Spatio-genomic heterogeneity within localized, multi-focal prostate cancer

Transcription

1 Spatio-genomic heterogeneity within localized, multi-focal prostate cancer Paul C. Boutros,1,2,3, Michael Fraser *,4, Nicholas J. Harding *,1, Richard de Borja *,1, Dominique Trudel *,5, Emilie Lalonde 1,2, Alice Meng 3, Pablo H. Hennings-Yeomans 1, Andrew McPherson 6, Veronica Y. Sabelnykova 1, Amin Zia 1, Natalie S. Fox 1,2, Julie Livingstone 1, Yu-Jia Shiah 1, Jianxin Wang 1, Timothy A. Beck 1, Cherry L. Have 5, Taryne Chong 1, Michelle Sam 1, Jeremy Johns 1, Lee Timms 1, Nicholas Buchner 1, Ada Wong 1, John D. Watson 1, Trent T. Simmons 1, Christine P ng 1, Gaetano Zafarana 4, Francis Nguyen 1, Xuemei Luo 1, Kenneth C. Chu 1, Stephenie D. Prokopec 1, Jenna Sykes 7, Alan Dal Pra 8, Alejandro Berlin 8, Andrew M. Brown 1, Michelle A. Chan- Seng-Yue 1, Fouad Yousif 1, Robert E. Denroche 1, Lauren C. Chong 1, Gregory M. Chen 1, Esther Jung 1, Clement Fung 1, Maud H.W. Starmans 1,9, Hanbo Chen 1, Shaylan K. Govind 1, James Hawley 1, Alister D Costa 1, Melania Pintilie 7, Daryl Waggott 1, Faraz Hach 6, Philippe Lambin 9, Lakshmi Muthuswamy 1,2, Colin S. Cooper 10,11, Rosalind Eeles 10,12, David E. Neal 13,14, Bernard Tetu 15, Cenk Sahinalp 6, Lincoln D. Stein 1, Neil Fleshner 16, Sohrab P. Shah 17,18,19, Colin C. Collins 20,21, Thomas J. Hudson 1,2, John D. McPherson 1,2, Theodorus van der Kwast 5, Robert G. Bristow 2,4,8, *These authors contributed equally to this work Corresponding authors: Paul.Boutros@oicr.on.ca and Rob.Bristow@rmp.uhn.on.ca 1 Ontario Institute for Cancer Research, Toronto, Ontario, Canada 2 Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada 3 Department of Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada 4 Ontario Cancer Institute, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada 5 Department of Pathology and Laboratory Medicine, Toronto General Hospital/University Health Network, Toronto, Ontario, Canada 6 School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada 7 Department of Biostatistics, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada 8 Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada 9 Department of Radiotherapy, Maastricht University, Maastricht, The Netherlands 10 Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK Page 1 of 28

2 11 - Department of Biological Sciences and School of Medicine, University of East Anglia, Norwich, UK 12 - Royal Marsden NHS Foundation Trust, London and Sutton, UK 13 - Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK 14 - Department of Surgical Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK 15 - Department of Pathology, Laval University, Quebec City, Quebec, Canada 16 Division of Urology, Princess Margaret Cancer Centre/University Health Network, Toronto, Ontario, Canada 17 Department of Pathology, University of British Columbia, Vancouver, British Columbia, Canada 18 Department of Computer Science, University of British Columbia, British Columbia, Canada 19 British Columbia Cancer Agency Research Centre, Vancouver, British Columbia, Canada 20 Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada 21 Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada Page 2 of 28

3 Supplementary Figures Supplementary Figure 1: Association of CNAs with Gleason Score We compared tumours with a Gleason Score of 3+4 (n=48) to those with a Gleason Score of 4+3 (n=27). There was no difference in median PGA (A), in the medium number of CNAs called (B) or in the frequencies of aberration of any individual gene s copy-number profile as shown by a volcano plot (C) and a histogram of unadjusted p-values (D). Page 3 of 28

4 Supplementary Figure 2: Association of CNAs with T-Category We compared tumours with a T-category T2a (n=48) to those with a T-category of T2b or T2c (n=27). There was no difference in median PGA (A), in the medium number of CNAs called (B) or in the frequencies of aberration of any individual gene s copy-number profile as shown by a volcano plot (C) and a histogram of unadjusted p-values (D). Page 4 of 28

5 Supplementary Figure 3: Association of CNAs with Pre-Treatment PSA We compared tumours with a pre-treatment PSA of 10 ng/ml or less (n=59) to those with a pretreatment PSA above 10 ng/ml (n=15 ). There was no difference in median PGA (A), in the medium number of CNAs called (B) or in the frequencies of aberration of any individual gene s copynumber profile as shown by a volcano plot (C) and a histogram of unadjusted p-values (D). Page 5 of 28

