Supporting InformationTucci et al. 10.1073/pnas.1110977109SI Materials and MethodsCell Culture and Transfection.All cell lines used were obtained fromAmerican Type Culture Collection, maintained at 37 °C in 5%CO2 in culture medium. H1299, PC3, MCF7, and MDA MB231cells were grown in RPMI, 250 μM L-glutamine (Gibco), peni-cillin/streptomycin 1 unit/mL (Gibco), 10% (vol/vol) FCS (In-vitrogen); ΔNp63α–SaOS-2–Tet–On cells were grown in RPMI,250 μM L-glutamine (Gibco), penicillin/streptomycin 1 unit/mL(Gibco), and 10% (vol/vol) Tet-free FCS (Clontech). To generateSaOS-2 clones with the inducible expression of ΔNp63 (Tet–Onsystem), we used a clone of SaOS-2 cells expressing Tet-re-sponsive transcriptional activator rtTA. A431 cells were grown inDMEM high glucose, 250 μM L-glutamine (Gibco), penicillin/streptomycin 1 unit/mL (Gibco), 10% (vol/vol) FCS (Invitrogen).ΔNp63α–PC3–Tet–On cells were grown in the same medium asparental PC3, supplemented with 25 μg/mL of zeocin (Invitrogen)and 4 μg/mL of blasticidin (Invitrogen). ΔNp63α–PC3–Tet–Oncells stable expressing the inhibitor of miR-205 were grown withthe additional selection of 500 μg/mL hygromicin B (Sigma-Al-drich). MiR205-PC3-Tet-On cells were grown in the presence of1 μg/mL of puromycin (Qiagen) and 500 μg/mL geneticin (Gib-co). All of the inducible lines were grown with Tet-free FCS(Clontech). RWPE-1 cells were grown in keratinocyte serum-freemedium supplemented with 0.05 mg/mL bovine pituitary extractand 5 ng/mL EGF (Gibco). DU145 were grown in Eagle’s min-imum essential medium and 10% (vol/vol) FCS (Invitrogen).Transfections were carried out by Lipofectamine 2000 reagent

(Invitrogen) according to the manufacturer’s instructions. Cellswere transfected with pre–miR-205 (50 nM) or anti–miR-205(100 nM), or FAM-labeled negative control (Ambion) using theSiPORT neoFX transfection agent (Ambion) according to themanufacturer’s instructions. After the indicated time, cells wereharvested for protein and RNA extraction. Each experiment wasperformed at least in triplicate.ΔNp63α–SaOS-2–Tet–On cells were treated with 2 μg/mL of

doxycycline for the indicated time points to induce ΔNp63 ex-pression. ΔNp63α–PC3–Tet–On and miR-205–PC3–Tet–On cellswere treated with 1 μg/mL of doxycycline for the indicated timepoints to induce ΔNp63 and miR-205 expression, respectively.PC3 and H1299 cells were transfected with one vector harboringthe human ΔNp63α coding sequence or with an empty vector;A431 cells were transfected with a mixture of three validatedsiRNAs targeting different portions of p63 (Ambion; 217143,4798, and 115456). MCF7 and MDA MB231 cells were trans-fected using the amaxa nucleofection method using solution Tand program X-001 and 8 pmol of GGACAGCAGCAUU-GAUCAA to silence ΔNp63 isoform in the MCF7 and a combi-nation of 4 pmol of AGUUAUUACCGAUCCACCA and 4pmol of UGAAUUCCUCAGACCAGAG to silence the TAp63isoform in the MDA MB231. For silencing of the endogenousp53 in the DU145 cells, we used 100 μM of oligo (Ambion; s607).

Generation of Stable Cell Lines. ΔNp63α–PC3–Tet–On cells weregenerated using the T-Rex system (Invitrogen) and kept underselection with zeocin and blasticidin, as described above. Forgeneration of miR-205–Tet–On cell lines, Phoenix packagingcells were transiently transfected with Lipofectamine 2000 (In-vitrogen). Retroviral-based plasmids encoding for the Tet-re-sponsive transcriptional activator rtTA or for a miR-30 cassetteembedding the mature sequence of miR-205, under the controlof the Tet-responsive element (TRE) were used. After 24 and48 h, retroviral supernatant was collected and sterile filtered

(0.45 μM). Cells were incubated for 8–16 h with the mediumcontaining the virus, supplemented with 4 μg/mL of polybrene(Sigma-Aldrich). Inducible cells were generated with two stepsof infection. First, cells were infected with a retrovirus containingthe Tet-responsive transcriptional activator rtTA. After selectionof at least 10 d with 500 μg/mL geneticin, cells were subjected toa second round of infection using a retrovirus carrying a miR-30cassette embedding the mature sequence of miR-205, under thecontrol of the TRE. Cells were then kept under selection with1 μg/mL of puromycin and 500 μg/mL geneticin.Lentiviral plasmids, HIV-based, coding for an inhibitor of

human miR-205 or a scramble control both CFP coupled (bothGenecopoeia), were used for lentiviral transfection. The 293Tacells (Genecopoeia) were transiently transfected, followingmanufacturer instructions, along with the HIV packaging plas-mids kit (Genecopoeia). After 24 and 48 h, lentiviral supernatantwas collected and sterile filtered (0.45 μM). After the addition of4 μg/mL of polybrene, it was used for overnight incubation oftarget cells, ΔNp63α–PC3–Tet–On. Cells were kept under se-lection with 500 μg/mL hygromycin B.

