The$mitochondrial$and$autosomal$mutation$landscapes$of$ prostate$cancer$ $supplementary$material$

Transcription

1 The$mitochondrial$and$autosomal$mutation$landscapes$of$ prostate$cancer$ $supplementary$material$ Johan Lindberg 1, Ian G. Mills 2-4, Daniel Klevebring 1, Wennuan Liu 5, Mårten Neiman 1, Jianfeng Xu 5, Pernilla Wikström 6, Peter Wiklund 7, Fredrik Wiklund 8, Lars Egevad 9, Henrik Grönberg 8. 1 Department of Medical Epidemiology and Biostatistics, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden. 2 Prostate Cancer Research Group, Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway. 3 Department of Urology, Oslo University Hospitals, Oslo, Norway. 4 Department of Cancer Prevention, Oslo University Hospitals, Oslo, Norway. 5 Center for Cancer Genomics, Wake Forest University School of Medicine, NC, USA. 6 Medical Biosciences, Pathology, Umeå University, Umeå, Sweden. 7 Department of Urology, Division of Surgery, Karolinska Hospital, Stockholm, Sweden. 8 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 9 Department of Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden.

2 Table$of$contents$ Supplementary,Text,...,1! Tissues,...,1! Analysis,of,exome,sequence,data,...,1! Validation,of,mitochondrial,sequence,data,quality,...,2! Analysis,of,RNA,sequencing,data,...,3! Estimation,of,cellular,frequencies,and,lowBpass,wholeBgenome,sequencing,...,4! Supplementary,Figures,...,5! Supplementary,Figure,1,...,5! Supplementary,Figure,2,...,6! Supplementary,Figure,3,...,7! Supplementary,references,...,8!!!!

3 Supplementary$Text$ Tissues& All samples were snap frozen in liquid nitrogen and stored at 80 C. Detailed information on handling of tissues was reported by Lindberg et al [1]. DNA and RNA were extracted from the same tumor samples using the Allprep kit (Qiagen, Hilden, Germany) according to standard protocol. RNA quality was assessed using the Agilent 2100 Bioanalyzer and RNA LabChips (Agilent, Santa Clara, CA, USA), which provides RNA integrity numbers (RIN). All RIN numbers were >7.2 (median: 9.3), which suggests excellent quality with little or no degradation. Analysis&of&exome&sequence&data& Except for tumor sample SWE-1 (2 75), all samples were sequenced using Illumina (San Diego, CA, USA) paired-end base pair (bp) sequencing. The sequence data were aligned to the human genome reference (hg19) using Burrows-Wheeler Aligner v0.6.1 (Dice Holdings Inc, New York, NY, USA) [2] with standard settings except parameter q (to trim off low-quality bases at the beginning or end of reads), which was set to 10. Duplicate reads were removed using the MarkDuplicates function in the software suit Picard v1.6.3 (Dice Holdings Inc, New York, NY, USA). Before identifying somatic variants, the sequence data were realigned and recalibrated using tools available in the Gene Analysis Toolkit (GATK) software bundle v1.6.4 (Broad Institute, Cambridge, MA, USA) [3]. Single nucleotide variants (SNVs) were identified using mutect v , a software tool developed at the Broad Institute (Cambridge, MA, USA) for the identification of somatic SNVs in tumor genomes. Only positions with 10 coverage in both tumor and normal genomes were considered for variant calling [4]. Although this marks the lower limit for mutant detection, 95% of variants harbored >15 coverage in both the tumor and the normal genome. Low-frequency variants (<10%) were filtered away if there was any evidence supporting the variant in the normal DNA, regardless of quality. In addition, we detected a subset of variants associated with misaligned reads near simple repeats. These were removed by implementing a strand-bias filter, since the variants were only supported by reads in one direction. Finally, a subset of variants was associated with low mapping quality, due to the presence of the same 100-mer located elsewhere in the genome save one position (namely, the false-positive SNV). These were removed using a mapping-quality filter requiring at least one read to harbor a mapping quality 30. MuTect has been validated in several publications, with excellent validation rates (>90%) [5 7]. We performed conventional Sanger sequencing on 24 autosomal highfrequency variants (>20%), which all were concordant between technologies (Supplementary Table 2). Validation of low-frequency variants is challenging, but the high concordance between variant allele frequencies using RNA sequencing (RNAseq) and exome data demonstrates a high level of performance even for lowfrequency variants (Supplementary Fig. 1). The Indel Genotyper v2.0 available in the! 1!