6 Supplementary Figure 4: MYCL1 Minimally Amplified Region MYCL1 amplified regions are shown for a representative subset of specimens. Lines indicate the region of copy number gain for the listed specimen. CPCG-0238 showed a larger ~1.1Mb amplification. CPCG-0189 showed the smallest gain, encompassing the minimally amplified region of ~8.5 kbp shown by the gray box. The median gain across all specimens was ~22 kbp (range 8.5kb 1.1 Mb). Page 6 of 28

7 Supplementary Figure 5: qpcr Validation of MYCL1 Amplification Custom TaqMan probes were designed to flank the MYCL1 locus on 1p34.3, across a region of 2 Mbp (+/- 1 Mbp on either side of the MYCL1 gene). Page 7 of 28

8 Supplementary Figure 6: Fluorescence in situ Hybridization (FISH) Analysis FISH was performed using probes directed to the MYCL1 locus on 1p34.3 (green) or to the centromeric region of chromosome 1 (CEP1; red). Results show analysis of benign prostate (FFPE) from a pathologically-verified, cancer-free cystoprostatectomy (A), showing 2 copies of MYCL1 and CEP1, or two FFPE prostate cancer specimens (CPCG-0364 and CPCG-0334), which were taken from a region of the index lesion immediately adjacent to the frozen specimens used for molecular analysis. Page 8 of 28

9 Supplementary Figure 7: Differential Gene Expression in MYCL1 Amplified Tumours We used mrna abundance microarrays to probe the downstream consequences of MYCL1 amplification. (A) shows large enrichment of differential expression in MYCL1-amplified tumours relative to non-mycl1 amplified tumours; (B) highlights some of the specific genes involved in a volcano plot; (C) and (D) shows similar differential abundance between MYCL1-amplified tumours and TP53-deleted tumours lacking MYCL1 amplification (note small sample sizes in this sub-group analysis lead to reduced statistical power). Page 9 of 28

10 Supplementary Figure 8: MYCL1 Clinical Correlates MYCL1 CNA status was not associated with pre-treatment PSA or T-category (A), but was inversely correlated to patient age and weakly associated with T2E events (B). All statistical analyses used Pearson s correlation or the point-biserial correlation. Page 10 of 28

11 Supplementary Figure 9: Patient-to-Cell-Line Transcriptome Profiles Two prostate cancer cell-lines (22RV1s and LNCAPs) had transfection of four separate MYCL1 isoforms, followed by mrna abundance profiling. The dotmaps on the left highlights genes showing statistical significance (q-value for the fold-change < 0.10) and large effect-sizes ( log 2 FoldChange > 1.5) in at least one cell-line (where both cell-lines meet these criteria, the larger effect-size is shown). The background shading gives the multiple-testing adjusted p-value, while the foreground dot-size and colour give the fold-change relative to empty-vector control. For each of these genes, we then compared their abundance in a subset of 16 MYCL1-wildtype and 8 MYCL1-amplified primary tumours that had whole-transcriptome profiling performed. The central heatmap gives abundance levels in each sample, while the red/white annotation bar at the top gives MYCL1 copy-number aberration status. Finally the two barplots on the right give the fold-change and p-value for each gene. In total 8/19 genes show p < 0.10 (relative to 2 expected by chance alone) even in this small patient cohort. Page 11 of 28

12 Supplementary Figure 10: Low-Input NGS Protocol Development of low-input library preparation Protocol. Table A displays the quality control metrics for three CPCG0184 samples as an example of the criteria used to evaluate library sequencing results. With the exception of # Lanes Sequenced and Sample Coverage, the values displayed are the average of the lane level metrics along with their standard deviation in brackets. PF Yield is the total number of pass filter bases available for alignment. Error Rate is the percentage of aligned bases which are not concordant with the reference. Reads/Start Point is a measure of library complexity it is the total number of reads divided by the number of unique mapping locations for those reads. Finally, Sample Coverage is the total filtered and collapsed coverage of the genome available for this sample from all lanes combined. Figure B shows a histogram of the insert distribution for an example lane from each of the three samples listed in Table A. Insert distribution is analogous to DNA fragment size; it is defined as the distance from the leftmost aligned base to the rightmost aligned based of a pair of reads. Page 12 of 28

