Mutations in the tumor suppressor p53 are the most frequent alterations in human cancer. These mutations include p53-inactivating mutations as well as oncogenic gain-of-function (GOF) mutations that endow p53 with capabilities to promote tumor progression. A primary challenge in cancer therapy is targeting stemness features and cancer stem cells (CSC) that account for tumor initiation, metastasis, and cancer relapse. Here we show that in vitro cultivation of tumors derived from mutant p53 murine bone marrow mesenchymal stem cells (MSC) gives rise to aggressive tumor lines (TL). These MSC-TLs exhibited CSC features as displayed by their augmented oncogenicity and high expression of CSC markers. Comparative analyses between MSC-TL with their parental mutant p53 MSC allowed for identification of the molecular events underlying their tumorigenic properties, including an embryonic stem cell (ESC) gene signature specifically expressed in MSC-TLs. Knockout of mutant p53 led to a reduction in tumor development and tumorigenic cell frequency, which was accompanied by reduced expression of CSC markers and the ESC MSC-TL signature. In human cancer, MSC-TL ESC signature–derived genes correlated with poor patient survival and were highly expressed in human tumors harboring p53 hotspot mutations. These data indicate that the ESC gene signature–derived genes may serve as new stemness-based prognostic biomarkers as well as novel cancer therapeutic targets.

Significance: Mesenchymal cancer stem cell-like cell lines express a mutant p53-dependent embryonic stem cell gene signature, which can serve as a potential prognostic biomarker and therapeutic target in cancer. Cancer Res; 78(20); 5833–47. ©2018 AACR.

Tumor development is a multistage process that is accompanied by gradual gain of intratumoral heterogeneity (1, 2). One of the theories accounting for tumor heterogeneity is the existence of a rare subpopulation of cells known as the cancer stem cells (CSC) that reside within tumors and harbor a unique ability to regenerate a tumor and capacities to metastasize and to resist chemotherapy (2). To study the nature of these rare CSCs, several methods for their isolation were described, mainly by identification of specific CSC surface markers (3). Although previous studies identified different CSC markers in various tumors, novel and more broadly expressed markers, which can be useful for early diagnostic and even for cancer therapy, are still urgently needed.

Mutations in the p53 gene are the most frequent alterations in human tumors (4, 5) that correlate with undifferentiated high-grade tumors (6–8). Most of the p53 mutations are missense mutations that produce full-length mutant p53 proteins, which not only lack the p53 tumor suppressor activity, but also gain new oncogenic functions that acquire the cell with enhance malignant properties (4, 5). Ample reports indicate a possible mutant p53 gain-of-function (GOF) activity in the acquisition of dedifferentiation and stemness phenotype that might lead to tumorigenesis. For example, mutant p53 GOF activity was shown to enhance the reprogramming efficiency of mouse embryonic fibroblasts (MEF) and augment the tumorigenesis capacity of the reprogrammed cells (9). Moreover, mutant p53 accumulation specifically in neuronal progenitors led to gliomagenesis, indicating improper maturation of neural stem cells by mutant p53 (10). Notably, bone marrow mesenchymal stem cells (BM-MSC) harboring p53 mutation undergo malignant transformation that induces sarcomagenesis (11). All in all, these studies imply that mutant p53 might have a GOF activity in stem cell transformation and dedifferentiation but the molecular profile underlying these processes is still poorly understood.

Here we show that in vitro cultivation of mutant p53 BM-MSC–derived tumors, led to the establishment of aggressive MSC tumor lines (TL). These MSC-TLs exhibited an enhanced tumorigenic capacity compared with their parental cells, mutant p53 MSCs, as shown by their ability to form rapidly growing tumors by the injection of as few as 1 × 102 cells. By transcriptome profiling, we found that MSC-TLs express elevated levels of CSC markers and a unique gene signature consisting of embryonic stem cell (ESC) genes. These results suggest that MSC-TLs may represent a mesenchymal CSC-like population that reside within mutant p53 MSC–derived tumors. Knocking-out mutant p53 significantly reduced tumor development and tumorigenic cell frequency and also resulted in a reduction in the expression of CSC markers and the identified ESC MSC-TL signature. Importantly, analyses of human tumor datasets showed that the ESC MSC-TL signature–derived genes correlated with poor patient survival and were highly expressed in various human cancers harboring p53-hotspot missense mutations. Altogether, our data suggest a novel role of mutant p53 in expanding mesenchymal CSC-like cells that display expression of a unique mutant p53–dependent gene signature comprising of ESC genes. The fact that this mesenchymal CSC–like signature contains embryonic genes, which are not tissue specific, might suggests that these signature-derived genes may serve as prognostic markers and potential cancer stemness therapeutic targets of mutant p53–dependent tumors and tumors at large.

Cells

Bone marrow MSCs were isolated from p53 WT or p53 Mut (R172H) mice and characterized as described previously (11). The different MSC isolates were grown in MSC-specific medium [MesenCult MSC Basal Medium (Mouse), STEMCELL Technologies] supplemented with 20% murine MesenCult MSC stimulatory supplement (Mouse; STEMCELL Technologies), 60 mg/mL penicillin, 100 mg/mL streptomycin, and 50 mg/mL kanamycin. p53 Mut MSC-TLs were established as follows; tumors were extracted and mechanically disaggregated by utilizing a cell strainer (542070, Greiner Bio-One). The isolated MSCs as mentioned previously (11), were examined for their authentication by the examination of their ability to differentiate to the mesodermal lineages adipocytes and osteocytes and also by verifying that the cells do not express hematopoietic markers. Tumor-derived cells were cultured on agar-coated plates in DMEM supplemented with 15% (v/v) FCS, 1 mmol/L sodium pyruvate, 2 mmol/L l-glutamine, 0.1 mmol/L nonessential amino acids, 0.1 mmol/L β-mercaptoethanol, 1,000 U/mL leukemia-inhibitory factor [ESG1107; (BD Biosciences) at a ratio of 1:1]. Cells were incubated at 37°C in a humidified atmosphere of 5% CO2. All cells were checked for Mycoplasma contamination and in all the analyses, we verify that the cells did not exceeded 20 in vitro passages.

Mice

C57BL/6 harboring p53 WT or p53 Mut (R172H; ref. 12), kindly provided by Professor Guillermina Lozano (MD Anderson Cancer Center, Houston, TX), athymic Nude-Foxn1nu, and NOD.CB17-Prkdcscid/NCrHsd (ENVIGO) were used. Animal protocols were approved by the Institutional Animal Care and Use Committee of the Weizmann Institute of Science (Rehovot, Israel).

CFU-fs assay

Freshly bone marrow nucleated cells derived from two p53 WT or two p53 Mut (R172H) mice were plated at cell density of 5 × 106 in 10-cm BD falcon plates (BD Biosciences). The cells were grown in MSC-specific medium as described above and refed once a week without further treatment. After 14 days, colonies of triplicates were stained with Giemsa and counted.

Cell proliferation assay

Cells were counted and plated (2.5 × 104 cells per well for 12-well plate or 1.4 × 105 cells for 6-well plate). Each day, cells in duplicate wells or plates were tripsinized and stained with Trypan blue, unstained cells were counted by using hemocytometer and light microscopy. The proliferation plots present results relative to day 1. Each curve represents p53 WT MSC or p53 Mut pMSC isolates or derived p53 Mut MSC-TLs accordingly.

Western blot analysis

Cells were lysed in TLB buffer (50 mmol/L Tris HCl, 100 mmol/L NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS) supplemented with Protease Inhibitor Cocktail (Sigma-Aldrich) for 15 minutes on ice, followed by centrifugation. Protein concentration was determined using BCA reagent (Thermo Fisher Scientific), 30–100 μg of protein extracts were separated by SDS-gel electrophoresis, and transferred to a nitrocellulose membrane at semi-dry conditions. Membranes were blocked using 5% dry milk in PBST (0.05% Tween in PBS), and incubated with anti-p53 (1c12, Cell Signaling Technology), anti-p21 (sc-397, Santa Cruz Biotechnology), anti-β-actin (sc-47778, Santa Cruz Biotechnology), anti-Sox2 (ab97959, Abcam), anti-Hmga1 (ab129153, ABCAM), anti-Slc16a1 (GTX54699, GeneTex), and anti-Etv5 (GTX5114394) followed by appropriate horseradish peroxidase–conjugated secondary antibodies and the signal was obtained by ECL Western Blotting Detection Reagent (Thermo Fisher Scientific) and ChemiDoc MP (Bio-Rad). In all the experiments, β-actin or GAPDH were used as a loading control.

Multispectral imaging flow cytometry analysis

For all the experiments, cells were imaged using multispectral imaging flow cytometry (ImageStreamX mark II imaging flow cytometer; Amnis Corp, part of EMD Millipore). Data were analyzed using image analysis software (IDEAS 6.2; Amnis Corp). Images were compensated for fluorescent dye overlap by using single-stained controls. Cells were gated for single cells, using the area and aspect ratio features, and for focused cells, using the Gradient RMS feature, as described previously (13).

For p53 staining, the cells were fixed with a fixation buffer (80% ethanol, 20% Hank's Balanced Salt Solution), washed and stained with a primary p53 antibody (Cell Signaling Technology, 1C12; 1 hour, 4°C, gentle agitation), and then washed once with PBS−/− and DAPI for DNA staining. Nuclear staining p53 was verified using the Similarity feature (calculated as log-transformed Pearson correlation coefficient between the DAPI and p53 staining).

