BioMed Research International

BioMed Research International / 2012 / Article

Research Article | Open Access

Volume 2012 |Article ID 254085 | 13 pages | https://doi.org/10.1155/2012/254085

TP53 Mutation, Epithelial-Mesenchymal Transition, and Stemlike Features in Breast Cancer Subtypes

Academic Editor: F. C. Schmitt
Received23 Mar 2012
Revised11 May 2012
Accepted14 Jun 2012
Published30 Jul 2012

Abstract

Altered p53 protein is prevalently associated with the pathologic class of triple-negative breast cancers and loss of p53 function has recently been linked to the induction of an epithelial-mesenchymal transition (EMT) and acquisition of stemness properties. We explored the association between TP53 mutational status and expression of some genes involved in the canonical TGF-β signaling pathway (the most potent EMT inducer) and in two early EMT associated events: loss of cell polarity and acquisition of stemness-associated features. We used a publicly accessible microarray dataset consisting of 251 p53-sequenced primary breast cancers. Statistical analysis indicated that mutant p53 tumors (especially those harboring a severe mutation) were consistent with the aggressive class of triple-negative cancers and that, differently from cell cultures, surgical tumors underexpressed some TGF-β related transcription factors known as involved in EMT (ID1, ID4, SMAD3, SMAD4, SMAD5, ZEB1). These unexpected findings suggest an interesting relationship between p53 mutation, mammary cell dedifferentiation, and the concomitant acquisition of stemlike properties (as indicated by the overexpression of PROM1 and NOTCH1 genes), which improve tumor cells aggressiveness as indicated by the overexpression of genes associated with cell proliferation (CDK4, CDK6, MKI67) and migration (CXCR4, MMP1).

1. Introduction

TP53 tumor suppressor is the most commonly altered gene in human breast cancer where it is mutated in about 30–40% [1]. TP53 gene mutations result in altered and stable p53 proteins that function as dominant negative with gain-of-function properties, including drug resistance, and contribute to malignant progression with detrimental effects on patient’s outcome [2]. In particular, clinical evidence indicated that altered p53 proteins are prevalently associated with the pathologic class of triple-negative breast cancers, that is, tumors characterized by the immunohistochemical expression of basal cytokeratins and epidermal growth factor receptor (EGFR), but negative for estrogen (ER), progesterone receptor (PR), and HER2 expression [35]. Recently, triple-negative tumors have been also associated with a new less common subtype, known as claudin-low [6].

Wild-type p53 functions as a sequence-specific DNA binding transcription factor that regulates a plethora of target genes involved in DNA repair, cell cycle control, apoptosis, senescence, angiogenesis, and other fundamental biological process [7]. Therefore, it is not surprising that mutations of TP53 gene or inactivations of its signaling pathway are prerequisite for the development of tumors. Most mutations occur within the central DNA binding domain (exons 5–8) and, in particular, at several specific amino acids required for DNA binding. According to the type of mutation (point mutation, deletion, insertion, or stop codon), p53 protein synthesis may be totally inhibited or may generate functionally altered molecules that affect cell homeostasis in a different manner [8]. In fact, it is well known that not all p53 mutations have equal effects: some of them confer loss of function, others have a dominant negative effect and still others are classified as wild-type-like protein and represent mutant forms with a limited biological effect [9, 10].

Recently, some excellent papers have provided experimental evidence linking loss of p53 function to the induction of epithelial-mesenchymal transition (EMT) and acquisition of stemness properties in different tumor cell lines [1113]. EMT is a key program in embryonic development, the aberrant reactivation of which may induce progression, invasion, dissemination, and finally metastasis in cancer cells. The most evident peculiarities of a cell undergoing EMT process are loss of the epithelial phenotype and acquisition of mesenchymal features and abnormal motility capabilities [14, 15]. The most potent inducer of EMT is transforming growth factor-β (TGF-β) that triggers the activity of several transcription factors (ZEB1/TCF8, ZEB2/SIP1, Snail, Slug, Twist, and Ids), which in turn repress the expression of genes coding for epithelial markers and activate the expression of mesenchymal genes [16]. According to recent acquisition, p53 should prevent EMT by repressing ZEB1 and ZEB2 expressions via miRNAs activity. Consequently, p53 loss-of-function should downregulate miRNAs expression, the transactivation of transcription factors promoting EMT, and the emergence of tumor cells with stemlike properties [1719].

