Journal of Biomedicine and Biotechnology

Volume 2012 (2012), Article ID 254085, 13 pages

doi:10.1155/2012/254085

Research Article

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

Danila Coradini, Marco Fornili, Federico Ambrogi, Patrizia Boracchi, and Elia Biganzoli

Department of Clinical and Community Health Sciences, Medical Statistics, Biometry and Bioinformatics, University of Milan, Via Vanzetti, 5 20133 Milan, Italy

Received 23 March 2012; Revised 11 May 2012; Accepted 14 June 2012

Academic Editor: F. C. Schmitt

Copyright © 2012 Danila Coradini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 [3–5]. 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 [11–13]. 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 [17–19].

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).

Table 1: Genes entered in the study.

Official gene symbol
Gene name
Entrez gene ID
Ensembl genomic location

Apical junctional complex
CDH1
Cadherin 1, type 1, E-cadherin (epithelial)
999
16q22.1

CDH2
Cadherin 2, type 1, N-cadherin (neuronal)
1000
18q12.1

CLDN1
Claudin 1
9076
3q28

CLDN2
Claudin 2
9075
Xq22.3

CLDN3
Claudin 3
1365
7q11.23

CLDN4
Claudin 4
1364
7q11.23

CLDN5
Claudin 5
7122
22q11.21

CLDN6
Claudin 6
9074
16p13.3

CLDN7
Claudin 7
1366
17p13.1

CLDN8
Claudin 8
9073
21q22.11

CLDN9
Claudin 9
9080
16p13.3

CLDN10
Claudin 10
9071
13q32.1

CLDN11
Claudin 11
5010
3q26.2

CLDN12
Claudin 12
9069
7q21.13

CLDN14
Claudin 14
23562
21q22.3

CLDN15
Claudin 15
24146
7q22.1

CLDN16
Claudin 16
10686
3q28

CLDN17
Claudin 17
26285
21q22.11

CLDN18
Claudin 18
51208
3q22.3

CLDN23
Claudin 23
137075
8p23.1

CTNNA1
Catenin (cadherin-associated protein), alpha 1
1495
5q31.2

CTNNB1
Catenin (cadherin-associated protein), beta 1
1499
3p22.1

CTNND1
Catenin (cadherin-associated protein), delta 1
1500
11q12.1

JAM2
Junctional adhesion molecule 2
58494
21q21.3

JAM3
Junctional adhesion molecule 3
83700
11q25

JUP
Junction plakoglobin
3728
17q21.2

MAGI1
Membrane associated guanylate kinase, WW and PDZ domain containing 1
9223
3p14.1