For cell-cycle analysis, we utilized Phase-Flow BrdU Cell Proliferation Kit (BioLegend, CA 92121). Cells after staining with antibody against BrdU were analyzed by multispectral imaging flow cytometry and the percentage of cells in each cell-cycle phase was determined according to level of staining intensity.

For CD44 staining, cells were incubated with the primary antibody for to detect CD44 (eBioscience, Clone IM7, 17-0441-81; 1 hour, 4°C, gentle agitation), washed once with PBS−/− and Hoechst for DNA staining. Cells were imaged using the multispectral imaging flow cytometry. Approximately 1 × 104 cells were collected from each sample and cells were then gated for high intensity of CD44 staining and their percentage was calculated and compared between samples. To identify small and circular cells, the area (the number of microns squared in a mask) and circularity (the degree of the object's deviation from a circle) were calculated (three isolates each in each group). The population of cells with low area and high circularity was gated and their percentage was calculated and compared between samples.

CellTiter-Glo luminescent cell viability assay

Cells were counted and plated (2,000 cells per well for 96-well plate). Each day, CellTiter-Glo kit reagent was added directly to cells (in triplicates) that lead to cell lysis and generation of a luminescent signal. The signal is proportional to the amount of ATP, which is considered to be directly proportional to the presence of metabolically active cells in the culture.

Allograft tumor formation assay

Cells were tripsinized, stained with Trypan blue (Sigma-Aldrich T8154), and live cells (unstained) were counted by using hemocytometer. Furthermore, cells were resuspended with PBS containing 1% of FCS and injected subcutaneously into Athymic Nude-Foxn1nu or NOD.CB17-Prkdcscid/NCrHsd (ENVIGO) between 6 and 8 weeks of age.

Bioluminescence in vivo imaging

Cells were infected with pBABE-hygro retroviral vector–expressing luciferase, to monitor tumor growth. Immunodeficient mice were subcutaneous injected with 1 × 102–103 infected cells into the nape of the neck. After 1 week, from the inoculation, mice were anesthetized and injected with luciferin and monitored by IVIS2000 system. Exposure time was 30 seconds for 20 images that were taken for each tumor. We selected the highest peak of luminescence values and normalized it to adjacent background. Tumors were allowed to reach to maximal size of 1 cm2 and then mice were sacrificed.

IHC staining

Four-micron–thick paraffin-embedded tissue sections were deparaffinized, rehydrated, and pretreated for antigen retrieval. For Sox2, CD44, and PCNA, the antigen retrieval was done in citric acid (pH 6), for 10 minutes, by microwave. For ALDH1 and CD34 staining, the antigen retrieval was done in ice-cold acetone for 7 minutes, then washed in PBS and additional 10 minutes in citric acid (pH 6) by microwave. Sections were than blocked and incubated with primary antibodies for at least 24 hours as in 4°C, as follows; PCNA (FL-261; sc-7907, Santa Cruz Biotechnology), ALDH1 (AF5896 R&D Systems), Sox2 (ab97959, Abcam), CD44 (553131, BD Pharmingen), and CD34 (ACL8927AP, Accurate). Sections were washed and incubated with appropriate secondary biotinylated IgG and avidin–biotin complex (Elite-ABC kit, Vector Laboratories) followed by DAB reaction (Sigma-Aldrich). CD34 antibody incubation followed by incubation with secondary Cy3-conjugated antibody (1:100, Jackson ImmunoResearch) for 30–60 minutes and with DAPI for nuclear staining. Quantification of the staining was done utilizing Image Pro Plus software.

RNA sequencing and analysis

Three independent p53 Mut MSCs isolates and two individual p53 Mut MSC-TLs derived from primary tumors of each MSC isolate, as well as additional four p53 Mut MSC-TLs derived from secondary tumors (overall ten p53 Mut MSC-TLs) were subjected to RNA sequencing analysis. Cells were plated and when they reach to 80% confluent, RNA was extracted using Direct-zol RNA MiniPrep (ZYMO RESEARCH). Libraries preparation were done using the INCPM-RNA-seq. Briefly, polyA fraction (mRNA) was purified from 500 ng of total RNA following by fragmentation and generation of double-stranded cDNA. Then, end repair, A base addition, adapter ligation, and PCR amplification steps were performed.

Libraries were evaluated by Qubit and TapeStation. Sequencing libraries were constructed with barcodes to allow multiplexing of 22 samples in two lanes. A total of 18 million single-end 60-bp reads were sequenced per sample on Illumina HiSeq 2500 V4 instrument. Reads were trimmed using cutadapt (http://dx.doi.org/10.14806/ej.17.1.200) and mapped to mm10 genome (downloaded from igenomes) using TopHat (v2.0.10; http://dx.doi.org/10.1186/gb-2013-14-4-r36). Counting over refseq genes proceeded using htseq-count (http://dx.doi.org/10.1093/bioinformatics/btu638; intersection-strict mode). Differential expression analysis was performed using DESeq2 (http://dx.doi.org/10.1186/s13059-014-0550-8) with the betaPrior, cooks Cutoff and independent filtering parameters set to False. The data presented in this study have been deposited in NCBI's Gene Expression Omnibus (14) and are accessible through GEO series accession number GSE118173 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE118173).

Reverse transcription and qRT-PCR

RNA was extracted using Direct-zol RNA MiniPrep (ZYMO RESEARCH). Reverse transcription and qRT-PCR were performed as described previously (11). The sequences of the specific gene primers are listed in Supplementary Table S1.

Knockout of mutant p53 by CRISPR technology

Cells were transfected with Px330 backbone–expressing sgRNA for targeting murine p53 and Cas9 by GeneExpresso in vitro DNA transfection reagent (EG-1031, Excellgen). The Px330 p53 plasmid was a gift from Tyler Jacks (Addgene plasmid 59910). After 48 hours from the infection, we plated 0.5 cell per well in a 96-well plate to generate single-cell clones. After the generation of single-cell clones, we analyzed all clones by Western blot analysis to detect p53 knockout clones. Two clones out of 71 clones were KO for mutant p53. We verified by DNA sequencing the DNA editing by the guided RNA molecule.

Obtaining ESC gene sets

Three gene sets were collected form independent studies. First, a previous study from our laboratory performed a RNA sequencing analysis on ESCs upon differentiation induction by retinoic acid (15). We reanalyze the raw data and genes that were significantly downregulated upon differentiation (Padj < 0.05, FC < 2, max reads > 10) were selected as the ESC genes list. For the second study performed using bioChIP-chIP analysis (16), we selected shared target genes of Nanog, Sox2, and Oct4. A third dataset was extracted from a study that performed a time course microarray experiment on reprogramed MEFs into induce pluripotent stem cells (iPSC) by infecting with retroviruses encoding for the four yamanaka reprogramming factors (Oct4, Sox2, klf4, and c-myc). We selected genes that had expression levels more 2-fold after 8 days of the reprogramming process compared with day 0) and excluded genes that were upregulated in the control (infection with retrovirus encoding for GFP; ref. 17).

Gene set enrichment analysis

First, for each comparison, we estimated the significant genes (Padj < 0.05, FC > 2, max reads > 10). Moreover, from each dataset, we estimated the number of genes that had at least 10 reads (max) in our RNA sequencing analysis and analyzed whether those lists are enriched in the differential gene expression in every comparison. We utilized The GeneProf hypergeometric probability calculator (https://www.geneprof.org/GeneProf/tools/hypergeometric.jsp) to estimate the enrichment P value of the different gene sets. Gene set enrichment analysis (GSEA) was also performed by using GSEA software (http://software.broadinstitute.org/gsea/index.jsp). The enrichment of specific datasets was considered significant when the nominal (NOM) P value and FDR q value were less than 0.05.

The Cancer Genome Atlas data analysis

Survival analysis.

Data for the survival analyses were downloaded from The Cancer Genome Atlas (TCGA) via “Xena.” We examined whether the combined expression of each possible gene pair from the human orthologs of the MSC-TL ESC gene list, affected patient survival by Cox proportional-hazards model via the R package “survival.” We classified the expression level per gene per sample as “high” or “low,” using median of each gene value as cutoff. Next, to distill the genes that can predict poor prognosis, we first examined the combined expression of each possible gene pair from the ESC gene list and selected the pairs that significantly predicted poor survival in patients in each cancer type (FDR < 0.05; HR > 2). Next, we selected the significant genes derived from the pairwise survival analyses and performed multigene survival analyses via backward stepwise selection and arrived at a final survival model based on gene signatures that predicted poor patient survival. Next, we selected the most significant genes derived from the latter gene signatures and examined whether the combine expression of the selected genes can predict poor patient survival (by Cox proportional-hazards model).

Pan-cancer expression analysis.

Mutation and expression (RNA-seq) data was downloaded from TCGA via the R package “CGDSR.” We used the subset “3_way_complete,” which contains all tumor samples that have mRNA, CNA, and sequencing data. Differential expression between groups was tested using a t test on log2-transformed normalized counts. The frequency of the gene expression was calculated and normalized to the overall gene expression by χ2 test. The results shown here are based upon data generated by TCGA research network: http://cancergenome.nih.gov.