So far, despite the availability of a huge amount of information on the transcript profile from microarray analysis, the interrelations among p53 mutations and genes involved in EMT have not been specifically assessed. Therefore, to investigate the association between TP53 mutational status and EMT process, we interrogated a publicly accessible microarray dataset consisting of 251 p53 sequenced primary breast cancers [20]. Adopting an unconventional approach, we did not use the whole transcript profile but we selected a priori panel of genes experimentally recognized as involved in the canonical TGF-β signaling pathway and in two early events associated with EMT: loss of epithelial cell polarity and acquisition of stemness-associated features. To delineate a more comprehensive picture of the relationship among p53 mutation, EMT, and tumor aggressiveness, we also considered some genes involved in cell proliferation, apoptosis, and metastatic spread.

2. Materials and Methods

2.1. Materials

As reported in the original paper [20], gene expression profile was determined by using the Affymetrix Human Genome HG-U133A and -B GeneChip, and microarray dataset while was available at the ArrayExpress website (http://www.ebi.ac.uk/arrayexpress/), with the accession number E-GEOD-3494. Patients and tumors characteristics were provided as supporting information in the original paper [20].

2.2. Gene Selection

According to the aim of the study, we selected 147 genes (Table 1). Specifically, the panel was composed of 27 genes recognized as involved in TGF-β-induced EMT, 57 involved in epithelial cell plasticity, 13 coding for stemlike properties and 31 involved in cell proliferation, apoptosis, and metastatic spread. In addition, to describe breast cancer subtypes, 19 genes coding for luminal and basal markers were also considered. The 147 genes corresponded to 352 Affymetrix probe sets, as verified by GeneAnnot system v2.0 (http://bioinfo2.weizmann.ac.il/geneannot/), that additionally provided information about the quality of each probe set in terms of sensitivity and specificity score [21] (Supplementary Table 1 available at doi: 10.1155/2012/254085).


Official gene symbol Gene nameEntrez gene IDEnsembl genomic location

Apical junctional complexCDH1Cadherin 1, type 1, E-cadherin (epithelial)99916q22.1
CDH2Cadherin 2, type 1, N-cadherin (neuronal)100018q12.1
CLDN1Claudin 190763q28
CLDN2Claudin 29075Xq22.3
CLDN3Claudin 313657q11.23
CLDN4Claudin 413647q11.23
CLDN5Claudin 5712222q11.21
CLDN6Claudin 6907416p13.3
CLDN7Claudin 7136617p13.1
CLDN8Claudin 8907321q22.11
CLDN9Claudin 9908016p13.3
CLDN10Claudin 10907113q32.1
CLDN11Claudin 1150103q26.2
CLDN12Claudin 1290697q21.13
CLDN14Claudin 142356221q22.3
CLDN15Claudin 15241467q22.1
CLDN16Claudin 16106863q28
CLDN17Claudin 172628521q22.11
CLDN18Claudin 18512083q22.3
CLDN23Claudin 231370758p23.1
CTNNA1Catenin (cadherin-associated protein), alpha 114955q31.2
CTNNB1Catenin (cadherin-associated protein), beta 114993p22.1
CTNND1Catenin (cadherin-associated protein), delta 1150011q12.1
JAM2Junctional adhesion molecule 25849421q21.3
JAM3Junctional adhesion molecule 38370011q25
JUPJunction plakoglobin372817q21.2
MAGI1Membrane associated guanylate kinase, WW and PDZ domain containing 192233p14.1
MARVELD2MARVEL domain containing 21535625q13.2
OCLNOccludin49505q13.2
PVRL1Poliovirus receptor-related 1 (herpesvirus entry mediator C)581811q23.3
PVRL2Poliovirus receptor-related 2 (herpesvirus entry mediator B)581919q13.32
PVRL3Poliovirus receptor-related 3259453q13.13
PVRL4Poliovirus receptor-related 4816071q23.3
SYMPKSymplekin818919q13.32
TJP1Tight junction protein 1 (zona occludens 1)708215q13.1
TJP2Tight junction protein 2 (zona occludens 2)94149q21.11
TJP3Tight junction protein 3 (zona occludens 3)2713419p13.3
VCLVinculin741410q22.2

