SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial. 2) TP53 mutation analysis in OCCC cell lines The primers used for TP53 sequencing have previously been described by Cooper et al (34). Tumour DNA was amplified using forward and reverse primer pairs in a 50μl reaction volume containing 20ng of 2 template DNA, 0.8 μM of each primer, 0.2 μM dNTP, 1.5 mM MgCl2, 0.3 units of AmpliTaq Gold Taq polymerase (Applied Biosystems, Warrington, UK), and 1 x PCR buffer, and amplified using the following cycle parameters: 95 °C for 10 min, followed by 35 cycles of 95 °C for 30 sec, 56 °C for 30 sec, 72 °C for 30 sec, and a subsequent final extension of 72 °C for 10 min and incubation of incubation of 4 °C. 5μl of PCR product was visualised after being run on a 2% w/v agarose gel. PCR products (45μl) were subsequently cleaned using Min-elute columns (Qiagen) according to the manufactures instructions and eluted in 50μl. Sequencing reactions were carried out with DNA Sequencing Kit BigDye Terminator v 1.1 Cycle Sequencing Ready Reaction Mix (Applied Biosystems, Warrington, UK), in a 10 μl reaction using 1.0mM of the forward or reverse primers, 4μl BigDye, 2 μl cleaned PCR product and 3.5μl nuclease free water, (with cycle parameters 96 °C 1 minute, followed by 25 cycles of 96 °C for 20 sec, 50 °C for 20 sec, 60 °C for 4 minutes and final incubation at 4 °C). 10 μl of the sequencing product was cleaned with the Dye-ex columns (Qiagen) according to manufacturers instructions. Products were dried at 70 °C for 30 minutes and resuspended in Hi-Di Formamide (Applied Biosystems, Warrington, UK) and run on an Applied Biosystems 3100 Genetic Analyser. Sequences were analysed with Mutation Surveyor software (Softgenetics, PA, USA). Mutations were confirmed by repeat PCR and sequencing reactions and by sequencing forward and reverse strands. 3) Quantitative Real-Time Reverse Transcriptase PCR (qRT-PCR) Briefly, RNA was extracted using the RNeasy FFPE RNA Isolation Kit (Qiagen) followed by an additional DNase treatment as previously described (14). RNA quantification, quality control and reverse transcription were performed as previously described (14). RNA samples were quantified using the Agilent 2100 Bioanalyzer with RNA Nano LabChip Kits (Agilent Biosystems). Criteria for use in reverse transcription was a concentration of >75ng/μl. In total, 30 with interpretable PPM1D gene copy 3 number status rendered optimal results. Reverse transcription was performed with Superscript III (Invitrogen) using 400ng of RNA per reaction. Triplicate reactions were performed for each sample (RT- positive), in addition to an RT-negative reaction to check for the absence of detectable DNA contamination. Quantitative real time PCR was performed using TaqMan® chemistry on the ABI Prism 7900HT(Applied Biosystems), using the standard curve method (14, 37). Assays were purchased from Applied Biosystems. All selected amplicons were less than 85bp to accommodate the degraded nature of the RNA. In addition, three reference genes (TBP, TFRC and MRPL19) were used, having been previously selected as effectively normalising for degradation of RNA (14). PPM1D expression levels were normalised to the geometric mean of the three reference genes. (Assay on demand ID: Hs00174609_m1-TFRC, Hs00608522_g1-MRPL19 and Hs00186230_m1-PPM1D). SUPPLEMENTARY FIGURE LEGENDS Supplementary Figure 1: Prediction Analysis of Microarrays (PAM) predictor of histological subtypes of primary ovarian cancers. A) One hundred and twenty-six genes were identified to classify correctly primary ovarian samples into clear cell, endometrioid, mucinous and serous cancers or normal ovary. B) Results of the predicted histological types subtypes based on the PAM predictor. Note that the misclassification rate is 18.3%. C) Posterior probabilities for ovarian clear cell carcinoma cell lines based on the PAM predictor. Note that all cell lines displayed a high correlation with the transcriptomic