Quantification of viable tumor microvascular characteristics by multispectral analysis

Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data

In oncology, intratumoural heterogeneity is closely linked with the efficacy of therapy, and can be partially characterized via tumour biopsies. Here we show that intratumoural heterogeneity can be characterized spatially via phenotype-specific, multi-view learning classifiers trained with data from dynamic positron emission tomography (PET) and multiparametric magnetic resonance imaging (MRI). Classifiers trained with PET-MRI data from mice with subcutaneous colon cancer quantified phenotypic changes resulting from an apoptosis-inducing targeted therapeutic and provided biologically relevant probability maps of tumour-tissue subtypes. When applied to retrospective PET-MRI data of patients with liver metastases from colorectal cancer, the trained classifiers characterized intratumoural tissue subregions in agreement with tumour histology. The spatial characterization of intratumoural heterogeneity in mice and patients via multimodal, multiparametric imaging aided by machine-learning may facilitate applications in precision oncology.

Image-based modeling of vascular organization to evaluate anti-angiogenic therapy

In tumor therapy anti-angiogenic approaches have the potential to increase the efficacy of a wide variety of subsequently or co-administered agents, possibly by improving or normalizing the defective tumor vasculature. Successful implementation of the concept of vascular normalization under anti-angiogenic therapy, however, mandates a detailed understanding of key characteristics and a respective scoring metric that defines an improved vasculature and thus a successful attempt. Here, we show that beyond commonly used parameters such as vessel patency and maturation, anti-angiogenic approaches largely benefit if the complex vascular network with its vessel interconnections is both qualitatively and quantitatively assessed. To gain such deeper insight the organization of vascular networks, we introduce a multi-parametric evaluation of high-resolution angiographic images based on light-sheet fluorescence microscopy images of tumors. We first could pinpoint key correlations between vessel length, straightness and diameter to describe the regular, functional and organized structure observed under physiological conditions. We found that vascular networks from experimental tumors diverted from those in healthy organs, demonstrating the dysfunctionality of the tumor vasculature not only on the level of the individual vessel but also in terms of inadequate organization into larger structures. These parameters proofed effective in scoring the degree of disorganization in different tumor entities, and more importantly in grading a potential reversal under treatment with therapeutic agents. The presented vascular network analysis will support vascular normalization assessment and future optimization of anti-angiogenic therapy.

Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver “virtual biopsies” within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.

Habitat Imaging of Tumors Enables High Confidence Sub-Regional Assessment of Response to Therapy

Imaging biomarkers are used in therapy development to identify and quantify therapeutic response. In oncology, use of MRI, PET and other imaging methods can be complicated by spatially complex and heterogeneous tumor micro-environments, non-Gaussian data and small sample sizes. Linear Poisson Modelling (LPM) enables analysis of complex data that is quantitative and can operate in small data domains. We performed experiments in 5 mouse models to evaluate the ability of LPM to identify responding tumor habitats across a range of radiation and targeted drug therapies. We tested if LPM could identify differential biological response rates. We calculated the theoretical sample size constraints for applying LPM to new data. We then performed a co-clinical trial using small data to test if LPM could detect multiple therapeutics with both improved power and reduced animal numbers compared to conventional t-test approaches. Our data showed that LPM greatly increased the amount of information extracted from diffusion-weighted imaging, compared to cohort t-tests. LPM distinguished biological response rates between Calu6 tumors treated with 3 different therapies and between Calu6 tumors and 4 other xenograft models treated with radiotherapy. A simulated co-clinical trial using real data detected high precision per-tumor treatment effects in as few as 3 mice per cohort, with p-values as low as 1 in 10,000. These findings provide a route to simultaneously improve the information derived from preclinical imaging while reducing and refining the use of animals in cancer research.

Using Variable Flip Angle (VFA) and Modified Look-Locker Inversion Recovery (MOLLI) T1 mapping in clinical OE-MRI

BACKGROUND AND PURPOSE

The imaging technique known as Oxygen-Enhanced MRI is under development as a noninvasive technique for imaging hypoxia in tumours and pulmonary diseases. While promising results have been shown in preclinical experiments, clinical studies have mentioned experiencing difficulties with patient motion, image registration, and the limitations of single-slice images compared to 3D volumes. As clinical studies begin to assess feasibility of using OE-MRI in patients, it is important for researchers to communicate about the practical challenges experienced when using OE-MRI on patients to help the technique advance.

MATERIALS AND METHODS

We report on our experience with using two types of T1 mapping (MOLLI and VFA) for a recently completed OE-MRI clinical study on oropharyngeal squamous cell carcinoma.

RESULTS

We report: (1) the artefacts and practical difficulties encountered in this study; (2) the difference in estimated T1 from each method used - the VFA T1 estimation was higher than the MOLLI estimation by 27% on average; (3) the standard deviation within the tumour ROIs - there was no significant difference in the standard deviation seen within the tumour ROIs from the VFA versus MOLLI; and (4) the OE-MRI response collected from either method. Lastly, we collated the MRI acquisition details from over 45 relevant manuscripts as a convenient reference for researchers planning future studies.

