Mutations in the TP53 gene are very common in human cancers, and are associated with poor clinical outcome. Transgenic mouse models lacking the Trp53 gene or that express mutant Trp53 transgenes produce tumours with malignant features in many organs. We previously showed the transcriptome of a p53-deficient mouse skin carcinoma model to be similar to those of human cancers with TP53 mutations and associated with poor clinical outcomes. This report shows that much of the 682-gene signature of this murine skin carcinoma transcriptome is also present in breast and lung cancer mouse models in which p53 is inhibited. Further, we report validated gene-expression-based tests for predicting the clinical outcome of human breast and lung adenocarcinoma. It was found that human patients with cancer could be stratified based on the similarity of their transcriptome with the mouse skin carcinoma 682-gene signature. The results also provide new targets for the treatment of p53-defective tumours.

Competing interests: The authors have read the journal’s policy and have the following conflicts: R.G.-E., J.-M.P., A.B.M.-C., M.S., P.L. and C.B. hold two patents for the genomic tests described in this study. Patent 1: Inventors: R. García Escudero, A.B. Martínez Cruz, M. Santos Lafuente and J.M. Paramio, Title: Genomic fingerprint of breast cancer, Request N°: PCT/ES2009/07028, Priority country: Spain, Priority date: 01/july/2009, Organism: CIEMAT. 2. Inventors: R. García Escudero, J.M. Paramio, Pedro Larrañaga, and Concepción Bielza, Title: Predictor test of global survival in lung adenocarcinoma, Request N°: P201031626, Priority country: Spain, Priority date: 05/november/2010, Organism: CIEMAT and UPM. In relation with employment, consultancy, or products in development the authors declare no conflict of interest. The conflict of interest that the authors are declaring does not alter their adherence to all the PLoS ONE policies on sharing data and materials.

This report shows the above 682-gene signature to be present in different GEMMs of BC and lung adenocarcinoma (LAd). Importantly, the similarities were strongest in those models involving p53 inhibition, and in the metastatic samples arising from some of them. Using this 682-gene signature, we obtained and validated GE tests able to stratify patients with these cancers into groups with significant differences in expected clinical outcome, and which showed high sensitivity in terms of the identification of patients with a potentially good outcome.

We previously reported that a 682-gene expression signature common to two skin carcinoma models lacking p53 (alone or combined with a lack of pRb, hereafter referred to as p53 ΔEC and p53 ΔEC ;pRb ΔEC respectively) in stratified epithelia [12] , [13] showed strong similarities to signatures of human primary carcinomas involving TP53 mutations (both truncating and point) arising in different anatomical locations. Bioinformatic tools used to examine the mouse skin carcinoma gene signature and transcriptomes of different types of human cancer showed a human signature of 20 overexpressed genes associated with TP53 mutation and a poor prognosis. Importantly, when patients with cancer were stratified depending on the expression of these genes, different clinical outcomes were observed: the stronger the expression, the lower the probability of surviving cancers such as breast carcinoma (BC) or multiple myeloma [12] .

There are different ways to search for correlations between tumour gene expression (GE) patterns and the clinical behaviour of tumours [4] . In the model-driven approach, the transcriptome of cells exposed to specific stimuli (such as a wound) or after the activation of specific oncogenic pathways, is used to determine a prognosis [5] , [6] . This approach has the drawback that the experimental model used might not accurately reflect the processes that occur in tumours. The advantage, however, is that the model system acts as a “filter” of genes that are important in oncogenic signalling. The use of genetically engineered mouse models (GEMMs) designed to emulate the genetic alterations found in human cancers represents a great advance in this area. The targeted over-expression of a particular oncogene or knockout of a specific tumour suppressor gene in a well defined genetic background offers many advantages for studying tumour progression initiated by genetic aberrations [7] . A major benefit of GEMMs over cellular systems is that mouse carcinomas contain tumour cells as well as stromal and endothelial cells, which all contribute to a tumour’s biology [8] . Thus, genome-wide GE profiles of primary carcinomas from GEMMs of cancer [9] , [10] , as well as comparisons between metastatic and primary mouse carcinoma samples, have been used to try to develop predictors of the outcome of human cancer [11] .

