Supplementary Material for: Patterns of Genomic Loss of Heterozygosity Predict Homologous Recombination Repair Defects in Epithelial Ovarian Cancer Victor Abkevich1, *, Kirsten M. Timms1, *, Bryan T. Hennessy2, Jennifer Potter1, Mark S Carey3, Larissa A Meyer4, Karen Smith-McCune5, Russell Broaddus6, Karen H Lu7, Jian Chen1, Thanh V Tran1, Deborah Williams1, Diana Iliev1, Srikanth Jammulapati1, Lisa M FitzGerald1, Thomas Krivak8, Julie A. DeLoia9, Alexander Gutin1, Gordon B. Mills3, Jerry S. Lanchbury1, 1 Myriad Genetics Inc., Salt Lake City, UT, USA 2 Beaumont Hospital and Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland 3 Department of Obstetrics and Gynecology, University of British Colombia, Vancouver, Canada 4 Department of Obstetrics, Gynecology and Reproductive Sciences, The University of Texas Medical School – Houston, TX, USA 5 Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA 6 Department of Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA 7 Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA 8 Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, USA 9 School of Public Health and Health Services, The George Washington University, Washington DC, USA *The first two authors contributed equally to this work Corresponding author: Kirsten Timms, Ph.D., Myriad Genetics, 320 Wakara Way, Salt Lake City, UT, 84108, USA. Phone: (801) 584-3759. Fax: (801) 584-3640. Email: [email protected] Supplementary Materials and Methods Cell Line Growth Cancer cell lines were grown in RPMI + 10% FBS + 1% penicillin/streptomycin media at 37 o C in T75 flasks until ~5x106 cell density. Exceptions were cell lines that required non-standard media, L-glutamine, or insulin. Cells grown in suspension were centrifuged for 5 minutes at 1700 rpm in a 1.5 mL centrifuge tube and the supernatant discarded. Cells grown in a monolayer had medium removed by aspiration, were washed with PBS, and trypsin solution added. After the cells detached they were collected in medium, transferred to a 1.5 mL microcentrifuge tube and centrifuged at 1700 rpm for 5 minutes. The supernatant was discarded. Isolated cells were resuspended in 200 µL PBS. Promoter Methylation qPCR Assays DNA methylation-sensitive and methylation-dependent restriction enzymes were used to selectively digest unmethylated or methylated genomic DNA, respectively. Post-digest DNA was quantified by real-time PCR using primers flanking the regions of interest. The relative concentrations of differentially methylated DNA are determined by comparing the amount of each digest with that of a mock digest. A cutoff of 0.10 was used to define samples as “methylated”. Methylation Microarrays The downloaded data from TCGA included the signal intensities from methylated (M) and unmethylated (U) probes from Infinium HumanMethylation27 microarrays (Illumina, San Diego, CA). The fraction of methylated probes was calculated (M/(I+M)). Data from this assay had higher noise than the qPCR assay (data not shown), consequently a cutoff of 0.35 was used to define samples as “methylated”. BRCA1 and CCP Signature Expression Assays BRCA1 and Cell Cycle Progression Signature Expression Assays RNA was treated with Amplification Grade Deoxyribonuclease I (Sigma-Aldrich Inc.) per manufacturer’s protocol with an extended incubation time of 30 minutes. Reverse transcription was performed using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA) per manufacturer’s instructions. Replicate preamplifications were run independently using the Taqman PreAmp Master Mix Kit (Applied Biosystems) protocol in a 5ul reaction volume. Two preamplification replicates were run at 8 and 18 cycles respectively for cell cycle gene assays. Three preamplification replicates were run at 18 cycles only for BRCA1 assays. The post-amplification products were diluted 1:5 in low-EDTA Tris-EDTA (TE). Quantitative Polymerase Chain Reaction (qPCR) was then performed and assessed on Gene Expression M48 Dynamic Arrays (Fluidigm, South San Francisco, CA) per manufacturer’s protocol. The comparative cycle threshold (CT) method was used to calculate relative gene expression. CTs from preamplification of different numbers of cycles were