4 GATK was applied to identify insertions/deletions (indels) [3]. Standard settings were used except for read-depth requirement, which was set to 10 in the tumor sample and 15 in the normal sample. After removal of germline variants, all indels in simple repeats plus 3 bp were removed due to an inflation of misaligned reads causing falsepositive indels in these regions. Subsequently, all indels were manually examined to remove false positives according to Ding et al [8], retaining 12% of putative indels. Both SNVs and indels were annotated using Ensembl s variation application program interface (Ensembl, Hinxton, UK) [9]. To avoid inflation of genes found in metastasis primary pairs or as high-low frequency mutations in tumor samples from the same prostate, common mutations were assigned only to the tumor tissue that harbored it in the highest fraction in all subsequent comparisons, unless stated otherwise. Also, due to high similarity between right and left lymph node metastases of SWE-52, only metastasis SWE-52B was used for calculations unless stated otherwise. MuSiC, a software suite available from The Genome Institute at Washington University (St. Louis, MO, USA), was used to identify significantly mutated genes (SMGs) [10]. MuSiC calculates contextdependent mutation rates (eg, C positions in CpG dinucleotides have elevated mutation rates and should therefore be tested separately), which are tested against a background mutation rate (nonsynonymous mutations were tested vs the nonsynonymous background mutation rate). An overall p value is subsequently obtained for each gene by three different approaches (Fisher exact test, a likelihood test, and convolution). After false-discovery rate (FDR) correction for multiple testing [11], we assumed the same stringency threshold as used previously [7], where genes with a convolution FDR <0.15 and also Fisher exact and/or likelihood FDR <0.15 were considered significant (Supplementary Table 3). Also, two genes, with only multiple mutations in the same tumor, were removed from the list of significantly mutated genes (SMGs). Validation&of&mitochondrial&sequence&data&quality& We used the first 11 normal DNA samples that were assayed on Affy6 (Affymetrix Inc, Santa Clara, CA, USA) to map copy number alterations (CNAs) for validation of the quality of the sequence data mapping to the mitochondrial genome. This allowed for independent verification of 118 mitochondrial positions, on average, per sample. Mitochondrial germline variants from the sequence data were identified using samtools pileup v (Dice Holdings Inc, New York, NY, USA) [12]. In 1183 positions, the reference allele was called in the sequence data. Affy6 demonstrated a disconcordant call in only one position. One hundred-seventeen positions harbored nonreference alleles from the sequence data, of which five were disconcordant versus Affy6. In all five positions, the sequence data identified a heteroplasmic variant due to the presence of a minor allele (fraction: 12 17%) not called by Affy6. Subsequently, we mapped the tumor and normal DNA (all Caucasian subjects, 12 tumor-normal pairs) independently to the Yoruban (NC_001807) mitochondrial genome to determine if the nonreference variation identified in the normal DNA concurred with the tumor sample. On average, each sample carried 38 nonreference alleles. Of 450 positions, 14 demonstrated disconcordance between tumor/normal pairs. In all 14 positions, the reference allele was present as a minor allele (fraction:! 2!

5 all <20%), causing a homoplasmic call in the tumor mitochondrial genome relatively a heteroplasmic call in the normal DNA. Note, when investigating each disconcordant position in detail, the reference allele was present in 11/14 positions of the tumor DNA, albeit in too low frequency for single nucleotide polymorphism detection. Subsequently, we mapped the tumor and normal DNA samples to the Caucasian reference NC_ to identify somatic mutations. When comparing mitochondrial mutation rate to the autosomal mutation rate in protein coding genes, the following ratio was calculated: (total number of mitochondrial electron transport chain [ETC] mutations summarized for all samples / total number of megabases of mitochondrial ETC sequence with sufficient coverage summarized over all samples) / (total number of autosomal coding mutations summarized over all samples / total number of megabases of targeted autosomal regions with sufficient coverage summarized over all samples): (55 / 0.656) / (2162 / ) = The difference in mutation rate made us investigate alternative approaches for identifying SMGs in the mitochondrial genome. There was no difference in mutation rates in transition/transversion categories between coding and noncoding variants. Therefore, we performed a simple frequency test using the displacement (D) loop as background. Since the D loop harbored nine mutations, no ETC gene was mutated at a significantly higher mutation rate. The same approach was undertaken using synonymous mutations as a background. No ETC genes reached statistical significance, due to small sample size relative to the possible number of nonsynonymous/synonymous variants for each gene. Analysis&of&RNA&sequencing&data& RNAseq was performed using the Ovation RNA-Seq System V2 (Nugen Technologies Inc, San Carlos, CA, USA) according to standard protocols. Samples were pooled in equimolar amounts, and sequenced in pairs of eight in one lane on the Hiseq 2000 instrument (San Diego, CA, USA). This yielded reads on average per sample (range: to ). On average, reads per sample mapped to autosomal nonribosomal regions (range: to ). The RNAseq data were aligned to the human genome (hg19) using Tophat v2.0.0 ( [13]. Tophat depends on the Bowtie aligner v (Dice Holdings Inc, New York, NY, USA) and discovers splice sites in a prebuilt transcriptome index file. We used the Ensembl database for these purposes [14]. Subsequently, transcript assembly and abundance estimation was performed using Cufflinks ( [15] with upper-quartile normalization [16] to increase the robustness of expression estimates. To assess a proliferation score for each tumor, we undertook a similar approach as Cuzick and colleagues [17] but with modifications, since they used real-time quantitative polymerase chain reaction to assess expression levels and we did not have access to a control set of samples to define a baseline expression level for each gene. First, genes with fragments per kilobase of transcript per million mapped reads <0.01 were set to 0. Subsequently, the median log 2 expression value was assessed for each gene suggested by Cuzick and colleagues [17] to be associated to prostate cancer proliferation (56 genes in total). For each expressed cell cycle gene, we calculated the following ratio per sample: R = log 2 (expression level in sample X / median expression level over all samples).! 3!