13 Supplementary Figure 11: Genomic Rearrangement Profile of Index Lesions (A) The number of copy-number aberrations (top panel), the proportion of the genome altered (middle panel) and the number of genomic rearrangements of different types (bottom panel) for the index lesion of each patient, as determined by SNP microarray and whole-genome sequencing. (B) The genomic rearrangement profile of the index lesion of each sample across the genome, with the top frequency bar indicating the proportion of samples that had an aberration in a specific 1 Mbp bin of the genome. The covariates note the presence of genomic rearrangements in three large public datasets (Baca et al., Weischenfeldt et al. and Berger et al.). Page 13 of 28

14 Supplementary Figure 12: OncoScan SNV Validation Germline SNV calls from the OncoScan SNP array were compared to predictions from those made by WGS in blood or frozen tumour specimens (right panel) or in FFPE specimens (left panel). In each case, almost all samples showed over 90% concordance. Page 14 of 28

15 Supplementary Figure 13: Validation Re-Sequencing Coverage A heatmap of the coverage (log-scale) for each sample subjected to deep amplicon resequencing on an IonTorrent PGM. Each column corresponds to a sample (top covariates) and each row to an individual amplicon in the validation experiment. Page 15 of 28

16 Supplementary Figure 14: Germline Call Overlap For each tumour that had multiple regions sequenced, we compared the set of germline SNV calls between regions using Venn diagrams (A-D). For each tumour the vast majority of germline calls were common to all regions, providing a lower-bound on the error-rate of our calling pipeline. Page 16 of 28

17 Supplementary Figure 15: Intra-Focal Heterogeneity of CPCG0099 Visualization of mutation pattern over two foci of a Gleason 7 prostate tumour (CPCG0099). Multiple slices were taken for pathological examination, with both H&E and ERG staining shown. Note that no positional information is available for the bottom sample. Tissue from each region was subject to WGS, and common SNVs and CNAs are shown in the central panel. The right panel shows a subset of selected targetable mutations. Page 17 of 28

18 Supplementary Figure 16: Intra-Focal Heterogeneity of CPCG0102 Visualization of mutation pattern over three foci of a Gleason 7 prostate tumour (CPCG0102). Multiple slices were taken for pathological examination, with both H&E and ERG staining shown. Note that no positional information is available for the bottom sample. Tissue from each region was subject to WGS, and common SNVs and CNAs are shown in the central panel. The right panel shows a subset of selected targetable mutations. Page 18 of 28

19 Supplementary Figure 17: Intra-Focal Heterogeneity of CPCG0183 Visualization of mutation pattern over four foci of a Gleason 7 prostate tumour (CPCG0183). Multiple slices were taken for pathological examination, with both H&E and ERG staining shown. Note that no positional information is available for the bottom sample. Tissue from each region was subject to WGS, and common SNVs and CNAs are shown in the central panel. The right panel shows a subset of selected targetable mutations. Page 19 of 28

20 Supplementary Figure 18: Intra-Focal Heterogeneity of CPCG0184 Visualization of mutation pattern over five foci of a Gleason 7 prostate tumour (CPCG0184). Multiple slices were taken for pathological examination, with both H&E and ERG staining shown. Note that no positional information is available for the bottom sample. Tissue from each region was subject to WGS, and common SNVs and CNAs are shown in the central panel. The right panel shows a subset of selected targetable mutations. Page 20 of 28

21 Supplementary Figure 19: Phylogenetic Modeling Approach An overview of the phylogenetic modeling strategy, involving tree generation (A and B), followed by use of the blood-based reference sample as an outlier (C), forcing the tree to be ultrametric (i.e. every branch tip has equal distance to the root) (D) and finally removing the reference sample (E). Page 21 of 28

22 Supplementary Figure 20: CNA-Based Phylogeny of CPCG0099 log 2 Ratio plots for CPCG0099 of relative tumour/normal coverage across the entire genome (blue) with red lines indicating segmented CN status. Regions are clustered using phylogenetic methods. Page 22 of 28

23 Supplementary Figure 21: CNA-Based Phylogeny of CPCG0102 log 2 Ratio plots for CPCG0102 of relative tumour/normal coverage across the entire genome (blue) with red lines indicating segmented CN status. Regions are clustered using phylogenetic methods. Page 23 of 28

24 Supplementary Figure 22: CNA-Based Phylogeny of CPCG0183 log 2 Ratio plots for CPCG0183 of relative tumour/normal coverage across the entire genome (blue) with red lines indicating segmented CN status. Regions are clustered using phylogenetic methods. Numbers on the tree give bootstrap confidence in the branching patterns. Page 24 of 28

25 Supplementary Figure 23: CNA-Based Phylogeny of CPCG0184 log 2 Ratio plots for CPCG0184 of relative tumour/normal coverage across the entire genome (blue) with red lines indicating segmented CN status. Regions are clustered using phylogenetic methods. Numbers on the tree give bootstrap confidence in the branching patterns. Page 25 of 28