Mutations in the TP53 tumour suppressor gene are very common in human cancers, and in most cases are associated with a poor clinical outcome. Although great efforts have been made to find specific therapies for TP53-mutant cancers [1] , none are currently used in the clinical setting. The lack of such therapies may be explained by the wide diversity of p53-related genomic alterations (point or truncating mutations, oncogenic or dominant-negative mutations, loss of heterozygosity, etc.) and by the presence of additional alterations in oncogenic signalling pathways [2] . Besides, such mutations are predictors of resistance to Nutlin-3a [3] , an inhibitor of the MDM2 E3 ligase that negatively regulates p53 protein levels. However, the sensitivity of human cancer cell lines to chemotherapeutic drugs is not associated to p53 mutations [3] . The search for effective therapies for mutant patients is therefore of prime importance. One way of arriving at a treatment might be to identify and validate molecular biomarkers of TP53-based carcinogenesis, some of which might be suitable as targets for therapy. An added value of p53-based biomarkers would be their potential use in predicting the response to cancer therapies, thus allowing for the personalised treatment of patients.

Patient risk scores (p53RS) are represented depending on ER, PR and HER2 status using the 40-gene test for breast cancer patients ( A ) and depending on EGFR and KRAS mutation status as calculated by the 36-gene genomic-clinic test for lung adenocarcinoma patients ( B ). Each dot represents an individual sample value. Horizontal green lines represent mean values in each sample group. Student’s Ttest analysis was done to find significant differences in score values between patient biomarker subgroups (threshold p-val<0.05). Patients were stratified based on the risk groups as of low (green), or high-intermediate (red) risks (see Materials and Methods ).

Currently, there are oncogene biomarkers defining molecular subtypes with different clinical outcome and/or targeted therapies in BC and LAd, as we have already mentioned for oestrogen receptor and breast cancer (see Fig. 3 ). The p53 dysfunction was analysed in these molecular subtypes by comparing the p53RS-derived values using the 40-gene test and 36-gene genomic-clinical test. For breast tumours, ER or progesterone receptor (PR) negative samples displayed higher risk score values than the positive ones, in line with their highest aggressive behavior ( Fig. 6A ) ( Table S9 ). HER2-positive carcinomas exhibited higher score values ( Fig. 6A ), also in agreement with worse clinical outcome. For LAd, EGFR-mutant tumours showed lower risk score values ( Fig. 6B ) ( Table S10 ), as expected due to their best clinical behavior. However, no significant differences were found between samples with or without KRAS mutations. Despite the mean differences in p53RS values, both 40-gene test ( Fig. 7 ) and 36-gene genomic-clinical test ( Fig. 8 ) stratified patients with significant survival differences independent on oncogene biomarker subgrouping.

A comparison between clinical outcome as predicted by the 40-gene test and p53 mutation status was performed using the Miller dataset. The genomic test showed greater sensitivity than the p53 mutation status in terms of predicting patients with a good prognosis (see comparisons of the low risk [L, green line] and p53-WT [pink line] groups; Fig. 5B ). Interestingly, patients without TP53 mutations but predicted to be at high risk by the 40-gene test showed poor survival potential (high risk and WT in Fig. 5C , red line). Importantly, these WT patients showed similar survival probabilities to the high risk TP53-mutant patients (high risk and MUT in Fig. 5C , dashed red line). A similar result was obtained when comparing the 40-gene test with the Miller GE-based predictor of p53 mutation status [28] ( Fig. S10 ). Multivariate Cox regression including both predictors showed the results of the 40-gene test to be better correlated with survival than the p53 mutation genomic predictor ( Table S6 ). These results indicate that the prediction of clinical outcome based on the 40-gene test to be more accurate than the TP53 mutation status, the consequence of its ability to detect poor outcome patients with no mutation and to discriminate low risk patients with greater sensitivity.