Mutant p53 enhanced the self-renewal capacity and oncogenicity of primary bone marrow MSCs

To examine the role of mutant p53 in stem cells transformation, we utilized a system containing BM-MSCs isolates harboring WT p53 or p53 R172H mutation, which is analogous to the frequent and known GOF hotspot mutation, R175H in human tumors (11). To corroborate whether mutant p53 MSCs isolates display loss of WT p53 activity, we examined the DNA damage response. As expected, upon doxorubicin treatment, p53 wild-type (p53 WT) MSCs showed an increase in nuclear p53 protein levels (Fig. 1A and B; Supplementary Fig. S1A) that was accompanied by the elevation of its target gene, p21 (Fig. 1A and C). In agreement with previous studies (4), mutant p53 was already accumulated in basal levels, regardless of the DNA damage response and there was no detection of p21 expression in p53-mutant (p53 Mut) MSCs upon doxorubicin treatment (Fig. 1A–C). Consistently, p53 Mut MSCs showed a higher proliferation rate compared with p53 WT MSCs (Fig. 1D). Therefore, no genuine p53 activity was detected in p53 Mut MSCs. As mentioned above, mutant p53 promote cancer development (4, 5). We further examined the tumorigenic potential. In agreement with our previous study (11), while all p53 Mut MSC isolates were able to form aggressive sarcomas, p53 WT MSCs lacked this ability (Supplementary Fig. S1B–S1D). Tumor-initiating capacity is ascribed to uncontrolled self-renewal pathways and acquisition of CSCs properties (18). Next, colony-forming unit-fibroblastic (CFU-Fs) assay showed that p53 Mut mice exhibited higher number of bone marrow stem cells compared with p53 WT mice (Fig. 1E), indicating a higher self-renewal ability of p53 Mut bone marrow–derived stem cells. Overall, mutant p53 enhanced the proliferation, tumorigenic potential, and the self-renewal of BM-MSCs.

Figure 1.

Mutant p53 enhanced the oncogenicity of primary bone marrow MSCs. Doxorubicin treatment (0.5 μg/mL) for 6 hours. A, Western blot analysis of p53 and p21. Representative experiment of three experiments. B, p53 expression by ImageStreamX. Images of representative cells (top). Representative plot of p53 intensity (bottom). C,p21 expression by qRT-PCR. Results are presented as mean ± SD of two experiments. D, Proliferation plot. *, P < 0.05 by repeated-measures ANOVA. Representative experiment of two experiments. E, CFU-Fs from the BM of p53 WT and p53 Mut mice (top). Bottom, the results are displayed as means ± SEM. *, P < 0.05 by two-tailed Student t test. Representative experiment of two experiments.

Figure 1.

Mutant p53 enhanced the oncogenicity of primary bone marrow MSCs. Doxorubicin treatment (0.5 μg/mL) for 6 hours. A, Western blot analysis of p53 and p21. Representative experiment of three experiments. B, p53 expression by ImageStreamX. Images of representative cells (top). Representative plot of p53 intensity (bottom). C,p21 expression by qRT-PCR. Results are presented as mean ± SD of two experiments. D, Proliferation plot. *, P < 0.05 by repeated-measures ANOVA. Representative experiment of two experiments. E, CFU-Fs from the BM of p53 WT and p53 Mut mice (top). Bottom, the results are displayed as means ± SEM. *, P < 0.05 by two-tailed Student t test. Representative experiment of two experiments.

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Mutant p53 MSC–derived tumor lines exhibited augmented tumorigenic capacity compared with their parental cells

Tumor intra-heterogeneity that has been accounted for tumor development, is attributed to the existence of multiple subclones within a tumor that have different tumorigenic abilities (1). To propagate the tumorigenic cells within p53 Mut MSC–derived tumors, we cultivated these tumors in vitro and established various MSC-TLs. p53 Mut MSC-TLs exhibited higher proliferation and viability capacity compared with their p53 Mut parental MSCs (pMSC; Fig. 2A; Supplementary Fig. S2A). Consistently, as expected, cell-cycle analysis revealed lower percentage of cells in the G1 phase and higher percentage of cells in the S-phase in p53 Mut MSC-TLs compared with their parental cells, p53 Mut pMSCs (Supplementary Fig. S2B and S2C). Furthermore, we found that p53 Mut MSC-TL–derived cells are smaller in size and more circular compared with their parental cells (Fig. 2B; Supplementary Fig. S2D–S2F). Interestingly, stem cells and iPSCs are characterized by high proliferation rate, small cell size, and circular morphology (19, 20). To explore the possibility that the MSC-TLs exhibit stem cell features, we examine CD44 expression, a common marker for CSCs in various cancer models (21, 22). The percentage of CD44high subpopulation in the p53 Mut MSC-TLs was higher compared with their parental cells (Fig. 2C). Next, an in vivo serial dilution transplantation assay showed higher tumor initiation capacity and tumorigenic cell frequency in p53 Mut MSC-TLs compared with their parental cells (Fig. 2D). Strikingly, as few as 1 × 102 cells from p53 Mut MSC-TLs were able to generate tumors within an average of 34.7 days, whereas subcutaneous injection of 3 × 106 p53 Mut pMSCs gave rise to tumors within 97 days (mean; Fig. 2E; Table 1). Consistently, in vivo imaging enabled the detection of tumors, following subcutaneously injection of 1 × 102–103 cells derived from p53 Mut MSC-TL, already within 14 days. Injection of the same amount of pMSCs yielded no tumor development (Fig. 2F; Supplementary Fig. S2G), even following 6 months. Furthermore, tumors derived from MSC-TLs displayed peripheral nerves and blood vessels invasion, reflecting aggressive features that were not detected in the pMSC-derived tumors (Fig. 2G). MSC-TL–derived tumors also demonstrated higher expression of the proliferative marker, PCNA, and of another common CSC marker (23), aldehyde dehydrogenase isoform 1 (ALDH1), compared with the pMSC-derived tumors (Fig. 2H; Supplementary Fig. S2H and S2I). Altogether, these results suggest that upon cultivation of mutant p53 pMSC–derived tumors, we enriched for highly tumorigenic cell lines with CSC features.

Figure 2.

Mutant p53 MSC–derived tumor lines exhibited augmented tumorigenic capacity compared with their parental cells. A, Proliferation plot. Representative experiment of two experiments. Light gray curve, p53 Mut pMSCs; dark gray curve, p53 Mut MSC-TLs. **, P < 0.01 by repeated-measures ANOVA. B, The 1 × 104–2 × 104 cells were imaged by ImageStreamX (left). The percentage of the subpopulation with low area and high circularity was determined. Results are presented as mean ± SEM. *, P < 0.05 by paired two-tailed Student t test (right). Representative plot of circularity versus area. The red gate contains the small and circular subpopulation. C, CD44 expression was determined by ImageStreamX. Top, the results are from two experiments and are presented as mean ± SEM. *, P < 0.05 by paired one-tailed Student t test. Bottom, representative image of CD44HIGH and CD44LOW expression levels. BF, bright field. D, Three p53 Mut MSC isolate and their derived MSC-TLs were subcutaneously injected into athymic Nude-Foxn1nu mice. Tumor take percentage is presented as mean ± SEM. Top, ***, P < 0.001 by proportion test. Bottom, tumorigenic cell frequency (http://bioinf.wehi.edu.au/software/elda/). CI, confidence interval. E, Distribution of tumors latency is presented as mean ± SEM. Survival analysis showed higher HR in the p53 Mut MSC-TL group compared with p53 Mut pMSC group (115-fold increase in tumor development; ***, P < 0.001). F, Bioluminescence monitoring of growing tumors in mice subcutaneously injected with p53 Mut pMSC isolate and a derived MSC-TL. Results are shown as means ± SEM. ****, P < 0.0001 by repeated-measures ANOVA (representative images in Supplementary Fig. S2G). G, a, Hematoxylin and eosin staining of MSC-TL–derived tumor indicating invasion of a peripheral nerve. Arrowheads identify the outline of the infiltrated nerve. b, Blood vessel invasion (vein). Arrowheads identify the severely damaged vein wall. The lumen is filled with neoplastic cells. A, artery. c, Invasive growth. The neoplastic cells form finger-like extensions (arrows), which infiltrate the surrounding skeletal muscle. d, High mitotic rate (arrows mark cells in mitoses). H, PCNA and ALDH1 expression in p53 Mut MSCs and MSC-TL–derived tumors by IHC staining (quantification and statistics in Supplementary Fig. S2H and S2I).

Figure 2.