CONCLUSION

We have reported our practical experience from an OE-MRI clinical study, with the aim that sharing this is helpful to researchers planning future studies. In this study, VFA was a more useful technique for using OE-MRI in tumours than MOLLI T1 mapping.

Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma

PURPOSE

This work aims to identify intratumoral habitats with distinct heterogeneity based on 2-deoxy-2-[¹⁸F]fluro-D-glucose positron emission tomography (PET)/X-ray computed tomography (CT) imaging, and to develop a subregional radiomics approach to predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC).

PROCEDURES

In total, 128 NPC patients (85 vs. 43 for primary vs. validation cohorts) who underwent pre-treatment PET/CT scan were enrolled retrospectively. Each tumor was partitioned into several phenotypically consistent subregions based on individual- and population-level clustering. For each subregion, 202 radiomics features were extracted to construct imaging biomarker for prognosis via Cox's proportional hazard model combined with forward stepwise feature selection. Relevance of imaging biomarkers and clinicopathological factors were assessed by multivariate Cox regression analysis and Spearman's correlation analysis. To investigate whether imaging biomarkers could provide complementary prognosis information beyond existing predictors, a scoring system was further developed for risk stratification and compared with AJCC staging system.

RESULTS

Three subregions (denoted as S1, S2, and S3) were discovered with distinct PET/CT imaging characteristics in the two cohorts. The prognostic performance of imaging biomarker S3 outperformed the whole tumor (C-index, 0.69 vs. 0.58; log-rank test, p < 0.001 vs. p = 0.552). Imaging biomarker S3 and AJCC stage were identified as independent predictors (p = 0.011 and 0.042, respectively) after adjusting for clinicopathological factors. The scoring system outperformed the traditional AJCC staging system (log-rank test, p < 0.0001 vs. p = 0.0002 in primary cohort and p = 0.0021 vs. p = 0.0277 in validation cohort, respectively).

CONCLUSIONS

Subregional radiomics analysis of PET/CT imaging has the potential to predict PFS in patients with NPC, which also provides complementary prognostic information for traditional predictors.

Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p < 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer.

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.

Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models

It is well-recognized that solid tumors are genomically, anatomically, and physiologically heterogeneous. In general, more heterogeneous tumors have poorer outcomes, likely due to the increased probability of harboring therapy-resistant cells and regions. It is hypothesized that the genomic and physiologic heterogeneity are related, because physiologically distinct regions will exert variable selection pressures leading to the outgrowth of clones with variable genomic/proteomic profiles. To investigate this, methods must be in place to interrogate and define, at the microscopic scale, the cytotypes that exist within physiologically distinct subregions ("habitats") that are present at mesoscopic scales. MRI provides a noninvasive approach to interrogate physiologically distinct local environments, due to the biophysical principles that govern MRI signal generation. Here, we interrogate different physiologic parameters, such as perfusion, cell density, and edema, using multiparametric MRI (mpMRI). Signals from six different acquisition schema were combined voxel-by-voxel into four clusters identified using a Gaussian mixture model. These were compared with histologic and IHC characterizations of sections that were coregistered using MRI-guided 3D printed tumor molds. Specifically, we identified a specific set of MRI parameters to classify viable-normoxic, viable-hypoxic, nonviable-hypoxic, and nonviable-normoxic tissue types within orthotopic 4T1 and MDA-MB-231 breast tumors. This is the first coregistered study to show that mpMRI can be used to define physiologically distinct tumor habitats within breast tumor models. SIGNIFICANCE: This study demonstrates that noninvasive imaging metrics can be used to distinguish subregions within heterogeneous tumors with histopathologic correlation.

Data-driven identification of tumor subregions based on intravoxel incoherent motion reveals association with proliferative activity

PURPOSE

Intravoxel incoherent motion (IVIM) analysis gives information on tissue diffusion and perfusion and may thus have a potential for e.g. tumor tissue characterization. This work aims to study if clustering based on IVIM parameter maps can identify tumor subregions, and to assess the relevance of obtained subregions by histological analysis.

METHODS

Fourteen mice with human neuroendocrine tumors were examined with diffusion-weighted imaging to obtain IVIM parameter maps. Gaussian mixture models with IVIM maps from all tumors as input were used to partition voxels into k clusters, where k = 2 was chosen for further analysis based on goodness of fit. Clustering was performed with and without the perfusion-related IVIM parameter D * , and with and without including spatial information. The validity of the clustering was assessed by comparison with corresponding histologically stained tumor sections. A Ki-67-based index quantifying the degree of tumor proliferation was considered appropriate for the comparison based on the obtained cluster characteristics.

RESULTS

The clustering resulted in one class with low diffusion and high perfusion and another with slightly higher diffusion and low perfusion. Strong agreement was found between tumor subregions identified by clustering and subregions identified by histological analysis, both regarding size and spatial agreement. Neither D * nor spatial information had substantial effects on the clustering results.

