Herein we provide a detailed molecular analysis of the spatial heterogeneity of clinically localized, multifocal prostate cancer to delineate new oncogenes or tumor suppressors. We initially determined the copy number aberration (CNA) profiles of 74 patients with index tumors of Gleason score 7. Of these, 5 patients were subjected to whole-genome sequencing using DNA quantities achievable in diagnostic biopsies, with detailed spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity in focal genomics. Multifocal tumors are highly heterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements. We identified and validated a new recurrent amplification of MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Moreover, we demonstrate divergent tumor evolution in multifocal cancer and, in some cases, tumors of independent clonal origin. These data represent the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Gene Expression Omnibus
Mohler, J. et al. NCCN clinical practice guidelines in oncology: prostate cancer. J. Natl. Compr. Canc. Netw. 8, 162–200 (2010).
D'Amico, A.V. et al. Cancer-specific mortality after surgery or radiation for patients with clinically localized prostate cancer managed during the prostate-specific antigen era. J. Clin. Oncol. 21, 2163–2172 (2003).
Buyyounouski, M.K., Pickles, T., Kestin, L.L., Allison, R. & Williams, S.G. Validating the interval to biochemical failure for the identification of potentially lethal prostate cancer. J. Clin. Oncol. 30, 1857–1863 (2012).
Villers, A., McNeal, J.E., Freiha, F.S. & Stamey, T.A. Multiple cancers in the prostate. Morphologic features of clinically recognized versus incidental tumors. Cancer 70, 2313–2318 (1992).
Nichol, A.M., Warde, P. & Bristow, R.G. Optimal treatment of intermediate-risk prostate carcinoma with radiotherapy: clinical and translational issues. Cancer 104, 891–905 (2005).
Taylor, B.S. et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010).
Lapointe, J. et al. Genomic profiling reveals alternative genetic pathways of prostate tumorigenesis. Cancer Res. 67, 8504–8510 (2007).
Paris, P.L. et al. Whole genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors. Hum. Mol. Genet. 13, 1303–1313 (2004).
Penney, K.L. et al. mRNA expression signature of Gleason grade predicts lethal prostate cancer. J. Clin. Oncol. 29, 2391–2396 (2011).
Lalonde, E. et al. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncol. 15, 1521–1532 (2014).
Olmos, D. et al. Prognostic value of blood mRNA expression signatures in castration-resistant prostate cancer: a prospective, two-stage study. Lancet Oncol. 13, 1114–1124 (2012).
Cortese, R. et al. Epigenetic markers of prostate cancer in plasma circulating DNA. Hum. Mol. Genet. 21, 3619–3631 (2012).
Ruijter, E.T., van de Kaa, C.A., Schalken, J.A., Debruyne, F.M. & Ruiter, D.J. Histological grade heterogeneity in multifocal prostate cancer. Biological and clinical implications. J. Pathol. 180, 295–299 (1996).
Lindberg, J. et al. Exome sequencing of prostate cancer supports the hypothesis of independent tumour origins. Eur. Urol. 63, 347–353 (2013).
Grasso, C.S. et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).
Barbieri, C.E. et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).
Ren, S. et al. RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell Res. 22, 806–821 (2012).
Prensner, J.R. et al. Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression. Nat. Biotechnol. 29, 742–749 (2011).
Kumar, A. et al. Exome sequencing identifies a spectrum of mutation frequencies in advanced and lethal prostate cancers. Proc. Natl. Acad. Sci. USA 108, 17087–17092 (2011).
Weischenfeldt, J. et al. Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer. Cancer Cell 23, 159–170 (2013).
Baca, S.C. et al. Punctuated evolution of prostate cancer genomes. Cell 153, 666–677 (2013).
Zhou, Z. et al. Synergy of p53 and Rb deficiency in a conditional mouse model for metastatic prostate cancer. Cancer Res. 66, 7889–7898 (2006).