Mutant p53 MSC–derived tumor lines exhibited augmented tumorigenic capacity compared with their parental cells. A, Proliferation plot. Representative experiment of two experiments. Light gray curve, p53 Mut pMSCs; dark gray curve, p53 Mut MSC-TLs. **, P < 0.01 by repeated-measures ANOVA. B, The 1 × 104–2 × 104 cells were imaged by ImageStreamX (left). The percentage of the subpopulation with low area and high circularity was determined. Results are presented as mean ± SEM. *, P < 0.05 by paired two-tailed Student t test (right). Representative plot of circularity versus area. The red gate contains the small and circular subpopulation. C, CD44 expression was determined by ImageStreamX. Top, the results are from two experiments and are presented as mean ± SEM. *, P < 0.05 by paired one-tailed Student t test. Bottom, representative image of CD44HIGH and CD44LOW expression levels. BF, bright field. D, Three p53 Mut MSC isolate and their derived MSC-TLs were subcutaneously injected into athymic Nude-Foxn1nu mice. Tumor take percentage is presented as mean ± SEM. Top, ***, P < 0.001 by proportion test. Bottom, tumorigenic cell frequency (http://bioinf.wehi.edu.au/software/elda/). CI, confidence interval. E, Distribution of tumors latency is presented as mean ± SEM. Survival analysis showed higher HR in the p53 Mut MSC-TL group compared with p53 Mut pMSC group (115-fold increase in tumor development; ***, P < 0.001). F, Bioluminescence monitoring of growing tumors in mice subcutaneously injected with p53 Mut pMSC isolate and a derived MSC-TL. Results are shown as means ± SEM. ****, P < 0.0001 by repeated-measures ANOVA (representative images in Supplementary Fig. S2G). G, a, Hematoxylin and eosin staining of MSC-TL–derived tumor indicating invasion of a peripheral nerve. Arrowheads identify the outline of the infiltrated nerve. b, Blood vessel invasion (vein). Arrowheads identify the severely damaged vein wall. The lumen is filled with neoplastic cells. A, artery. c, Invasive growth. The neoplastic cells form finger-like extensions (arrows), which infiltrate the surrounding skeletal muscle. d, High mitotic rate (arrows mark cells in mitoses). H, PCNA and ALDH1 expression in p53 Mut MSCs and MSC-TL–derived tumors by IHC staining (quantification and statistics in Supplementary Fig. S2H and S2I).

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Table 1.

Mutant p53 MSC–derived tumor lines exhibited augmented tumorigenic capacity compared with their parental cells

Cell typeNumber of injected cellsTumor/miceTumor take (%)Days to tumor detection (mean)
p53 Mut MSCs (1) 102 0/4 
p53 Mut MSCs (1) 103 0/4 
p53 Mut MSCs (1) 105 0/4 
p53 Mut MSC-TL (1) 102 3/4 75 33 
p53 Mut MSC-TL (1) 103 3/4 75 28.3 
p53 Mut MSC-TL (1) 105 4/4 100 21.7 
p53 Mut MSCs (2) 102 0/4 
p53 Mut MSCs (2) 103 0/4 
p53 Mut MSCs (2) 105 0/4 
p53 Mut MSCs (2) 3 × 106 4/5 80 73.5 
p53 Mut MSC-TL (2) 102 4/9 44.4 42.2 
p53 Mut MSC-TL (2) 103 9/9 100 29.4 
p53 Mut MSC-TL (2) 105 9/9 100 18.7 
p53 Mut MSCs (3) 102 0/4 
p53 Mut MSCs (3) 103 0/4 
p53 Mut MSCs (3) 105 1/4 25 144 
p53 Mut MSCs (3) 3 × 106 9/9 100 120.6 
p53 Mut MSC-TL (3) 102 9/11 81.8 29 
p53 Mut MSC-TL (3) 103 9/14 64.2 23.1 
p53 Mut MSC-TL (3) 105 5/5 100 15.2 
Cell typeNumber of injected cellsTumor/miceTumor take (%)Days to tumor detection (mean)
p53 Mut MSCs (1) 102 0/4 
p53 Mut MSCs (1) 103 0/4 
p53 Mut MSCs (1) 105 0/4 
p53 Mut MSC-TL (1) 102 3/4 75 33 
p53 Mut MSC-TL (1) 103 3/4 75 28.3 
p53 Mut MSC-TL (1) 105 4/4 100 21.7 
p53 Mut MSCs (2) 102 0/4 
p53 Mut MSCs (2) 103 0/4 
p53 Mut MSCs (2) 105 0/4 
p53 Mut MSCs (2) 3 × 106 4/5 80 73.5 
p53 Mut MSC-TL (2) 102 4/9 44.4 42.2 
p53 Mut MSC-TL (2) 103 9/9 100 29.4 
p53 Mut MSC-TL (2) 105 9/9 100 18.7 
p53 Mut MSCs (3) 102 0/4 
p53 Mut MSCs (3) 103 0/4 
p53 Mut MSCs (3) 105 1/4 25 144 
p53 Mut MSCs (3) 3 × 106 9/9 100 120.6 
p53 Mut MSC-TL (3) 102 9/11 81.8 29 
p53 Mut MSC-TL (3) 103 9/14 64.2 23.1 
p53 Mut MSC-TL (3) 105 5/5 100 15.2 

Embryonic gene signature expressed in mutant p53 MSC-TLs

To identify gene networks that underlie the accentuated oncogenic activity of p53 Mut MSC-TLs, we subjected three independent parental p53 Mut MSCs isolates, two individual p53 Mut MSC-TLs derived from primary tumors of each MSC isolate, as well as additional four p53 Mut MSC-TLs derived from secondary tumors, which exhibited similar tumorigenic capacity as the primary MSC-TLs (Supplementary Fig. S3A) to transcriptome profiling. While the principal component analysis and hierarchical clustering revealed transcriptional variances between individual p53 Mut MSC-TLs, the largest transcriptome variation was observed between p53 Mut pMSCs and their derived MSC-TLs (Supplementary Fig. S3B and S3C). This finding indicates a tumorigenic transcriptional core that is shared between all p53 Mut MSC-TLs. The aggressive nature of p53 Mut MSC-TLs led us to examine whether they express a CSC signature. We utilized a defined murine CSC signature derived from head and neck squamous cell carcinoma (24). We found that this CSC signature was enriched (P = 2.2 × 10−16) and the CSC signature–derived genes were more frequent in the upregulated gene fraction of p53 Mut MSC-TLs compared with their parental cells (χ2 test, P = 0.0003; Fig. 3A; Supplementary Table S2). Next, GSEA showed that p53 Mut MSC-TL signature was enriched for stemness and undifferentiated cancer gene sets (Fig. 3B). Epithelial-to-mesenchymal transition (EMT) was shown to be associated with acquisition of stemness and CSC phenotype (25). We further found that an EMT gene signature is enriched in p53 Mut MSC-TLs compared with their parental cells, p53 Mut pMSCs (Fig. 3B). Analyzing the genes that were upregulated in p53 Mut MSC-TLs compared with their parental cells, by Ingenuity Pathway Analysis (IPA) tool (26) linked them to biological functions including cell movement, proliferation, and survival, whereas this analysis showed downregulation of genes associated with organismal death and growth failure (Fig. 3C; Supplementary Table S3). The activated canonical pathways in the p53 Mut MSC-TLs, according to IPA, unveiled cancer-associated pathways including paxilin, Gα12/13, PAK, integrin, and CDK5 signaling (Supplementary Table S3). Interestingly, IPA also highlighted the activation of stem cell upstream regulators in the p53 Mut MSC-TLs that are involved in different developmental pathways, including Wnt, Hedgehog, and Notch pathways (Fig. 3D; Supplementary Table S3). Furthermore, the activated upstream regulators also included oncogenic pluripotency regulators, Myc and Klf4, as well as embryonic stem cells regulators including EZH2 and BRG1. On the contrary, upstream regulators that were inhibited in the p53 Mut MSC-TLs comprised of tumor suppressors including CDKN1B, TSC2, STK11, mir-141-3p, and mir-34a-5p (Fig. 3D; Supplementary Table S3). Notably, mir-34-5p was shown to negatively regulate the Wnt signaling pathway (27, 28). Furthermore, IPA analysis, identified, as the highest scored network in p53 Mut MSC-TLs, a gene network associated with cellular, embryonic, and organismal development (Supplementary Fig. S3D; Supplementary Table S3). To further examine these observations, we obtained murine ESC signature datasets from three independent published studies (Supplementary Table S2). A hypergeometric probability test was aimed to examine whether the differential genes between p53 Mut MSC-TLs and p53 Mut pMSCs, contained higher number of genes than randomly expected, from a particular ESC dataset, indicating ESC dataset enrichment. To that end, we obtained a dataset from a previous comparative analysis of p53 WT murine ESCs before and after retinoic acid–induced differentiation (15). We focused on genes that were downregulated during ESCs differentiation, because these genes, most likely, represent the ESC expression signature. This gene set was enriched in p53 Mut MSC-TLs and included 129 overexpressed genes as compared with the parental MSCs (P = 0.02). A second gene set was extracted from a published study identifying target genes of embryonic transcription factors (16). Shared target gene list of the ESC transcriptional factors; Nanog, Sox2, and Oct4 yielded a significant enrichment score in the upregulated gene fraction of p53 Mut MSC-TLs compared with their parental cells (P = 0.005). Finally, we analyzed a gene set comprising of genes that were upregulated upon induction of iPSCs (17). This list contained genes that showed, at least, 2-fold increase in their expression following 8 days of pluripotency induction. Consistently, the reprogramming gene set contained 39 overexpressed genes in the p53 Mut MSC-TLs and yielded a significant enrichment score (P = 1.23 × 10−7). However, enrichment analysis of the downregulated genes in the p53 Mut MSC-TLs compared with their parental cells did not yielded a significant P value in any ESC dataset. Overall, we detected 184 ESC genes derived from the three datasets that were upregulated in the p53 Mut MSC-TLs (Fig. 3E; Supplementary Table S2). The ESC transcription factor, Sox2, known to be essential for maintaining pluripotency of ESCs, was shared by all the ESC datasets and was highly expressed in the p53 Mut MSC-TLs as compared with their parental cells (Fig. 3F and G). The expression of additional ESC signature–derived genes were validated (Supplementary Fig. S3E). Interestingly, ESC gene signature derived genes that were downregulated in p53 Mut MSC-TLs were associated with promoting apoptosis and attenuating cell proliferation (Supplementary Fig. S3F). Overall, p53 Mut MSC-TLs highly expressed cancer and CSC-associated genes and an ESC gene signature.