CONCLUSIONS

The results of this study show that IVIM parameter maps can be used to identify tumor subregions using a data-driven framework based on Gaussian mixture models. In the studied tumor model, the obtained subregions showed agreement with proliferative activity.

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

Probabilistic classification of tumour habitats in soft tissue sarcoma

The purpose of this work is to propose a method to characterize tumour heterogeneity on MRI, using probabilistic classification based on a reference tissue. The method uses maps of the apparent diffusion coefficient (ADC), T₂ relaxation, and a calculated map representing high-b-value diffusion-weighted MRI (denoted simDWI) to identify up to five habitats (i.e. sub-regions) of tumours. In this classification method, the parameter values (ADC, T₂ , and simDWI) from each tumour voxel are compared against the corresponding parameter probability distributions in a reference tissue. The probability that a tumour voxel belongs to a specific habitat is the joint probability for all parameters. The classification can be visualized using a custom colour scheme. The proposed method was applied to data from seven patients with biopsy-confirmed soft tissue sarcoma, at three time-points over the course of pre-operative radiotherapy. Fast-spin-echo images with two different echo times and diffusion MRI with three b-values were obtained and used as inputs to the method. Imaging findings were compared with pathology reports from pre-radiotherapy biopsy and post-surgical resection. Regions of hypercellularity, high-T₂ proteinaceous fluid, necrosis, collagenous stroma, and fibrosis were identified within soft tissue sarcoma. The classifications were qualitatively consistent with pathological observations. The percentage of necrosis on imaging correlated strongly with necrosis estimated from FDG-PET before radiotherapy (R²  = 0.97) and after radiotherapy (R²  = 0.96). The probabilistic classification method identifies realistic habitats and reflects the complex microenvironment of tumours, as demonstrated in soft tissue sarcoma.

Data-driven mapping of hypoxia-related tumor heterogeneity using DCE-MRI and OE-MRI

PURPOSE

Previous work has shown that combining dynamic contrast-enhanced (DCE)-MRI and oxygen-enhanced (OE)-MRI binary enhancement maps can identify tumor hypoxia. The current work proposes a novel, data-driven method for mapping tissue oxygenation and perfusion heterogeneity, based on clustering DCE/OE-MRI data.

METHODS

DCE-MRI and OE-MRI were performed on nine U87 (glioblastoma) and seven Calu6 (non-small cell lung cancer) murine xenograft tumors. Area under the curve and principal component analysis features were calculated and clustered separately using Gaussian mixture modelling. Evaluation metrics were calculated to determine the optimum feature set and cluster number. Outputs were quantitatively compared with a previous non data-driven approach.

RESULTS

The optimum method located six robustly identifiable clusters in the data, yielding tumor region maps with spatially contiguous regions in a rim-core structure, suggesting a biological basis. Mean within-cluster enhancement curves showed physiologically distinct, intuitive kinetics of enhancement. Regions of DCE/OE-MRI enhancement mismatch were located, and voxel categorization agreed well with the previous non data-driven approach (Cohen's kappa = 0.61, proportional agreement = 0.75).

CONCLUSION

The proposed method locates similar regions to the previous published method of binarization of DCE/OE-MRI enhancement, but renders a finer segmentation of intra-tumoral oxygenation and perfusion. This could aid in understanding the tumor microenvironment and its heterogeneity. Magn Reson Med 79:2236-2245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Purpose

We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies.

Procedures

Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2–3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology.

Results

While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (r_necrotic = 0.92, r_{peri-necrotic} = 0.82 and r_viable = 0.98) with the histology.

Conclusions

The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures.

Electronic supplementary material

The online version of this article (doi:10.1007/s11307-016-1009-y) contains supplementary material, which is available to authorized users.

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.

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.

Antagonist antibodies to vascular endothelial growth factor receptor 2 (VEGFR-2) as anti-angiogenic agents

Interaction of numerous signaling pathways in endothelial and mesangial cells results in exquisite control of the process of physiological angiogenesis, with a central role played by vascular endothelial growth factor receptor 2 (VEGFR-2) and its cognate ligands. However, deregulated angiogenesis participates in numerous pathological processes. Excessive activation of VEGFR-2 has been found to mediate tissue-damaging vascular changes as well as the induction of blood vessel expansion to support the growth of solid tumors. Consequently, therapeutic intervention aimed at inhibiting the VEGFR-2 pathway has become a mainstay of treatment in cancer and retinal diseases. In this review, we introduce the concepts of physiological and pathological angiogenesis, the crucial role played by the VEGFR-2 pathway in these processes, and the various inhibitors of its activity that have entered the clinical practice. We primarily focus on the development of ramucirumab, the antagonist monoclonal antibody (mAb) that inhibits VEGFR-2 and has recently been approved for use in patients with gastric, colorectal, and lung cancers. We examine in-depth the pre-clinical studies using DC101, the mAb to mouse VEGFR-2, which provided a conceptual foundation for the role of VEGFR-2 in physiological and pathological angiogenesis. Finally, we discuss further clinical development of ramucirumab and the future of targeting the VEGF pathway for the treatment of cancer.