ApoptosisBAXBCL2-associated X protein58119q13.33
BCL2B-cell CLL/lymphoma 259618q21.33
CASP2Caspase 2, apoptosis-related cysteine peptidase8357q34
ERBB2V-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (avian)206417q12
MDM2Mdm2 p53 binding protein homolog (mouse)419312q15
TP53Tumor protein p53715717p13.1
TP53INP1Tumor protein p53 inducible nuclear protein 1942418q22.1
XIAPX-linked inhibitor of apoptosis331Xq25

Basal markersACTA1Actin, alpha 1, skeletal muscle581q42.13
CD44CD44 molecule (Indian blood group)96011p13
EGFREpidermal growth factor receptor19567p11.2
KRT5Keratin 5385212q13.13
KRT6AKeratin 6A385312q13.13
KRT6BKeratin 6B385412q13.13
KRT14Keratin 14386117q21.2
KRT17Keratin 17387217q21.2
TP63Tumor protein p6386263q28
VIMVimentin743110p13

Cell cycleCCNB3Cyclin B385417Xp11.22
CCND1Cyclin D159511q13.3
CCNE2Cyclin E291348q22.1
CDK2Cyclin-dependent kinase 2101712q13.2
CDK4Cyclin-dependent kinase 4101912q14.1
CDK6Cyclin-dependent kinase 610217q21.2
CDKN1ACyclin-dependent kinase inhibitor 1A (p21, Cip1)10266p21.2
CDKN1BCyclin-dependent kinase inhibitor 1B (p27, Kip1)102712p13.1
CDKN2ACyclin-dependent kinase inhibitor 2A (melanoma, p16, CDK4)10299p21.3
CDKN2BCyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4)10309p21.3
CDKN2DCyclin-dependent kinase inhibitor 2D (p19, inhibits CDK4)103219p13.2
GAS1Growth arrest-specific 126199q21.33
MKI67Antigen identified by monoclonal antibody Ki-67428810q26.2

Cell polarity complexesCRB1Crumbs homolog 1(Drosophila)234181q31.3
CRB3Crumbs homolog 3 (Drosophila)9235919p13.3
DLG1Discs, large homolog 1 (Drosophila)17393q29
DLG2Discs, large homolog 2 (Drosophila)174011q14.1
DLG3Discs, large homolog 3 (Drosophila)1741Xq13.1
DLG4Discs, large homolog 4 (Drosophila)174217p13.1
DLG5Discs, large homolog 5 (Drosophila)923110q22.3
INADLInaD-like (Drosophila)102071p31.3
MPP5Membrane protein, palmitoylated 5 (MAGUK p55 subfamily member 5)6439814q23.3
LLGL1Lethal giant larvae homolog 1 (Drosophila)399617p11.2
LLGL2Lethal giant larvae homolog 2 (Drosophila)399317q25.1
PARD3Par-3 partitioning defective 3 homolog (C. elegans)5628810p11.21
PARD3BPar-3 partitioning defective 3 homolog B (C. elegans)1175832q33.3
PARD6APar-6 partitioning defective 6 homolog alpha (C. elegans)5085516q22.1
PARD6BPar-6 partitioning defective 6 homolog beta (C. elegans)8461220q13.13
PARD6GPar-6 partitioning defective 6 homolog gamma (C. elegans)8455218q23
PRKCIProtein kinase C, iota55843q26.2
PRKCZProtein kinase C, zeta55901p36.33
SCRIBScribbled homolog (Drosophila)235138q24.3