Figure 3.

Embryonic gene signature expressed in mutant p53 MSC-TLs. RNA sequencing of p53 Mut pMSCs and p53 Mut MSC-TLs. A, Heatmap of CSC gene signature–derived genes. B, GSEA enrichment plots. C, Diseases or functions that are increased or decreased in the p53 Mut MSC-TLs versus p53 Mut MSCs according to IPA. D, Activated or inactivated upstream regulators in p53 Mut MSC-TLs versus p53 Mut MSCs according to IPA. E, Heatmaps representing the three ESC gene dataset–derived genes. F,Sox2 expression by qRT-PCR. **, P < 0.01 by two-tailed Student t test. G, IHC staining of Sox2 in MSCs and MSC-TL–derived tumors. Four tumors (two tumors per slide) in each group were stained and the average of the percentage of Sox2-positive cells of, at least, 13 random fields in each slide was estimated. The results are presented as mean ± SEM. ***, P < 0.001 by two-tailed Student t test. Representative images are presented.

Figure 3.

Embryonic gene signature expressed in mutant p53 MSC-TLs. RNA sequencing of p53 Mut pMSCs and p53 Mut MSC-TLs. A, Heatmap of CSC gene signature–derived genes. B, GSEA enrichment plots. C, Diseases or functions that are increased or decreased in the p53 Mut MSC-TLs versus p53 Mut MSCs according to IPA. D, Activated or inactivated upstream regulators in p53 Mut MSC-TLs versus p53 Mut MSCs according to IPA. E, Heatmaps representing the three ESC gene dataset–derived genes. F,Sox2 expression by qRT-PCR. **, P < 0.01 by two-tailed Student t test. G, IHC staining of Sox2 in MSCs and MSC-TL–derived tumors. Four tumors (two tumors per slide) in each group were stained and the average of the percentage of Sox2-positive cells of, at least, 13 random fields in each slide was estimated. The results are presented as mean ± SEM. ***, P < 0.001 by two-tailed Student t test. Representative images are presented.

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Mutant p53 knockout reduced tumor-initiating capacity of mutant p53 MSC-TL subclones

The gain of oncogenic activities by mutant p53 that promote tumor development is a well-accepted notion (4, 5). To determine whether the enhanced tumorigenic capacity of MSC-TLs is mutant p53 dependent, we knocked out mutant p53 in a MSC-TL (Supplementary Fig. S4A). Knockout (KO) of mutant p53 resulted in a significant reduction in tumor-initiating capacity, tumor development and tumorigenic cell frequency (Fig. 4A–D). Tumors derived from p53 KO MSC-TL subclones were significantly smaller compared with tumors derived from p53 Mut MSC-TL subclones, exemplified by tumor weight and size (Fig. 4A and B). Furthermore, tumor latency was longer upon mutant p53 KO (Fig. 4C). Notably, injection of as low as 1 × 102 showed significant reduction in the incidence of tumors upon KO of mutant p53 (Fig. 4D). Furthermore, injection of 10 cells, yielded tumors only upon injection of mutant p53 expressing MSC-TL subclones (Fig. 4C and D), indicating a mutant p53 GOF activity in expanding tumorigenic cell population. Tumors derived from mutant p53 KO MSC-TL subclones displayed wider area of tumor necrosis, as well as a reduction in tumor vascularization, as determined by CD34 staining, compared with tumors derived from mutant p53 MSC-TL subclones (Fig. 4E; Supplementary Fig. S4B). This phenomenon is in line with the notion that mutant p53 exhibits a GOF in angiogenesis (4, 29). Furthermore, upon mutant p53 KO there was a significant reduction in the expression of the two CSC markers, CD44 and ALDH1, which were shown to be highly expressed in p53 Mut MSC-TLs compared with their parental cells (Fig. 4F and G). Altogether, these results suggest that mutant p53 enhances tumor growth, angiogenesis, survival, expression of CSC markers, and most importantly expansion of tumor-initiating cells within the MSC-TLs.

Figure 4.

Mutant p53 knockout reduced the tumor-initiating capacity of mutant p53 MSC-TL subclones. p53 Mut and p53 KO MSC-TL subclones were subcutaneously injected into athymic nude-Foxn1nu mice (see the blot of mutant p53 KO in Supplementary Fig. S4). A, Fourteen days after injection of 1 × 105 cells, tumors were removed. Tumors weight are presented as mean ± SEM. ***, P < 0.001 by two-tailed Student t test. B, A total of 1 × 104 cells from each group (n = 3) were subcutaneously injected for tumor growth observation. The results are shown as mean ± SEM. *, P < 0.05 by log-transformed rate t test. C, Tumor latency is presented as mean ± SEM. Survival analysis showed higher HR in the p53 Mut MSC-TL group compared with p53 KO group (5.2-fold increase in tumor development; **, P < 0.01). D, Percentage of tumor take is presented as mean ± SEM. Number of mice in each cell amount engraftment; n = 10, 1 × 101; n = 17, 1 × 102; n = 7, 1 × 103 in each group. Top, **, P < 0.01 by proportion test. Bottom, tumorigenic cell frequency (http://bioinf.wehi.edu.au/software/elda/). CI, confidence interval. E, Representative hematoxylin and eosin staining of p53 KO (a+b) and p53 Mut (c+d) MSC-TL subclone–derived tumors. a, Low magnification of a p53 KO MSC-TL subclone–derived tumor with extensive necrosis, seen as pink-stained areas (asterisks). The boxed area is shown in b. c, Low magnification of a p53 Mut MSC-TL subclone–derived tumor composed of viable tissue. The boxed area is shown in d. F, IHC analysis of ALDH1 and CD44. ALDH1 analysis contained sections from three tumors in each group and the CD44 analysis contained sections from four tumors in each group. The staining area in, at least, 10 random fields in each slide was estimated. Representative images are presented. G, The results are presented as mean ± SEM. ***, P < 0.001 by two-tailed Student unpaired t test.

Figure 4.

Mutant p53 knockout reduced the tumor-initiating capacity of mutant p53 MSC-TL subclones. p53 Mut and p53 KO MSC-TL subclones were subcutaneously injected into athymic nude-Foxn1nu mice (see the blot of mutant p53 KO in Supplementary Fig. S4). A, Fourteen days after injection of 1 × 105 cells, tumors were removed. Tumors weight are presented as mean ± SEM. ***, P < 0.001 by two-tailed Student t test. B, A total of 1 × 104 cells from each group (n = 3) were subcutaneously injected for tumor growth observation. The results are shown as mean ± SEM. *, P < 0.05 by log-transformed rate t test. C, Tumor latency is presented as mean ± SEM. Survival analysis showed higher HR in the p53 Mut MSC-TL group compared with p53 KO group (5.2-fold increase in tumor development; **, P < 0.01). D, Percentage of tumor take is presented as mean ± SEM. Number of mice in each cell amount engraftment; n = 10, 1 × 101; n = 17, 1 × 102; n = 7, 1 × 103 in each group. Top, **, P < 0.01 by proportion test. Bottom, tumorigenic cell frequency (http://bioinf.wehi.edu.au/software/elda/). CI, confidence interval. E, Representative hematoxylin and eosin staining of p53 KO (a+b) and p53 Mut (c+d) MSC-TL subclone–derived tumors. a, Low magnification of a p53 KO MSC-TL subclone–derived tumor with extensive necrosis, seen as pink-stained areas (asterisks). The boxed area is shown in b. c, Low magnification of a p53 Mut MSC-TL subclone–derived tumor composed of viable tissue. The boxed area is shown in d. F, IHC analysis of ALDH1 and CD44. ALDH1 analysis contained sections from three tumors in each group and the CD44 analysis contained sections from four tumors in each group. The staining area in, at least, 10 random fields in each slide was estimated. Representative images are presented. G, The results are presented as mean ± SEM. ***, P < 0.001 by two-tailed Student unpaired t test.

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Mutant p53 knockout led to a reduction in the expression of the ESC gene signature expression in MSC-TL subclones

Next, we examined whether the reduction in tumorigenicity following mutant p53 KO is associated with a decrease in the ESC gene signature expression, described above. Therefore, we subjected mutant p53 MSC-TLs and the corresponding mutant p53 KO MSC-TL subclones to a transcriptome profiling by RNA sequencing (Supplementary Table S4). IPA analysis of the differential genes upon KO of mutant p53 in the MSC-TLs revealed increase of biological functions such as organismal death, morbidity, and mortality and decrease of cell migration and proliferation (Fig. 5A; Supplementary Table S5). Interestingly, we identified inactivation of several cancer-related pathways upon mutant p53 KO. These included integrin, paxilin, Gα12/13, PAK, and CDK5 signaling pathways that were previously shown to be activated in the MSC-TLs compared with their parental cells. Additional cancer-related pathways that were inhibited upon mutant p53 KO included RhoA, CXCR4, PDGF, ILK, and VEGF signaling pathways. Mutant p53 abolishment also led to the downregulation of genes associated with mouse ESC pluripotency pathway (Supplementary Table S5). Finally, we detected downregulation of two reprogramming regulators Myc and Klf4 following mutant p53 depletion (Fig. 5B). Furthermore, we found that the p53 Mut MSC-TL ESC signature was significantly downregulated upon mutant p53 abolishment (P = 5 × 10−8; Fig. 5C; Supplementary Table S4). Notably, the ESC genes, shown to be downregulated upon retinoic acid differentiation induction (15), yielded a further enrichment in the downregulated genes upon mutant p53 knockout (P = 5.9 × 10−9; Fig. 5D; Supplementary Table S4).