centered by the average of the genes on the replicate that were in common between all replicates. The resulting values were normalized first by the average C Ts of the housekeeper genes then by the average of the normalized CTs of each assay on all samples from the first cohort to yield ∆∆CT. CCP score (Cuzick et al., 2011) and relative BRCA1 expression was calculated as the average of the negative of the ∆∆CTs of the cell-cycle genes and BRCA1 assays, respectively. Identification of Samples with Loss of BRCA1 Expression: BRCA1 and BRCA2 mRNA levels are expected to correlate with the expression of cell cycle genes (Whitfield et al., 2002), therefore any lack of correlation might suggest the presence of BRCA1 or BRCA2 mutations affecting the transcription of these genes. While there appears to be a linear relationship between cell cycle expression and BRCA1 expression in a majority of samples, we observed a small group of samples with elevated CCP score (Cuzick et al., 2011) and low BRCA1 expression. The threshold for identifying patients with abnormal BRCA1 expression was defined using robust linear regression in a large set of ovarian cancer samples (n = 234). BRCA1 expression was regressed on CCP score using iteratively re-weighted least squares (IWLS), a method for linear regression that places lower weight on outliers. In this application we used Huber weights. A 99% prediction interval for the resulting regression was calculated. Points failing below the lower limit of the interval were considered abnormal. Under the assumption that CCP score and BRCA1 expression are linearly correlated, the choice of a 99% interval controls the false positive rate at 0.005. Affymetrix 500K GeneChip arrays The Affymetrix GeneChip Mapping NspI or StyI Assay Kit was used in the generation of biotinylated DNA for Affymetrix Mapping 500K NspI or StyI microarray hybridizations (each assay was prepared separately). Genomic DNA (250 ng) was digested with NspI or StyI restriction enzyme and adaptors were added to restriction fragment ends with T4 DNA ligase. Adaptor-modified samples were PCR amplified using Clontech Titanium Taq, which generated an amplified product of average size between 200 and 1,100 bp. Amplification products were purified using a Clontech DNA amplification cleanup kit. 90 µg of purified DNA was fragmented using Affymetrix Fragmentation Reagent. Biotin-labeling of the fragmented sample was accomplished using the GeneChip DNA Labeling Reagent. Biotin-labeled DNA was hybridized on NspI or StyI Affymetrix microarrays at 49 oC for 16 to 18 hours in the Affymetrix rotation oven. After hybridization, probe array wash and stain procedures were carried out on the automatic Affymetrix Fluidics Stations as per manufacturer’s manual and microarrays were scanned and raw data was collected by Affymetrix GeneChip Scanner 3000. CN and LOH analysis of SNP microarray data The algorithm is designed to determine the most likely allele specific copy number (ASCN) at each SNP location. The corresponding likelihood explicitly takes into account contamination of a cancer DNA sample with non-cancer stromal cell DNA. The algorithm reconstructs ASCN based on SNP data from tumor samples; it is not designed to utilize SNP data from matching normal samples if available. The algorithm infers probabilistically SNP genotypes in the normal DNA. The inference is based on presence of normal DNA in a tumor sample as well as SNP allele frequencies in the population. It should be noted that if contamination with normal DNA is low (for example, in cancer cell lines), the inference is based on allele frequencies alone, and, therefore, the reconstructed ASCN may be affected by the ethnicity of a sample donor. A similar algorithm for CN analysis is described in detail in Abkevich et al., 2010. The algorithm used in this paper was implemented in two versions, one for analysis of Affymetrix 500K GeneChip array data generated internally, and the other for analysis of GenomeWideSNP6 Affymetrix array data downloaded from the TCGA web site (http://tcga- data.nci.nih.gov/tcga/dataAccessMatrix.htm?diseaseType=OV). The latter array, in addition to SNP probes, contains a number of