Assessment of Tumor Angiogenesis: Dynamic Contrast-enhanced MR Imaging and Beyond

Dynamic contrast-enhanced (DCE) MR imaging is used increasingly often to evaluate tumor angiogenesis and the efficacy of antiangiogenic drugs. In clinical practice DCE-MR imaging applications are largely centered on lesion detection, characterization, and localization. In research, DCE-MR imaging helps inform decision making in early-phase clinical trials by showing efficacy and by selecting dose and schedule. However, the role of these techniques in patient selection is uncertain. Future research is required to optimize existing DCE-MR imaging methods and to fully validate these biomarkers for wider use in patient care and in drug development.

A Population-Based Gaussian Mixture Model Incorporating 18F-FDG PET and Diffusion-Weighted MRI Quantifies Tumor Tissue Classes

The aim of our study was to create a novel Gaussian mixture modeling (GMM) pipeline to model the complementary information derived from(18)F-FDG PET and diffusion-weighted MRI (DW-MRI) to separate the tumor microenvironment into relevant tissue compartments and follow the development of these compartments longitudinally.

METHODS

Serial (18)F-FDG PET and apparent diffusion coefficient (ADC) maps derived from DW-MR images of NCI-H460 xenograft tumors were coregistered, and a population-based GMM was implemented on the complementary imaging data. The tumor microenvironment was segmented into 3 distinct regions and correlated with histology. ANCOVA was applied to gauge how well the total tumor volume was a predictor for the ADC and (18)F-FDG, or if ADC was a good predictor of (18)F-FDG for average values in the whole tumor or average necrotic and viable tissues.

RESULTS

The coregistered PET/MR images were in excellent agreement with histology, both visually and quantitatively, and allowed for validation of the last-time-point measurements. Strong correlations were found for the necrotic (r = 0.88) and viable fractions (r = 0.87) between histology and clustering. The GMM provided probabilities for each compartment with uncertainties expressed as a mixture of tissues in which the resolution of scans was inadequate to accurately separate tissues. The ANCOVA suggested that both ADC and (18)F-FDG in the whole tumor (P = 0.0009, P = 0.02) as well as necrotic (P = 0.008, P = 0.02) and viable (P = 0.003, P = 0.01) tissues were a positive, linear function of total tumor volume. ADC proved to be a positive predictor of (18)F-FDG in the whole tumor (P = 0.001) and necrotic (P = 0.02) and viable (P = 0.0001) tissues.

CONCLUSION

The complementary information of (18)F-FDG and ADC longitudinal measurements in xenograft tumors allows for segmentation into distinct tissues when using the novel GMM pipeline. Leveraging the power of multiparametric PET/MRI in this manner has the potential to take the assessment of disease outcome beyond RECIST and could provide an important impact to the field of precision medicine.

Cluster analysis of quantitative parametric maps from DCE-MRI: application in evaluating heterogeneity of tumor response to antiangiogenic treatment

PURPOSE

The objective of this study was to compare a clustering approach to conventional analysis methods for assessing changes in pharmacokinetic parameters obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during antiangiogenic treatment in a breast cancer model.

MATERIALS AND METHODS

BALB/c mice bearing established transplantable her2+ tumors were treated with a DNA-based antiangiogenic vaccine or with an empty plasmid (untreated group). DCE-MRI was carried out by administering a dose of 0.05 mmol/kg of Gadocoletic acid trisodium salt, a Gd-based blood pool contrast agent (CA) at 1T. Changes in pharmacokinetic estimates (K(trans) and vp) in a nine-day interval were compared between treated and untreated groups on a voxel-by-voxel analysis. The tumor response to therapy was assessed by a clustering approach and compared with conventional summary statistics, with sub-regions analysis and with histogram analysis.

RESULTS

Both the K(trans) and vp estimates, following blood-pool CA injection, showed marked and spatial heterogeneous changes with antiangiogenic treatment. Averaged values for the whole tumor region, as well as from the rim/core sub-regions analysis were unable to assess the antiangiogenic response. Histogram analysis resulted in significant changes only in the vp estimates (p<0.05). The proposed clustering approach depicted marked changes in both the K(trans) and vp estimates, with significant spatial heterogeneity in vp maps in response to treatment (p<0.05), provided that DCE-MRI data are properly clustered in three or four sub-regions.

CONCLUSIONS

This study demonstrated the value of cluster analysis applied to pharmacokinetic DCE-MRI parametric maps for assessing tumor response to antiangiogenic therapy.

Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care.

Integrative analysis of cancer imaging readouts by networks

Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time‐space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node‐ and/or edge‐valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre‐clinical and clinical impact.