Epithelial-mesenchymal transitionAKT1V-akt murine thymoma viral oncogene homolog 120714q32.33
AKT2V-akt murine thymoma viral oncogene homolog 220819q13.2
BMP1Bone morphogenetic protein 16498p21.3
ID1Inhibitor of DNA binding 1, dominant negative helix-loop-helix protein339720q11.21
ID2Inhibitor of DNA binding 2, dominant negative helix-loop-helix protein33982p25.1
ID3Inhibitor of DNA binding 3, dominant negative helix-loop-helix protein33991p36.12
ID4Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein34006p22.3
SMAD2SMAD family member 2408718q21.1
SMAD3SMAD family member 3408815q22.33
SMAD4SMAD family member 4408918q21.2
SMAD5SMAD family member 540905q31.1
SMAD6SMAD family member 6409115q22.31
SMAD7SMAD family member 7409218q21.1
SMURF1SMAD specific E3 ubiquitin protein ligase 1571547q22.1
SMURF2SMAD specific E3 ubiquitin protein ligase 26475017q24.1
SNAI1Snail homolog 1 (Drosophila)661520q13.13
SNAI2Snail homolog 2 (Drosophila)65918q11.21
TCF3Transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47)692919p13.3
TGFBITransforming growth factor, beta-induced, 68 kDa70455q31.1
TGFBR1Transforming growth factor, beta receptor 170469q22.33
TGFBR2Transforming growth factor, beta receptor II70483p24.1
TGFBR3Transforming growth factor, beta receptor III70491p22.1
TGIF2TGFB-induced factor homeobox 26043620q11.23
TWIST1Twist homolog 1 (Drosophila)72917p21.1
TWIST2Twist homolog 2 (Drosophila)1175812q37.3
ZEB1Zinc finger E-box binding homeobox 1693510p11.22
ZEB2Zinc finger E-box binding homeobox 298392q22.3

Luminal markersCD24CD24 molecule1001339416q21
ESR1Estrogen receptor 120996q25.1
GATA3GATA binding protein 3262510p14
KRT7Keratin 7385512q13.13
KRT8Keratin 8385612q13.13
KRT18Keratin 18387512q13.13
KRT19Keratin 19388017q21.2
MUC1Mucin 1, cell surface associated45821q22
PGRProgesterone receptor524111q22.1

Metastasis-related genesCXCR4Chemokine (C-X-C motif) receptor 478522q22.1
CXCR5Chemokine (C-X-C motif) receptor 564311q23.3
MMP1Matrix metallopeptidase 1 (interstitial collagenase)431211q22.2
MMP2Matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase)431316q12.2
MMP3Matrix metallopeptidase 3 (stromelysin 1, progelatinase)431411q22.2
MTA1Metastasis associated 1911214q32.33
MTA2Metastasis associated 1 family, member 2921911q12.3
MTA3Metastasis associated 1 family, member 3575042p21
PAK6P21 protein (Cdc42/Rac)-activated kinase 65692415q15.1
TIMP1TIMP metallopeptidase inhibitor 17076Xp11.23

Stemlike featuresABCG2ATP-binding cassette, sub-family G (WHITE),member 294294q22.1
ALDH1A1Aldehyde dehydrogenase 1 family, member A12169q21.13
ALDH1A3Aldehyde dehydrogenase 1 family, member A322015q26.3
BMI1BMI1 polycomb ring finger oncogene64810p12.2
JAG1Jagged 118220p12.2
JAG2Jagged 2371414q32.33
NANOGNanog homeobox7992312p13.31
NOTCH1Notch 148519q34.3
NOTCH2Notch homolog 2 (Drosophila)48531p12
NOTCH3Notch homolog 3 (Drosophila)485419p13.12
NOTCH4Notch homolog 4 (Drosophila)48556p21.32
NUMBNumb homolg (Drosophila)865014q24.3
PROM1Prominin 188424p15.32

2.3. Statistical Analysis

As some genes are recognized by more than a single probe set, each of which characterized by an individual specificity and sensitivity that differently contribute to gene expression value, a gene expression mean value was calculated after weighting each probe set for its own sensitivity and specificity score. Specifically, each expression value (already log2 transformed in the original dataset) was multiplied for the semisum of sensitivity and specificity scores of the corresponding probe set.