probes for non-polymorphic locations across the human genome. These probes are informative for CN analysis but are not directly informative for LOH analysis. Statistical Analysis The p-values presented in this paper were generated using the Kolmogorov-Smirnov test which is well suited for this particular analysis. To confirm that the obtained p-values are reliable, they were recalculated using an exact Fisher test. For example, for the first cohort out of 44 BRCA deficient samples 42 have an HRD score above 10 (this cutoff was determine as optimal for separating BRCA deficient samples from BRCA intact samples in all cohorts in the Kolmogorov-Smirnov test), while out of 88 BRCA intact samples only 27 have HRD score above 10. The corresponding p-value according to Fisher test is 10-13, while we obtained p-value=10-11 using the Kolmogorov-Smirnov test. For the second cohort out of 18 BRCA deficient samples 16 have an HRD score above 10, while out of 25 BRCA intact samples only 4 have an HRD score above 10. The corresponding p-value according to Fisher test is 2*10-6, while a pvalue=10-5 was obtained using the Kolmogorov-Smirnov test. For the third cohort out of 146 BRCA deficient samples 129 have HRD score above 10, while out of 288 BRCA intact samples only 87 have HRD score above 10. The corresponding p-value according to Fisher test is 10-32, while a p-value=10-29 was obtained using the Kolmogorov-Smirnov test. In all of these comparisons an even more significant p-value was obtained using Fisher test. This is because in the Kolmogorov-Smirnov the p-value is already adjusted for the multiple testing required for determination of the optimal cutoff for separating BRCA deficient samples from BRCA intact samples, while in Fisher test there is no such adjustment. Supplementary Results Effect of contamination with normal tissue on determination of HR deficiency and HRD score It is expected that at high levels of contamination of tumor samples with normal tissue, the molecular assays used in this study to define HR deficiency (mutation detection, quantifying RNA expression and promoter methylation) might be unreliable. In addition, HRD score estimation might be compromised at high contamination. Therefore, we have investigated the dependence of the obtained results on the degree of contamination. First,we examined whether the distribution of HRD scores depends on the degree of contamination (Supplementary Figure S7). According to linear regression test this correlation was not significant (p-value=0.94). However, at the highest contamination level of 90% there was an unusually high number of samples with HRD score zero. This suggests that HRD score can be reliably estimated at contamination of up to 85%. Supplementary Figure S8 shows the distribution of contamination in carriers and non-carriers of deleterious mutations in BRCA1 and BRCA2 genes. Mutations can be detected at contamination levels up to 90%. Moreover, according to Kolmogorov-Smirnov test the difference in the degree of contamination among carriers and non-carriers of deleterious mutations in BRCA1 and BRCA2 genes is only marginally significant (p=0.03). This suggests that the results of mutation screening are reliable for all but highly contaminated samples. Supplementary Figure S9 shows the distribution of contamination in samples with methylated BRCA1 or RAD51C genes as well as samples with low expression of BRCA1 gene and all other samples. It is clear that detection of methylation and low RNA expression is strongly affected by contamination. Indeed, the highest level of contamination in samples with methylated BRCA1 or RAD51C or low expression of BRCA1 gene is 65%. This suggests that these assays are unreliable at contamination above 65%. In conclusion, it appears that the detection of promoter methylation and low RNA expression are most sensitive to high levels of contamination and become unreliable at contamination above 65%. Therefore, all samples with contamination above 65% have been excluded from the statistical analysis. Supplementary Tables Supplementary Table S1. Patient and cancer characteristics. NA = not applicable. Total Number of Patients Age at diagnosis