Integrating Cancer Vaccines in the Standard-of-Care of Ovarian Cancer: Translating Preclinical Models to Human.
Ontology highlight
ABSTRACT: As the majority of ovarian cancer (OC) patients are diagnosed with metastatic disease, less than 40% will survive past 5 years after diagnosis. OC is characterized by a succession of remissions and recurrences. The most promising time point for immunotherapeutic interventions in OC is following debulking surgery. Accumulating evidence shows that T cells are important in OC; thus, cancer vaccines capable of eliciting antitumor T cells will be effective in OC treatment. In this review, we discuss different cancer vaccines and propose strategies for their incorporation into the OC standard-of-care regimens. Using the murine ID8 ovarian tumor model, we provide evidence that a cancer vaccine can be effectively combined with OC standard-of-care to achieve greater overall efficacy. We demonstrate several important similarities between the ID8 model and OC patients, in terms of response to immunotherapies, and the ID8 model can be an important tool for evaluating combinatorial regimens and clinical trial designs in OC. Other emerging models, including patient-derived xenograft and genetically engineered mouse models, are continuing to improve and can be useful for evaluating cancer vaccination therapies in the near future. Here, we provide a comprehensive review of the completed and current clinical trials evaluating cancer vaccines in OC.
Project description:Immunotherapy has emerged as one of the most promising approaches for ovarian cancer treatment. The tumor microenvironment (TME) is a key factor to consider when stimulating antitumoral responses as it consists largely of tumor promoting immunosuppressive cell types that attenuate antitumor immunity. As our understanding of the determinants of the TME composition grows, we have begun to appreciate the need to address both inter- and intra-tumor heterogeneity, mutation/neoantigen burden, immune landscape, and stromal cell contributions. The majority of immunotherapy studies in ovarian cancer have been performed using the well-characterized murine ID8 ovarian carcinoma model. Numerous other animal models of ovarian cancer exist, but have been underutilized because of their narrow initial characterizations in this context. Here, we describe animal models that may be untapped resources for the immunotherapy field because of their shared genomic alterations and histopathology with human ovarian cancer. We also shed light on the strengths and limitations of these models, and the knowledge gaps that need to be addressed to enhance the utility of preclinical models for testing novel immunotherapeutic approaches.
Project description:Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC.
Project description:Ovarian cancer remains a major issue for gynecological oncologists, and most patients are diagnosed when the disease is already advanced with a poor chance of survival. Debulking surgery followed by platinum-taxane chemotherapy is the current standard of care, but based on several different strategies currently under evaluation, some encouraging data have been published in the last 4 to 5 years. This review provides a state-of-the-art overview of the available alternatives to conventional treatment and the most promising new combinations. For example, neoadjuvant chemotherapy does not seem to be inferior to primary debulking. Despite its outcome improvements, intraperitoneal chemotherapy struggles for acceptance due to the heavy toxicity. Dose-dense chemotherapy, after showing an impressive efficacy in Asian populations, has not produced equal results in a European cohort, and the results of alternative platinum doublets are not superior to those of carboplatin and paclitaxel. In this setting, adherence to a maintenance therapy after first-line treatment and multiple (primarily antiangiogenic) agents appears to be effective. Although many questions, including the duration of maintenance treatment and the use of bevacizumab beyond progression, remain unanswered, new biologic agents, such as poly(ADP-ribose) polymerase (PARP) inhibitors, nintedanib, and mitogen-activated protein/extracellular signal-regulated kinase (MEK) inhibitors, have emerged as potential therapeutic options in the very near future. Based on the multiplicity of available strategies, the histological and molecular features of the tumor, in addition to patient's clinical condition and disease state, continue to gain importance in guiding treatment choices.
Project description:Because of the inherent complexity of coupled nonlinear biological systems, the development of computational models is necessary for achieving a quantitative understanding of their structure and function in health and disease. Statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks. Multiscale modeling links networks to cells, organs, and organ systems. Computational approaches are used to characterize anatomic shape and its variations in health and disease. In each case, the purposes of modeling are to capture all that we know about disease and to develop improved therapies tailored to the needs of individuals. We discuss advances in computational medicine, with specific examples in the fields of cancer, diabetes, cardiology, and neurology. Advances in translating these computational methods to the clinic are described, as well as challenges in applying models for improving patient health.
