Mice Are Not Humans: The Case of p53
Authors: Martin Fischer
Journal: Trends in cancer (2020)

Mouse models represent powerful tools in cancer research that have influenced much of our understanding of tumor development and our current anticancer strategies. Yet, central signaling pathways differ considerably between mouse and human, including gene regulatory networks (GRNs) of the most important tumor suppressor p53 and its oncogenic sibling p63. Mouse models represent powerful tools in cancer research that have influenced much of our understanding of tumor development and our current anticancer strategies. Yet, central signaling pathways differ considerably between mouse and human, including gene regulatory networks (GRNs) of the most important tumor suppressor p53 and its oncogenic sibling p63. Mouse models are widely used in basic cancer research and preclinical cancer studies, despite their limited translation potential being extensively discussed [1.Gould S.E. et al.Translational value of mouse models in oncology drug development.Nat. Med. 2015; 21: 431-439Crossref PubMed Scopus (187) Google Scholar,2.Rangarajan A. Weinberg R.A. Comparative biology of mouse versus human cells: modelling human cancer in mice.Nat. Rev. Cancer. 2003; 3: 952-959Crossref PubMed Scopus (420) Google Scholar]. Murine models remain an approximation of the human system, as mice are not only smaller and die at a younger age; they also have an increased metabolic rate and develop a different spectrum of tumors than humans do. Luckily, humans are less susceptible to cancer development than mice. Transcriptional landscapes are important in cancer research. Transcription factors represent key nodes in most oncogenic signaling pathways, and they shape transcriptional landscapes through their gene regulatory networks (GRNs), which comprise all their differentially regulated target genes. Yet, the Mouse ENCODE project reported significant differences in cis-regulatory regions, which recruit transcription factors to DNA to regulate target gene expression, between mice and humans. For example, only ~36% of mouse DNAse I hypersensitive sites (DHSs), which reflect open chromatin regions that are accessible to productive transcription factor binding, and ~22% of mouse transcription factor footprints, which are DNA segments that are protected by transcription factor binding, were found to be conserved in humans [3.Vierstra J. et al.Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.Science. 2014; 346: 1007-1012Crossref PubMed Scopus (162) Google Scholar,4.Stergachis A.B. et al.Conservation of trans-acting circuitry during mammalian regulatory evolution.Nature. 2014; 515: 365-370Crossref PubMed Scopus (152) Google Scholar]. Consequently, several transcription factors, including important regulators of cancer development, such as MYC, display very limited conservation of their binding site repertoires [4.Stergachis A.B. et al.Conservation of trans-acting circuitry during mammalian regulatory evolution.Nature. 2014; 515: 365-370Crossref PubMed Scopus (152) Google Scholar,5.Cheng Y. et al.Principles of regulatory information conservation between mouse and human.Nature. 2014; 515: 371-375Crossref PubMed Scopus (180) Google Scholar]. Similarly, the FANTOM project revealed that more than 56% of promoters that regulate protein-coding genes have diverged between mouse and human [6.Young R.S. et al.The frequent evolutionary birth and death of functional promoters in mouse and human.Genome Res. 2015; 25: 1546Crossref PubMed Scopus (30) Google Scholar]. Recent studies have revealed that GRNs of the well-studied p53 transcription factor family members p53 and p63 differ substantially between mouse and human [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar,8.Sethi I. et al.Evolutionary re-wiring of p63 and the epigenomic regulatory landscape in keratinocytes and its potential implications on species-specific gene expression and phenotypes.Nucleic Acids Res. 2017; 45: 8208-8224Crossref PubMed Scopus (20) Google Scholar]. Given that p63 (ΔNp63) serves a crucial role in squamous cell carcinoma development and p53 is key to the development of almost all tumors, the extensive difference in their GRNs, through which they mediate their function, potentially affects the successful translation of many preclinical results from mouse models to humans. To improve the translation of anticancer strategies and to enable a better assessment of preclinical data from mouse models, it is important to gain a more complete picture of shared and distinct processes between the two species. In human cancer no other gene is mutated as often as is TP53, which encodes for the p53 tumor suppressor. In addition to p53, the p53 transcription factor family comprises p53's ancient siblings p63 and p73. All three family member have multiple N- and C-terminal isoforms that bind to overlapping targets but can elicit different functions. While full-length p53 serves as a universal tumor suppressor, full-length p73 is a key regulator of multiciliated cell differentiation, full-length p63 ensures quality control in germ line cells, and ΔNp63 functions in epidermal development and as an oncogene in squamous cell carcinomas. Because of their highly conserved DNA binding domain (DBD), p53 homologs from multiple species can specifically trans-activate a common cis-regulatory element, the p53 response element (p53RE). The p53 family DBD recognizes two decameric half sites with consensus sequences that differ slightly between the family members: p53 prefers RRRCWWGYYY, while p63 binds RRRCNNGYYY (R=A/G, W=A/T, Y=C/T, N=A/T/G/C) [9.Riege K. et al.Dissecting the DNA binding landscape and gene regulatory network of p63 and p53.bioRxiv. 