Range Median Unknown Follow-up time (days) Range Median Unknown Stage 1 2 3 4 Unknown Histology Serous (high grade) Serous (low grade) Serous (unknown grade) Non-serous Mixed Unknown Grade 1 2 3 4 Unknown Residual disease after surgery 0 <= 1 cm > 1 cm Unknown Surgery Yes No Unknown Chemotherapy Yes Platinum (cis or carboplatin)-based (no taxane) Platinum plus Taxane (paclitaxel or docetaxel)-based No Unknown First cohort 152 Second cohort 53 Third cohort 435 37 - 88 59 4 (2.6%) 38 - 77 56 0 30 - 89 59 0 20 - 5570 1127 5 (3.3%) 213 - 3294 701 0 8 - 5480 874 2 (0.5%) 9 (5.9%) 14 (9.2%) 107 (70.4%) 21 (13.8%) 1 (0.7%) 0 0 46 (86.8%) 7 (13.2%) 0 6 (1.4%) 21 (4.8%) 338 (77.7%) 69 (15.9%) 1 (0.2%) 125 (87.2%) 8 (5.3%) 0 8 (5.3%) 10 (6.6%) 1 (0.7%) 39 (73.6%) 1 (1.9%) 0 4 (7.6%) 1 (1.9%) 8 (15.1%) 424 (97.5%) 3 (0.7%) 8 (1.8%) 0 0 0 8 (5.3%) 18 (11.8%) 126 (82.9%) 0 0 1 (1.9%) 12 (22.6%) 40 (75.5%) 0 0 2 (0.5%) 50 (11.5%) 373 (85.8%) 1 (0.2%) 8 (1.8%) 9 (5.9%) 95 (62.5%) 40 (26.3%) 8 (5.3%) 0 44 (83%) 9 (17%) 0 84 (19.3%) 200 (46.0%) 102 (23.5%) 49 (11.3%) 152 (100%) 0 0 53 (100%) 0 0 386 (88.7%) 0 49 (11.3%) 52 (98.1%) 1 (1.9%) 51 (96.2%) 0 1 (1.9%) 399 (91.7%) NA NA 23 (5.3%) 13 (3.0%) 139 (91.5%) 12 (7.9%) 127 (83.6%) 7 (4.6%) 6 (4%) Supplementary Table S2. Number of samples used in each assay. Assay Affymetrix 500K SNP arrays BRCA1 and BRCA2 tumor sequencing BRCA1 and BRCA2 germline sequencing CCP and BRCA1 qPCR BRCA1 and BRCA2 methylation analysis Other HR gene methylation analysis Cohort 1 Number of samples Reason assay was not applied to all samples Cohort 2 Number of samples Reason assay was not applied to all samples 152 not applicable 53 not applicable 150 sequencing failed normal tissue not available or no mutation detected in tumor insufficient tissue for RNA extraction insufficient DNA for analysis insufficient DNA for analysis 52 sequencing failed normal tissue not available or no mutation detected in tumor 19 137 126 92 11 53 34 0 not applicable insufficient DNA for analysis insufficient DNA for analysis Supplementary Table S3: Immortalized tumor cell lines used in this study. Cell Line Tissue Cell Line Tissue CRL7482 Breast ML46 Ovarian HTB121 Breast MPSCI Ovarian HTB27 Breast HOC1 Ovarian CRL2343 Breast OAW42 Ovarian HTB21 Breast SW626 Ovarian HTB127 Breast OCC1 Ovarian CRL2316 Breast 59M Ovarian CRL1897 Breast OC316 Ovarian CRL1902 Breast IGROV1 Ovarian CRL2321 Breast EFO21 Ovarian CRL2329 Breast OVCAR8 Ovarian HTB19 Breast A2780 Ovarian CRL2315 Breast DOV13 Ovarian CRL2865 Breast EFO27 Ovarian CRL2322 Breast HEY Ovarian CRL2330 Breast SKOV3 Ovarian CRL2324 Breast OVCAR3 Ovarian CRL2338 Breast OVCA420 Ovarian HTB30 Breast OVCA429 Ovarian ACC308 Breast FUOV1 Ovarian CRL1504 Breast UPN251 Ovarian HTB122 Breast CCL221 Colon ACC589 Breast CRL2102 Colon CRL2326 Breast CCL247 Colon HTB22 Breast HTB79 Pancreatic HTB126 Breast CRL7721 Breast CRL2335 Breast HTB20 Breast CRL2336 Breast HTB131 Breast CRL2314 Breast Supplementary Table S4. Promoter methylation assays used (SABiosciences). Gene Symbol Description Assay catalog ID MDC1 Mediator or DNA damage checkpoint 1 MePH08721-2A PARP1 Poly(ADP-ribose) polymerase 1 MePH02379-2A BRCA1 Breast Cancer 1, early onset MePH28472-1A BRCA2 Breast Cancer 2, early onset MePH28473-1A RAD50 RAD50 homolog MePH28350-1A RAD51C RAD51 homolog C MePH22389-1A PALB2 Partner and localizer of BRCA2 MePH28516-1A CHEK2 CHK2 checkpoint homolog MePH28264-1A ATM Ataxia telangiectasia mutated MePH28470-1A RAD51 RAD51 homolog MePH19071-2A Supplementary Table S5. qPCR assays used (Applied Biosystems). Gene Symbol Assay Catalog ID Assay Function ASF1B Hs00216780_m1 CCP ASPM Hs00411505_m1 CCP BIRC5 Hs00153353_m1 CCP BUB1B Hs01084828_m1 CCP C18orf24 Hs00536843_m1 CCP CDC20 Hs03004916_g1 CCP CDC2 Hs00364293_m1 CCP CDCA3 Hs00229905_m1 CCP CDCA8 Hs00983655_m1 CCP CDKN3 Hs00193192_m1 CCP CENPF Hs00193201_m1 CCP CENPM Hs00608780_m1 CCP CEP55 Hs00216688_m1 CCP DLGAP5 Hs00207323_m1 CCP DTL Hs00978565_m1 CCP FOXM1 Hs01073586_m1 CCP KIAA0101 Hs00207134_m1 CCP KIF11 Hs00189698_m1 CCP KIF20A Hs00993573_m1 CCP KIF4A Hs01020169_m1 CCP MCM10 Hs00960349_m1 CCP NUSAP1 Hs01006195_m1 CCP ORC6L Hs00204876_m1 CCP PBK Hs00218544_m1 