Generation of autochthonous mouse models of clear cell renal cell carcinoma: mouse models of renal cell carcinoma

Hou, Weibin; Ji, Zhigang

doi:10.1038/s12276-018-0059-4

Cited by 21 publications

(17 citation statements)

References 82 publications

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“…Indeed transgenic mice with reporter alleles display specific expression of PAX8 in adult PTC and FTSEC 20,36 . Importantly, VHL inactivation 36 , when combined with other genetic lesions in Pax8-expressing cells, leads to neoplastic lesions of different stage and penetrance 37,38 suggesting proximal tubules as a potential source of RCC.…”

Section: Discussionmentioning

confidence: 99%

PAX8 activates metabolic genes via enhancer elements in Renal Cell Carcinoma

et al. 2019

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Transcription factor networks shape the gene expression programs responsible for normal cell identity and pathogenic state. Using Core Regulatory Circuitry analysis (CRC), we identify PAX8 as a candidate oncogene in Renal Cell Carcinoma (RCC) cells. Validation of large-scale functional genomic screens confirms that PAX8 silencing leads to decreased proliferation of RCC cell lines. Epigenomic analyses of PAX8-dependent cistrome demonstrate that PAX8 largely occupies active enhancer elements controlling genes involved in various metabolic pathways. We selected the ferroxidase Ceruloplasmin (CP) as an exemplary gene to dissect PAX8 molecular functions. PAX8 recruits histone acetylation activity at bound enhancers looping onto the CP promoter. Importantly, CP expression correlates with sensitivity to PAX8 silencing and identifies a subset of RCC cases with poor survival. Our data identifies PAX8 as a candidate oncogene in RCC and provides a potential biomarker to monitor its activity.

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Section: Discussionmentioning

confidence: 99%

PAX8 activates metabolic genes via enhancer elements in Renal Cell Carcinoma

et al. 2019

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“…“Mouse models” recapitulate physiological variables contributing to disease progression, which are absent in vitro and allow for the derivation of clinically relevant outputs. We describe briefly a few mouse models and assays employed in tumor biology; interested readers may refer to other detailed articles [70,71,72]. The metastatic cascade is routinely captured by spontaneous models for metastasis that involves ectopic/orthotopic tumor cell transplantation in immunocompromised mice.…”

Section: Snapshot Assays For Metastasis Assessmentmentioning

confidence: 99%

Uncoupling Traditional Functionalities of Metastasis: The Parting of Ways with Real-Time Assays

Varankar

Bapat

2019

JCM

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The experimental evaluation of metastasis overly focuses on the gain of migratory and invasive properties, while disregarding the contributions of cellular plasticity, extra-cellular matrix heterogeneity, niche interactions, and tissue architecture. Traditional cell-based assays often restrict the inclusion of these processes and warrant the implementation of approaches that provide an enhanced spatiotemporal resolution of the metastatic cascade. Time lapse imaging represents such an underutilized approach in cancer biology, especially in the context of disease progression. The inclusion of time lapse microscopy and microfluidic devices in routine assays has recently discerned several nuances of the metastatic cascade. Our review emphasizes that a complete comprehension of metastasis in view of evolving ideologies necessitates (i) the use of appropriate, context-specific assays and understanding their inherent limitations; (ii) cautious derivation of inferences to avoid erroneous/overestimated clinical extrapolations; (iii) corroboration between multiple assay outputs to gauge metastatic potential; and (iv) the development of protocols with improved in situ implications. We further believe that the adoption of improved quantitative approaches in these assays can generate predictive algorithms that may expedite therapeutic strategies targeting metastasis via the development of disease relevant model systems. Such approaches could potentiate the restructuring of the cancer metastasis paradigm through an emphasis on the development of next-generation real-time assays.

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“…In addition, no suitable genetically engineered mouse model (GEMM) for RCC that recapitulates with fidelity the metastatic process exists to date [30][31][32][33] . It is true that around 10 different GEMMs for RCC have been developed 34 37 . The HR reported in their article is lower than in our study.…”

Section: Discussionmentioning

confidence: 99%

A multi-layered systems approach for renal cell carcinoma

Rudewicz

Souleyreau

et al. 2020

Preprint

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Acknowledgments:This work was supported by grants from the PlanCancer («Systems Biology of Renal Cell Carcinoma using an Experimental RCC model» (C18005GS, SystemsRCC)) and from the university Bordeaux (G2P project) to AB. LC and AE were supported by post-doctoral fellowships from the Region "Nouvelle Aquitaine". The Crispr/cas9 construct was gratefully provided by Michel Tremblay (Goodman Cancer Center, McGill, University). The authors would like to thank the lentiviral production plateform Vect'UB for EGFP lentivirus, the "service commun des animaleries" for animal production and housing, and the Bordeaux Imaging Center (BIC). 2 AbstractIn order to discover molecular pathways and players in renal cancer development and metastasis, we developed a mouse model to generate sequentially more aggressive and specialized cell lines. Multiple cell lines for primary tumor growth, survival in the blood circulation and lung metastasis or metastatic spread from the primary tumor were generated and analyzed using a multi-layered approach which includes large-scale transcriptome, genome and methylome analyses. Transcriptome and methylome analyses demonstrated distinct clustering in three different groups. Remarkably, DNA sequencing did not show significant genomic variations in the different groups which indicates absence of clonal selection during the in vivo amplification process. Transcriptome analysis revealed several markers and signatures of tumor aggressiveness which were validated, at the mRNA and protein level, in patient cohorts from TCGA, local biobanks and clinical trials. This also includes soluble markers. In particular, SAA2 and CFB were highly predictive for survival and tumor progression. Methylome analysis of full-length DNA allowed clustering of the same groups and revealed clinically predictive signatures. We also uncovered IL34 as a key regulator of renal cell carcinoma (RCC) progression which was also functionally validated in vivo, and a mathematical model of IL34-dependent primary tumor growth and metastasis development was provided. These results indicate that such multilayered analysis in a RCC animal model leads to meaningful results that are of translational significance. 3

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Generation of autochthonous mouse models of clear cell renal cell carcinoma: mouse models of renal cell carcinoma

Cited by 21 publications

References 82 publications

PAX8 activates metabolic genes via enhancer elements in Renal Cell Carcinoma

PAX8 activates metabolic genes via enhancer elements in Renal Cell Carcinoma

Uncoupling Traditional Functionalities of Metastasis: The Parting of Ways with Real-Time Assays

A multi-layered systems approach for renal cell carcinoma

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