Prediction Analysis for Microarray (PAM) analysis was used to identify genes associated with the TP53 mutation status. PAM methodology minimizes the classification error using cross validation. For the selected genes, shrunken centroids across the different mutation groups were plotted. The FDR level was estimated through a permutation method.

To identify the tumors characterized by a similar intrinsic phenotype (tumor subtypes), unsupervised hierarchical cluster analysis was performed using the subset of genes coding for luminal and basal markers, HER-2, and claudins. The choice of the number of clusters to be used was supported by mean silhouette values [22]. PAM methodology was used to detail differential gene expression among clusters [23].

Principal Components Analysis (PCA) and PCA-based biplots were used to assess gene expression among clusters [24]. Moreover, for evaluating the associations among genes, specific subsets, not used to build PCA, were passively projected over the PCA-based biplots of intrinsic phenotypes.

All analyses were performed using open source software R 2.14.1 packages stats, cluster, and HDMD (http://www.R-project.org/).

3. Results and Discussion

As described in the original paper [20], the cases series was composed of 251 tumors, 58 of which characterized by a TP53 mutation. Of the 58 mutant tumors, 37 had point mutations and 21 had “severe” mutations, that is, insertions ( 𝑛 = 3 ), deletions ( 𝑛 = 1 1 ), and stop codons ( 𝑛 = 7 ), that result in frame shift and truncations with deleterious functional consequences.

To identify genes differentially expressed between mutant and wild-type p53 tumors, we first applied a PAM analysis on the overall cases series (Figure 1). According to the expected loss-of-function, mutant p53 tumors were characterized by the underexpression of genes under p53 control (i.e., CDKN1A, coding for p21, and TP53INP, coding for the p53-inducible nuclear protein 1). In addition, they were showed as underxpressed several genes coding for epithelial cell polarity complex (DLG4, DLG5, INADL, PARD3B, PARD6B) and apical junctional components (CLDN11, JAM2, JAM3, MAGI1, OCLN, PVRL2, TJP1, TJP3). Conversely, mutant p53 tumors overexpressed CDH2, the gene coding for N-cadherin, and some genes involved in cell proliferation (CCNE2, CDK4, CDK6, CDKN2A, CDKN2D, MKI67) and metastatic spread (CXCR4, MMP1). An overall similar pattern of expression was found when we interrogated another publicly available microarray dataset where p53 mutational status was known [3] using the same panel of 147 genes. Despite the different microarray platform used (42K cDNA microarrays instead of Affymetrix GeneChip), mutant p53 tumors were associated with a dramatic underexpression of genes coding for apical junctional components (INADL, JAM2, JAM3, MAGI1, PARD6B, PVRL2) coupled with the overexpression of genes coding for N-cadherin (CDH2) or involved in cell proliferation and metastatic spread (CCNE2, CDK4, CDK6, MKI67, MTA1) (Supplementary Figure 1). Notably, in both datasets, p53 mutant tumors were associated with the overexpression of PROM1, supporting the experimental evidence indicating the relation among p53 mutation and the reacquisition of some stemlike properties according to an EMT-like process [12, 13].