CCP PLK1 Hs00153444_m1 CCP PRC1 Hs00187740_m1 CCP PTTG1 Hs00851754_u1 CCP RAD51 Hs00153418_m1 CCP RAD54L Hs00269177_m1 CCP RRM2 Hs00357247_g1 CCP TK1 Hs01062125_m1 CCP TOP2A Hs00172214_m1 CCP CLTC Hs00191535_m1 CCP Housekeeper MMADHC Hs00739517_g1 CCP Housekeeper MRFAP1 Hs00738144_g1 CCP Housekeeper PPP2CA Hs00427259_m1 CCP Housekeeper PSMA1 Hs00267631_m1 CCP Housekeeper PSMC1 Hs02386942_g1 CCP Housekeeper RPL13A Hs03043885_g1 CCP Housekeeper RPL37 Hs02340038_g1 CCP Housekeeper RPL38 Hs00605263_g1 CCP Housekeeper RPL4 Hs03044647_g1 CCP Housekeeper RPL8 Hs00361285_g1 CCP Housekeeper RPS29 Hs03004310_g1 CCP Housekeeper SLC25A3 Hs00358082_m1 CCP Housekeeper TXNL1 Hs00355488_m1 CCP Housekeeper UBA52 Hs03004332_g1 CCP Housekeeper BRCA1 Hs00173233_m1 BRCA1 BRCA1 Hs00173237_m1 BRCA1 BRCA1 Hs01556190_m1 BRCA1 BRCA1 Hs01556191_m1 BRCA1 GUSB Hs99999908_m1 BRCA1 Housekeeper HMBS Hs00609297_m1 BRCA1 Housekeeper SDHA Hs00188166_m1 BRCA1 Housekeeper UBC Hs00824723_m1 BRCA1 Housekeeper YWHAZ Hs00237047_m1 BRCA1 Housekeeper Supplementary Table S6. BRCA1, BRCA2, and RAD51C defects detected in the study cohorts. 1 – Two of these mutations were excluded from the analysis because one copy of BRCA2 remained intact. Cohort N BRCA1 BRCA1 BRCA2 BRCA1 + mutation mutation methylation Total N RAD51C RAD51C methylation methylation BRCA2 and/or low + BRCA1 mutation expression mutation 1 152 1 23 8 13 45 89 2 1 2 53 0 11 3 5 19 ND ND ND 3 435 0 51 341 64 1491 435 11 0 Supplementary Table S7. BRCA1 and BRCA2 Mutations in Cancer Cell Lines. Cell line Tissue Gene Variant Classification Comment Reversion IGROV1 Ovarian BRCA1 IVS21+1G>C Suspected Heterozygous No Yes deleterious UPN251 Ovarian BRCA1 1199del29 Deleterious Homozygous 1246delA Deleterious Homozygous OC316 Ovarian BRCA1 5579delA Deleterious Heterozygous No CRL2324 Breast BRCA1 R1751X Deleterious Homozygous No BRCA2 E1593X Deleterious Heterozygous No CRL2336 Breast BRCA1 5385insC Deleterious Homozygous No CRL2330 Breast BRCA2 5811delA Deleterious Heterozygous No HTB20 Breast BRCA2 S3094X Deleterious Heterozygous No CCL247 Colon BRCA2 8249insA Deleterious Heterozygous No CCL221 Colon BRCA2 3827DelGT Deleterious Heterozygous No 5579delA Deleterious Heterozygous HTB79 Pancreas BRCA2 6174delT Deleterious Homozygous No OVCAR8 Ovarian BRCA1 Methylation Suspected Homozygous No Homozygous No Homozygous No deleterious EFO21 Ovarian BRCA1 Methylation Suspected deleterious CRL2314 Ovarian BRCA1 Methylation Suspected deleterious Supplementary Figure Legends Supplementary Figure S1. Percent methylation for BRCA1 for the first cohort. Methylation of the BRCA1 promoter was measured for 126 samples from the first cohort. The distribution, which can be roughly described as Poisson distribution in combination with a long tail, was used to define a cut-off for HR deficiency for methylation somewhere between 10 and 15%. Supplementary Figure S2. Comparison between CCP score and expression of BRCA1. The line corresponds to the cut-off for low expression, samples below this line are considered BRCA1 deficient due to loss of expression. Only samples without mutations in BRCA1 or BRCA2 or methylation of BRCA1 are shown. Expression of BRCA1 and BRCA2 was measured for 137 out of 152 samples from the first cohort, and for all samples from the second cohort. Expression outliers were not observed for BRCA2, or for other genes from the HR pathway (MDC1, PARP1, RAD50, RAD51, RAD51C, PALB2, CHEK2, ATM). However there were a significant number of outliers with very low expression of BRCA1. Supplementary Figure S3. Whole chromosome LOH in tumor samples. Blue circles correspond to BRCA1 or BRCA2 deficient samples. Red circles correspond to BRCA1 and BRCA2 intact samples. Combined area under blue and red circles is the same. The area under each individual circle is proportional to the number of samples with the corresponding number of LOH regions. Figure 3a. Number of LOH regions covering whole chromosomes for the first cohort. Figure 3b. Number of LOH regions covering whole chromosomes for the third cohort. Supplementary Figure S4. Correlation between HRD scores and HR deficiency calculated for different LOH region length cut-offs for the