Computational Multiplexing Imaging merges imaging and networks.

Networks show signatures of tumor heterogeneity and phenotypic profiles observed in‐vivo.

A profile ensemble establishes a tumor fingerprint, and this constitutes a novel type of marker.

Personalized treatment is embedded in a systems medicine approach.

Mapping in vivo tumor oxygenation within viable tumor by 19F-MRI and multispectral analysis

Quantifying oxygenation in viable tumor remains a major obstacle toward a better understanding of the tumor micro-environment and improving treatment strategies. Current techniques are often complicated by tumor heterogeneity. Herein, a novel in vivo approach that combines (19)F magnetic resonance imaging ((19)F-MRI) R 1 mapping with diffusion-based multispectral (MS) analysis is introduced. This approach restricts the partial pressure of oxygen (pO2) measurements to viable tumor, the tissue of therapeutic interest. The technique exhibited sufficient sensitivity to detect a breathing gas challenge in a xenograft tumor model, and the hypoxic region measured by MS (19)F-MRI was strongly correlated with histologic estimates of hypoxia. This approach was then applied to address the effects of antivascular agents on tumor oxygenation, which is a research question that is still under debate. The technique was used to monitor longitudinal pO2 changes in response to an antibody to vascular endothelial growth factor (B20.4.1.1) and a selective dual phosphoinositide 3-kinase/mammalian target of rapamycin inhibitor (GDC-0980). GDC-0980 reduced viable tumor pO2 during a 3-day treatment period, and a significant reduction was also produced by B20.4.1.1. Overall, this method provides an unprecedented view of viable tumor pO2 and contributes to a greater understanding of the effects of antivascular therapies on the tumor's microenvironment.

Indexed distribution analysis for improved significance testing of spatially heterogeneous parameter maps: application to dynamic contrast-enhanced MRI biomarkers

PURPOSE

To develop significance testing methodology applicable to spatially heterogeneous parametric maps of biophysical and physiological measurements arising from imaging studies.

THEORY

Heterogeneity can confound statistical analyses. Indexed distribution analysis (IDA) transforms a reference distribution, establishing correspondences across parameter maps to which significance tests are applied.

METHODS

Well-controlled simulated and clinical K(trans) data from a dynamic contrast-enhanced magnetic resonance imaging study of bevacizumab were analyzed using conventional significance tests of parameter averages, histogram analysis, and IDA. Repeated pretreatment scans provided negative control; a post treatment scan provided positive control.

RESULTS

Histogram analysis was insensitive to simulated and known effects. Simulation: conventional analysis identified treatment effect (P ≈ 5 × 10(-4)) and direction, but underestimated magnitude (relative error 67-81%); IDA identified treatment effect (P = 0.001), magnitude, direction, and spatial extent (100% accuracy). Bevacizumab: conventional analysis was sensitive to treatment effect (P = 0.01; 95% confidence interval on K(trans) decrease: 23-37%); IDA was sensitive to treatment effect (P < 0.05; K(trans) decrease approximately 25%), inferred its spatial extent to be 94-96%, and inferred that K(trans) decrease is independent of baseline value, an inference that conventional and histogram analyses cannot make.

CONCLUSIONS

In the presence of heterogeneity, IDA can accurately infer the magnitude, direction, and spatial extent of between samples of parametric maps, which can be visualized spatially with respect to the original parameter maps.

Multimodal microvascular imaging reveals that selective inhibition of class I PI3K is sufficient to induce an antivascular response

The phosphatidylinositol 3-kinase (PI3K) pathway is a central mediator of vascular endothelial growth factor (VEGF)-driven angiogenesis. The discovery of small molecule inhibitors that selectively target PI3K or PI3K and mammalian target of rapamycin (mTOR) provides an opportunity to pharmacologically determine the contribution of these key signaling nodes in VEGF-A-driven tumor angiogenesis in vivo. This study used an array of micro-vascular imaging techniques to monitor the antivascular effects of selective class I PI3K, mTOR, or dual PI3K/mTOR inhibitors in colorectal and prostate cancer xenograft models. Micro-computed tomography (micro-CT) angiography, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), vessel size index (VSI) MRI, and DCE ultrasound (DCE-U/S) were employed to quantitatively evaluate the vascular (structural and physiological) response to these inhibitors. GDC-0980, a dual PI3K/mTOR inhibitor, was found to reduce micro-CT angiography vascular density, while VSI MRI demonstrated a significant reduction in vessel density and an increase in mean vessel size, consistent with a loss of small functional vessels and a substantial antivascular response. DCE-MRI showed that GDC-0980 produces a strong functional response by decreasing the vascular permeability/perfusion-related parameter, K (trans). Interestingly, comparable antivascular effects were observed for both GDC-980 and GNE-490 (a selective class I PI3K inhibitor). In addition, mTOR-selective inhibitors did not affect vascular density, suggesting that PI3K inhibition is sufficient to generate structural changes, characteristic of a robust antivascular response. This study supports the use of noninvasive microvascular imaging techniques (DCE-MRI, VSI MRI, DCE-U/S) as pharmacodynamic assays to quantitatively measure the activity of PI3K and dual PI3K/mTOR inhibitors in vivo.