Western Blotting. Proteins were extracted with RIPA buffer con-taining mixture inhibitors (Roche) and concentration was de-termined using a Bradford dye-based assay (Bio-Rad). Totalprotein (30 μg) was subjected to SDS/PAGE followed by im-munoblotting with appropriate antibodies at the recommendeddilutions. The blots were then incubated with peroxidase linkedsecondary antibodies followed by enhanced-chemiluminescentdetection using Super Signal chemiluminescence kit (ThermoScientific). Antibodies used were as follows: ZEB1 (1:500; SantaCruz), HA-HRP (1:5,000; Sigma), p63 (1:1,000; Santa Cruz), E-cadherin (1:1,000; Cell Signaling), vimentin (1:1,000; Cell Sig-naling), p53 (1:1,000; Santa Cruz), p21 (1:1,000, Santa Cruz), Bax(1:1,000, Santa Cruz), ZEB2/SIP1 (1:1,000, Santa Cruz), actin(1:1,000; Santa Cruz), and GAPDH (1:10,000; Sigma-Aldrich).

Coimmunoprecipitation. Immunoprecipitation was performed inPC3, DU145, and SaOS-2 cells using Protein G-Sepharose (GEHealthcare) and following manufacturer guidelines. DO-1 (SantaCruz) and 4A4 (Santa Cruz) mouse monoclonal antibodies wereused to immunoprecipitate p53 and p63, respectively. Pull-downwith GST_p63DBD with mutp53 from rabbit reticulocyte lysateswas performed as described (1).

RNA Extraction and qRT-PCR. Total RNA was isolated from cellsusing TRIzol (Invitrogen) according to the manufacturer’s in-structions. RNA samples were treated with RNase-free DNase I(Invitrogen). Total RNA was reverse transcribed using Super-Script III reverse transcriptase and oligo(dT) primer (Invitrogen).qRT-PCR was performed in an ABI PRISM 7000 SequenceDetection system (Applied Biosystem) with SYBR green readymix (Applied Biosystem) and specific primers (SI Materials andMethods). The GAPDH gene (Invitrogen) was used as internalcontrol. For miRNA detection, RNA was reverse transcribedusing a TaqMan MicroRNA Reverse Transcription kit and qRT-PCR was performed with TaqMan universal master mix (AppliedBiosystem) and specific primers for miR-205. RNU6B (AppliedBiosystem) was used as internal control. Gene and miR expres-sion was defined from the threshold cycle (Ct), and relative ex-pression levels were calculated by using the 2-ΔΔCt method afternormalization with reference to expression of housekeepinggenes (GAPDH or RNU6B).

Tucci et al. www.pnas.org/cgi/content/short/1110977109 1 of 27

Primers for qRT-PCR. Human ΔNp63 for GAAGAAAGGACAG-CAGCATTGAHuman ΔNp63 rev GGGACTGGTGGACGAGGAGHuman TA p63 for GGACTGTATCCGCATGCAGHuman TAp63 rev GAGCTGGGCTGTGCGTAGHuman total p63 for TAACACAGACCACGCGCAGAHuman total p63 rev GAATACGTCCAGGTGGCCGAHuman ZEB1 for GCACCTGAAGAGGACCAGAGHuman ZEB1 rev TGCATCTGGTGTTCCATTTTHuman E-cadherin for CCCACCACGTACAAGGGTCHuman E-cadherin rev CTGGGGTATTGGGGGCATCHuman keratin 14 for CGACCTGGAAGTGAAGATCCGHuman keratin 14 rev CACACTCATGCGCAGGTTCAAHuman sharp-1 for CGTCTTTGGAGTTGACATGGHuman sharp-1 rev GGGCAGCTTTGAGAACTAGCHuman IKKα for GAAAAGGCCATCCACTATGCHuman IKKα rev TCACCATCTCTGTGCTGTCHuman GAPDH for AGCCACATCGCTCAGACACHuman GAPDH rev GCCCAATACGACCAAATCC

Promoter Cloning. The luciferase reporter plasmid, whose ex-pression is controlled by the miR-205 promoter sequence, wasmodified from the pGL3-Basic vector (Promega). miR-205promoter sequence containing the three p53 responsive elementswas amplified using specific oligos (forward 5′-CTCTGATCG-TGCATGTGCTT-3′ and reverse 5′-AGACAGACTCCGGTG-GAATG-3′; 2110 bp), cloned in the PCR 2.1 vector (Invitrogen),and restricted with NheI and XbaI. This fragment was thencloned in the luciferase vector that was previously restricted withthe same enzymes.

Luciferase Reporter Assay. For luciferase assays, SaOS-2 cells wereseeded in triplicate into 12-well plates and cotransfected with250 ng of a luciferase reporter plasmid driven by the miR-205(miR-205WT ormutated RE-II or RE-III) promoter and 10 ng ofRenilla luciferase cDNA (pRL-CMV; Promega), together with750 ng of ΔNp63α-HA or pcDNA-HA as a control. Luciferaseactivity was assayed 24 h after transfection by a dual luciferasereporter assay system (Promega) and normalized against Renillaluciferase activity; light emission was measured over 10 s usingGloMax (Promega) luminometer. Mut-RE-II and mut-RE-IIIhave been obtained by mutating the core binding sequenceGGGTACTTA and TACGGG, respectively.

Chromatin Immunoprecipitation. ΔNp63α–SaOS-2 inducible cells(5 × 106) were crosslinked for 10 min, in a solution containing 1%formaldehyde, and ChIP assays were performed using aMAGnifyChIP system (Invitrogen), referring to the manufacturer’s in-structions. Cell lysates were sonicated to obtain chromatin frag-ments of ∼700 bp. The immunocomplex was immunoprecipitatedusing a specific antibody anti-HA (16B12; Covance) and non-specific IgG as a technical control. Collected DNA fragmentswere tested by PCR using three set of primers located in the miR-205 promoter, amplifying the p53 putative responsive elements(RE) found in the miR-205 promoter. RE-I: 5′-CATCTGCCC-CAACCCATCCA-3′ 205-RE1-F, 5′-AAAGAAATGCCCATC-AACCCT-3′ 205-RE1-R (119 bp); RE-II: 5′-GGCTGTCCTTC-CTAGATAAGTC-3′ 205-RE2-F, 5′-TGCCAATGTGAAATC-CAGA-3′ 205-RE2-R; RE-III: 5′-GGATTTCACATTGGCAA-ACGG-3′ 205-RE3-F, 5′-CTACTCTGAGCTTGCTTACCA-3′205-RE3-R (150 bp). The p53 RE located on hMDM2, used aspositive control, was amplified with one set of primers: forward5′-GGTTGACTCAGCTTTTCCTCTTG-3′ and reverse 5′-GG-AAAATGCATGGTTTAAATAGCC-3′ (119 bp).