A ) The AURKA, AURKB and PLK1 genes within the 40-gene test are overexpressed in human BC with p53 mutations. Tumour samples were ordered by p53 Risk Score as determined by the 40-gene test; risk groups are shown as low (green), intermediate (blue) or high (red) risk. Note the existence of high risk tumours without p53 mutations. B ) Comparison of patient stratification determined using the 40-gene test and p53 mutation status in the Miller BC dataset. The survival curves of both stratification methods are shown simultaneously for the same patient dataset. p-val: significance of survival differences (log-rank test). C ) Combination of the 40-gene test and p53 mutation status for stratifying patients with BC. Patients are grouped as p53-WT (L, I and high risk groups) or p53-MUT (low, intermediate and high risk groups). Only one sample out of 251 was classified as of low risk and p53-MUT; this was not included in the graph. p-val: significance of survival differences (log-rank test).

The calculation of the risk score for the BC and LAd patients was based on the GE profiles of the p53-deficient tumours, not on the presence/absence of p53 mutations in sample patients as previously reported for BC predictors [28] , [31] . Given the importance of p53 alterations in the appearance of human cancer, great effort has been directed towards the development of therapies that restore p53 function [1] . However, no such treatments are yet available in the clinical setting. Another possibility is to identify molecular biomarkers associated with p53 alterations that offer themselves as therapeutic targets. To examine this, we selected genes that are overexpressed in p53-mutant human BC tumours (Miller dataset, Table S2 ) [28] , and for which specific inhibitors are in preclinical testing: AURKA, AURKB and PLK1 ( Fig. 5A ). These inhibitors, if validated clinically, might be usable for the treatment of patients with p53 mutations. Importantly, the overexpression of the AURKA, AURKB and PLK1 genes was also observed in non-p53 mutant tumours within the high risk group as assessed by the 40-gene test ( Fig. 5A ), showing that some patients with poor outcome suffering p53-WT tumours may also benefit from such therapies. To search for any potential anti-tumoral effect of these inhibitors in tumour samples with p53 deficiency, the GE profiles of human cancer cell lines and xenografts sensitive to targeting therapies were compared to the 682-gene signature. The similarities observed indicate their potential susceptibility to these agents. The human cancer xenografts that responded to AURKA inhibitors were found to be more similar to the mouse p53-deficient tumours than those that did not respond ( Fig. S9A ) [32] . Further, those cell lines sensitive to targeted therapies against AURKB and PLK showed strong similarities to the p53-deficient mouse carcinomas ( Fig. S9B ) [33] . Importantly, these sensitive cell lines included not only BC and LAd cell lines, but cells of other organs, suggesting an effect of these inhibitors in different cancer types. Another approach to search for targeted therapies that might be useful in p53-deficient tumours was performed using the Connectivity Map resource [34] ( Materials and Methods ). Briefly, we search for small molecule bioactive compounds (dubbed perturbagens) able to induce GE profiles with the reverse pattern of that observed in the 682-signature, so that they could be used to treat p53-deficient tumours. The results indicate that inhibitors of histone deacetylases (such as trichostatin A or vorinostat) are between the most significant perturbagens that may repress the 682-signature pattern ( Table 2 ). Interestingly, the antipsychotic drug thioridazine also represses the p53-deficient carcinoma GE profiles, in line with recent evidences demonstrating that the drug antagonizes dopamine receptors that are expressed on cancer stem cells and on breast cancer cells [35] .

Using metagenomic comparisons of GEMMs ( Fig. 2 ), the time-course inhibition of p53 was seen to involve the progressive appearance of the 682-gene signature with BC formation. In addition, p53 restoration in mouse lung adenomas and adenocarcinomas led to the disappearance of the signature; other authors have reported tumour cell loss to occur as well [18] . A similar result was obtained for the 40-gene signature in the BC model, and for the 36-gene signature in the LAd model ( Fig. S7 ). These findings support the idea of a major role for p53 in the control of the genes in both signatures. Network analyses of the 40-gene and 36-gene proteins in relation to p53 and pRb (since the 682-signature was obtained from the common transcriptomes of the p53 ΔEC and p53 ΔEC ;pRb ΔEC models [12] ) showed both p53 and pRb to be direct regulators of most of these proteins ( Fig. S8A and C ). Further, these signature genes appear to be important regulators of processes involved in carcinogenesis such as apoptosis, differentiation and proliferation ( Fig. S8B and D ).