Tumor-stroma interaction in orthotopic primary pancreatic cancer xenografts during hedgehog pathway inhibition

To test the effects of hedgehog (Hh) pathway inhibition on the stroma of orthotopically grown primary pancreatic cancer xenografts, and investigate the potential to monitor these effects non-invasively using magnetic resonance imaging (MRI), mice bearing orthotopically grown primary pancreatic cancer xenografts were treated with the Hh neutralizing antibody 5E1. Pathway inhibition was determined by RT-PCR using primer sets for human and mouse Hh pathway genes, and effects on stroma assessed by automated image analysis of tissue sections stained for collagen and α-smooth muscle actin (αSMA). MRI provided quantitative biomarkers of stromal density based on magnetization transfer (MT-MRI) and dynamic contrast enhancement (DCE-MRI). Modest growth inhibition was seen in both models tested using 5E1, but was greater in OCIP19, which showed high expression of mouse Hh pathway genes and an extensive fibrous stroma. However, despite profound inhibition of both mouse and human Hh pathway genes, in neither model did we observe depletion of the stroma. Alignment of MT-MRI ratio images to histological sections showed co-registration with areas of fibrosis, although this was confounded by the presence of tumor necrosis. Due to the lack of stromal depletion by 5E1 it was not possible to determine the utility of MT-MRI for monitoring this effect. Cancer- and stromal cell-derived Hh signaling elements are expressed in orthotopic primary pancreatic cancer xenografts, and selective targeting is growth-inhibitory. In contrast to some recent reports, growth inhibition does not involve attenuation of the tumor stroma, pointing to additional effects of Hh signaling in pancreatic cancer.

Response of HT29 colorectal xenograft model to cediranib assessed with 18 F-fluoromisonidazole positron emission tomography, dynamic contrast-enhanced and diffusion-weighted MRI

Cediranib is a small-molecule pan-vascular endothelial growth factor receptor inhibitor. The tumor response to short-term cediranib treatment was studied using dynamic contrast-enhanced and diffusion-weighted MRI at 7 T, as well as (18) F-fluoromisonidazole positron emission tomography and histological markers. Rats bearing subcutaneous HT29 human colorectal tumors were imaged at baseline; they then received three doses of cediranib (3 mg/kg per dose daily) or vehicle (dosed daily), with follow-up imaging performed 2 h after the final cediranib or vehicle dose. Tumors were excised and evaluated for the perfusion marker Hoechst 33342, the endothelial cell marker CD31, smooth muscle actin, intercapillary distance and tumor necrosis. Dynamic contrast-enhanced MRI-derived parameters decreased significantly in cediranib-treated tumors relative to pretreatment values [the muscle-normalized initial area under the gadolinium concentration curve decreased by 48% (p=0.002), the enhancing fraction by 43% (p=0.003) and K(trans) by 57% (p=0.003)], but remained unchanged in controls. No change between the pre- and post-treatment tumor apparent diffusion coefficients in either the cediranib- or vehicle-treated group was observed over the course of this study. The (18) F-fluoromisonidazole mean standardized uptake value decreased by 33% (p=0.008) in the cediranib group, but showed no significant change in the control group. Histological analysis showed that the number of CD31-positive vessels (59 per mm(2) ), the fraction of smooth muscle actin-positive vessels (80-87%) and the intercapillary distance (0.17 mm) were similar in cediranib- and vehicle-treated groups. The fraction of perfused blood vessels in cediranib-treated tumors (81 ± 7%) was lower than that in vehicle controls (91 ± 3%, p=0.02). The necrotic fraction was slightly higher in cediranib-treated rats (34 ± 12%) than in controls (26 ± 10%, p=0.23). These findings suggest that short-term treatment with cediranib causes a decrease in tumor perfusion/permeability across the tumor cross-section, but changes in vascular morphology, vessel density or tumor cellularity are not manifested at this early time point.

Do imaging biomarkers relate to outcome in patients treated with VEGF inhibitors?

The management of solid tumors has been transformed by the advent of VEGF pathway inhibitors. Early clinical evaluation of these drugs has used pharmacodynamic biomarkers derived from advanced imaging such as dynamic MRI, computed tomography (CT), and ultrasound to establish proof of principle. We have reviewed published studies that used these imaging techniques to determine whether the same biomarkers relate to survival in renal, hepatocellular, and brain tumors in patients treated with VEGF inhibitors. Data show that in renal cancer, pretreatment measurements of K(trans) and early pharmacodynamic reduction in tumor enhancement and density have prognostic significance in patients treated with VEGF inhibitors. A weaker, but significant, relationship is seen with subtle early size change (10% in one dimension) and survival. Data from high-grade glioma suggest that pretreatment fractional blood volume and K(trans) were prognostic of overall survival. However, lack of control data with other therapies prevents assessment of the predictive nature of these biomarkers, and such studies are urgently required.