As regards the genes related to the canonical TGF-β signaling pathway, mutant p53 tumors showed the downregulation of many genes coding for pivotal elements of this pathway (ID1, ID4, SMAD3, SMAD4, SMAD5, TGFBR2, TGFBR3, ZEB1) coupled with the overexpression of SMURF2, coding for a SMAD-specific E3 ubiquitin protein ligase, and TGIF2, coding for a transcriptional repressor interacting with TGF-β activated SMAD proteins (Figure 2). A similar pattern of expression (i.e., downregulation of SMAD2, SMAD3, SMAD5, SMAD7, TGFBR2, ZEB1, and overexpression of SMURF2 and TGIF2) was found in Langerod dataset [3] (Supplementary Figure 2). This unexpected finding could be explained taking into account that TGF-β is a multifunctional cytokine and a powerful tumor suppressor that governs many aspects of mammary epithelial cells physiology and homeostasis [25]. Consistent with the notion that estrogen receptor and TGF-β signaling pathways are major regulators during mammary gland development [26], it is not surprising that p53 mutant tumors concomitantly underexpressed ESR1 (coding for ERα), TGFBR2, TGFBR3 (coding for TGF-β receptors) and ID1, ID4, SMAD3, SMAD4, SMAD5, ZEB1 (coding for key elements of the pathway).

When severe and missense mutations were considered separately, PAM analysis provided evidence that severe TP53 mutations were responsible for the differential gene expression observed in mutant with respect to wild-type p53 tumors, even though some important alterations were already present in missense TP53 mutations, as, for example, the downregulation of some apical junctional components or the overexpression of genes related to cell proliferation and invasion. As shown in Figure 3, in addition to the expected decrease in the expression of TP53, CDKN1A, and TP53INP1, tumors harboring severe mutations were characterized by a dramatic overexpression of genes associated with proliferation (CDK4, CDK6, MKI67) and metastatic spread (CXCR4, MMP1), and by underexpression of several genes involved in epithelial cell identity (DLG5, INADL, JAM2, JAM3, MAGI1, OCLN, PARD3B, PARD6B, PVRL2, SCRIB, TJP1 and TJP3).

As regards the association between missense or severe TP53 mutation and EMT-related genes, Figure 4 indicates that, with respect to tumors harboring a missense mutation, those with severe mutations were characterized by the overexpression of SMURF2, SNAI1, and TGIF2 genes. Of particular interest is the overexpression of SNAI1 gene because of the concomitant overexpression of NOTCH1 pointed out by PAM analysis in tumors with severe TP53 mutation (Figure 3). Indeed, Notch signalling pathway, which is implicated as an important contributor to EMT in tumorigenesis, has been recently suggested to play a direct role on the expression of the Snail transcription factor [27].

With respect to missense TP53 mutations, severe ones were also characterized by an increased expression of PROM1, the gene encoding for prominin, a pentaspan transmembrane glycoprotein (CD133) often overexpressed on cancer cells, where it is thought to function in maintaining stem cell properties by suppressing differentiation. This finding is in agreement with the hypothesis that basal cancers, which have been proposed to have a stem cell origin, are virtually all TP53 mutants and express high levels of PROM1 transcript and protein [28]. Unfortunately, since in Langerod dataset [3] severe mutations accounted for only three cases, we were unable to verify all these observations in an independent dataset.

One of the aims of the study was to explore the relationship among p53 mutation, EMT, and tumor aggressiveness, a peculiar characteristic of certain breast cancer subtypes (especially basal-like phenotype). To this specific aim, we performed an unsupervised hierarchical cluster analysis, using the subset of genes coding for luminal and basal markers, HER-2, and claudins, and we looked at the distribution of p53 mutations according to tumor subtype. The analysis indicated that mutant p53 tumors distributed into three main clusters (Figure 5). Of the 58 mutant p53 tumors, 23 were included in Cluster 1, 17 in Cluster 2, and 18 in Cluster3. However, looking at the relative percentage, we found that, on the total number of tumors in each cluster, only 17% (23/133) of Cluster 1 and 19% (18/95) of Cluster 3 tumors had p53 mutations, whereas 74% (17/23) of Cluster 2 tumors did have. Notably, 10 of these 17 mutations were severe mutations.