A ) Kaplan-Meier curves for overall survival (OS) for the pooled population of patients with lung cancer in three datasets including patients with all disease stages. Patients were stratified based on the 36-gene test as of low (green), intermediate (blue) or high (red) risk (see Materials and Methods ). B ) Kaplan-Meier curves for early stage patients (Stages IA and IB). Patients were stratified based on the 36-gene test as of low (green), intermediate (blue) or high (red) risk. C ) Kaplan-Meier curves for patients profiled using qRT-PCR and FFPE samples. Patients were stratified based on the 36-gene test as of low (green), or high-intermediate (red) risks (see Materials and Methods ). Owing to the small sample size, the intermediate and high risk groups were pooled. p-val: significance of survival differences (log-rank test).

Using the same approach used with BC, an optimal group of 36 probesets corresponding to 30 genes (36-gene test) was obtained to predict overall survival ( Materials and Methods , Fig. S1 , Fig. S2 [panels A, D and E], Tables S4 and S5 ). Shedden et al. [30] reported that the accuracy of genomic predictors of LAd outcome could be improved by incorporating certain clinical variables. Thus, a clinical predictor test was developed including tumour stage, patient gender and age ( Fig. S5A ). The combination of both genomic and clinical information (36-gene genomic-clinical test) increased the prediction accuracy, of overall survival, allowing patients to be stratified into three risk groups (low, intermediate and high) using the same approach as for BC. Validation in 3 external microarray GE datasets showed the accuracy of the combined test with the pooled patients (n = 313) ( Fig. 4A ), or in individual datasets ( Fig. S6 ). More importantly, it also accurately predicted clinical outcome among early stage patients ( Fig. 4B , Fig. S6 ). As the number of reported human LAd samples that we have used for validation is lower when compared to human BC, we decided to add new LAd samples by performing GE from FFPE tumour blocks. This analysis would also aid to demonstrate the feasibility of the 36-gene genomic-clinical predictor using FFPE tissue. Validation was performed using quantitative real-time PCR (qRT-PCR) ( Materials and Methods , Fig. S5B ). The results confirmed that the genomic-clinical test stratified patients with different survival probabilities ( Fig. 4C ) with similar accuracy to that seen for ‘fresh’ (i.e., non-FFPE) samples profiled using GE microarrays (area under the curve [AUC] = 0.72, p-val = 1.4×10 −9 for microarrays; AUC = 0.70, p-val = 0.05 for qRT-PCR). Univariate Cox regression analysis including all patients in the validation datasets (n = 362) showed significant risk differences between patient strata. The hazard ratio (HR) for OS at 5 years was 14.14 times higher (95% CI = 3.46 to 57.83, p-val = 0.0002) than in the high than the low risk groups. In addition, the hazard ratio (HR) for OS at 5 years was 7.60 times higher (95% CI 1.82 to 31.78, p-val = 0.005) for the high risk group than the intermediate risk group.

Most breast cancers are oestrogen receptor positive (ER+) and are treated with adjuvant hormonal therapy, such as tamoxifen. Interestingly, although the 40-gene test was developed using data from patients that received no such treatment, it predicted the outcome for such hormonally-treated patients as well ( Fig. 3B ). A possible explanation for this is that this test identifies tumours with inherent malignant behavior, and which are therefore less prone to respond to adjuvant therapy. Alternatively, it may be that high risk patients with BC suffer inhibition of the p53-dependent pathway linked to ER signalling pathways [24] – [27] . In agreement with this hypothesis it should be noted that a reduced response to tamoxifen has been reported in patients with BC carrying TP53 mutations [28] , [29] ( Fig. 3C ).