Measurement of p63 Stability. DU145, PC3, and H1299 cells wereplated in six-well plates, transfected, and kept in culture. After24 h, fresh medium containing 10 μM cycloheximide was added to

the culture. Protein levels were determined by collecting cells atthe indicated time points and performing Western blotting asdescribed above. The relative amount of p63 protein was eval-uated by densitometry and normalized on GAPDH or actin.

Wound Healing Assay.PC3, RWPE-1, and DU145 cells were platedin six-well plates, transfected, and kept in culture. After 24 h, cellswere scraped with a p10 tip (time 0 h), washed several times inPBS, and monitored with a phase contrast 10× objective using atime-lapse microscope (Zeiss; 200M) with the MetaMorph sys-tem. Images were obtained every 15 min for the duration of 36 h.Movies were made using the ImageJ software and are repre-sentative of three independent experiments. For fluorescencetime-lapse imaging, PC3 cells were transfected with pcDNA orΔNp63 in combination with GFP-spectrin, scratched, and spec-trin behavior was assessed using a time-lapse microscope, takingpictures every 15 min for 16 h. Movies were made using theImageJ software and are representative of a total of nine moviesof three independent experiments.DU145 cells and DU145 cells transiently transfected with a

construct harboring the mature sequence of human miR-205,GFP labeled, were counted and then 8 × 104 cells per each wereplated together in the same well (final cell number seeded 1.6 ×105 in 12-well plates); 24 h later, cells were scraped with a p10 tip(time 0 h), washed several times in PBS, and monitored with aphase contrast 10× objective using a time-lapse microscope (Zeiss;200M) with the MetaMorph software program. Images were ob-tained every 10 min for the duration of 24 h. Cell tracking wasmade using the AxioVision software program and is representa-tive of at least three independent experiments.

ImmunofluorescenceMicroscopy.For immunofluorescence staining,PC3 cells were transfected with ΔNp63 or with an empty vector, inpresence of GFP-actin, whereas ΔNp63α–PC3–Tet–On or miR-205–PC3–Tet–On cells were either left untreated or induced with1 μg/mL of doxycycline. Cells were seeded in 12-well plates ontoglass coverslips and after 24 h scraped as described above. At 0, 2,and 6 h after the scratch, cells were fixed in 4% (vol/vol) para-formaldehyde (15 min) and then permeabilized (10 min in 0.5%Triton X-100 and 3% (wt/vol) BSA in PBS). A monoclonal an-tibody against GM130 (1:200; BD Transduction Laboratories)was used to label the Golgi apparatus for 1 h at room tempera-ture. After three washings (10 min in PBS), the cells were in-cubated with Alexa Fluor 568-conjugated secondary antibody(1:1,000; Jackson Immuno Research Laboratories). After threewashes (10 min, PBS), actin filaments were stained with GFP-phalloidin for 30 min. Afterward, nuclei were stained with DAPI(1:10,000) for 10 min; this step was followed by a further wash (10min in PBS). The coverslips were then mounted with Aqua-polymount antifading solution (Polysciences) onto glass slidesand observed under a confocal microscope (Zeiss; LSM 510).

Flow Cytometry Analysis. Flow cytometry was performed as de-scribed in ref. 2. Briefly, PC3, RWPE-1, and DU145 cells wereharvested after treatment with 0.025% trypsin for 3 min at 37 °Cand then, after addition of 10% (vol/vol) FCS in PBS, the cellswere centrifuged, washed with PBS, and fixed in 70% (vol/vol)cold ethanol. The harvesting of cells using these conditions led tohigh yield of undamaged cells. After fixation, cells were washedwith PBS, treated for 15 min at 37 °C with RNase (100 μg/mL),and then stained with propidium iodide (10 μg/mL in the darkfor 30 min). The samples were then analyzed using a FACScanflow cytometer (Becton Dickinson).

Tumor Metastasis in Vivo. Stably infected cells (1.5 × 106 cells permouse) were suspended in 200 μL of RPMI serum-free mediumand injected through the tail vein into 5- to 8-wk-old BALB/cnude male mice (purchased from Charles River). ΔNp63α and

Tucci et al. www.pnas.org/cgi/content/short/1110977109 2 of 27

miR-205 expression was induced in vivo by treating mice with2 mg/mL doxycycline in 5% (vol/vol) sucrose through theirdrinking water. The solution was prepared freshly every 2–3 dand bottles were covered with aluminum due to doxycyclinephotosensitivity. Animals were killed after 3 wk (32 mice) or6 wk (30 mice) and lungs were harvested for immunohistochem-istry. Tissues were fixed in paraformaldehyde and total number oflung metastases was counted using a stereomicroscope. All ex-periments on mice were conducted according to institutional,national, and European regulations. Animal protocols were ap-proved by the ethics committee of Ghent University.