Figure 5.

Mutant p53 knockout led to a reduction in the expression of the ESC gene signature expression in MSC-TL subclones. Transcriptional analysis of p53 Mut and p53 KO MSC-TL subclones by RNA sequencing. A, Diseases or functions that were increased or decreased following KO of p53 Mut according to IPA. B,Myc- and Klf4-normalized counts. C, Heatmap of MSC-TL ESC gene signature–derived genes following KO of p53 Mut. D, The volcano plot presents differential genes (black) and ESC-derived genes that were downregulated (blue) or upregulated (red) upon KO of mutant p53 in MSC-TL subclones. E, Heatmap of PRC2 target genes following KO of p53 Mut. F, The volcano plot presents differential genes (black) and PRC2 target genes that were downregulated (blue) and upregulated (red) upon KO of mutant p53 in MSC-TL subclones. G, qRT-PCR analysis of ESC genes' expression. H, Western blot analysis of p53- and ESC-derived genes.

Figure 5.

Mutant p53 knockout led to a reduction in the expression of the ESC gene signature expression in MSC-TL subclones. Transcriptional analysis of p53 Mut and p53 KO MSC-TL subclones by RNA sequencing. A, Diseases or functions that were increased or decreased following KO of p53 Mut according to IPA. B,Myc- and Klf4-normalized counts. C, Heatmap of MSC-TL ESC gene signature–derived genes following KO of p53 Mut. D, The volcano plot presents differential genes (black) and ESC-derived genes that were downregulated (blue) or upregulated (red) upon KO of mutant p53 in MSC-TL subclones. E, Heatmap of PRC2 target genes following KO of p53 Mut. F, The volcano plot presents differential genes (black) and PRC2 target genes that were downregulated (blue) and upregulated (red) upon KO of mutant p53 in MSC-TL subclones. G, qRT-PCR analysis of ESC genes' expression. H, Western blot analysis of p53- and ESC-derived genes.

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The polycomb repressive complex 2 (PRC2) regulates the pluripotency of ESCs by repressing development-associated genes (30). We found that a gene set of PRC2-repressed target genes (31) was enriched and more frequent in the upregulated gene fraction following mutant p53 KO (Enrichment P = 1 × 10−15, χ2 test P = 0.02; Fig. 5E and F; Supplementary Table S4). This might suggest that in p53 Mut MSC-TL subclones PRC2 is active, thus contributing to the ESC signature expression phenotype. Moreover, the expression of Sox2, previously shown to be elevated in the different MSC-TLs compared with their parental cells, was downregulated following mutant p53 KO (Fig. 5G and H). We validated the downregulation of additional ESC signature derived genes following mutant p53 KO (Fig. 5G and H; Supplementary Fig. S5A). By utilizing BindDB tool (32), we showed that the proximal promoters of mutant p53 MSC-TL–dependent genes are enriched for chromatin transcriptional activation marks in ESCs (Supplementary Fig. S5B). These findings suggest that mutant p53 induces the expression of ESC gene signature in MSC-TLs.

The MSC-TL ESC signature–derived genes are associated with poor patient survival and correlate with human tumors harboring p53 missense mutations

To assess the relevance of the murine MSC-TL ESC signature in human patients with cancer, we utilized datasets provided by TCGA. We focused on the human orthologs of ESC gene signature, which was detected in the MSC-TLs, and additional mutant-dependent ESC genes. We identified unique gene signatures, whose combined expression predicted poor patient survival in several cancer types (Fig. 6A; Supplementary Fig. S6; Supplementary Table S6, detailed in Material and Methods section). Of note, a single-gene survival analysis yielded lower HRs compared with the combined expression of genes, indicating the importance of analyzing the combined expression of genes (Supplementary Table S6). Furthermore, the combined expression of four transcription factors, derived from the MSC-TL ESC gene signature, E2F2, HMGA1, SOX2, and ZIC2 predicted poor patient survival in various cancer types (Supplementary Fig. S6; Supplementary Table S6).

Figure 6.

The MSC-TL ESC signature–derived genes are associated with poor patient survival and correlate with human tumors harboring p53 missense mutations. A, Kaplan–Meier graph of the indicated datasets showing significant relationship between genes expression levels and patient survival. B, Volcano plot of the identified MSC-TL ESC gene signature and mutant p53–dependent ESC genes in the p53-hotspot mutations TCGA samples versus other p53 statuses TCGA samples (χ2 test, P = 0.01). C, Volcano plot of a previously described human ESC signature in the p53-hotspot mutations tumor samples versus other p53 statuses samples in TCGA datasets (χ2 test, P = 0.004). D, Box plots of RNA expression in TCGA samples harboring one of the p53-hotspot mutations versus other p53 statuses. Statistical analysis was determined by t test on log2-transformed normalized counts (P < 0.05; −1.5 > FC > 1.5). FDR corrected P value is indicated on the top of the box plot.

Figure 6.

The MSC-TL ESC signature–derived genes are associated with poor patient survival and correlate with human tumors harboring p53 missense mutations. A, Kaplan–Meier graph of the indicated datasets showing significant relationship between genes expression levels and patient survival. B, Volcano plot of the identified MSC-TL ESC gene signature and mutant p53–dependent ESC genes in the p53-hotspot mutations TCGA samples versus other p53 statuses TCGA samples (χ2 test, P = 0.01). C, Volcano plot of a previously described human ESC signature in the p53-hotspot mutations tumor samples versus other p53 statuses samples in TCGA datasets (χ2 test, P = 0.004). D, Box plots of RNA expression in TCGA samples harboring one of the p53-hotspot mutations versus other p53 statuses. Statistical analysis was determined by t test on log2-transformed normalized counts (P < 0.05; −1.5 > FC > 1.5). FDR corrected P value is indicated on the top of the box plot.

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To explore a correlation between p53 hotspot mutations and the expression of the ESC gene signature we performed a pan-cancer gene expression analysis according to TCGA datasets. Datasets derived from different cancer types that includes tumor samples with a characterized p53 status were studied (Supplementary Table S7). As described previously, missense mutations in the p53 gene are the most common mutations found in human cancers and usually occur in six hotspot amino acids in the DNA-binding domain of the protein. These mutations produce full-length mutant p53 proteins that were shown to exhibit oncogenic GOF (4, 5). Similar to the IARC p53 database, these six hotspot mutations were highly frequent in the TCGA database (Supplementary Fig. S7). Of note, we compared between tumor samples harboring one of the p53-hotspot missense mutations and the entire tumor samples that harbor other p53 mutations or WT p53. We assumed that tumors expressing WT p53 probably entailed inactivation of p53-associated pathways, due to mutations in other downstream genes (33). The frequency of highly expressed MSC-TL ESC genes in tumors harboring hotspot missense p53 mutations was significantly higher compared with the rest of tumor samples (Fig. 6B; Supplementary Table S8;χ2 test P = 0.01). By analyzing a previously described human ESC signature (30), we found a higher proportion of human ESC–derived genes that were highly expressed in tumors harboring one of the six p53 hotspot missense mutations compared with the rest of tumor samples (χ2 test P = 0.004; Fig. 6C; Supplementary Table S8). Importantly, SOX2 was upregulated in p53-hotspot missense mutations tumor samples. Box plot of SOX2 and additional ESC genes are presented (Fig. 6D; Supplementary Fig. S8). These analyses indicated that like in our mouse model, expression of mutant p53 proteins in human tumors correlated with the expression of the presently described p53 Mut MSC-TL–associated ESC gene signature and human ESC signature.