first cohort. Corresponding log10(p-value) are on the y-axis. The relationship between the cut-off of the size of LOH regions and the significance of correlation of the HRD score with HR deficiency was investigated, this dependence was found to be relatively weak in the interval of the sizes of LOH regions from 11 to 21 Mb. The cut-off of 15 Mb approximately in the middle of the interval was arbitrarily selected for further analysis. The rational for this selection rather than selecting the cut-off with the lowest p-value is that the latter cut-off is more sensitive to statistical noise present in the data. Supplementary Figure S5. Comparison of HRD scores in three groups of BRCA1 and BRCA2 deficient samples for the combined data from all three cohorts. Row A: 46 carriers of germline mutations in BRCA1; B: 24 carriers of somatic mutations in BRCA1; C: 82 samples with either methylation or low expression of BRCA1; D: 27 carriers of germline mutations in BRCA2; E: 9 carriers of somatic mutations in BRCA2. Three samples were excluded from the analysis: one with both methylation of BRCA1 and a germline mutation in BRCA1, one with both methylation of RAD51C and a germline mutation in BRCA1, and one with both germline and somatic mutations in BRCA2. Green circles correspond to carriers of somatic mutations, red circles correspond to carriers of germline mutations, and blue circles correspond to samples with either methylation or low expression of the BRCA1 gene. The combined area under the green, red, and blue circles is the same. The area under each individual circle is proportional to the number of samples with the corresponding number of LOH regions. Supplementary Figure S6. Comparison of HRD scores of BRCA1, BRCA2, and RAD51C deficient samples. Blue circles correspond to BRCA1 deficient samples, red circles correspond to BRCA2 deficient samples, and green circles correspond to RAD51C deficient samples. The combined area under red, blue, and green circles is the same. The area under each individual circle is proportional to the number of samples with the corresponding number of LOH regions. Supplementary Figure S7. Comparison of HRD scores of samples with different degree of contamination with benign tissue. The area under each individual circle is proportional to the number of samples with the corresponding number of HRD scores and degree of contamination. Supplementary Figure S8. Correlation between contamination of tumor samples with benign tissue and results of BRCA1 and BRCA2 sequencing. Blue circles correspond to samples with deleterious mutations in BRCA1 and/or BRCA2 genes. Red circles correspond to the remaining samples. Combined area under blue and red circles is the same. The area under each individual circle is proportional to the number of samples with the corresponding contamination. Supplementary Figure S9. Correlation between contamination of tumor samples with benign tissue and results of methylation and expression analysis of BRCA1 and RAD51C genes. Blue circles correspond to samples with either methylated BRCA1 or RAD51C genes and/or samples with low BRCA1 expression. Red circles correspond to the remaining samples. Combined area under blue and red circles is the same. The area under each individual circle is proportional to the number of samples with the corresponding contamination. Supplementary References Abkevich V, Iliev D, Timms KM, Tran T, Skolnick M, Lanchbury JS, Gutin A. (2010) Computational method for estimating copy numbers in normal samples, cancer cell lines, and solid tumors using array comparative genomic hybridization. Journal of Biomedicine and Biotechnology Epub 2010 Jul 8. Cuzick J, Swanson GP, Fisher G, Brothman A, Berney DM, Reid JE, Mesher D, Speights VO, Stankiewicz E, Foster CS, Møller H, Scardino P, Warren JD, Park J, Younus A, Flake DD 2 nd, Wagner S, Gutin A, Lanchbury JS, Stone S, Transatlantic Prostate Group. (2011) Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncology 12: 245-55. Whitfield ML, Sherlock G, Saldanha AJ, Murray JI, Ball CA, Alexander KE, Matese JC, Perou CM, Hurt MM, Brown PO, Botstein D. (2002) Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell 13: 1977-2000.