Colon tumor growth and antivascular treatment in mice: complementary assessment with MR elastography and diffusion-weighted MR imaging

PURPOSE

To investigate the potential value of magnetic resonance (MR) elastography and diffusion-weighted (DW) MR imaging in the detection of microstructural changes of murine colon tumors during growth and antivascular treatment.

MATERIALS AND METHODS

The study was approved by the regional ethics committee for animal care. Sixty Balb-C mice, bearing ectopic and orthotopic colon tumors, were monitored for 3 weeks with high-resolution T2-weighted MR imaging, three-dimensional steady-state MR elastography, and DW MR imaging at 7 T. The same imaging protocol was performed 24 hours after injection of combretastatin A4 phosphate (CA4P) in 12 mice. The absolute value of the complex shear modulus (|G*|) and the apparent diffusion coefficient (ADC) were measured in the viable zones of tumors and compared with microvessel density (MVD), cellularity, and micronecrosis by using the Pearson correlation coefficient.

RESULTS

During tumor growth, |G*| increase was correlated with MVD (r = 0.70 [P = .08] and r = 0.78 [P = .002], for both the ectopic and orthotopic models, respectively). Moreover, the ectopic tumors displayed decreased ADC, which correlated with increased cellularity (r = 0.77, P = .04), whereas no changes in ADC and cellularity were observed in orthotopic tumors. After CA4P administration, |G*| decreased in the ectopic model (P < .0001), similar to the MVD evolution (P = .03), whereas no significant changes in |G*| (P = .7) and MVD (P = .6) were observed in the orthotopic model. ADC increased in both models (P = .047 and P = .01 for the ectopic and the orthotopic models, respectively) in relation to increased micronecrosis.

CONCLUSION

Imaging of mechanical properties and diffusivity provide complementary information during tumor growth and regression that are respectively linked to vascularity and tumor cell alterations, including cellularity and micronecrosis.

Quantification of antiangiogenic and antivascular drug activity by kinetic analysis of DCE-MRI data

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to quantify the response of tumors to vascular targeting agents. Tumor response is frequently assessed using mathematical models that describe the distribution of contrast agents over time as a function of fundamental characteristics of vascular physiology. Generally, mathematical models of biological systems are abstractions that attempt to retain fundamental physiologic characteristics, thereby allowing for multiple potential modeling approaches and structures. Various DCE-MRI modeling techniques are discussed in this article.

Quantitative MR imaging: physical principles and sequence design in abdominal imaging

Quantitative magnetic resonance (MR) imaging seeks to quantify fundamental biologic and MR-inducible tissue properties, in contrast to the routine application of MR imaging in the clinic, in which differences in MR parameters are used to generate contrast for subsequent subjective image analysis. Fundamental parameters that are commonly quantified by using MR imaging include proton density, diffusion, T1 relaxation, T2 and T2* relaxation, and magnetization transfer. Applications of these MR imaging-quantifiable parameters to abdominal imaging include oncologic imaging, evaluation of diffuse liver disease, and assessment of splenic, renal, and pancreatic disease. An understanding of the inherent physical principles underlying the basic quantitative parameters as well as the commonly used pulse sequences requisite to their derivation is critical, as this field is rapidly growing and its use will likely continue to expand in the clinic. The full potential of quantitative MR imaging applied to abdominal imaging has yet to be realized, but the myriad applications reported to date will undoubtedly continue to grow.

Erratum to: Ungersma SE, Pacheco G, Ho C, Yee SF, Ross J, van Bruggen N, Peale FV Jr, Ross S, Carano RA. Vessel imaging with viable tumor analysis for quantification of tumor angiogenesis. Magn Reson Med 2010;63:1637–1647

Imaging of tumor microvasculature has become an important tool for studying angiogenesis and monitoring antiangiogenic therapies. Ultrasmall paramagnetic iron oxide contrast agents for indirect imaging of vasculature offer a method for quantitative measurements of vascular biomarkers such as vessel size index, blood volume, and vessel density (Q). Here, this technique is validated with direct comparisons to ex vivo micro-computed tomography angiography and histologic vessel measurements, showing significant correlations between in vivo vascular MRI measurements and ex vivo structural vessel measurements. The sensitivity of the MRI vascular parameters is also demonstrated, in combination with a multispectral analysis technique for segmenting tumor tissue to restrict the analysis to viable tumor tissue and exclude regions of necrosis. It is shown that this viable tumor segmentation increases sensitivity for detection of significant effects on blood volume and Q by two antiangiogenic therapeutics [anti-vascular endothelial growth factor (anti-VEGF) and anti-neuropilin-1] on an HM7 colorectal tumor model. Anti-vascular endothelial growth factor reduced blood volume by 36±3% (p<0.0001) and Q by 52±3% (p<0.0001) at 48 h post-treatment; the effects of anti-neuropilin-1 were roughly half as strong with a reduction in blood volume of 18±6% (p<0.05) and a reduction in Q of 33±5% (p<0.05) at 48 h post-treatment.

