| Literature DB >> 28879168 |
Kyle M Schachtschneider1, Regina M Schwind1, Jordan Newson2, Nickolas Kinachtchouk2, Mark Rizko3, Nasya Mendoza-Elias3, Paul Grippo4, Daniel R Principe4, Alex Park3, Nana H Overgaard5, Gregers Jungersen5, Kelly D Garcia6, Ajay V Maker7, Laurie A Rund8, Howard Ozer4, Ron C Gaba1, Lawrence B Schook1,8.
Abstract
Despite an improved understanding of cancer molecular biology, immune landscapes, and advancements in cytotoxic, biologic, and immunologic anti-cancer therapeutics, cancer remains a leading cause of death worldwide. More than 8.2 million deaths were attributed to cancer in 2012, and it is anticipated that cancer incidence will continue to rise, with 19.3 million cases expected by 2025. The development and investigation of new diagnostic modalities and innovative therapeutic tools is critical for reducing the global cancer burden. Toward this end, transitional animal models serve a crucial role in bridging the gap between fundamental diagnostic and therapeutic discoveries and human clinical trials. Such animal models offer insights into all aspects of the basic science-clinical translational cancer research continuum (screening, detection, oncogenesis, tumor biology, immunogenicity, therapeutics, and outcomes). To date, however, cancer research progress has been markedly hampered by lack of a genotypically, anatomically, and physiologically relevant large animal model. Without progressive cancer models, discoveries are hindered and cures are improbable. Herein, we describe a transgenic porcine model-the Oncopig Cancer Model (OCM)-as a next-generation large animal platform for the study of hematologic and solid tumor oncology. With mutations in key tumor suppressor and oncogenes, TP53R167H and KRASG12D , the OCM recapitulates transcriptional hallmarks of human disease while also exhibiting clinically relevant histologic and genotypic tumor phenotypes. Moreover, as obesity rates increase across the global population, cancer patients commonly present clinically with multiple comorbid conditions. Due to the effects of these comorbidities on patient management, therapeutic strategies, and clinical outcomes, an ideal animal model should develop cancer on the background of representative comorbid conditions (tumor macro- and microenvironments). As observed in clinical practice, liver cirrhosis frequently precedes development of primary liver cancer or hepatocellular carcinoma. The OCM has the capacity to develop tumors in combination with such relevant comorbidities. Furthermore, studies on the tumor microenvironment demonstrate similarities between OCM and human cancer genomic landscapes. This review highlights the potential of this and other large animal platforms as transitional models to bridge the gap between basic research and clinical practice.Entities:
Keywords: cancer models; clinical needs; oncology; oncopig; pigs; translational medicine
Year: 2017 PMID: 28879168 PMCID: PMC5572387 DOI: 10.3389/fonc.2017.00190
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 6.244
Figure 1Development and utilization of the Oncopig Cancer Model (OCM). (A) Diagram of the Oncopig transgene cassette located on chromosome 18. A Lox-Stop-Lox sequence prevents expression of KRAS and TP53. Exposure to Cre recombinase results in site specific recombination between the two recognition sites (LoxP), resulting in removal of the Stop sequence and subsequent expression of KRAS and TP53. (B) Diagram of the breeding scheme used to produce Oncopigs for experimental use. The original male Oncopig homozygous for an MHC haplotype and carrying a single transgene cassette located on chromosome 18 is bred to a non-transgenic female to produce offspring heterozygous for the transgene cassette and MHC haplotype. The resulting heterozygous offspring are further bred to produce Oncopig offspring homozygous for both the transgene cassette and an MHC haplotype. Male homozygous offspring can then be bred to a variety of transgenic or non-transgenic pig breeds (depicted through varying color) to produce genetically diverse Oncopigs for experimental purposes, all of which harbor a single copy of the transgene cassette and a shared MHC haplotype.