Gene Expression and Clustering Analysis. Dataset from Taylor et al.(3) GSE21032 was used for this analysis. Samples were selectedto have both miR-205 and p63 mRNA (exon level) data avail-able. Samples were then clustered by their expression of miR-205and TP63 probes. We used an unsupervised Bayesian clusteringmethodology developed in Markert et al. (4) and Vazquez (5).This method finds an optimal number of clusters and optimalclustering from an input of various preclusterings generated bya library of standard clustering methods. Optimization is basedon a Bayesian formulation of a Gaussian mixture model.Epithelial–mesenchymal transition (EMT) signatures were

downloaded from the Broad Institute Online portal (Fig. S8G).Signature scores were calculated using GSEA (6, 7). ΔNp63–miR-205 up- and down-regulation signatures were derived byfold-change analysis from Taylor et al. (3) EMT and ΔNp63–miR-205 signature scores were calculated for the validation da-taset, Sboner et al. GSE16560 (8). These data were clustered bytheir ΔNp63–miR-205 signature scores using the unsupervisedBayesian methodology.Pearson correlation coefficients were calculated using the

matlab routine corr. Kaplan–Meier analysis was performed usingthe matlab routine kmplot.To analyze the relation between ΔNp63, miR-205, and cancer

development (with special regards to metastases and recurrence),we used correlation and clustering analysis on a recently pub-lished dataset by Taylor et al. (3), which contains mRNA ex-pression data recorded on Affymetrix Human Exon 1.0ST Array,microRNA expression (Agilent-019118 Human miRNA Micro-array 2.0), and DNA copy number data for 218 prostate cancersamples. The data are available online on the GEO database(accession number for the superseries GSE21032). The set con-tains 181 primary tumor samples and 37 metastases, as well asnormal samples and some cell lines and xenografts. Extensiveclinical records are available for 131 of the primary tumor sam-ples. Data are also available from the Memorial Sloan-KetteringCancer Center (MSKCC) Prostate Cancer Genomics Data Portalat http://cbio.mskcc.org/prostate-portal/. We downloaded thepreprocessed expression data for the Exon Array and microRNAplatforms (preprocessing included standard qspline normaliza-tion) from the GEO database. Cell lines and xenografts wereexcluded from the analysis. Among the remaining tissue sampleswe collected, a total of 139 samples for which both microRNAand mRNA expression were available, including 98 primary tu-mors, 13 metastases, and 28 normal samples.Samples were then clustered by their expression, using all

probes for TP63 and the expression of the miR-205. For clus-tering, we applied a Bayesian optimized clustering methodology,as previously described in Markert et al. (4). It makes use ofa Bayesian clustering optimizer developed in Vazquez (5), whichoptimizes a given clustering based on a Bayesian formulation ofa Gaussian mixture model. The optimizer returns an alteredclustering with possibly lower number of clusters than the initialclustering (correcting for overfitting) and possibly different dis-tribution of samples in clusters, together with a score (free-en-ergy) quantifying the quality of fit to a Gaussian mixture model.The clustering methodology applies this optimizer to a set of

input clusterings generated by a library of various standardclustering methods. The final optimal clustering is chosen amongthe optimized output clusterings by minimizing the free-energyscore and thus optimizing the fit. As in Markert et al. (4), inputclusterings are generated using two approaches. The first ap-proach generates clusterings of the continuous data matrix usinga variety of standard clustering methods as available in thematlab toolbox, and a large range of predefined cluster numbers(as required by most clustering methods). The second approachis a two-step procedure, generating a discretized data matrix inthe first step and then clustering it using standard methods fordiscrete data such as Hamming- or Jaccard-based linkage. Thediscretized data matrix is itself generated by applying the Bayesianoptimizer to clusterings of each column, created by the methodsin the first, continuous approach.Overall, this methodology produces a stable clustering fit to a

Gaussianmixturemodel,withanoptimized (andthusunsupervised)number of groups, and optimized over a large array of standardclustering methods including k means and many types of metric-linkage combinations. See Markert et al. (4) for more details.We used a two-tailed t test to calculate significance of the

expression levels in the resulting clusters for all probes of TP63on the Exon array, to discern the isoform specificity. Notably, theclustering was generated using all probes from all exons of TP63in an unsupervised manner; however, probe expression levelanalysis showed highly significant differences between the clus-ters only in those probes that are found in the ΔNp63 isoforms.TAp63-specific probes were not significantly altered among theclusters (Fig. S8F). After creating the clustering, we analyzedclinical variables on the resulting clusters and used two-tailedt test and Fischer’s exact test to assess significance of alterationsbetween the clusters (Fig. S8). Kaplan–Meier analysis was ap-plied using both time to biochemical recurrence and overallsurvival. Because survival was censored for all samples in thisselection, we suppressed the censoring to obtain survival trendsbased on the data within the 5-y follow-up period. Censoring wasused for biochemical recurrence (Fig. 5 C and D) and is in-dicated by black markers on the probability curves.We furthermore downloaded genetic alterations in the TP53

pathway from the MSKCC Prostate Cancer Genomics DataPortal to control for the presence of TP53 mutants; however, onlyone mutant was contained in the dataset and thus TP53 mutationwas insignificant in the dataset.Pearson rank correlation was calculated including two-tailed

P values (matlab routine corr) to discern functional relationshipsbetween the ΔNp63–miR205 complex and EMT-mediator andmarker genes ZEB1, SIP1, CDH1, VIM, CDH2, and FN1. Wealso calculated average expression levels of EMT cluster genes(Fig. 5B).Expression signatures characterizing EMT were downloaded

from the Broad Institute portal at http://www.broadinstitute.org/gsea/msigdb as indicated in Fig. S8G, and signature scores foreach sample were calculated using GSEA (6, 7, 9). Pearsoncorrelation coefficients and P values between the signaturescores and the miR-205 and ΔNp63 expression were calculated(Fig. S8D). Significance for the signature score association wascalculated using a random test (n = 100,000) and was used fordisplaying the heatmap in Fig. 5B.Because survival data were censored in the Taylor et al. (3)

dataset, we sought to support the survival trend discerned fromTaylor et al. (3) on a second dataset. Because microRNA ex-pression together with mRNA expression was to our knowledgenot available in secondary public datasets, we derived gene listscharacterizing miR-205 expression by computing the genes thatwere most significantly correlated (miR-205_up signature) andanticorrelated (miR-205_dn signature) with miR-205 expression.Whole-transcript expression data from Taylor et al. (3) wereused for this analysis and correlation was calculated using both