2020; (Published online June 12, 2020. https://doi.org/10.1101/2020.06.11.145540)Google Scholar]. Notably, most p53REs also function as p63RE and many p63REs serve as p53REs. Together, transcription factors of the p53 family share many binding sites but regulate largely nonoverlapping gene sets. Given the shared binding sites and the possibility of p53 family members – particularly p63 and p73 – to form heterotetramers, presumably all p53 family members are affected by the evolutionary turnover of p53REs and p63REs that has been observed. Cis-regulatory elements mediate transcription factor DNA binding, and functionally active cis-regulatory elements are often associated with open chromatin. Yet, ENCODE data showed that most open chromatin regions, as assayed by DHSs and transcription factor footprints, are not conserved between mouse and human [3.Vierstra J. et al.Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution.Science. 2014; 346: 1007-1012Crossref PubMed Scopus (162) Google Scholar,4.Stergachis A.B. et al.Conservation of trans-acting circuitry during mammalian regulatory evolution.Nature. 2014; 515: 365-370Crossref PubMed Scopus (152) Google Scholar]. The recent global analyses of the p53 and p63 GRN identified species-specific p53REs and p63REs as the main source underlying the mouse- and human-specific DNA binding landscape and ultimately the GRN of p53 and p63 [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar,8.Sethi I. et al.Evolutionary re-wiring of p63 and the epigenomic regulatory landscape in keratinocytes and its potential implications on species-specific gene expression and phenotypes.Nucleic Acids Res. 2017; 45: 8208-8224Crossref PubMed Scopus (20) Google Scholar]. Two distinct processes shape the species-specific cis-regulatory elements. First, a considerable number of species-specific cis-regulatory elements has been introduced during evolution through transposable elements, including p53REs in long terminal repeats in the human genome [10.Bourque G. et al.Evolution of the mammalian transcription factor binding repertoire via transposable elements.Genome Res. 2008; 18: 1752-1762Crossref PubMed Scopus (348) Google Scholar] and in fuzzy tandem repeats in the mouse genome [11.Simeonova I. et al.Fuzzy tandem repeats containing p53 response elements may define species-specific p53 target genes.PLoS Genet. 2012; 8e1002731Crossref PubMed Scopus (14) Google Scholar]. Indeed, ~39% of mouse and ~48% of human p53 binding sites were identified in regions that are not present in the other species (Figure 1) [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar]. Similarly, ~23% of identified mouse p63 binding sites resided in regions that could not be aligned to the human genome [8.Sethi I. et al.Evolutionary re-wiring of p63 and the epigenomic regulatory landscape in keratinocytes and its potential implications on species-specific gene expression and phenotypes.Nucleic Acids Res. 2017; 45: 8208-8224Crossref PubMed Scopus (20) Google Scholar]. Second, the evolutionary turnover – the birth and death – of cis-regulatory elements altered the binding profiles of p53 and p63 in genomic regions that are shared between mouse and human [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar,8.Sethi I. et al.Evolutionary re-wiring of p63 and the epigenomic regulatory landscape in keratinocytes and its potential implications on species-specific gene expression and phenotypes.Nucleic Acids Res. 2017; 45: 8208-8224Crossref PubMed Scopus (20) Google Scholar]. In the case of p53, only ~9% of mouse p53 binding sites were found to be conserved in their respective human genomic region (~5% of total mouse p53 peaks; Figure 1A) [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar]. Similarly, only ~25% of mouse p63 binding sites were also occupied by p63 in their corresponding human region [8.Sethi I. et al.Evolutionary re-wiring of p63 and the epigenomic regulatory landscape in keratinocytes and its potential implications on species-specific gene expression and phenotypes.Nucleic Acids Res. 2017; 45: 8208-8224Crossref PubMed Scopus (20) Google Scholar]. Loss of p53 and p63 binding to the corresponding region in the other species typically coincided with loss of a p53RE and p63RE, respectively [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar,8.Sethi I. et al.Evolutionary re-wiring of p63 and the epigenomic regulatory landscape in keratinocytes and its potential implications on species-specific gene expression and phenotypes.Nucleic Acids Res. 2017; 45: 8208-8224Crossref PubMed Scopus (20) Google Scholar]. These studies revealed that p53REs and p63REs have undergone exceptionally fast turnover during evolution when compared with most other cis-regulatory elements [4.Stergachis A.B. et al.Conservation of trans-acting circuitry during mammalian regulatory evolution.Nature. 