Figure 2Utilization of the Oncopig Cancer Model (OCM) for biomarker discovery and validation. (A) Tumor and blood samples are taken from Oncopigs for biomarker screening studies. Samples are processed immediately (fresh), flash-frozen, or formalin-fixed paraffin-embedded (FFPE). Blood samples can immediately undergo Ficoll density centrifugation for isolation of peripheral blood mononuclear cells (PBMCs). Fresh tumor samples can be used to produce tumor cell lines and isolate tumor-infiltrating lymphocytes (TILs). The flash-frozen samples can be used for deep sequencing (i.e., whole-exome sequencing, RNA-seq, miRNA-seq, and DNA methylation analysis). FFPE samples can be utilized for immunohistochemistry (IHC) of the tumor and the tumor microenvironment. Results obtained from these analyses can be correlated with patient outcomes to identify predictive biomarkers. (B) Proposed testing of clinical management with immunotherapy. As new immunotherapy agents and combinations are developed, optimal combinations and subtype susceptibility must be determined. Patients experiencing durable responses that are sustained even off treatment require new concepts in risk management and mitigation, while making the most of the clinical benefit. Overall, a phased approach can be tested in which aggressive combination regimens that achieve frequent responses can be followed by maintenance with less aggressive and safer regimens, reaching the point of weaning responsive animals off treatment. Identifying biomarkers will be crucial for optimal clinical management. Adapted from Ref. (122).
Potential predictive biomarkers for immunotherapy.
| Type | Source | Biomarker | Clinical significance |
|---|---|---|---|
| Liquid | Serum | IL-6 | High-dose IL-2 treatment failure and shorter overall survival associated with high levels in metastatic renal cell carcinoma |
| CRP | High-dose IL-214 resistance associated with high levels; decreasing levels during ipilimumab therapy associated with disease control and survival | ||
| VEGF | Lack of response to high-dose IL-2 is associated with high levels and decreased overall survival | ||
| LDH | Ipilimumab therapeutic benefit predicted by low pretreatment levels; decreasing levels during ipilimumab therapy associated with disease control and survival | ||
| sCD25 | Ipilimumab therapy resistance predicted by high levels | ||
| NY-SEO-1 antibody | Greater likelihood to respond to CTLA-4 blockade predicted by seropositivity | ||
| Cellular | Peripheral blood | Neutrophils/leukocytes | High-dose IL-2 treatment failure and shorter overall survival associated with high counts |
| Lymphocytes | High-dose IL-2 therapy response associated with immediate lymphocytosis | ||
| CD8+ T cells | Clinical benefit to CTLA-4 blockade associated with presence | ||
| Absolute lymphocyte count | Increasing counts during ipilimumab therapy associated with improved overall survival | ||
| Eosinophils | Increasing counts during ipilimumab therapy associated with improved overall survival | ||
| CD4 + ICOS + T cells | Increase in frequency after ipilimumab therapy | ||
| Myeloid-derived suppressor cells | Ipilimumab therapy benefit predicted by low frequency | ||
| Tumor | PD-L1 | ||
| Tumor-infiltrating lymphocytes | CD4 + ICOShigh T cells | Clinical benefit of ipilimumab correlated with increased frequency | |
| CD8 + T cells | PD-1/PD-L1 expression predicts response to PD-1 blockade | ||
| Genomic | Tumor | Tumor mutation loads | Predicts clinical benefit of ipilimumab and PD-1 blockade |
| Mismatch repair | Predicts clinical benefit of PD-1 blockade | ||
Adapted from Ref. (.
Oncopig cell isolates successfully transformed in vitro.
| Cell type/origin | Isolated | Transformed |
|---|---|---|
| Fibroblasts | Yes | Yes |
| Hepatocytes | Yes | Yes |
| Pancreatic ductal cells | Yes | Yes |
| Dermal epithelial cells | Yes | Yes |
| Splenocytes | Yes | Yes |
| Ovarian surface epithelial cells | Yes | Yes |
| Fallopian tube secretory epithelial cells | Yes | Yes |
| Renal proximal tubule epithelial cells | Yes | Yes |
| Bone marrow (no specific cell isolation) | Yes | Yes |
| Testis (no specific cell isolation) | Yes | Yes |
| Skeletal muscle (no specific cell isolation) | Yes | Yes |
List of OCM cell types for which isolation and transformation have been attempted.