Supplementary materials and methods




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NameSupplementary materials and methods
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Supplementary materials and methods

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Meta-analysis

A systematic search was conducted in the PubMed database for original publications between 1988 and 2017. Studies with a small sample size (< 50 cases) and repeated reports from the same authors or centers were excluded. Information on patient age, geographical location, TNM stage and pathological grade of BCa and IPCa, prostate slicing thickness, and overall survival was eventually extracted from 57 studies (supplementary material, Table S2). Review Manager Software (version 5.3) was used for meta-analysis.
Whole-exome sequencing

Genomic DNA of tumor and paired normal tissue was extracted from ten FFPE sections with a thickness of 10 μm using a QIAamp DNA FFPE Tissue Kit (Qiagen, MD, USA). DNA was quantified by Qubit (Life Technologies, NY, USA) and DNA integrity was examined by agarose gel electrophoresis. A paired-end DNA library was generated according to the manufacturer’s protocols (Agilent, CA, USA). Briefly, genomic DNA was sonicated into 180- to 280-bp fragments using an S220 sonicator (Covaris, MA, USA) and purified by AMPure SPRI beads from Agencourt (Beckman Coulter, IN, USA). The DNA fragments were enriched by six cycles of PCR using SureSelect ILM Indexing Pre Capture PCR Reverse Primer and SureSelect Primer (Agilent). The size distributions of the libraries were examined on a Bioanalyzer DNA 1000 chip (Agilent). 500 ng DNA was subjected to whole-exome capture, using Agilent’s SureSelect Human All Exon V5 Kit. The captured DNA–RNA hybrids were recovered using Dynabeads MyOne Streptavidin T1 from Dynal (Life Technologies, NY, USA). DNA was eluted and desalted using Qiagen MinElute PCR purification columns. The purified products were then amplified using the SureSelect ILM Indexing Post Capture Forward PCR Primer and PCR Primer Index 1 through Index 16 (Agilent). 50 Mb DNA sequences of 334 378 exons from 20 965 genes were captured. DNA libraries were sequenced on the Hiseq 4000 platform (Illumina, CA, USA) according to the manufacturer’s instructions for paired-end 150-bp reads. The targeted sequencing depth was 200. The sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra) under accession number SRP110960.
Sequence alignment and variant calling

Clean reads in FastQ format from the Illumina platform were aligned to UCSC hg19 human genome by Burrows–Wheeler Aligner (BWA) to get mapping results in BAM format. SAMtools, Picard (http://broadinstitute.github.io/picard/), and GATK (https://software.broadinstitute.org/gatk/) were used to filter BAM files, for local realignment, and for base quality recalibration to generate final BAM files for computation of the sequence coverage and depth. The somatic SNVs were detected by MuTect with an additional filter described below. The somatic indels were detected by GATK Somatic Indel Detector. ANNOVAR (http://annovar.openbioinformatics.org/en/latest/) was performed to do annotation for VCF (Variant Call Format). Variants obtained from previous steps were compared against SNPs present in the dbSNP and 1000 Genomes databases (1000 Genomes Project Consortium) to discard known SNPs. The retained non-synonymous SNVs were submitted to PolyPhen (http://genetics.bwh.harvard.edu/pph2/) and SIFT (http://sift.jcvi.org/) for functional prediction. Control-FREEC was utilized to detect somatic CNVs.
Filters for FFPE samples

An additional filter was applied to exclude artefactual mutations produced by formalin-fixed and paraffin-embedded (FFPE) specimens. In brief, after removal of duplicates and soft clipped reads, data were analyzed in MuTect with these parameters (align quality: 30; strand bias: 0.05; keep the mutation site with the highest align quality if more than one mutation site was examined within 11 bp; keep the mutation sites supported by at least three different reads). Furthermore, we filtered out single strand bias based on a read pair orientation of more than 20:1.
Identification of putative driver mutations

All identified non-synonymous mutations were compared with potential driver genes in bladder cancer or present in the COSMIC cancer gene census. Putative driver mutations were determined if they satisfied one of the following criteria: (1) either the exact mutation, the same mutation site or at least three mutations located within 15 bp of the variant were found in COSMIC and (2) if the candidate gene was marked as recessive in COSMIC and the variant was predicted to be deleterious and had a SIFT score < 0.05 or a PolyPhen score > 0.995.
Clonality indices

Clonal relatedness was determined by clonality indices calculations as previously reported [9]. Briefly, the probability of observing a given mutation in both samples is calculated on the basis of 131 TCGA bladder urothelial carcinomas (http://www.cbioportal.org/study?id=blca_tcga_pub#mutations), and then the clonality index is calculated on the basis of this probability. The cut-off for clonal relatedness was calculated from the mutational data of the 131 unrelated bladder urothelial carcinomas from TCGA. The positive control was randomly selected from 131 unrelated bladder urothelial carcinomas in duplicate, and the negative control was an equivalent number of randomly selected pairs of bladder urothelial carcinomas from TCGA. To define the optimum cut-offs, the R package ‘ROCR’ was used to maximize accuracy. The median cut-off was 65.03 (95% confidence interval 46.83–77.40), with a median accuracy of 95% (95% confidence interval 93.69–96.79%), a median sensitivity of 95.38% (95% confidence interval 93.08–97.44%), and a median specificity of 94.87% (95% confidence interval 92.56–97.44%). All the algorithms for these calculations have been described by Schultheis et al [9].


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