Cross-visit tumor sub-segmentation and registration with outlier rejection for dynamic contrast-enhanced MRI time series data

Clinical trials of anti-angiogenic and vascular-disrupting agents often use biomarkers derived from DCE-MRI, typically reporting whole-tumor summary statistics and so overlooking spatial parameter variations caused by tissue heterogeneity. We present a data-driven segmentation method comprising tracer-kinetic model-driven registration for motion correction, conversion from MR signal intensity to contrast agent concentration for cross-visit normalization, iterative principal components analysis for imputation of missing data and dimensionality reduction, and statistical outlier detection using the minimum covariance determinant to obtain a robust Mahalanobis distance. After applying these techniques we cluster in the principal components space using k-means. We present results from a clinical trial of a VEGF inhibitor, using time-series data selected because of problems due to motion and outlier time series. We obtained spatially-contiguous clusters that map to regions with distinct microvascular characteristics. This methodology has the potential to uncover localized effects in trials using DCE-MRI-based biomarkers.

Multiparametric imaging of tumor response to therapy

There is an increasing opportunity to perform multifunctional imaging at a variety of organ sites with relatively short examination times. Each technique yields quantitative parameters that reflect specific aspects of the underlying tumor or tissue biology. Many biomarkers have emerged that provide unique information on tumor behavior, including response to treatment. The multiparametric approach combines the information from different functional imaging techniques; this goes beyond what can be achieved by using any single functional technique, thus allowing an improved understanding of biologic processes and of responses to therapeutic interventions. Multiparametric imaging has many potential clinical roles; it is useful for pharmaceutical drug development and for predicting therapeutic efficacy.

Quantifying antivascular effects of monoclonal antibodies to vascular endothelial growth factor: insights from imaging

PURPOSE

Little is known concerning the onset, duration, and magnitude of direct therapeutic effects of anti-vascular endothelial growth factor (VEGF) therapies. Such knowledge would help guide the rational development of targeted therapeutics from bench to bedside and optimize use of imaging technologies that quantify tumor function in early-phase clinical trials.

EXPERIMENTAL DESIGN

Preclinical studies were done using ex vivo microcomputed tomography and in vivo ultrasound imaging to characterize tumor vasculature in a human HM-7 colorectal xenograft model treated with the anti-VEGF antibody G6-31. Clinical evaluation was by quantitative magnetic resonance imaging in 10 patients with metastatic colorectal cancer treated with bevacizumab.

RESULTS

Microcomputed tomography experiments showed reduction in perfused vessels within 24 to 48 h of G6-31 drug administration (P

CONCLUSION

These data suggest that VEGF-specific inhibition induces rapid structural and functional effects with downstream significant antitumor activity within one cycle of therapy. This finding has important implications for the design of early-phase clinical trials that incorporate physiologic imaging. The study shows how animal data help interpret clinical imaging data, an important step toward the validation of image biomarkers of tumor structure and function.

Collapse

Key Words

• angiogenesis
• biomarker
• imaging
• vegf

Collapse

MESH Headings

• Adolescent
• Angiogenesis Inhibitors/therapeutic use
• Animals
• Antibodies, Monoclonal/pharmacology
• Antibodies, Monoclonal/therapeutic use
• Antibodies, Monoclonal, Humanized
• Bevacizumab
• Cell Line, Tumor
• Colorectal Neoplasms/blood supply
• Colorectal Neoplasms/drug therapy
• Diagnostic Imaging
• Drug Delivery Systems
• Female
• Humans
• Mice
• Mice, Nude
• Neovascularization, Pathologic/drug therapy
• Vascular Endothelial Growth Factor A/immunology
• Xenograft Model Antitumor Assays

Collapse

Grants

• A6086 Cancer Research UK
• G0902173 Medical Research Council
• C19221/A6086 Cancer Research UK

Collapse

Affiliation(s)

James P B O'Connor Imaging Science and Biomedical Engineering, School of Cancer and Imaging Sciences, University of Manchester, Manchester, United Kingdom. james.o'
Richard A D Carano
Andrew R Clamp
Jed Ross
Calvin C K Ho
Alan Jackson
Geoff J M Parker
Chris J Rose
Franklin V Peale
Michel Friesenhahn
Claire L Mitchell
Yvonne Watson
Caleb Roberts
Lynn Hope
Sue Cheung
Hani Bou Reslan
Mary Ann T Go
Glenn J Pacheco
Xiumin Wu
Tim C Cao
Sarajane Ross
Giovanni A Buonaccorsi
Karen Davies
Jurjees Hasan
Paula Thornton
Olivia del Puerto
Napoleone Ferrara
Nicholas van Bruggen
Gordon C Jayson

Collapse