Spontaneous and Induced Animal Models for Cancer Research

Onaciu, Anca; Munteanu, Raluca; Munteanu, V.C.; Gulei, Diana; Ráduly, Lajos; Pîrlog, Radu; Atanasov, Atanas G.; Korban, Schuyler S.; Irimie, Alexandru; Berindan‐Neagoe, Ioana

doi:10.3390/diagnostics10090660

Cited by 55 publications

(40 citation statements)

References 255 publications

(267 reference statements)

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“…The pathogenesis of OVC is complicated and, therefore, improved animal models are required to investigate the underlying mechanisms of OVC. Nowadays, more induced and less spontaneous models of small animals were used for disease researches, however spontaneous cancer model similar with human metabolism can offer more translatable results ( 32 ). In the present study, four M. fortis females with spontaneous OVC were identified, some of which were hereditary.…”

Section: Discussionmentioning

confidence: 99%

De novo assembly and transcriptome characterization: Novel insights into the mechanisms of primary ovarian cancer in Microtus fortis

Gao²,

Du³

et al. 2021

Mol Med Rep

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The natural incidence of primary epithelial ovarian cancer (oVc) in adult female voles of some established strains of Microtus fortis is relatively high. M. fortis oVc has some pathological similarities to human epithelial oVc, therefore M. fortis represents the latest and most valuable animal model for studying human oVc. The lack of available genetic information for M. fortis limits the use of common immunological methods; thus, high-throughput sequencing technologies have been used to reveal the mechanisms of primary oVc in M. fortis. The individuals with cancer were diagnosed using histopathologic hematoxylin and eosin staining. The present study used rna-sequencing (rna-seq) technology to establish a de novo assembly of the M. fortis transcriptome produced 339,830 unigenes by the short reads assembly program Trinity. comparisons were made between oVc and healthy ovarian tissue (oV) and between fallopian tube cancer (FTc) and healthy fallopian tube (FT) tissues using rna-seq analysis. a total of 3,434 differentially expressed genes (deGs) were identified in oVc tissue compared with oV tissue using rna-Seq by Expectation-Maximization software, including 1,950 significantly upregulated and 1,484 significantly downregulated genes. There were 2,817 DEGs identified in the FTC tissues compared with the FT tissue, including 1,762 significantly upregulated and 1,055 significantly downregulated genes.Pathway enrichment analysis revealed that upregulated transcripts in the oVc vs. oV groups were involved in cell growth and proliferation-associated pathways, whereas the downregulated deGS in the oVc vs. oV groups were enriched in steroid biosynthesis-related pathways. Furthermore, the tumor suppressor gene, p53, was downregulated in the FTc and oVc compared with the FT and oV groups, respectively; whereas, genes that promoted cell migration, such as ras-related protein rap-1b, ras homolog family member a and rac1, were upregulated. in summary, to the best of our knowledge, the present study characterized the M. fortis de novo transcriptome of oV and FT tissues and to perform RNA-seq quantification to analyze the differences in healthy and cancerous oV and FT tissues. These results identified pathways that differed between cancerous and healthy M. fortis tissues. analysis of these pathways may help to reveal the pathogenesis of primary oVc in M. fortis in future work.

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Section: Discussionmentioning

confidence: 99%

De novo assembly and transcriptome characterization: Novel insights into the mechanisms of primary ovarian cancer in Microtus fortis

Gao²,

Du³

et al. 2021

Mol Med Rep

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“…Induced models have gained recognition in the recent years due to ease and availability of various protocols and techniques. Further, unlike transgenic models that may cause a shortened lifespan, this approach can be used to grow UMs at different sites at various stages of development in animals ( 121 ).…”

Section: Genetically Engineered Models (Gems)mentioning

confidence: 99%

Animal models of uveal melanoma

Uner¹,

Gandrakota²,

Azarcon³

et al. 2022

Ann Eye Sci

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Animal models are crucial for the study of tumorigenesis and therapies in oncology research. Though rare, uveal melanoma (UM) is the most common intraocular tumor and remains one of the most lethal cancers. Given the limitations of studying human UM cells in vitro, animal models have emerged as excellent platforms to investigate disease onset, progression, and metastasis. Since Greene’s initial studies on hamster UM, researchers have dramatically improved the array of animal models. Animals with spontaneous tumors have largely been replaced by engrafted and genetically engineered models. Inoculation techniques continue to be refined and expanded. Newer methods for directed mutagenesis have formed transgenic models to reliably study primary tumorigenesis. Human UM cell lines have been used to generate rapidly growing xenografts. Most recently, patient-derived xenografts have emerged as models that closely mimic the behavior of human UM. Separate animal models to study metastatic UM have also been established. Despite the advancements, the prognosis has only recently improved for UM patients, especially in patients with metastases. There is a need to identify and evaluate new preclinical models. To accomplish this goal, it is important to understand the origin, methods, advantages, and disadvantages of current animal models. In this review, the authors present current and historic animal models for the experimental study of UM. The strengths and shortcomings of each model are discussed and potential future directions are explored.

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“…Circumventing some of these difficulties, experimental genetic models have been developed to assess the interplay between distinct molecular lesions and between cancer cells and the TME during cancer progression. Among those, diverse mice models have been generated via a variety of methods, including chemical or physical mutagenesis (gamma rays, X-rays, UV-light, and particle radiation), viral infection, insertion of transgenes, homologous recombination, and, more recently, genome editing by the CRISPR technology [ 20 ]. These models can mirror complex mutational patterns observed in tumour samples, as well as interactions taking place within a microenvironment.…”

Section: Oncogenesis: a Dynamic Process Of Multifactorial Naturementioning

confidence: 99%

In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer

Selvaggio

Chaouiya

Janody

2021

IJMS

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The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.

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Spontaneous and Induced Animal Models for Cancer Research

Cited by 55 publications

References 255 publications

De novo assembly and transcriptome characterization: Novel insights into the mechanisms of primary ovarian cancer in Microtus fortis

De novo assembly and transcriptome characterization: Novel insights into the mechanisms of primary ovarian cancer in Microtus fortis

Animal models of uveal melanoma

In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer

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