p53 mutations are the most common alterations in human tumors that lead to the gain of oncogenic traits, which promote cancer development (4, 5). CSCs were suggested to be an important milestone underlying oncogenicity. The intrinsic stemness potential of adult stem cells, together with their long life span, enabling mutations accumulation, make them suitable candidates to be the cell of origin of CSCs. p53 was shown maintaining the normal equilibrium between self-renewal and differentiation of stem cells (21, 34). Various studies support the notion that loss of WT p53 in stem cells leads to tumor initiation (21). Here, we demonstrate that BM-MSCs derived from p53-mutant mice exhibit an augmented sarcomagenesis and higher self-renewal capacity. This is in agreement with previous studies showing that p53 preserves the genomic integrity of MSCs and prevents their malignant transformation (11, 35). Cultured MSCs, similarly to cancer cells, exhibit intra-population heterogeneity with variable proliferation and differentiation capacities and display a repertoire of surface markers (36). Accordingly, the low frequency of tumorigenic cells seen within the parental p53 Mut MSCs may account for intra-population heterogeneity with a restricted number of tumorigenic cells within the population (Fig. 2D). Our data shows that by culturing mutant p53 MSC–derived tumors, we enriched for aggressive TLs. These MSC-TLs exhibited CSC expression markers and an augmented tumorigenic capacity, by the injections of as few as 1 × 102. Notably, under the same conditions, no tumors were evident upon the injection of their parental p53 Mut MSCs. Transcriptome analysis revealed that the augmented tumorigenic capacity of the established p53 Mut MSC-TLs was associated with the expression of CSC gene signature and a unique gene signature consisting of ESC genes. ESC genes that were downregulated in p53 Mut MSC-TLs were linked with positive regulation of apoptosis and cell proliferation attenuation. These results indicate that the aggressive p53 Mut MSC-TLs adopt only a part of the ESC signature, which favors tumorigenesis, but suppress the part of the ESC signature that negatively regulates the tumorigenic processes. MSC-TLs derived from identical parental MSCs were clustered together. This transcriptional clustering and the shared tumorigenic phenotype by all p53 Mut MSC-TLs in conjunction with intra-population heterogeneity in MSCs (36) might indicate the existence of a common origin within the mutant p53 MSC population rather than a stochastic event. Overall, these results suggest that in vitro cultivation of the mutant p53 MSC–derived tumors indeed selected for a CSC-like population that reside within MSC-derived tumors. This led to the identification of a mesenchymal CSC gene signature.

p53 inactivation in human breast tumors were shown to correlate with expression of an ESC signature (37). Furthermore, WT p53 was shown to negatively regulate the expression of ESC transcription factors (21). These findings suggest that the ESC signature expression in tumors lacking WT p53 activity may result from the absence of a p53-mediated repression of ESC factors' expression.

As mentioned above, p53 hotspot missense mutations are the most common alteration in human tumors that produce mutant p53 proteins that exhibit oncogenic traits that are beyond the abrogation of the normal tumor suppressor activity (4, 5). We and others have shown a mutant p53 GOF activity in promoting EMT (38, 39). Notably, the EMT was shown to exert stem cell characteristics (25). Furthermore, mutant p53 was shown to induce dedifferentiation of a human osteosarcoma cell line (40). In addition, mutant p53 was shown to facilitate the reprogramming process and to generate iPSCs with tumorigenic ability (9). Recently, we reported that mutant p53 upregulate the expression of CSC markers in colon cell lines (41). These observations might indicate a mutant p53 GOF activity in enhancing cancer plasticity and stemness phenotype, thus promoting the malignant process. However, the molecular profiling of endowing cells with stemness phenotype by mutant p53 protein remains to be elucidated.

The observation that p53 knockout in p53 Mut MSC-TL led to a reduction of tumorigenesis and CSC markers' expression indicates a role of mutant p53 in expanding the CSC population. As expected, we found downregulation of cancer-associated genes following mutant p53 KO. Interestingly, the ESC signature, which was identified in the p53 Mut MSC-TLs, was also downregulated following mutant p53 abrogation. This ample change in ESC gene signature expression, regulated by the presence of mutant p53 protein, rather than loss of WT p53 function, is novel. The significant differences in transcriptome profile between p53 Mut MSC-TL and p53 Mut KO MSC-TL subclones implies an involvement of a broader transcriptional regulation, such as an epigenetic regulation. Indeed, we showed that promoters of the mutant p53 MSC-TL–dependent genes are enriched for chromatin transcriptional activation marks in ESCs. Furthermore, we found that Polycomb Repressive Complex 2 (PRC2) target genes are downregulated in p53 Mut MSC-TL subclones. This may indicate that, as in ESC cells, PRC2 is active in p53 Mut MSC-TL subclones and accordingly can negatively regulate the expression of different development-associated genes by histone methyltransferase activity. Altogether, these analyses may imply that one possible mechanism in which mutant p53 regulates the expression of the ESC derive genes is by regulating the epigenetic mark state. Furthermore, the BindDB tool allowed us also to identify transcription factors that bind to the promoters of the mutant p53 MSC-TL–dependent genes, thus suggesting that they may be involved in mechanisms in which mutant p53 regulates the ESC gene signature (Supplementary Fig. S5B). In all, we suggest a novel mutant p53 GOF activity in upregulating the expression of a mesenchymal CSC-like signature that comprised of ESC genes.

Core developmental transcription programs were shown to be shared between human and mouse species (42). Therefore, it was of interest to examine the human relevance of the p53 Mut MSC-TLs ESC gene signature. We identified specific gene signatures, derived from the human orthologs of p53 Mut MSC-TL ESC gene signature, which predicted poor patient survival in different cancer types. When performing pan-cancer expression analyses, we found that the human orthologs of the identified MSC-TL ESC signature were preferentially expressed in human cancers harboring p53 hotspot missense mutations. Furthermore, analyzing a previously defined human ESC signature, which was shown to correlate with high-grade breast tumors with poor clinical outcome (30), showed similar preferential expression. These observations might explain the association between p53 mutations, and high-grade tumors with poor prognosis (6–8, 43, 44).

In conclusion, our study demonstrates that mutant p53 GOF activity is associated with the enrichment of mesenchymal CSC-like population along with upregulation of a unique signature containing ESC genes. This ESC gene signature was also relevant to patient outcome and to human tumors harboring p53-hotspot missense mutations. Notably, carcinomas occasionally undergo epithelial-to-mesenchymal transition, a process that was also associated with acquisition of stemness traits (25). Thus, our data might suggest that the specific genes in the ESC signature, identified in our MSC model, can be regarded as a broader signature of CSCs and tumor progression toward advanced disease. Gene members of this newly defined signature may therefore serve as prognostic biomarkers and new therapeutic targets for targeting cancer stemness and CSCs in tumors, thereby preventing cancer recurrence.

No potential conflicts of interest were disclosed.

Conception and design:G. Koifman, H. Solomon, A. Molchadsky, V. Rotter

Development of methodology:G. Koifman, V. Rotter

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.):G. Koifman, Y. Shetzer, V. Rotter

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis):G. Koifman, Y. Shetzer, R. Rotkopf, V. Rotter

Writing, review, and/or revision of the manuscript:G. Koifman, H. Solomon, A. Molchadsky, V. Rotter