Tucci et al. www.pnas.org/cgi/content/short/1110977109 3 of 27

Spearman and Pearson coefficients, across all samples includingnormals, and across tumors only. Significance was determined asthe maximal P value among these four associated correlationP values. Probes were sorted by significance, and significanceP value <1.0E-05 was used as a cutoff for the mir-205_dn sig-nature (20 genes) and P = 1.0E-06 was used for the miR-205_upsignature (153 genes). Notably, this analysis detected two con-firmed miR-205 target genes that were highly significantly anti-correlated (EPB41L4B, P < 1.0E-05 and FAM84B, P < 1.0E-04),and TP63 is contained in the coexpressed group (P < 1.0E-06).We then used GSEA with the two gene lists (see below) on avalidation dataset of 281 prostate tumor samples from Sboneret al. (8), which was downloaded in preprocessed form from theGEO database (GSE16560). Samples were clustered by theirsignature scores, using the Bayesian arbiter methodology de-scribed above. This resulted in three clusters indicating loss andfunction of the miR-205 axis and one intermediate/indeter-minate group. We analyzed survival among these clusters usingKaplan–Meier analysis with the censored survival data (Fig.S8E). We also calculated EMT signature scores on this set andcomputed Pearson correlation coefficients and P values betweenall signatures.miR-205_up (miR-205 coexpressed genes) are as follows:

RASD2, NEFH, DUSP3, tcag7.893, FREQ, MYL9,LOC729468, VEGFA, CNN1, TLN1, PDZRN4, GPRASP1,ALDH1A2, RGN, MYH11, FLNA, ARMCX1, TAGLN,RHOB, PGM5, ACTG2, CRISPLD2, ROR2, THSD4, C5orf4,

SCNN1A, ARHGAP20, SYNM, hCG_1783494, LGALS3BP,CSRP1, USP31, PCP4, TEAD1, TPM1, MYOCD, ZFP36L2,DMD, LPAR1, PDGFD, ANXA6, CEACAM1, TLR3, ZNF185,EDNRA, HES1, LOC100133842, NNT, GPRC5B, SORL1,TNS1, TFCP2L1, CLDN1, ACACB, TBC1D1, RBMS2, PRNP,RBMS2P, TCEAL2, MBNL2, PDK4, SVIL, LOC100133706,LOC651397, PDGFC, LOC100133793, ROBO1, LDHB,LOC100129436, LOC100131438, ATP1B1, CTSB, GNAL,LOC642132, GLI3, ITGA3, ANO1, EPAS1, TUBA4A,ZFP36L1, CX3CL1, ID1, PPP1R3C, UNQ6228, MET, ATP2B4,SCPEP1, NR3C1, PTPLA, TRAF3IP2, KRT6B, ITGB4, CD59,LAMB3, RBMS3, KRT6C, KRT17P3, HOXD11, RBBP8,LOC648605, AOX1, SMAD3, SNAI2, TSPAN2, ZNF204,CYP3A5, TMLHE, C21orf63, DST, TNC, SCRN1, ST5, APO-BEC3C, FRMD6, ANXA2P2, ETS2, PGRMC1, KRT23,ANXA1, ANXA2, L3MBTL4, JUB, ITGB6, CPA6, ALS2CR4,PLP2, CDH3, KRT14, PDLIM1, GSTP1, ACSF2, MAML2,FHL2, NTN4, NAV2, ST6GALNAC2, HSPA4L, MYOF,MEIS2, HOXD10, KRT17, PCDH7, LOC730427, MPZL2,FGFR2, GABRE, SLC14A1, FLRT3, KRT15, LOC642587,TP63, TRIM29, and KRT5.miR-205_down (miR-205 antisense expressed genes) are as

follows: HOXC6, SLC10A5, COL28A1, CGREF1, hCG_1995004,ZNF285A, IMPA1P, MAST2, C2orf72, FAM151B, SGEF,C8orf45, EPB41L4B, KALRN, TBC1D30, C8orf34, POLN,LOC100129203, CYP2J2, and MMP26.

1. Straub WE, et al. (2010) The C-terminus of p63 contains multiple regulatory elementswith different functions. Cell Death Dis 1:e5.1.

2. Sayan BS, et al. (2010) Differential control of TAp73 and ΔNp73 protein stability by thering finger ubiquitin ligase PIR2. Proc Natl Acad Sci USA 107(29):12877–12882.

3. Taylor BS, et al. (2010) Integrative genomic profiling of human prostate cancer. CancerCell 18:11–22.

4. Markert EK, Mizuno H, Vazquez A, Levine AJ (2011) Molecular classification of prostatecancer using curated expression signatures. Proc Natl Acad Sci USA 108(52):21276–21281.

5. Vazquez A (2008) Bayesian approach to clustering real value, categorical and networkdata: Solution via variational methods. arXiv:0805.2689v3.

6. Subramanian A, et al. (2005) Gene set enrichment analysis: A knowledge-basedapproach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA102(43):15545–15550.

7. Mootha VK, et al. (2003) PGC-1alpha-responsive genes involved in oxidativephosphorylation are coordinately downregulated in human diabetes. Nat Genet 34(3):267–273.

8. Sboner A, et al. (2010) Molecular sampling of prostate cancer: A dilemma forpredicting disease progression. BMC Med Genomics 16:3–8.

9. Sarrió D, et al. (2008) Epithelial-mesenchymal transition in breast cancer relates to thebasal-like phenotype. Cancer Res 68:989–997.