MARVELD2
MARVEL domain containing 2
153562
5q13.2

OCLN
Occludin
4950
5q13.2

PVRL1
Poliovirus receptor-related 1 (herpesvirus entry mediator C)
5818
11q23.3

PVRL2
Poliovirus receptor-related 2 (herpesvirus entry mediator B)
5819
19q13.32

PVRL3
Poliovirus receptor-related 3
25945
3q13.13

PVRL4
Poliovirus receptor-related 4
81607
1q23.3

SYMPK
Symplekin
8189
19q13.32

TJP1
Tight junction protein 1 (zona occludens 1)
7082
15q13.1

TJP2
Tight junction protein 2 (zona occludens 2)
9414
9q21.11

TJP3
Tight junction protein 3 (zona occludens 3)
27134
19p13.3

VCL
Vinculin
7414
10q22.2

Apoptosis
BAX
BCL2-associated X protein
581
19q13.33

BCL2
B-cell CLL/lymphoma 2
596
18q21.33

CASP2
Caspase 2, apoptosis-related cysteine peptidase
835
7q34

ERBB2
V-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (avian)
2064
17q12

MDM2
Mdm2 p53 binding protein homolog (mouse)
4193
12q15

TP53
Tumor protein p53
7157
17p13.1

TP53INP1
Tumor protein p53 inducible nuclear protein 1
94241
8q22.1

XIAP
X-linked inhibitor of apoptosis
331
Xq25

Basal markers
ACTA1
Actin, alpha 1, skeletal muscle
58
1q42.13

CD44
CD44 molecule (Indian blood group)
960
11p13

EGFR
Epidermal growth factor receptor
1956
7p11.2

KRT5
Keratin 5
3852
12q13.13

KRT6A
Keratin 6A
3853
12q13.13

KRT6B
Keratin 6B
3854
12q13.13

KRT14
Keratin 14
3861
17q21.2

KRT17
Keratin 17
3872
17q21.2

TP63
Tumor protein p63
8626
3q28

VIM
Vimentin
7431
10p13

Cell cycle
CCNB3
Cyclin B3
85417
Xp11.22

CCND1
Cyclin D1
595
11q13.3

CCNE2
Cyclin E2
9134
8q22.1

CDK2
Cyclin-dependent kinase 2
1017
12q13.2

CDK4
Cyclin-dependent kinase 4
1019
12q14.1

CDK6
Cyclin-dependent kinase 6
1021
7q21.2

CDKN1A
Cyclin-dependent kinase inhibitor 1A (p21, Cip1)
1026
6p21.2

CDKN1B
Cyclin-dependent kinase inhibitor 1B (p27, Kip1)
1027
12p13.1

CDKN2A
Cyclin-dependent kinase inhibitor 2A (melanoma, p16, CDK4)
1029
9p21.3

CDKN2B
Cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4)
1030
9p21.3

CDKN2D
Cyclin-dependent kinase inhibitor 2D (p19, inhibits CDK4)
1032
19p13.2

GAS1
Growth arrest-specific 1
2619
9q21.33

MKI67
Antigen identified by monoclonal antibody Ki-67
4288
10q26.2

Cell polarity complexes
CRB1
Crumbs homolog 1(Drosophila)
23418
1q31.3

CRB3
Crumbs homolog 3 (Drosophila)
92359
19p13.3

DLG1
Discs, large homolog 1 (Drosophila)
1739
3q29

DLG2
Discs, large homolog 2 (Drosophila)
1740
11q14.1

DLG3
Discs, large homolog 3 (Drosophila)
1741
Xq13.1

DLG4
Discs, large homolog 4 (Drosophila)
1742
17p13.1

DLG5
Discs, large homolog 5 (Drosophila)
9231
10q22.3

INADL
InaD-like (Drosophila)
10207
1p31.3

MPP5
Membrane protein, palmitoylated 5 (MAGUK p55 subfamily member 5)
64398
14q23.3

LLGL1
Lethal giant larvae homolog 1 (Drosophila)
3996
17p11.2

LLGL2
Lethal giant larvae homolog 2 (Drosophila)
3993
17q25.1

PARD3
Par-3 partitioning defective 3 homolog (C. elegans)
56288
10p11.21

PARD3B
Par-3 partitioning defective 3 homolog B (C. elegans)
117583
2q33.3

PARD6A
Par-6 partitioning defective 6 homolog alpha (C. elegans)
50855
16q22.1

PARD6B
Par-6 partitioning defective 6 homolog beta (C. elegans)
84612
20q13.13

PARD6G
Par-6 partitioning defective 6 homolog gamma (C. elegans)
84552
18q23

PRKCI
Protein kinase C, iota
5584
3q26.2

PRKCZ
Protein kinase C, zeta
5590
1p36.33

SCRIB
Scribbled homolog (Drosophila)
23513
8q24.3

Epithelial-mesenchymal transition
AKT1
V-akt murine thymoma viral oncogene homolog 1
207
14q32.33

AKT2
V-akt murine thymoma viral oncogene homolog 2
208
19q13.2

BMP1
Bone morphogenetic protein 1
649
8p21.3

ID1
Inhibitor of DNA binding 1, dominant negative helix-loop-helix protein
3397
20q11.21

ID2
Inhibitor of DNA binding 2, dominant negative helix-loop-helix protein
3398
2p25.1

ID3
Inhibitor of DNA binding 3, dominant negative helix-loop-helix protein
3399
1p36.12