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases):G. Koifman, V. Rotter

Study supervision:V. Rotter

Other (performing and analyzing the in vivo experiments):S. Eizenberger

Other (performing experiments): G. Lonetto

Other (assisted in performing experiments):N. Goldfinger

We thank Dr. Tali Shalit and Dr. Gil Hornung for RNA sequencing analysis. We also thank Dr. Raya Eilam and Dr. Ori Brenner for their help with IHC and pathologic analyses, respectively. In addition, we thank Dr. Ziv Porat for his help with Multispectral imaging flow cytometry (IFC) data analysis. We would like to acknowledge TCGA research network for providing TCGA datasets. The work by V. Rotter was supported by a Center of Excellence Grant from the Israel Science Foundation, a Center of Excellence Grant from the Flight Attendant Medical Research Institute, and the Israel Cancer Research Fund (ICRF). V. Rotter is the incumbent of the Norman and Helen Asher Professorial Chair of Cancer Research at The Weizmann Institute.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Marjanovic
ND
,
Weinberg
RA
,
Chaffer
CL
. 
Cell plasticity and heterogeneity in cancer
.
Clin Chem
2013
;
59
:
168
79
.
2.
Visvader
JE
,
Lindeman
GJ
. 
Cancer stem cells in solid tumours: accumulating evidence and unresolved questions
.
Nat Rev Cancer
2008
;
8
:
755
68
.
3.
Medema
JP
. 
Cancer stem cells: the challenges ahead
.
Nat Cell Biol
2013
;
15
:
338
44
.
4.
Brosh
R
,
Rotter
V
. 
When mutants gain new powers: news from the mutant p53 field
.
Nat Rev Cancer
2009
;
9
:
701
13
.
5.
Muller
PA
,
Vousden
KH
. 
Mutant p53 in cancer: new functions and therapeutic opportunities
.
Cancer Cell
2014
;
25
:
304
17
.
6.
Junttila
MR
,
Karnezis
AN
,
Garcia
D
,
Madriles
F
,
Kortlever
RM
,
Rostker
F
, et al
Selective activation of p53-mediated tumour suppression in high-grade tumours
.
Nature
2010
;
468
:
567
71
.
7.
Ahmed
AA
,
Etemadmoghadam
D
,
Temple
J
,
Lynch
AG
,
Riad
M
,
Sharma
R
, et al
Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary
.
J Pathol
2010
;
221
:
49
56
.
8.
Fujimoto
K
,
Yamada
Y
,
Okajima
E
,
Kakizoe
T
,
Sasaki
H
,
Sugimura
T
, et al
Frequent association of p53 gene mutation in invasive bladder cancer
.
Cancer Res
1992
;
52
:
1393
8
.
9.
Sarig
R
,
Rivlin
N
,
Brosh
R
,
Bornstein
C
,
Kamer
I
,
Ezra
O
, et al
Mutant p53 facilitates somatic cell reprogramming and augments the malignant potential of reprogrammed cells
.
J Exp Med
2010
;
207
:
2127
40
.
10.
Wang
Y
,
Yang
J
,
Zheng
H
,
Tomasek
GJ
,
Zhang
P
,
McKeever
PE
, et al
Expression of mutant p53 proteins implicates a lineage relationship between neural stem cells and malignant astrocytic glioma in a murine model
.
Cancer Cell
2009
;
15
:
514
26
.
11.
Shetzer
Y
,
Kagan
S
,
Koifman
G
,
Sarig
R
,
Kogan-Sakin
I
,
Charni
M
, et al
The onset of p53 loss of heterozygosity is differentially induced in various stem cell types and may involve the loss of either allele
.
Cell Death Differ
2014
;
21
:
1419
31
.
12.
Lang
GA
,
Iwakuma
T
,
Suh
YA
,
Liu
G
,
Rao
VA
,
Parant
JM
, et al
Gain of function of a p53 hot spot mutation in a mouse model of Li-Fraumeni syndrome
.
Cell
2004
;
119
:
861
72
.
13.
George
TC
,
Fanning
SL
,
Fitzgerald-Bocarsly
P
,
Medeiros
RB
,
Highfill
S
,
Shimizu
Y
, et al
Quantitative measurement of nuclear translocation events using similarity analysis of multispectral cellular images obtained in flow
.
J Immunol Methods
2006
;
311
:
117
29
.
14.
Edgar
R
,
Domrachev
M
,
Lash
AE
. 
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
.
Nucleic Acids Res
2002
;
30
:
207
10
.
15.
Rivlin
N
,
Katz
S
,
Doody
M
,
Sheffer
M
,
Horesh
S
,
Molchadsky
A
, et al
Rescue of embryonic stem cells from cellular transformation by proteomic stabilization of mutant p53 and conversion into WT conformation
.
Proc Natl Acad Sci U S A
2014
;
111
:
7006
11
.
16.
Kim
J
,
Chu
J
,
Shen
X
,
Wang
J
,
Orkin
SH
. 
An extended transcriptional network for pluripotency of embryonic stem cells
.
Cell
2008
;
132
:
1049
61
.
17.
Koga
M
,
Matsuda
M
,
Kawamura
T
,
Sogo
T
,
Shigeno
A
,
Nishida
E
, et al
Foxd1 is a mediator and indicator of the cell reprogramming process
.
Nat Commun
2014
;
5
:
3197
.
18.
Pardal
R
,
Molofsky
AV
,
He
S
,
Morrison
SJ
. 
Stem cell self-renewal and cancer cell proliferation are regulated by common networks that balance the activation of proto-oncogenes and tumor suppressors
.
Cold Spring Harb Symp Quant Biol
2005
;
70
:
177
85
.
19.
Smith
ZD
,
Sindhu
C
,
Meissner
A
. 
Molecular features of cellular reprogramming and development
.
Nat Rev Mol Cell Biol
2016
;
17
:
139
54
.
20.
Courtot
AM
,
Magniez
A
,
Oudrhiri
N
,
Feraud
O
,
Bacci
J
,
Gobbo
E
, et al
Morphological analysis of human induced pluripotent stem cells during induced differentiation and reverse programming
.
Biores Open Access
2014
;
3
:
206
16
.
21.
Rivlin
N
,
Koifman
G
,
Rotter
V
. 
p53 orchestrates between normal differentiation and cancer
.
Semin Cancer Biol
2015
;
32
:
10
7
.
22.
Zoller
M
. 
CD44: can a cancer-initiating cell profit from an abundantly expressed molecule?
Nat Rev Cancer
2011
;
11
:
254
67
.
23.
Ma
I
,
Allan
AL
. 
The role of human aldehyde dehydrogenase in normal and cancer stem cells
.
Stem Cell Rev
2011
;
7
:
292
306
.
24.
Chen
D
,
Wu
M
,
Li
Y
,
Chang
I
,
Yuan
Q
,
Ekimyan-Salvo
M
, et al
Targeting BMI1(+) cancer stem cells overcomes chemoresistance and inhibits metastases in squamous cell carcinoma
.
Cell Stem Cell
2017
;
20
:
621
34
.
25.
Mani
SA
,
Guo
W
,
Liao
MJ
,
Eaton
EN
,
Ayyanan
A
,
Zhou
AY
, et al
The epithelial-mesenchymal transition generates cells with properties of stem cells
.
Cell
2008
;
133
:
704
15
.
26.
Kramer
A
,
Green
J
,
Pollard
J
 Jr
,
Tugendreich
S
. 
Causal analysis approaches in Ingenuity Pathway Analysis
.
Bioinformatics
2014
;
30
:
523
30
.
27.
Kim
NH
,
Kim
HS
,
Kim
NG
,
Lee
I
,
Choi
HS
,
Li
XY
, et al
p53 and miRNA-34 are suppressors of canonical Wnt signaling
.
Sci Signal
2011
;
4
:
ra71
.
28.
Hashimi
ST
,
Fulcher
JA
,
Chang
MH
,
Gov
L
,
Wang
S
,
Lee
B
. 
MicroRNA profiling identifies miR-34a and miR-21 and their target genes JAG1 and WNT1 in the coordinate regulation of dendritic cell differentiation
.
Blood
2009
;
114
:
404
14
.
29.
Bossi
G
,
Marampon
F
,
Maor-Aloni
R
,
Zani
B
,
Rotter
V
,
Oren
M
, et al
Conditional RNA interference in vivo to study mutant p53 oncogenic gain of function on tumor malignancy
.
Cell Cycle
2008
;
7
:
1870
9
.
30.
Ben-Porath
I
,
Thomson
MW
,
Carey
VJ
,
Ge
R
,
Bell
GW
,
Regev
A
, et al
An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors
.
Nat Genet
2008
;
40
:
499
507
.
31.
Ku
M
,
Koche
RP
,
Rheinbay
E
,
Mendenhall
EM
,
Endoh
M
,
Mikkelsen
TS
, et al
Genomewide analysis of PRC1 and PRC2 occupancy identifies two classes of bivalent domains
.
PLoS Genet
2008
;
4
:
e1000242
.
32.
Livyatan
I
,
Aaronson
Y
,
Gokhman
D
,
Ashkenazi
R
,
Meshorer
E
. 
BindDB: an integrated database and webtool platform for "Reverse-ChIP" epigenomic analysis
.
Cell Stem Cell
2015
;
17
:
647
8
.
33.
Joerger
AC
,
Fersht
AR
. 
The p53 pathway: origins, inactivation in cancer, and emerging therapeutic approaches
.
Annu Rev Biochem
2016
;
85
:
375
404
.
34.
Solozobova
V
,
Blattner
C
. 
p53 in stem cells
.
World J Biol Chem
2011
;
2
:
202
14
.
35.
Rodriguez
R
,
Rubio
R
,
Menendez
P
. 
Modeling sarcomagenesis using multipotent mesenchymal stem cells
.
Cell Res
2012
;
22
:
62
77
.
36.
Pevsner-Fischer
M
,
Levin
S
,
Zipori
D
. 
The origins of mesenchymal stromal cell heterogeneity
.
Stem Cell Rev
2011
;
7
:
560
8
.
37.
Mizuno
H
,
Spike
BT
,
Wahl
GM
,
Levine
AJ
. 
Inactivation of p53 in breast cancers correlates with stem cell transcriptional signatures
.
Proc Natl Acad Sci U S A
2010
;
107
:
22745
50
.
38.
Kogan-Sakin
I
,
Tabach
Y
,
Buganim
Y
,
Molchadsky
A
,
Solomon
H
,
Madar
S
, et al
Mutant p53(R175H) upregulates Twist1 expression and promotes epithelial-mesenchymal transition in immortalized prostate cells
.
Cell Death Differ
2011
;
18
:
271
81
.
39.
Dong
P
,
Karaayvaz
M
,
Jia
N
,
Kaneuchi
M
,
Hamada
J
,
Watari
H
, et al
Mutant p53 gain-of-function induces epithelial-mesenchymal transition through modulation of the miR-130b-ZEB1 axis
.
Oncogene
2013
;
32
:
3286
95
.
40.
Di Fiore
R
,
Marcatti
M
,
Drago-Ferrante
R
,
D'Anneo
A
,
Giuliano
M
,
Carlisi
D
, et al
Mutant p53 gain of function can be at the root of dedifferentiation of human osteosarcoma MG63 cells into 3AB-OS cancer stem cells
.
Bone
2014
;
60
:
198
212
.
41.
Solomon
H
,
Dinowitz
N
,
Pateras
IS
,
Cooks
T
,
Shetzer
Y
,
Molchadsky
A
, et al
Mutant p53 gain of function underlies high expression levels of colorectal cancer stem cells markers
.
Oncogene
2018
;
37
:
1669
84
.
42.
Xue
Z
,
Huang
K
,
Cai
C
,
Cai
L
,
Jiang
CY
,
Feng
Y
, et al
Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing
.
Nature
2013
;
500
:
593
7
.
43.
Andersson
J
,
Larsson
L
,
Klaar
S
,
Holmberg
L
,
Nilsson
J
,
Inganas
M
, et al
Worse survival for TP53 (p53)-mutated breast cancer patients receiving adjuvant CMF
.
Ann Oncol
2005
;
16
:
743
8
.
44.
Murakami
I
,
Hiyama
K
,
Ishioka
S
,
Yamakido
M
,
Kasagi
F
,
Yokosaki
Y
. 
p53 gene mutations are associated with shortened survival in patients with advanced non-small cell lung cancer: an analysis of medically managed patients
.
Clin Cancer Res
2000
;
6
:
526
30
.

Supplementary data