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Fig. S1. TAp63 and ΔNp63 drive the expression of miR-205. H1299 cells were transfected with either an empty vector or HA-ΔNp63 (A) or with TAp63 (B), andendogenous levels of miR-205 were assessed by qRT-PCR 48 h posttransfection. (C) qRT-PCR of ΔNp63 and TAp63 endogenous levels in MCF7 cells. (D) MCF-7cells were transfected with either a scrambled control (Scr) or a siRNA-targeting ΔNp63, and endogenous levels of ΔNp63 and miR-205 were assessed by qRT-PCR 48 h posttransfection. (E) qRT-PCR of ΔNp63 and TAp63 endogenous levels in MDA MB231 cells. (F) MDA MB231 cells were transfected either witha scrambled control (Scr) or with a combination of siRNAs targeting TAp63 and endogenous levels of TAp63 and miR-205 were assessed by qRT-PCR 48 hposttransfection. (G) ΔNp63α–SaOS-2–Tet–On cells were treated with 2 μg/mL doxycycline (Doxy) for 24, 48, and 72 h and endogenous levels of miR-205 wereassessed by qRT-PCR. Induction of p63 is shown in the Western blot. (H) ΔNp63α–SaOS-2–Tet–On cells were treated with or without doxycycline 2 μg/mL to

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overexpress the human ΔNp63 protein (see Western blot) and collect every hour for 12 h. Endogenous levels of ΔNp63, miR-205 (Upper), keratin 14 (K14),sharp-1, and IKKα (Lower) were assessed by qRT-PCR and the results normalized to RNU6B for miR-205, and to the GAPDH gene for the others. Data representmean ± SD of four different experiments analyzed in triplicate. (I) Induction kinetics of ΔNp63 direct transcriptional targets (miR-205, K14, sharp-1, and IKKα)analyzed by qRT-PCR following expression of ΔNp63 in SaOS-2–Tet–On cells. (J) The miR-205 promoter region containing the three p53 consensus sites (p53RE).All qRT-PCR results were normalized to RNU6B or GAPDH. Data represent mean ± SD of three different experiments analyzed in triplicate. *P < 0.05.

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Fig. S2. ΔNp63 regulates ZEB1 through its target miR-205. (A) miR-205–PC3–Tet–On cells were treated with 1 μg/mL doxycycline (Doxy) for 24 h and qRT-PCRof miR-205 induction levels were performed. (B) PC3 cells and (C) H1299 cells were transfected with 50 nM pre–miR-205 or negative control (Neg Ctrl) for 24 hand the cell lysates were analyzed for ZEB1 expression by Western blotting and qRT-PCR. (D) A431 cells were transfected with 100 nM anti–miR-205 or Neg Ctrlfor 72 h and the levels of ZEB1 were assessed by qRT-PCR and Western blotting. The qRT-PCR results were normalized to the GAPDH gene. Data representmean ± SD of three different experiments analyzed in triplicate. MiR-205 levels were assessed by qRT-PCR to verify the transfection efficiency. (E) Westernblotting of endogenous levels of ZEB1 protein in ΔNp63α–SaOS-2–Tet–On cells. Levels of actin were used as loading control. (F) ΔNp63α–PC3–Tet–On cells or thesame cell line bearing a stable expression of anti–miR-205 were treated with 1 μg/mL Doxy for 24 h. Endogenous levels of ZEB1 were assessed by Westernblotting. (G) Western blotting of ZEB2 protein endogenous levels in miR-205–PC3–Tet–On cells treated with 1 μg/mL Doxy for 24 h. Percentage of inhibition ofZEB2 protein is expressed relative to untreated miR-205–PC3–Tet–On cells and normalized to GAPDH levels.

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Fig. S3. ΔNp63 and miR-205 inhibit cell migration. Scratch assays of PC3 cells transfected with an empty vector or HA-ΔNp63 (A) and with 50 nM pre–miR-205or Neg Ctrl (B). Monolayers were scratch wounded with a p10 tip and filmed for 36 h under light time-lapse microscope. The wound closure is illustrated byshowing the wound immediately (0 h) and 36 h after the scratch. Images are representative of nine fields from three independent experiments. See also MoviesS1, S2, S3, and S4. (C) PC3 cells were transfected as in Fig. 3A and qRT-PCR of miR-205 levels was performed 24, 48, and 72 h after the scratch to verity thetransfection efficiency. Results were normalized to RNU6B. Data represent mean ± SD of three different experiments analyzed in triplicate. (D) Cell cycleanalysis was performed by flow cytometry in PC3 cells transfected for 48 h with either an empty vector or HA-ΔNp63.

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Fig. S4. ΔNp63 and miR-205 inhibit polarization. (A) PC3 cells, transfected with an empty vector (Left) or with ΔNp63 (Right) along with GFP-phalloidin(green), were grown in monolayers and wounded. Localization of the Golgi (red) was determined using immunofluorescence and cells were counterstainedwith DAPI (blue). Representative pictures are shown. (B) ΔNp63α–PC3–Tet–On cells untreated (Left) or induced with 1 μg/mL doxycycline (Right) for 24 h weregrown in monolayers and wounded. (C) Localization of the Golgi complex in >50 cells around the edge of the wound at 0, 2, and 6 h after scratch. (D) miR-205–PC3–Tet–On cells untreated (Left) or induced with 1 μg/mL doxycycline (Right) for 24 h were grown in monolayers and wounded. (E) Localization of the Golgicomplex in >50 cells around the edge of the wound at 0, 2, and 6 h after scratch. Data are representatives of mean ± SEM of three or more independentexperiments. *P < 0.05, **P < 0.01, ***P < 0.001. (F) Stills taken from movies showing the motility of empty vector (Left) and ΔNp63-expressing cells (Right)following transfection with GFP-spectrin. Data are representative of three independent experiments of a total of nine movies. See also Movies S9 and S10.