26 Supplementary Figure 24: Batch-Effects Removal in mrna Profiling Batch-effect removal in mrna data. Prior to batch-effect removal the two experimental batches cluster closely together (A), while after they show distinct patterns of mrna abundance (B). Page 26 of 28

27 Supplementary Table Legends Supplementary Table 1: Gene-Wise CNA Profiles for All Patients For each sample that received OncoScan SNP array interrogation of copy-number aberrations (n=75) this table gives for each gene whether it is amplified (1), deleted (-1) or unchanged (0). Additionally each gene is annotated with the Ensembl gene and transcript IDs, the chromosome, the starting and ending base-pair and the gene-symbol from both HUGO and HGNC. Supplementary Table 2: Regions of Recurrent CNAs GISTIC analysis of copy-number array data identified regions of recurrent copy-number alteration (rows). The columns give the name for each region, its chromosomal location (both arm and precise coordinates and probes involved) and statistical support (q-values, and amplitude estimates). For each patient, a coding of 0 (no event) vs. 1/2 (event) is given. Supplementary Table 3: GISTIC Genes Genes identified in recurrent GISTIC peaks are listed, along with their individual locations, Cytobands, q-values and gene-symbols. Supplementary Table 4: Validation of MYCL1 and MYC Amplification We performed quantitative PCR using probes directed to the putatively amplified regions of either MYCL1 or MYC, using a probe directed against RPPH1 (RNaseP H) as a control gene. Overall validation rates are shown. Supplementary Table 5: Summary of Flanking qpcr We performed qpcr analysis using the indicated probes, which flank the MYCL1 locus (which encompasses the probe shown in yellow) over a region of ~2 Mbp. NCI-H510A non-small cell lung cancer cells were used a positive control for MYCL1 amplification, since these cells contain a ~2.9 Mbp amplification of chromosome 1p, including the entire region covered by these probes. PC3 prostate cancer cells were used as a negative control. Supplementary Table 6: Genomic Instability Associated with MYC Family Gain For each Myc family member, we assessed the mean, median and standard-deviation of PGA and the total number of CNAs detected. Supplementary Table 7: Differential CNAs Associated with MYCL1 Amplification For each gene, we compared its frequency of CNA in MYCL1-amplified tumours vs. that in MYCamplified tumours. This table genes the gene ID (both Ensemble gene and transcript) along with genesymbols and genomic location. It lists the frequency of occurrence in MYCL1-amplified tumours, in MYC-amplified, the p-value from a proportion test, and the multiple-testing adjusted q-value. Page 27 of 28

28 Supplementary Table 8: MYCL1-Associated Transcriptome Dysregulation Comparison of tumours harbouring MYCL1-amplifications (n = 8) to those without (n=16) identified 294 genes showing differential abundance (q < 0.05, Bayesian-moderated t-test, see Methods). A list of gene-symbols for these genes is given here. Supplementary Table 9: Patient Annotation Key clinical information about each patient, including age at treatment, diagnostic Gleason Score, clinical T-category, Biochemical Recurrence status and ERG fusion status. Supplementary Table 10: Tumour Cellularity Analysis For each tumour sample subject to whole-genome sequencing tumour cellularity was assessed both by a urological pathologist (CellularityPath) and by the Qpure algorithm executed on SNP microarray data (CellularityQpure). Supplementary Table 11: Sequencing Statistics Overview of whole-genome sequencing. For each tumour and region, the collapsed coverage for blood (replicated for each region) and tumour are given, along with the input material type for the tumour sequencing, and the number of SNVs (of various functional categories), CNAs and Genomic Rearrangements. The number of somatic events in FFPE samples is elevated, likely due to artifacts of the FFPE procedure. Supplementary Table 12: All Genomic Rearrangements All detected Genomic Rearrangements, along with their chromosomal positions and a categorization of the rearrangement type, genes involved, and the score output from the destruct algorithm are given. Supplementary Table 13: Functional SNVs All detected functional somatic SNVs, along with their genomic locations, base-change and their status in each sequenced tumour region. Supplementary Table 14: WGA Effects Comparison of samples with and without WGA amplification based on the identity of SNPs detected by the OncoScan microarray platform. Supplementary Table 15: Pathway Analysis of MYCL1-Associated mrna Differences The GOEAST tool was used to assess functional enrichment amongst genes showing different mrna abundance between MYCL1-amplified and MYCL1-neutral tumours. Supplementary Table 16: Effects of WGA on SNP-Array Performance Comparison of concordance of SNP calls between matched WGA and non-wga specimens on the OncoScan array platform. Page 28 of 28