A ) Kaplan-Meier curves of distant metastasis-free survival (DMFS) for a pooled population of 12 GE datasets of patients with BC. Patients were stratified based on the 40-gene test as of low (green), intermediate (blue) or high (red) risk (see Materials and Methods ). B ) Kaplan-Meier curves of DMFS from ER+, tamoxifen-treated women with BC. Patients were stratified based on the 40-gene test as of low (green), intermediate (blue) or high (red) risk. C ) Kaplan-Meier curves for ER+, tamoxifen-treated patients with breast cancer in the Miller dataset. Patients were stratified depending on the presence (red) or absence (green) of p53 mutations. p-val: significance of survival differences (log-rank test).

For human BC, a subgroup of 40 probesets, corresponding to 32 genes (40-gene test), was selected based on optimal distant metastasis prediction accuracy and small gene set size ( Materials and Methods , Figs. S1 and S2A-C , Tables S2 and S3 ). The 40-gene test stratified BC patients into three risk groups: high, intermediate and low. The prediction accuracy of the test was validated in 12 additional datasets, comprising a total of 2993 tumour samples, 4 different endpoints, and 2 microarray platforms (Affymetrix and Agilent) ( Fig. 3A , Figs. S3 and S4 , Table S2 ). Multivariate Cox regression analysis including both genomic and clinical variables showed the 40-gene test to discriminate patient risk groups independent of clinical prognostic factors ( Table 1 ).

Given the similarities between the mouse skin signature and those of mouse lung and BC (see above) and human tumours arising in these organs [12] , the question arose as to whether the 682-gene signature could be used to develop prognostic tests for these human cancers. To develop such genomic predictors, the rodent signature was combined with GE data for primary human BC or LAd samples with known survival data.

A ) SV40 Large-T antigen expression in mammary gland was analysed at various time-points during carcinoma formation in transgenic WAP-TNP8 mice. Heatmaps of 682-gene signature transcripts from normal mammary glands (green), primary breast carcinomas (red) and mammary samples with transgene expression at 1, 2, 3, 4 and 5 months (blue) are shown (upper panel). B ) p53 expression was induced in lung adenomas and adenocarcinomas in the Kras LA2/+ ;Trp53 LSL/LSL ;Rosa26Cre ERT2 mouse model. The heatmaps of the 682-gene signature transcripts from normal lungs (green), lung adenomas (orange) and adenocarcinomas (red) (treated and untreated) are shown (upper panel). In A and B , sample groups are ordered from left to right based on increasing Pearson correlation with the centroid template based on the 682-gene signature. Probesets are ordered from top to bottom based on T-values (see Materials and Methods ). The number of samples in each group is shown under the heatmap. The correlation values for individual samples with the centroid are shown in the middle panel. Values range from −1 (negative correlation, bluish background) to +1 (positive correlation, reddish background). The significance of the correlation for each sample is shown in the lower panel as –log 10 (p-val). The red line indicates a p-val of 0.01.

Since the p53-deficient primary skin samples profiled were overt carcinomas, it cannot be ruled out that other oncogenic events may be acting as major players in their transcriptome deregulation, and therefore in the similarities seen with human primary tumours with poor outcome. To detect any direct implication of p53 protein activity in the GE pattern, breast and lung GEMMs in which p53 expression levels could be modulated were examined. In the WAP-TNP8 model, time-course analyses of p53 inhibition by means of SV40 large T-antigen expression (1, 2, 3, 4 and 5 months) showed a progressive increase in the overexpression of already overexpressed (plus a reduction in the expression of already underexpressed) 682-signature genes in mammary carcinomas ( Fig. 2A ). In addition, the restoration of Trp53 expression with tamoxifen in Kras LA2/+ ;Trp53 LSL/LSL ;Rosa26Cre ERT2 mouse lung adenomas and adenocarcinomas reduced the overexpression (and induced the underexpression) of 682-signature mRNAs ( Fig. 2B ). As previously reported [18] , tamoxifen-dependent p53 induction in these malignant lung adenocarcinomas leads to significant tumour cell loss. These results directly associate tumour reduction (upon p53 expression) with the disappearance of the 682-gene signature, indicating that its transcriptional regulation is dependent on p53. This confirms that this signature is common to both p53-altered human and mouse carcinomas.