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Fig. S5. Mutant p53 correlates with the loss of p63. (A) qRT-PCR of relative expression of ΔNp63 and TA63 in RWPE-1 cells. (B) DU145 cells were transfectedwith pre–miR-205 or Neg Ctrl and the levels of ZEB1, p63, and p53 proteins were assessed by Western blotting. RWPE-1 cells were transfected with an emptyvector and with a p53 mutant, R175H, and protein levels of ZEB1 and p63 (C), p21 (D), and Bax (E) were assessed by Western blotting. (F) H1299 cells weretransfected with an empty vector and with different p53 mutants as indicated, and endogenous levels of miR-205 were assessed by qRT-PCR. Induction of HA-p53 is shown in the Western blot. qRT-PCR results were normalized to GAPDH or RNU6B and data represent mean ± SD of three different experiments an-alyzed in triplicate. (G) p63 protein steady-state levels relative to Fig. 4D were evaluated by densitometry and normalized to GAPDH. (H) Decay of p63 proteinlevels in the presence of 10 μM cycloheximide (CHX) in PC3 cells reexpressing p53 WT and in H1299 cells (I) transfected with a p53 mutant, R175H.

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Fig. S6. p63 interacts with p53 mutants. (A and B) Full controls of coimmunoprecipitations of mutant p53 and p63 relative to Fig. 4F. Percentage of pull-downover input is indicated. One of three independent experiment is shown. (C) DU145 cells were transfected with ΔNp63 and coimmunoprecipitated with theindicated antibody. (D) SaOS-2 cells were cotransfected with ΔNp63, p53 WT, or the indicated p53 mutants and coimmunoprecipitated with the indicatedantibody. Percentage of pull-down over input is indicated. One of three independent experiments is shown. (E and F) Pull-down with GST_p63DBD withmutp53 from rabbit reticulocyte lysates. Incubation at 4 °C (E) and room temperature and 37 °C for 3 h (F).

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Fig. S7. Mutant p53 inhibits cell migration. (A) RWPE-1, and DU145 transfected with 50 nM pre–miR-205 or Neg Ctrl were scratch wounded with a p10 tip andfilmed for 12 h under light time-lapse microscope. The wound closure is illustrated by showing the wound immediately (0 h) and 12 h after the scratch. Imagesare representative of nine fields from three independent experiments. See also Movies S11, S12, and S13. (B) qRT-PCR for miR-205 levels was performed 24 hafter the scratch to verify the transfection efficiency. (C) Cell cycle analysis was performed by flow cytometry in RWPE-1 cells and in transfected DU145 cells. (D)DU145 cells and DU145 cells transiently transfected with a construct harboring the mature sequence of human miR-205 GFP labeled were plated together inthe same well. Monolayers were scratch wounded with a p10 tip and filmed for 24 h under light time-lapse microscope. Image is representative of threeindependent experiments. (E) Tortuosity, (F) straight distance, (G) mean velocity, and (H) total distance of cell were analyzed using the AxioVision softwareprogram. Data represent mean ± SEM of three different experiments. ***P < 0.001. (I) DU145 cells were transfected with a scrambled control or an oligosilencing the endogenous mutant p53 and were scratch wounded with a p10 tip and filmed for 24 h under light time-lapse microscope. The wound closure isillustrated by showing the wound immediately (0 h) and 24 h after the scratch. Images are representative of nine fields from three independent experiments.See also Movies S14 and S15. (J) Cell cycle analysis was performed by flow cytometry in transfected DU145 cells.

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Fig. S8. ΔNp63–miR-205 functionality affects invasion potential and other clinical parameters in human cancer. Samples from Taylor et al. (1) were clusteredaccording to their TP63–miR-205 expression and clusters were analyzed for clinical parameters. (A) Loss of the ΔNp63–miR-205 axis was significantly associatedwith increased metastatic events and lymph node invasion (P < 0.05, Fisher’s exact test) and was associated with higher Gleason scores (B) and increasedprostate-specific antigen (PSA) levels (C). (D) Statistics for Fig. 5B, correlation between signature scores, and expression values across all samples. Colors refer tothe direction of the correlation: blue for anticorrelation, red for correlation. (Upper) Actual correlation coefficients. Coefficients are values between −1 and 1,with −1 indicating perfect anticorrelation and +1 indicating perfect correlation. (Lower) P values (likelihood of a coefficient being large or small; whenever thecoefficient is negative, anticorrelation, the coloring is blue, also in the P values). Functional relationship with EMT was measured by computing Pearsoncorrelation coefficients between expression of ΔNp63 and miR-205 and signature scores for expression signatures characterizing EMT. A signature pair derivedby Sarrio et al. (2) in a basal-like breast cancer cell line manifested significant anticorrelation between EMT up-regulation and expression values suggestinginduction of EMT under loss of ΔNp63 and miR-205. A signature derived by collecting all genes annotated for EMT in the gene ontology (GO) databaseproduced a contrary result. Genes within the GO term signature are not separated for directionality of the association and might include many different up- ordown-regulators, collected over the many different experimental settings for which annotations were generated. Due to the combined directional nature ofEMT (loss of epithelial markers, gain of mesenchymal markers), the experimentally derived Sarrio_EMT signatures were considered more reliable in thisanalysis. (E) Survival probability and signature scores calculated for 281 prostate cancer samples from Sboner et al. (3) (GSE16560). Expression patterns relatedto ΔNp63–miR-205 function in the Taylor et al. (1) dataset were predictive of survival outcome in this second prostate tumor set (2). Lists of highly significantlymiR-205 coexpressed genes and miR-205 antisense expressed genes were derived from the Taylor et al. (1) dataset and used as expression signatures predictingmiR-205 activity. Samples were clustered by their signature scores, with clusters indicating loss or function of the ΔNp63–miR-205 axis as imputed by thesignatures. The plot shows Kaplan–Meier analysis of survival data from the extensive 30-y follow-up time in this watchful waiting cohort. (F) Expression datafor miR-205 and 26 probes along the exons of TP63 were available for 111 prostate cancer samples (98 primary tumors and 13 metastases) from Taylor et al. (1)(GSE21032). The 26 probes scan both the TAp63-specific exons, ΔNp63-specific exons, and common p63 exons, as indicated in the second column. These ex-pression data were clustered in an unsupervised manner. The three resulting clusters (clusters are as in Fig. 5: cluster 1 is ΔNp63/miR-205 loss, cluster 2 is in-termediate, and cluster 3 is ΔNp63/miR-205 function) were then analyzed for significantly altered expression of the single probes to detect those thatcontributed most to the clustering. Probes for TP63 are listed with their isoform specificity (Left bar) and average expression values of the probes on the

