Abstract

Vascular disrupting agents (VDAs) have entered clinical trials for over 15 years. As the leading VDA, combretastatin A4 phosphate (CA4P) has been evaluated in combination with chemotherapy and molecular targeting agents among patients with ovarian cancer, lung cancer and thyroid cancer, but still remains rarely explored in human liver cancers. To overcome tumor residues and regrowth after CA4P monotherapy, a novel dual targeting pan-anticancer theragnostic strategy, i.e., OncoCiDia, has been developed and shown promise previously in secondary liver tumor models. Animal model of primary liver cancer is time consuming to induce, but of value for more closely mimicking human liver cancers in terms of tumor angiogenesis, histopathological heterogeneity, cellular differentiation, tumor components, cancer progression and therapeutic response. Being increasingly adopted in VDA researches, multiparametric magnetic resonance imaging (MRI) provides imaging biomarkers to reflect in vivo tumor responses to drugs. In this article as a chapter of a doctoral thesis, we overview the construction and clinical relevance of primary and secondary liver cancer models in rodents. Target selection for CA4P therapy assisted by enhanced MRI using hepatobiliary contrast agents (CAs), and therapeutic efficacy evaluated by using MRI with a non-specific contrast agent, dynamic contrast enhanced (DCE) imaging, diffusion weighted imaging (DWI) are also described. We then summarize diverse responses among primary hepatocellular carcinomas (HCCs), secondary liver and pancreatic tumors to CA4P, which appeared to be related to tumor size, vascularity, and cellular differentiation. In general, imaging-histopathology correlation studies allow to conclude that CA4P tends to be more effective in secondary liver tumors and in more differentiated HCCs, but less effective in less differentiated HCCs and implanted pancreatic tumor. Notably, cirrhotic liver may be responsive to CA4P as well. All these could be instructive for future clinical trials of VDAs.

Highlights

  • As a potential class of anticancer therapy, vascular disrupting agents (VDAs) have been vigorously explored over the past 20 years [1,2,3]

  • The findings suggest that expanded tumor compressed local hepatic vein (HV)

  • This phenomenon might be explained by two possibilities: 1) these large blood vessels could originate from existing hepatic vessels where the normal tubulin cytoskeleton is not affected by Combretastatin A4 phosphate (CA4P); and 2) such wide tumor vasculature was targeted by CA4P, but only partially vascular shutdown occurred because of the enlarged lumen, while remaining blood flow could still feed the associated tumor cells

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Summary

Introduction

As a potential class of anticancer therapy, vascular disrupting agents (VDAs) have been vigorously explored over the past 20 years [1,2,3]. Dynamic contrast enhanced (DCE)-MRI contributes to evaluation of tumor vascular properties such as blood flow, blood volume, vascular permeability and extravascular extracellular space [37,38,39], whereas apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) helps to distinguish the cytolytic necrosis of less restricted diffusion caused by therapy from the non-responded viable tumoral cells [29,31,40,41,42] In this context, methodologically ensured imaging-histopathology co-localization has been proven crucial to the longstanding reliability of experimental interpretations and final research conclusions [34,35]

Animal Models of Primary and Secondary Liver Cancers Usable for VDAs Studies
Primary Liver Cancer Model in Rats
Complex
Clinical
Hepatobiliary CAs
Nonspecific MRI CA
Dynamic
Update of Antitumor Effects of CA4P in Primary and Secondary Liver Cancers
CA4P Dose-Related Efficacy
Vascularity and Differentiation of HCCs in Relation to CA4P Efficacies
Distinct Volume-Efficacy Relation between Micro-HCCs and Macro-HCCs
Study Limitations and Practical
Future Perspectives of VDA in Experimental Liver Cancers
Conclusions
Findings
A Dual-targeting Anticancer Approach
Full Text
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