PCA-based biplots, drawn using the same subset of genes of hierarchical cluster analysis (Figure 6), showed that Cluster 2 tumors were positively associated with genes related to basal phenotype (KRT5, KRT6A, KRT6B, KRT14, KRT17, EGFR) and with a panel of claudin-coding genes (CLDN1, CLDN6, CLDN10), whereas they were negatively associated with the majority of genes related to luminal phenotype. Conversely, Cluster 3 tumors were positively associated with genes related to luminal phenotype (ESR1, GATA3, MUC1, PGR, KRT18) and with a different panel of claudin-coding genes (CLDN3, CLDN4, CLDN7) and negatively associated with genes related to basal phenotype. Cluster 1 tumors showed a less clear-cut phenotype according to the more heterogeneous nature of this cluster, even though they appeared prevalently, associated with genes related to basal phenotype (KRT5, KRT6B, KRT14, KRT17, TP63). Remarkably, Cluster 2 tumors also showed the concomitant underexpression of ERBB2 gene providing evidence that these tumors had a gene expression profile consistent with the pathologic class of triple-negative tumors (Supplementary Figure 3), which are characterized by the expression of basal cytokeratins (mainly Krt5) and EGFR, but do not express estrogen and progesterone receptors, and HER2.

When we looked at the expression of EMT-associated genes according to clusters (Figure 7), we found that Cluster 2, consistent with the pathologic class of triple-negative cancers, showed a gene expression profile similar to that of tumors harboring a severe p53 mutation. Conversely, Cluster 3, consistent with the luminal-like phenotype, had a pattern of expression similar to that of wild-type p53 tumors whereas the phenotypically heterogenous Cluster 1 looks like the group of tumors with a missense p53 mutation. In particular, Cluster 2 (consistent with the pathologic class of triple-negative cancers and akin to severe p53 mutated tumors) was characterized by the underexpression of SMAD2, SMAD5, ZEB1, and TGFBR3 and the overexpression of SMURF2, TGIF2, and SNAI1, in agreement with the gene profile observed in tumors harboring a severe TP53 mutation (Figure 4).

Notably, when the subset of genes related to stemness properties was passively projected over the PCA-based biplots provided in Figure 6, Cluster 2 tumors were positively associated with PROM1 and NOTCH1, and negatively associated with ALDH1A1, BMI1, NUMB (Figure 8). Similar to the latter but opposite in the sign, was the pattern of association shown by Cluster 3 tumors.

The imbalance in Numb/Notch pathway observed in Cluster 2 tumors, associated with the overexpression of SNAI1, is of particular interest because the involvement of this pathway in differentiation program and epithelial cancer progression and metastasis. Numb is an evolutionary conserved protein that plays a critical role in cell-fate determination, including control of asymmetric cell division, endocytosis, cell adhesion, cell migration, and ubiquitination of specific substrates as p53. Loss of Numb causes increased activity of the oncogene Notch1 and for this reason, low expression of Numb and high levels of Notch1 have been associated with tumor progression and used as markers of tumor aggressiveness, especially in basal-like breast cancer [29]. The aggressiveness of this group of tumors was corroborated by the observation that Cluster 2 tumors were prevalently poorly differentiated (17/23 tumors were Grade III) with respect to Cluster 1 and Cluster 3 tumors, and by the positive association with genes promoting cell proliferation and metastatic spread. In this context, it can be viewed the overexpression of MMP1 and CXCR4, and the downregulation of TIMP1. Indeed, MMP1 encodes for a matrix metalloproteinase family member (specifically, a collagenase) involved in the breakdown of extracellular matrix whereas TIMP1 encodes for a specific tissue inhibitor of metalloproteases, including MMP-1. Because MMP1 is a target gene for wild-type p53 activity, the functional inactivation of the protein results in a gene overexpression that allows tumor cell migration after degradation of basement membrane and cell detachment [30, 31]. The concomitant overexpression of CXCR4 due to a gain-of-function mutant p53 [32, 33], further contributes to enhance tumor cell migration and metastatic spread [34]. In fact, CXCR4 encodes a C-X-C motif chemokine receptor specific for stromal cell-derived factor-1 (SDF-1/CXCL12), a member of the family of chemoattractant molecules, physiologically involved in the migration of immune cells. The CXCL12/CXCR4 signaling axis is also known to be important for tumor cell migration: CXCR4 expressed on tumor cells, provides a means of homing for metastatic cells to target organs [35]. Due to its implication with tumor dissemination, CXCR4 overexpression has been linked to a poor prognosis in breast cancer patients [35].