2014; 515: 365-370Crossref PubMed Scopus (152) Google Scholar], including binding sites of the E2F4-containing DREAM trans-repressor complex, which mediates p53-dependent gene downregulation [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar]. The unusually large size of p53REs and p63REs may explain this phenomenon. It has been shown that cis-regulatory elements that exceed ten base pairs are particularly susceptible to alterations during evolution [12.Tuğrul M. et al.Dynamics of Transcription factor binding site evolution.PLoS Genet. 2015; 11e1005639Crossref PubMed Scopus (53) Google Scholar], and the p53RE and p63RE comprise 20 base pairs. Dramatically different DNA binding landscapes and a large number of species-specific direct target genes reflect the limited conservation of the p53REs and p63REs. The majority of direct target genes are mouse- (N = 509) or human- (N = 329) specific, while only 86 have been identified as conserved (Figure 2) [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar]. The large set of species-specific p53 target genes contains multiple transcription factors (Figure 2), which likely contribute to the species-specific p53-dependent regulation of more than a thousand protein-coding genes. These genes were significantly related to the biological processes of DNA damage response, metabolism, and cell cycle regulation, as determined by gene ontology enrichment analyses [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar]. Despite the stark differences in their GRNs between mouse and human, the role of p53 and p63 as tumor suppressor and master regulator of stratifying epithelia, respectively, is clearly conserved. The evolutionary rewiring of their GRNs rather affect the mechanisms that enable p53 and p63 to perform these roles. Mouse models are often used to decipher the function of p53 and p63 to inform strategies for cancer treatment [13.Cheok C.F. et al.Translating p53 into the clinic.Nat. Rev. Clin. Oncol. 2011; 8: 25-37Crossref PubMed Scopus (282) Google Scholar]. It was long thought that p53 and p63 function similarly in mouse and human, with only a few target genes differing between the two species, but the GRNs of p53 and p63 have undergone extensive rewiring during evolution; therefore, we must be particularly cautious when translating findings that involve the p53 transcription factor family. Cancer research relies heavily on mouse models to identify candidate targets that affect tumor development and growth. Given the limitations to mimic human cancer in mouse models, several improvements have been made in the past years and new mouse models have emerged to bridge the gap between mouse and human. For example, cancer cells or small tumor tissues derived from patients can be injected into immunodeficient mice to generate patient-derived xenograft models, which recapitulate a human tumor better than tumors of genetically modified mouse models. Another important development are humanized mouse models, which allow us to circumvent species differences through reconstituting a human immune system in immunodeficient mice; for example, through engraftment of human peripheral blood mononuclear cells or human CD34+ hematopoietic stem cells [14.Walsh N.C. et al.Humanized mouse models of clinical disease.Annu. Rev. Pathol. 2017; 12: 187-215Crossref PubMed Scopus (292) Google Scholar]. The humanized mouse models can be combined with patient-derived xenografts to further improve cancer models. In agreement with comparative data of the mouse and human p53 GRN [7.Fischer M. Conservation and divergence of the p53 gene regulatory network between mice and humans.Oncogene. 2019; 38: 4095-4109Crossref PubMed Scopus (16) Google Scholar], a recent gene expression profiling study showed that the regulation of p53 target genes differed substantially between mouse and human blood samples in response to gamma radiation. Yet, the response was similar between blood samples from humans and humanized mice engrafted with hematopoietic stem cells [15.Ghandhi S.A. et al.Discordant gene responses to radiation in humans and mice and the role of hematopoietically humanized mice in the search for radiation biomarkers.Sci. Rep. 2019; 919434Crossref PubMed Scopus (8) Google Scholar], further supporting the use of humanized mouse models to generate data that are more meaningful to humans. Mouse models have had and will continue to have an important role in the identification and validation of cancer vulnerabilities. However, when we generate data in mouse models, we must be aware of their shortcomings. The assumption with mouse models has been that key tumor suppressor and oncogenic pathways function largely similar to those in humans. Since that is apparently not the case, we need to understand the differences between mouse and human more precisely to improve the translation of research results. Mouse models are powerful tools, but we need to have an informed look at them. M.F. is supported through research grant FI 1993/2-1 from the German Research Foundation (DFG). Funding for open access charge: Leibniz Institute on Aging – Fritz Lipmann Institute (FLI), Jena, Germany. The FLI is a member of the Leibniz Association and is financially supported by the Federal Government of Germany and the State or Thuringia.