Project description:Ovarian cancer (OC) represents the most dismal gynecological cancer. Pathobiology is poorly understood, mainly due to lack of appropriate study models. Organoids, defined as self-developing three-dimensional in vitro reconstructions of tissues, provide powerful tools to model human diseases. Here, we established organoid cultures from patient-derived OC, in particular from the most prevalent high-grade serous OC (HGSOC). Testing multiple culture medium components identified neuregulin-1 (NRG1) as key factor in maximizing OC organoid development and growth, although overall derivation efficiency remained moderate (36% for HGSOC patients, 44% for all patients together). Established organoid lines showed patient tumor-dependent morphology and disease characteristics, and recapitulated the parent tumor's marker expression and mutational landscape. Moreover, the organoids displayed tumor-specific sensitivity to clinical HGSOC chemotherapeutic drugs. Patient-derived OC organoids provide powerful tools for the study of the cancer's pathobiology (such as importance of the NRG1/ERBB pathway) as well as advanced preclinical tools for (personalized) drug screening and discovery.
Project description:The power of proteomics allows unparalleled opportunity to query the molecular mechanisms of a malignant cell and the tumor microenvironment in patients with ovarian cancer and other solid tumors. This information has given us insight into the perturbations of signaling pathways within tumor cells and has aided the discovery of new drug targets for the tumor and possible prognostic indicators of outcome and disease response to therapy. Proteomics analysis of serum and ascites has also given us sources with which to discover possible early markers for the presence of new disease and for the progression of established cancer throughout the course of treatment. Unfortunately, this wealth of information has yielded little to date in changing the clinical care of these patients from a diagnostic, prognostic, or treatment perspective. The rational examination and translation of proteomics data in the context of past clinical trials and the design of future clinical trials must occur before we can march forward into the future of personalized medicine.
Project description:PurposePatient-derived tumor xenografts (PDXs) can provide more reliable information about tumor biology than cell line models. We developed PDXs for epithelial ovarian cancer (EOC) that have histopathologic and genetic similarities to the primary patient tissues and evaluated their potential for use as a platform for translational EOC research.Materials and methodsWe successfully established PDXs by subrenal capsule implantation of primary EOC tissues into female BALB/C-nude mice. The rate of successful PDX engraftment was 48.8% (22/45 cases). Hematoxylin and eosin staining and short tandem repeat analysis showed histopathological and genetic similarity between the PDX and primary patient tissues.ResultsPatients whose tumors were successfully engrafted in mice had significantly inferior overall survival when compared with those whose tumors failed to engraft (p=0.040). In preclinical tests of this model, we found that paclitaxel-carboplatin combination chemotherapy significantly deceased tumor weight in PDXs compared with the control treatment (p=0.013). Moreover, erlotinib treatment significantly decreased tumor weight in epidermal growth factor receptor-overexpressing PDX with clear cell histology (p=0.023).ConclusionPDXs for EOC with histopathological and genetic stability can be efficiently developed by subrenal capsule implantation and have the potential to provide a promising platform for future translational research and precision medicine for EOC.
Project description:Preclinical models of alcohol use disorder (AUD) have advanced theoretical, mechanistic, and pharmacological study of the human condition. "Liking" and "wanting" behaviors reflect core processes underlying several models of AUD. However, the development and application of translational models of these preclinical approaches are at an incipient stage. The goal of this study was to examine how intravenous free-access and progressive-ratio, operant-response human alcohol self-administration paradigms can be used as translational human model parallels of preclinical "liking" and "wanting." Participants were 40 adults (mean age = 23.7, SD = 2.0; 45% female) of European descent who reported 12.6 drinking days (SD = 5.2) out of the previous 30 (average = 4.1 drinks per drinking day [SD = 1.7]). Individuals diverged in their alcohol self-administration behavior, such that free-access and progressive-ratio paradigm outcomes were not significantly correlated (p = 0.44). Free-access alcohol seeking was related to enjoying alcohol (p < 0.001), but not craving (p = 0.48), whereas progressive-ratio seeking at similar levels of alcohol exposure was related to craving (p = 0.02), but not enjoying (p = 0.30). Family history of alcoholism, venturesomeness traits, and disinhibition traits were unrelated (ps > 0.70) to preferred level of breath alcohol concentration (BrAC) in the free-access session, a measure of liking alcohol. Family history of alcoholism, disinhibition traits, and recent drinking history were significantly related (ps < 0.05) to alcohol seeking in the progressive-ratio paradigm, a measure of wanting alcohol. We conclude that intravenous alcohol self-administration paradigms show promise in modeling behaviors that characterize and parallel alcohol "liking" and "wanting" in preclinical models. These paradigms provide a translational link between preclinical methods and clinical trials.
