) and more extensively tauopathy (TauP301S). Blocking complement function by deleting C3 rescues plaque-associated synapse loss in PS2APP mice and ameliorates neuron loss and brain atrophy in TauP301S mice, improving neurophysiological and behavioral measurements. In addition, C3 protein is elevated in AD patient brains, including at synapses, and levels and processing of C3 are increased in AD patient CSF and correlate with tau. These results demonstrate that complement activation contributes to neurodegeneration caused by tau pathology and suggest that blocking C3 function might be protective in AD and other tauopathies.

Therapeutic resistance and susceptibility is shaped by cooperative multi-compartment tumor adaptation.

Emerging research suggests that multiple tumor compartments can influence treatment responsiveness and relapse, yet the search for therapeutic resistance mechanisms remains largely focused on acquired genomic alterations in cancer cells. Here we show how treatment-induced changes occur in multiple tumor compartments during tumor relapse and can reduce benefit of follow-on therapies. By using serial biopsies, next-generation sequencing, and single-cell transcriptomics, we tracked the evolution of multiple cellular compartments within individual lesions during first-line treatment response, relapse, and second-line therapeutic interventions in an autochthonous model of melanoma. We discovered that although treatment-relapsed tumors remained genetically stable, they converged on a shared resistance phenotype characterized by dramatic changes in tumor cell differentiation state, immune infiltration, and extracellular matrix (ECM) composition. Similar alterations in tumor cell differentiation were also observed in more than half of our treatment-relapsed patient tumors. Tumor cell-state changes were coincident with ECM remodeling and increased tumor stiffness, which alone was sufficient to alter tumor cell fate and reduce treatment responses in melanoma cell lines in vitro. Despite the absence of acquired mutations in the targeted pathway, resistant tumors showed significantly decreased responsiveness to second-line therapy intervention within the same pathway. The ability to preclinically model relapse and refractory settings-while capturing dynamics within and crosstalk between all relevant tumor compartments-provides a unique opportunity to better design and sequence appropriate clinical interventions.

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.

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.

Changes in the Synaptic Proteome in Tauopathy and Rescue of Tau-Induced Synapse Loss by C1q Antibodies.

Synapse loss and Tau pathology are hallmarks of Alzheimer's disease (AD) and other tauopathies, but how Tau pathology causes synapse loss is unclear. We used unbiased proteomic analysis of postsynaptic densities (PSDs) in Tau-P301S transgenic mice to identify Tau-dependent alterations in synapses prior to overt neurodegeneration. Multiple proteins and pathways were altered in Tau-P301S PSDs, including depletion of a set of GTPase-regulatory proteins that leads to actin cytoskeletal defects and loss of dendritic spines. Furthermore, we found striking accumulation of complement C1q in the PSDs of Tau-P301S mice and AD patients. At synapses, C1q decorated perisynaptic membranes, accumulated in correlation with phospho-Tau, and was associated with augmented microglial engulfment of synapses and decline of synapse density. A C1q-blocking antibody inhibited microglial synapse removal in cultured neurons and in Tau-P301S mice, rescuing synapse density. Thus, inhibiting complement-mediated synapse removal by microglia could be a potential therapeutic target for Tau-associated neurodegeneration.

Tumor Elastography and Its Association with Collagen and the Tumor Microenvironment.

Purpose: The tumor microenvironment presents with altered extracellular matrix (ECM) and stroma composition, which may affect treatment efficacy and contribute to tissue stiffness. Ultrasound (US) elastography can visualize and quantify tissue stiffness noninvasively. However, the contributions of ECM and stromal components to stiffness are poorly understood. We therefore set out to quantify ECM and stroma density and their relation to tumor stiffness.Experimental Design: A modified clinical ultrasound system was used to measure tumor stiffness and perfusion during tumor growth in preclinical tumor models. In vivo measurements were compared with collagen mass spectroscopy and automatic analysis of matrix and stromal markers derived from immunofluorescence images.Results: US elastography estimates of tumor stiffness were positively correlated with tumor volume in collagen and myofibroblast-rich tumors, while no correlations were found for tumors with low collagen and myofibroblast content. US elastography measurements were strongly correlated with ex vivo mechanical testing and mass spectroscopy-based measurements of total collagen and immature collagen crosslinks. Registration of ultrasound and confocal microscopy data showed strong correlations between blood vessel density and T-cell density in syngeneic tumors, while no correlations were found for genetic tumor models. In contrast to collagen density, which was positively correlated with stiffness, no significant correlations were observed for hyaluronic acid density. Finally, localized delivery of collagenase led to a significant reduction in tumor stiffness without changes in perfusion 24 hours after treatment.Conclusions: US elastography can be used as a potential biomarker to assess changes in the tumor microenvironment, particularly changes affecting the ECM. Clin Cancer Res; 24(18); 4455-67. ©2018 AACR.

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.

Monitoring and Targeting Anti-VEGF Induced Hypoxia within the Viable Tumor by 19F-MRI and Multispectral Analysis.

The effect of anti-angiogenic agents on tumor oxygenation has been in question for a number of years, where both increases and decreases in tumor pO2 have been observed. This dichotomy in results may be explained by the role of vessel normalization in the response of tumors to anti-angiogenic therapy, where anti-angiogenic therapies may initially improve both the structure and the function of tumor vessels, but more sustained or potent anti-angiogenic treatments will produce an anti-vascular response, producing a more hypoxic environment. The first goal of this study was to employ multispectral (MS) 19F-MRI to noninvasively quantify viable tumor pO2 and evaluate the ability of a high dose of an antibody to vascular endothelial growth factor (VEGF) to produce a strong and prolonged anti-vascular response that results in significant tumor hypoxia. The second goal of this study was to target the anti-VEGF induced hypoxic tumor micro-environment with an agent, tirapazamine (TPZ), which has been designed to target hypoxic regions of tumors. These goals have been successfully met, where an antibody that blocks both murine and human VEGF-A (B20.4.1.1) was found by MS 19F-MRI to produce a strong anti-vascular response and reduce viable tumor pO2 in an HM-7 xenograft model. TPZ was then employed to target the anti-VEGF-induced hypoxic region. The combination of anti-VEGF and TPZ strongly suppressed HM-7 tumor growth and was superior to control and both monotherapies. This study provides evidence that clinical trials combining anti-vascular agents with hypoxia-activated prodrugs should be considered to improved efficacy in cancer patients.