profile of primary ovarian clear cell carcinomas. Supplementary Figure 2: PPM1D shRNA induces knockdown of PPM1D mRNA and protein. A) Quantitative real time RT-PCR assessment of PPM1D mRNA levels in HeLa, KOC7C and TOV21G 24 hours after transfection with scrambled control (SC) or PPM1D shRNA (shRNA). B) Western blot illustrating the protein expression levels of PPM1D in HeLa, KOC7C and TOV21G at baseline and 24 hours after transfection with scramble control or PPM1D shRNA. SUPPLEMENTARY TABLE CAPTIONS Supplementary Table 1 – Gains Losses and amplifications for 12 OCCC cell lines Chrom = Chromosome, Start = Start of region, End = End of region, Start.bac = bacterial artificial chromosome at start of region, End.bac = bacterial artificial chromosome at end of region, BACs = No. of bacterial artificial chromosomes within region of copy number alteration, Length MB = size of copy number alteration in MB, maxM = maximum M-value (aws ratio) within region, Mirna = micro RNAs, aCGH.CNVs = previously reported microarray comparative genomic hybridisation copy number variations/ polymorphisms. Supplementary Table 2 – Recurrent gains and losses in ≥4 OCCC cell lines 2 Chrom = Chromosome, Start = Start of region, End = End of region, Start.bac = bacterial artificial chromosome at start of region, End.bac = bacterial artificial chromosome at end of region, BACs = No. of bacterial artificial chromosomes within region of copy number alteration, Length MB = size of copy number alteration in MB, maxM = maximum M-value (aws ratio) within region, Mirna = micro RNAs, aCGH CNVs = previously reported microarray comparative genomic hybridisation copy number variations/ polymorphisms. Supplementary Table 3 – Regions of high level amplification (Log2ratio>0.8) in OCCC cell lines Chrom = Chromosome, Start = Start of region, End = End of region, Start.bac = bacterial artificial chromosome at start of region, End.bac = bacterial artificial chromosome at end of region, Mirna = micro RNAs, aCGH.CNVs = previously reported microarray comparative genomic hybridisation copy number variations/ polymorphisms. Supplementary Table 4 – smallest region of overlap for amplified regions (Log2ratio>0.4) in 2 or more OCCC cell lines Chrom = Chromosome, Start = Start of region, End = End of region, Start.bac = bacterial artificial chromosome at start of region, End.bac = bacterial artificial chromosome at end of region, Mirna = micro RNAs, aCGH.CNVs = previously reported microarray comparative genomic hybridisation copy number variations/ polymorphisms. Supplementary Table 5 – List of genes whose expression correlate with copy number gains and/ or amplification. Symbol = HUGO gene symbol, refseq: NCBI Reference Sequence, unigene: NCBI UniGene reference, Start (kb) = Start of region in kilobases, End (kb) = End of region in kilobases, t.test.p.gain: two-tailed p value for correlations between gains and/ or amplification and mRNA expression levels. 3 Supplementary Table 6 – Gene networks identified as significantly enriched using the Ingenuity Pathways Analysis software based on the list of genes whose expression correlated with copy number gains and/ or gene amplification. In bold, genes whose expression significantly correlated with copy number gains and/ or gene amplification. Ingenuity Pathway Analysis scores >30 are considered highly significant. Supplementary Table 7 – List of genes whose expression correlate with deletion. Symbol = HUGO gene symbol, refseq: NCBI Reference Sequence, unigene: NCBI UniGene reference, Start (kb) = Start of region in kilobases, End (kb) = End of region in kilobases, t.test.p.loss: two-tailed p value for correlations between losses (deletions) and mRNA expression levels. Supplementary Table 8 – Gene networks identified as significantly enriched using the Ingenuity Pathways Analysis software based on the list of genes whose expression correlated with copy number losses. In bold, genes whose expression significantly