Heatmaps of the 682-gene signature transcripts from ( A ) primary breast carcinomas and normal mammary glands from different transgenic GEMMs (upper panel), and from ( B ) primary and metastatic lung adenocarcinomas and normal lungs from different transgenic GEMMs (upper panel) ( Table S1 ) are shown. The T-values returned by Student’s t-test comparisons between normal skin and carcinoma samples in which the 682-gene signature was determined (GSE11990) were used to build a centroid template. The Pearson correlation coefficient (and the corresponding p-value) with respect to the centroid was calculated for each mouse sample. Samples were ordered from left to right based on increasing correlation. Probesets are ordered from top to bottom based on T-values (see Materials and Methods ). Samples within blue rectangles are normal skin samples and skin tumour samples. The number of samples in each group is shown under the heatmaps. Pearson values are shown in the middle panel. Values range from −1 (negative correlation, bluish background) to +1 (positive correlation, reddish background). The significance value for the correlation is shown in the lower panel as –log 10 (p-val). The red line indicates p-val = 0.01. Genotypes highlighted in red are models with p53 alterations significantly correlated with the 682-signature. Samples highlighted in pink are metastases. In ( B ), the Kras (1) and Kras/Lkb1 L/L (1) samples are from the GSE6135 dataset; the Kras (2) and Kras/Lkb1 L/L (2) samples are from the GSE21581 dataset.

Given the significant GE similarities between these mouse skin tumours and human BC and LAd with a p53 mutation, in the present work the 682-gene signature was sought in GEMMs of BC and LAd showing p53 inhibition. Raw GE data were downloaded from the GEO database ( Table S1 ) [10] , [11] , [16] – [22] and similarities with the 682-gene tumour signature sought by calculating Pearson correlations (see Materials and Methods ). Metagenomic comparisons showed carcinomas from specific BC ( Fig. 1A ) and LAd ( Fig. 1B ) GEMMs to have GE profiles very similar to those of mouse skin carcinoma. With respect to BC, models of p53 inactivation via the expression of the SV40 large T-antigen (C3(1)Tag and WAP-TNP8 models) [20] , [23] , and the p53 fl/fl ;MMTV-cre transplant model [23] , were among the most similar (highlighted in red, Fig. 1A ). Significant similarities were seen with the 682-gene signature for a LAd model in which p53 expression is repressed in the presence of an oncogenic Kras G12D allele (Kras LA2/+ ;Trp53 LSL/LSL ;Rosa26Cre ERT2 model) [18] (highlighted in red, Fig. 1B ). Importantly, the p53-deficient skin carcinomas shared GE patterns with metastatic samples arising in a Kras/p53 R172H and a Kras/Lkb1 L/L LAd GEMM [10] , [11] , confirming their aggressive molecular properties (highlighted in pink, Fig. 1B ). Importantly, most Kras/p53 R172H metastatic samples lose the wild type (WT) Trp53 allele during malignant transformation [10] . These comparisons between GEMMs show that the 682-gene skin signature is significantly present in p53-deficient mouse lung and mammary carcinomas, and might be considered a common signature of p53-deficient carcinoma GEMMs.

Genome-wide microarray analyses have shown human aggressive and/or TP53-mutant tumours to possess transcriptomes resembling the 682-gene mouse skin carcinoma signature [12] . These similarities are particularly noticeable for human BC and LAd [12] . Further, the transcriptome of the mouse skin carcinomas shows strong similarities to that of embryonic stem cells (ESC), suggesting that p53 deficiency induces a potent de-differentiation process in epithelial cells [12] . p53-mutant human BCs show these ESC signatures too [14] . This is in agreement with the locally invasive properties of these mouse tumours, and their propensity to metastasise to distant organs [15] .