6 Although the majority of genes were expressed in all samples, some were not. There was no significant correlation between the fraction of expressed cell cycle genes and the amount of sequence data per sample (r = 0.13; p = ). Therefore, we undertook two approaches to assess a final proliferation score: (1) log 2 [mean (2^R of all expressed cell cycle genes for sample X)], and (2) 95th quartile of all expressed cell cycle gene log 2 ratios (R) for sample X. We chose approach the second approach because it had to a better correlation to overall Gleason score (r = 0.4; p = 0.002). Estimation&of&cellular&frequencies&and&low<pass&whole<genome& sequencing& The same software, PyClone v0.2, used by Shah and colleagues to estimate cellular fractions harboring individual variants in triple-negative breast cancer [18] was applied to our exome data, in combination with Affy6 or low-pass whole genome sequencing data (Supplementary Table 1) using standard settings. Only sequence data supporting the reference/variant allele with base quality 20 or mapping quality 30 were used for raw frequency estimates. Also, SNVs with read depth <20 were excluded to increase the robustness of the cellular frequency estimations. PyClone clusters variants with similar frequencies, giving them similar cellular frequencies. Therefore, to facilitate plotting of bee swarms of cellular fractions of individual variants (Fig. 3, Supplementary Fig. 2-3), a small constant (eg, in the interval for Fig. 3) was added to each variant. Since samples SWE-54/SWE-55 were not processed using Affy6, we performed low-pass whole-genome sequencing using the same library preparation protocol as described previously [1], but by sequencing without performing targeted capture. We obtained, on average, 1.8-fold coverage throughout the human genome (range: in the whole set of samples from tumor samples SWE-54/ SWE-55). CNA profiles from low-pass whole-genome sequencing data were obtained using BIC-seq [19] with default parameters, except for a 1000 bp bin size, a window size of 300 reads, and lambda set to 2. After segmentation, regions that did not reach significance after adjustment for multiple testing (using correction = TRUE in BICseq:::getSummary) were forced to a log 2 ratio of 0.! 4!

7 Supplementary$Figures$ Supplementary&Figure&1& Variant fraction from RNA sequencing Variant fraction from exome sequencing Supplementary Fig. 1. Comparison of allelic fraction of mitochondrial variants obtained from RNAseq data and exome data. Only positions with 10x coverage in the RNAseq data, and bases with 20 in quality were accounted for. In total, 78 positions harbored enough coverage to be include in the comparison. The Pearson correlation coefficient was 0.93 (p-value = 2.2e-16).! 5!

8 Supplementary&Figure&2& 1 Estimated proportion of cells harboring variant SWE-55A SWE-55B Supplementary Fig. 2. Comparison of SNV cellular frequency estimates from multiple samples of SWE-55. The cellular frequency estimation procedure is described in the Supplementary Text. All variants identified in either SWE-55A or SWE-55B were investigated in both samples. Lines demark variants with supporting sequence data in both samples. The lines are colored according to the color of the sample harboring the variant in highest cellular frequency. In total, out of 50 SNVs identified in both samples, there were sequence data supporting SNVs in 43 positions for SWE-55A and 39 position in SWE-55B. Note, SWE-55A harbored the lowest cancer cellularity of all samples (~50%). Thereof the low estimated maximum SNV cellularity. Nevertheless, the variants present at the highest estimated proportion in the primary biopsy sample were also present in the cluster of highest estimated SNV proportions of the bone metastasis, although it was collected 7.6 years after the initial primary biopsy procedure. This suggests a monoclonal common origin.! 6!

9 Supplementary,Figure,3, Estimated proportion of cells harboring variant 0.5 Estimated proportion of cells harboring variant 0.5 Estimated proportion of cells harboring variant SWE-54A SWE-54B SWE-54A SWE-54C SWE-54B SWE-54C Supplementary Fig. 3. Comparison of SNV cellular frequency estimates from multiple samples of SWE-54. The same analysis as described for SWE-55 (Supplementary Fig. 2) was performed for SWE-54. In total, 155 unique SNVs were found in the targeted regions of all three samples. High-quality sequence data supported the presence of 140, 53 and 70 of the 155 variants in SWE-54A, SWE-54B and SWE-54C, respectively. Note, in the rightmost panel, the metastases samples have a high degree of similarity. In contrast (leftmost and middle panel), it is evident that the major clone of the sampled primary tumor tissue was not seeding the metastasis, as the high-cellular frequency variants of the metastases samples were present at low frequency in the primary tumor tissue. SWE-54A harbored the highest mutation rate of all samples in this dataset (6.6 mutations/mb), interestingly; this was not the case for the metastasis samples (1.0 mutations/mb).! 7

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