Project description:ObjectiveThe value of cell lines for pre-clinical work lies in choosing those with similar characteristics. Selection of cell lines is typically based on patient history, histological subtype at diagnosis, mutation patterns, or signaling pathways. Although recent studies established consensus regarding molecular characteristics of ovarian cancer cell lines, data on in vivo tumorigenicity remains only sporadically available, impeding translation of in vitro work to xenograft models.MethodsWe introduced 18 ovarian cancer cell lines into athymic nude mice through subcutaneous, intraperitoneal, and ovary intrabursal routes, and observed tumor development over 6weeks. We also profiled cell line gene expression and identified differentially expressed gene sets based on their ability to form tumors in the subcutaneous or intraperitoneal locations. Representative cell lines were further subjected to proteomic analyses.ResultsOvarian cancer cell lines showed variable ability to grow in mice when implanted subcutaneous, intraperitoneal, or intrabursal. While some cell lines grew well in both SC and IP locations, others showed a strong propensity to grow in one location only. Gene expression profiles suggested that cell lines showing preference for IP growth had gene expression patterns more similar to primary tumors.ConclusionsWe report the tumorigenicity of 17 human ovarian cancer cell lines and one mouse cell line in three distinct anatomical locations, and associated gene networks. Growth patterns and histopathology, linked to molecular characteristics, provide a valuable resource to the research community, and better guide the choice of cell lines for in vitro studies to translate efficiently into xenograft testing.
Project description:IntroductionNovel therapies that effectively kill both differentiated cancer cells and cancer initiating cells (CICs), which are implicated in causing chemotherapy-resistance and disease recurrence, are needed to reduce the morbidity and mortality of ovarian cancer. These studies used monoclonal antibody (mAb) 376.96, which recognizes a B7-H3 epitope expressed on ovarian cancer cells and CICs, as a carrier molecule for targeted α-particle radioimmunotherapy (RIT) in preclinical models of human ovarian cancer.MethodsmAb 376.96 was conjugated to the chelate 2-(4-isothiocyanotobenzyl)-1,4,7,10-tetraaza-1,4,7,10-tetra-(2-carbamoylmethyl)-cyclododecane (TCMC) and radiolabeled with 212Pb, a source of α-particles. In vitro Scatchard assays determined the specific binding of 212Pb-376.96 to adherent differentiated or non-adherent CIC-enriched ES-2 and A2780cp20 ovarian cancer cells. Adherent ovarian cancer cells and non-adherent CIC-enriched tumorspheres treated in vitro with 212Pb-376.96 or the irrelevant isotype-matched 212Pb-F3-C25 were assessed for clonogenic survival. Mice bearing i.p. ES-2 or A2780cp20 xenografts were injected i.p. with 0.17-0.70MBq 212Pb-376.96 or 212Pb-F3-C25 and were used for in vivo imaging, ex vivo biodistribution, and therapeutic survival studies.Results212Pb-376.96 was obtained in high yield and purity (>98%); Kd values ranged from 10.6-26.6nM for ovarian cancer cells, with 104-105 binding sites/cell. 212Pb-376.96 inhibited the clonogenic survival of ovarian cancer cells up to 40 times more effectively than isotype-matched control 212Pb-F3-C25; combining 212Pb-376.96 with carboplatin significantly decreased clonogenic survival compared to either agent alone. In vivo imaging and biodistribution analysis 24h after i.p. injection of 212Pb-376.96 showed high peritoneal retention and tumor tissue accumulation (28.7% ID/g in ES-2 ascites, 73.1% ID/g in A2780cp20 tumors); normal tissues showed lower and comparable uptake for 212Pb-376.96 and 212Pb-F3-C25. Tumor-bearing mice treated with 212Pb-376.96 alone or combined with carboplatin survived 2-3 times longer than mice treated with 212Pb-F3-C25 or non-treated controls.ConclusionThese results support additional RIT studies with 212Pb-376.96 for future evaluation in patients with ovarian cancer.