Discussion

The p53 pathway is one of the most important tumour suppression mechanisms; mutations affecting it are commonly found in the majority of cancer types. The correlation between such mutations and tumour malignancy, suggests the need for more detailed characterization of this pathway. High throughput technologies such as genome-wide GE analysis or next generation sequencing (NGS) may help to determine the alterations in individual tumours, which would allow personalized treatments and ultimately improve the care that could be offered to patients. However, arriving at effective personalized medicine depends on the availability of appropriate analysis model systems and adequate clinical evaluation/validation. The present work discusses a p53-deficient tumour mouse model system with molecular features leading to tumour aggressiveness, and the development and validation of GE signatures that can predict clinical outcomes in human BC and LAd. The results show the genes making up these signatures to be surrogate markers of p53-dependent pathway alterations, and possible candidates for targeting therapies.

We previously reported a mouse 682-gene signature seen in p53-deficient skin tumours to show significant molecular similarities to human cancer transcriptomes (such as those of BC and LAd) involving TP53 mutations and/or poor outcome. The present results show that such similarities are also present in GEMMs of BC and LAd carcinoma in which p53 expression or function is inhibited, confirming our previous findings. They also show that human and mouse carcinomas arising in different organs such as skin, lung and breast show strong similarities upon p53 alteration. Similar findings were reported by Deeb et al. [9], which identified a gene signature associated with clinical outcome of human BC, LAd and prostate cancer using GEMMs expressing SV40 T/t antigens. An explanation for the similarities in molecular profile between tumours of different organs may be that p53 inhibition induces an overall process of de-differentiation, giving rise to an ESC-like phenotype. This would agree with our previous results showing ESC signatures in the mouse skin carcinomas [12], with findings showing that tumour aggressiveness is predicted by these ESC GE profiles [36], and with the presence of such profiles in p53-mutant human BC tumours [14]. The observation that p53 inhibition in different organs induces a common GE program associated with poor clinical outcome also reinforces the direct role of p53 protein in the suppression of malignancy. Similarly, the present results show that time-dependent inhibition of p53 in a BC model or restoration of p53 expression in tumours in a LAd model is significantly correlated with the 682-, 40-, and 36-gene signatures in vivo (Fig. 2 and S7). Pathway analysis showed that p53 directly inhibits the genes overexpressed in the 40-gene and 36-gene signatures. Collectively these findings strengthen and support the major roles of p53 in multiple tissues of different organisms, and demonstrate that these gene signatures are surrogate biomarkers of p53 inhibition during carcinoma progression. Using the Oncomine database, the analysis of the transcriptome of human cell lines and xenografts with sensitivity to drugs designed against AURKA, AURKB or PLK1 kinases show a profile similar to that seen in the described mouse skin carcinomas. These similarities indicate that tumours with such profiles may respond to these therapeutic agents, providing alternative therapies for TP53-mutant patients. Importantly, the Millenium company has recently reached Phase III clinical trials with the AURKA inhibitor MLN8237 for the treatment of haematological and solid tumours. These kinases have roles in mitosis, a process deeply de-regulated in p53-mutant tumours [37]. In addition, both AURKA and PLK1 are directly regulated by p53 (Fig. S8A). We suggest that the efficacy of the inhibitors of these kinases in tumours overexpressing them probably depends on both the presence of p53 mutations and p53 pathway inhibition independent of TP53 mutation (as assessed by the 40-gene test). Thus, there are reports indicating that inhibitors to AURKA or to PLK display better efficacy with p53 mutation [38]–[40]. Using the Connectivity Map resource, we found that HDAC, mTOR, PIK3CA or topoisomerase II inhibitors might be beneficial for tumours with similar profile to our p53-deficient mouse carcinomas. Some of these compounds are being analyzed in clinical trials for cancer treatment, or already approved by the FDA (such as vorinostat for cutaneous T-cell lymphoma).