Surprisingly, on the contrary, it should be the negative association, pointed out by PCA-based biplots, between Cluster 2 tumors and BMI1 expression. That, because the role of BMI1 gene in self-renewal of stem cells and as an oncogene in many human cancers where it induces EMT. Although Bmi1 overexpression has been correlated with poor prognosis in several tumor types, a recent study has indicated that, in breast cancer, high Bmi1 expression is limited to the luminal subtype and that it is associated with a good outcome [36]. Under this light, the positive association that we observed between BMI1 expression and Cluster 3 tumors, consistent with the luminal-like phenotype, seems to provide a transcriptomic support to this clinical evidence.

Cluster 1 tumors, which are prevalently p53 wild-type, are more difficult to categorize. Dissimilarly from basal-like and luminal-like, these tumors had an indefinite phenotype characterized by the coexpression of luminal and basal cytokeratins. In addition, the overexpression of several transcription factors (ID2, ID4, SNAI2, TWIST1, ZEB2), known to be under TGF-β control, and the concomitant overexpression of some genes coding for stemlike properties (ABCG2, JAG1, JAG2, NANOG, NOTCH4) makes it difficult to have a correct interpretation of the results. Indeed, it is not easy to establish whether such a phenotypical heterogeneity represents an intermediate step of an EMT-like process, in which tumor cells gain characteristics of mesenchymal cells but have not completely lost epithelial characteristics, or it is simply due to the individual heterogeneity of the tumors forming the cluster.

4. Conclusions

Aim of this in silico study was to investigate the association between TP53 mutational status and expression of a panel of genes related to TGF-β induced EMT and stemlike features, using a publicly accessible microarray dataset consisting of 251 p53-sequenced primary breast cancers. According to recent experimental evidence linking loss of p53 function, induction of EMT and acquisition of stemness properties in different tumor cell lines [1113], we expected an evident positive association between EMT-related genes and p53 mutations, in particular with severe p53 mutations. In addition, since clinical evidence indicates that p53 mutations are prevalently associated with the pathologic class of triple-negative breast cancers, we expected an overexpression of EMT-related genes in this specific subset of tumors. Our analysis supports the notion that mutant p53 tumors (especially those harboring a severe p53 mutation) were consistent with the aggressive clinic class of triple-negative cancers, but it clearly indicates that, differently from cell cultures [1113], surgical tumors did not overexpress TGF-β-related transcription factors. Taking into account the physiological role of TGF-β in mammary gland differentiation [25, 26], these unexpected findings seem to suggest an interesting relationship between p53 mutation, mammary cell dedifferentiation, and the concomitant acquisition of stemlike properties which improve tumor cells aggressiveness.

Supplementary Materials

Supplementary Table: lists the 352 Affymetrix probe sets corresponding to the 147 genes entered in the study. For each probe set sensitivity and specificity score are also provided.

Supplementary Figure 1: shows shrunken centroids for wild-type TP53 and mutant TP53 tumors in Langerod dataset (for details see Statistical Analysis description).

Supplementary Figure 2: shows the boxplots of the genes associated with EMT in wild-type (WT) or mutant (mut) TP53 tumors with respect to overall case series (all), in Langerod dataset (for details see Statistical Analysis description).

Supplementary Figure 3: shows the boxplots of the ESR1, PGR, ERBB2, TP53 and TP53INP genes in the three main clusters identified by unsupervised hierarchical cluster analysis using the subset of genes coding for luminal and basal markers, ERBB2 and claudins (for details see Statistical Analysis description).

  1. Supplementary Table
  2. Supplementary Figure 1
  3. Supplementary Figure 2
  4. Supplementary Figure 3

References

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