ID4
Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein
3400
6p22.3

SMAD2
SMAD family member 2
4087
18q21.1

SMAD3
SMAD family member 3
4088
15q22.33

SMAD4
SMAD family member 4
4089
18q21.2

SMAD5
SMAD family member 5
4090
5q31.1

SMAD6
SMAD family member 6
4091
15q22.31

SMAD7
SMAD family member 7
4092
18q21.1

SMURF1
SMAD specific E3 ubiquitin protein ligase 1
57154
7q22.1

SMURF2
SMAD specific E3 ubiquitin protein ligase 2
64750
17q24.1

SNAI1
Snail homolog 1 (Drosophila)
6615
20q13.13

SNAI2
Snail homolog 2 (Drosophila)
6591
8q11.21

TCF3
Transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47)
6929
19p13.3

TGFBI
Transforming growth factor, beta-induced, 68 kDa
7045
5q31.1

TGFBR1
Transforming growth factor, beta receptor 1
7046
9q22.33

TGFBR2
Transforming growth factor, beta receptor II
7048
3p24.1

TGFBR3
Transforming growth factor, beta receptor III
7049
1p22.1

TGIF2
TGFB-induced factor homeobox 2
60436
20q11.23

TWIST1
Twist homolog 1 (Drosophila)
7291
7p21.1

TWIST2
Twist homolog 2 (Drosophila)
117581
2q37.3

ZEB1
Zinc finger E-box binding homeobox 1
6935
10p11.22

ZEB2
Zinc finger E-box binding homeobox 2
9839
2q22.3

Luminal markers
CD24
CD24 molecule
100133941
6q21

ESR1
Estrogen receptor 1
2099
6q25.1

GATA3
GATA binding protein 3
2625
10p14

KRT7
Keratin 7
3855
12q13.13

KRT8
Keratin 8
3856
12q13.13

KRT18
Keratin 18
3875
12q13.13

KRT19
Keratin 19
3880
17q21.2

MUC1
Mucin 1, cell surface associated
4582
1q22

PGR
Progesterone receptor
5241
11q22.1

Metastasis-related genes
CXCR4
Chemokine (C-X-C motif) receptor 4
7852
2q22.1

CXCR5
Chemokine (C-X-C motif) receptor 5
643
11q23.3

MMP1
Matrix metallopeptidase 1 (interstitial collagenase)
4312
11q22.2

MMP2
Matrix metallopeptidase 2 (gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase)
4313
16q12.2

MMP3
Matrix metallopeptidase 3 (stromelysin 1, progelatinase)
4314
11q22.2

MTA1
Metastasis associated 1
9112
14q32.33

MTA2
Metastasis associated 1 family, member 2
9219
11q12.3

MTA3
Metastasis associated 1 family, member 3
57504
2p21

PAK6
P21 protein (Cdc42/Rac)-activated kinase 6
56924
15q15.1

TIMP1
TIMP metallopeptidase inhibitor 1
7076
Xp11.23

Stemlike features
ABCG2
ATP-binding cassette, sub-family G (WHITE),member 2
9429
4q22.1

ALDH1A1
Aldehyde dehydrogenase 1 family, member A1
216
9q21.13

ALDH1A3
Aldehyde dehydrogenase 1 family, member A3
220
15q26.3

BMI1
BMI1 polycomb ring finger oncogene
648
10p12.2

JAG1
Jagged 1
182
20p12.2

JAG2
Jagged 2
3714
14q32.33

NANOG
Nanog homeobox
79923
12p13.31

NOTCH1
Notch 1
4851
9q34.3

NOTCH2
Notch homolog 2 (Drosophila)
4853
1p12

NOTCH3
Notch homolog 3 (Drosophila)
4854
19p13.12

NOTCH4
Notch homolog 4 (Drosophila)
4855
6p21.32

NUMB
Numb homolg (Drosophila)
8650
14q24.3

PROM1
Prominin 1
8842
4p15.32

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 (), deletions (), and stop codons (), 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].

Figure 1: Shrunken centroids for wild-type TP53 and mutant TP53 tumors. Left-sided bars indicate lower expression in the subgroups relative to overall centroid; right-sided bars indicate higher expression in subgroups relative to overall centroid.

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).

Figure 2: Boxplots of the genes associated with EMT in wild-type (WT) or mutant (mut) TP53 tumors with respect to overall case series (all).

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).

Figure 3: Shrunken centroids for wild-type TP53 an mutant (missense or severe mutation) TP53 tumors. Left-sided bars indicate lower expression in the subgroups relative to overall centroid; right-sided bars indicate higher expression in subgroups relative to overall centroid.

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].

Figure 4: Boxplots of the genes associated with EMT in wild-type (WT), missense (MS), or severe (SV) TP53 mutation with respect to overall case series (all).

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.

Figure 5: Tumor dendrogram from clustering 251 tumors and 30 genes associated with intrinsic breast cancer phenotypes (luminal, basal, ERBB2, and claudins). Black bars denote tumors with a wild-type p53; orange bars, tumors with a mutant p53 (missense mutation); red bars, tumors with a mutant p53 (severe mutation).

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.

(a)

(b)

Figure 6: Active PCA-based biplots of intrinsic breast cancer phenotype-related genes. Samples are colored according to the mutational p53 status (wild-type in green and mutant in blue) and are labeled according to the corresponding cluster (1, 2, or 3). Loading for basal genes are represented by red arrows, luminal genes by pink arrows, claudin genes by orange arrows, and ERBB2 gene by yellow arrow.

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).

Figure 7: Boxplots of the genes associated with EMT in the three main clusters identified by unsupervised hierarchical cluster analysis using the subset of genes coding for luminal and basal markers, HER-2 and claudins.

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.

(a)

(b)

Figure 8: Passive projectionof stemlike-related genes in the space of the first two principal components (PC), (a), and in the space of first and third PCs, (b), of intrinsic phenotype gene set. Loading for EMT or stemlike genes are represented in black.

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 [11–13], 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 [11–13], 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.

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