A number of genomic tests have been developed for human BC outcome based upon GE profiles, although only a small number have seen clinical implementation [41]. Since TP53 mutation is a predictor of poor prognosis, some of these BC tests based on GE are designed to predict TP53 mutational status [28], [31]. However, the 40-gene test discriminates poor outcome tumours with no TP53 mutation, demonstrating its greater sensitivity than mutational analysis in the detection of patients with low survival potential. This might be the consequence of other molecular alterations that produce p53 pathway inhibition being present, either in the upstream regulators or downstream effectors of p53.

The BC tumours produced in the MMTV-c-myc models show different degrees of similarity with the 682-gene signature, with about 50% of samples returning positive Pearson correlation values in the GSE15904 dataset (which contains 80 carcinomas). Although this model is poorly metastatic [42], [43], a signature of metastatic potential has been described in a subgroup of its tumour types [16]. Cooperation between p53 and c-myc may exist in p53ΔEC and p53ΔEC;pRbΔEC skin carcinomas since they overexpress c-myc targets [12]. The close similarity of BC models owed to the expression of the SV40 T-antigen used in the C3(1)-Tag and WAP-TNP8 mice is due to the Large T-antigen deactivating p53 and pRb. These deactivations are also likely in human basal-like tumours since these are known to harbour p53 mutations [44], to have a high mitotic rate, and to show the greatest expression of proliferation genes, which are known E2F targets [45]. In addition, an interspecies comparison of mouse BC models and human BC samples has also shown strong similarities between SV40-derived models and human basal-like tumours at the genome-wide transcriptome level [46], confirming the present results. Finally, it has recently been described that a subgroup of carcinomas in the p53fl/fl;MMTV-cre transplant model, also with strongly 682-gene-like signatures, show marked enrichment in functional tumour-initiating cells in limiting dilution transplantation assays [47]. These findings further underscore the ESC characteristics of the p53-deficient skin carcinoma model.

The molecular and pathway changes that occur between primary carcinomas and metastases in the LAd Kras/Lkb1L/L model have been associated with the enrichment of GE signatures associated with the ESC phenotype, and the activation of epithelial-mesenchymal-transition (EMT), focal adhesion and oncogenic signalling (EGFR or ERBB2) [11]. These associations agree with the present results: the p53ΔEC and p53ΔEC;pRbΔEC skin models both show ESC signatures as well as the deregulation of EMT markers [12]. Whether the transition from primary to invasive tumours in the Kras/Lkb1L/L model is facilitated by early mutations inhibiting the p53-dependent pathway or in the p53 alleles themselves remains to be determined. Carcinomas arising through KrasG12D expression and homozygous p53 inhibition in the KrasLA2/+;Trp53LSL/LSL;Rosa26CreERT2 model showed a better correlation with 682-gene than with the KrasG12D model (which has WT p53). This agrees with the reported high malignancy of double transgenic mouse tumours [48], [49].

The results of our retrospective validation of the BC 40-gene test in about 3000 patients from 12 different cohorts strongly suggests its clinical usefulness, although further validation involving prospective testing is required. Moreover, the validation showed this BC test to be independent of the microarray platform used in the datasets, as also seen for the LAd 36-gene predictor. However, for LAd, the number of GE-based datasets for outcome prediction testing was more limited. Nonetheless, LAd predictors are very necessary in early stage patients if the right form of clinical management is to be adopted. Here we show that the 36-gene genomic-clinical predictor to be of high sensitivity in terms of predicting good outcome in the patients in these validation cohorts, stratifying patients of all disease stages in terms of clinical outcome. Remarkably, this test also appeared to be of use with FFPE-samples/qRT-PCR, again providing good patient stratification. Further restrospective validation studies are necessary with larger numbers of patients.

In conclusion, the present results indicate that mouse skin carcinoma models with p53-deficiency show significant similarities to mouse BC and LAd models with functional inhibition of p53. These similarities can be exploited in the development of accurate predictors of human BC and LAd clinical outcome. Additional genomic testing to predict clinical behavior should be tried with other cancer types associated with p53-dependent malignancy. We already have preliminary data showing that predictors for prostate adenocarcinoma, multiple myeloma, and glioblastoma might be obtained.