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clusters are given. Blue indicates significant underexpression; red, significant overexpression; and gray background, no significant alteration compared withthe overall mean. All probes specific for the TAp63 isoforms fail to show statistical significance, whereas all of the ΔNp63-specific or p63-common probes reachstatistical significance, indicating a specific association with ΔNp63. (G) Datasets and expression signatures used in analysis. List of the two datasets analyzedand of the EMT signature used are shown. Expression data for Taylor et al. (1) and Sboner et al. (2) were downloaded from the GEO database (accessionnumbers and references listed in Top tiers). Expression signatures characterizing EMT were downloaded from the Broad Institute database using the searchterm epithelial mesenchymal transition. Signature references and Web links are listed in the Middle tiers. Expression signatures characterizing miR-205coexpressed genes (miR-205_up) and antisense expressed genes (miR-205_dn) were derived from Taylor et al. (1) and are given in SI Materials and Methods.

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Fig. S9. p63 and miR-205 expression inhibit metastasis in vivo. A total of 1.5 × 106 scrambled control–PC3–Tet–On (10 mice), ΔNp63α–PC3–Tet–On (10 mice), ormiR-205–PC3–Tet–On (10 mice) cells were injected through the tail vein of BALB/c nude male mice. ΔNp63α and miR-205 expression was induced withdoxycycline through their drinking water. Animals were killed after 6 wk and total number of lung metastasis >2 mm was counted using a stereomicroscope.

1. Taylor BS, et al. (2010) Integrative genomic profiling of human prostate cancer. Cancer Cell 18:11–22.2. Sarrió D, et al. (2008) Epithelial-mesenchymal transition in breast cancer relates to the basal-like phenotype. Cancer Res 68:989–997.3. Sboner A, et al. (2010) Molecular sampling of prostate cancer: A dilemma for predicting disease progression. BMC Med Genomics 16:3–8.

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Movie S1. Migration of PC3 cells transfected with an empty vector. Cell migration was monitored using light time-lapse imaging of wound-scratch assays.Movie is representative of at least three independent experiments and in total nine movies.

Movie S1

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Movie S2. Migration of PC3 cells transfected with ΔNp63. Cell migration was monitored using light time-lapse imaging of wound-scratch assays. Movie isrepresentative of at least three independent experiments and in total nine movies.

Movie S2

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Movie S3. Migration of PC3 cells transfected with a negative control (Neg Ctrl). Cell migration was monitored using light time-lapse imaging of wound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S3

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Movie S4. Migration of PC3 cells transfected with a miR-205 precursor (pre–miR-205). Cell migration was monitored using light time-lapse imaging of wound-scratch assays. Movie is representatives of at least three independent experiments and in total nine movies.

Movie S4

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Movie S5. Migration of PC3 cells transfected with an empty vector and a negative control (Neg Ctrl). Cell migration was monitored using light time-lapseimaging of wound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S5

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Movie S6. Migration of PC3 cells transfected with ΔNp63 and a negative control (Neg Ctrl). Cell migration was monitored using light time-lapse imaging ofwound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S6

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Movie S7. Migration of PC3 cells transfected with an empty vector and a miR-205 inhibitor (anti–miR-205). Cell migration was monitored using light time-lapse imaging of wound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S7

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Movie S8. Migration of PC3 cells transfected with ΔNp63 and a miR-205 inhibitor (anti–miR-205). Cell migration was monitored using light time-lapse imagingof wound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S8

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Movie S9. Motility of PC3 cells transfected with an empty vector. PC3 cells were transfected with an empty vector in combination with GFP-spectrin. Themotility of the cells was monitored using fluorescence time-lapse confocal microscopy. Movie is representative of at least three independent experiments andin total nine movies.

Movie S9

Movie S10. Motility of PC3 cells transfected with ΔNp63. PC3 cells were transfected with ΔNp63 in combination with GFP-spectrin. The motility of the cells wasmonitored using fluorescence time-lapse confocal microscopy. Movie is representative of at least three independent experiments and in total nine movies.

Movie S10

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Movie S11. Migration of RWPE-1. Cell migration was monitored using light time-lapse imaging of wound-scratch assays. Movie is representative of at leastthree independent experiments and in total nine movies.

Movie S11

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Movie S12. Migration of DU145 cells transfected with a negative control (Neg Ctrl). Cell migration was monitored using light time-lapse imaging of wound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S12

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Movie S13. Migration of DU145 cells transfected with a miR-205 precursor (pre–miR-205). Cell migration was monitored using light time-lapse imaging ofwound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S13

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Movie S14. Migration of DU145 cells transfected with a scrambled control (Scr). Cell migration was monitored using light time-lapse imaging of wound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S14

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Movie S15. Migration of DU145 cells transfected with a oligo silencing the endogenous p53 mutant (si-p53). Cell migration was monitored using light time-lapse imaging of wound-scratch assays. Movie is representative of at least three independent experiments and in total nine movies.

Movie S15

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