Edwards, J., Krishna, N.S., Witton, C.J. & Bartlett, J.M. Gene amplifications associated with the development of hormone-resistant prostate cancer. Clin. Cancer Res. 9, 5271–5281 (2003).
Pugh, T.J. et al. The genetic landscape of high-risk neuroblastoma. Nat. Genet. 45, 279–284 (2013).
Rushlow, D.E. et al. Characterisation of retinoblastomas without RB1 mutations: genomic, gene expression, and clinical studies. Lancet Oncol. 14, 327–334 (2013).
Penn, L.J., Brooks, M.W., Laufer, E.M. & Land, H. Negative autoregulation of c-Myc transcription. EMBO J. 9, 1113–1121 (1990).
Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).
Bashashati, A. et al. Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling. J. Pathol. 231, 21–34 (2013).
Song, S. et al. qpure: a tool to estimate tumor cellularity from genome-wide single-nucleotide polymorphism profiles. PLoS ONE 7, e45835 (2012).
Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).
Berger, M.F. et al. The genomic complexity of primary human prostate cancer. Nature 470, 214–220 (2011).
Lindberg, J. et al. The mitochondrial and autosomal mutation landscapes of prostate cancer. Eur. Urol. 63, 702–708 (2013).
Samuels, Y. et al. High frequency of mutations of the PIK3CA gene in human cancers. Science 304, 554 (2004).
Janku, F. et al. PIK3CA mutation H1047R is associated with response to PI3K/AKT/mTOR signaling pathway inhibitors in early-phase clinical trials. Cancer Res. 73, 276–284 (2013).
Sangai, T. et al. Biomarkers of response to Akt inhibitor MK-2206 in breast cancer. Clin. Cancer Res. 18, 5816–5828 (2012).
Djulbegovic, M. et al. Screening for prostate cancer: systematic review and meta-analysis of randomised controlled trials. BMJ 341, c4543 (2010).
Zafarana, G. et al. Copy number alterations of c-MYC and PTEN are prognostic factors for relapse after prostate cancer radiotherapy. Cancer 118, 4053–4062 (2012).
Locke, J.A. et al. NKX3.1 haploinsufficiency is prognostic for prostate cancer relapse following surgery or image-guided radiotherapy. Clin. Cancer Res. 18, 308–316 (2012).
Locke, J.A. et al. Allelic loss of the loci containing the androgen synthesis gene, StAR, is prognostic for relapse in intermediate-risk prostate cancer. Prostate 72, 1295–1305 (2012).
Cooper, C.S. et al. Analysis of the genetic phylogeny of multifocal prostate cancer identifies multiple independent clonal expansions in neoplastic and morphologically normal prostate tissue. Nat. Genet. 47, 367–372 (2015).
Ishkanian, A.S. et al. High-resolution array CGH identifies novel regions of genomic alteration in intermediate-risk prostate cancer. Prostate 69, 1091–1100 (2009).
Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
Mermel, C.H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).
Dai, M. et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005).
Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).
Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).
Smyth, G.K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article3 (2004).
Fisher, S. et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011).
O'Connor, B.D., Merriman, B. & Nelson, S.F. SeqWare Query Engine: storing and searching sequence data in the cloud. BMC Bioinformatics 11 (suppl. 12), S2 (2010).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 41, D8–D20 (2013).
Li, H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27, 718–719 (2011).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
Ouedraogo, M. et al. The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes. PLoS ONE 7, e50653 (2012).
Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).
Fuentes Fajardo, K.V. et al. Detecting false-positive signals in exome sequencing. Hum. Mutat. 33, 609–613 (2012).
Forbes, S.A. et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945–D950 (2011).
McPherson, A. et al. nFuse: discovery of complex genomic rearrangements in cancer using high-throughput sequencing. Genome Res. 22, 2250–2261 (2012).
Wang, J. et al. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nat. Methods 8, 652–654 (2011).
Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Ewing, B., Hillier, L., Wendl, M.C. & Green, P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8, 175–185 (1998).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).
Chen, H. & Boutros, P.C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12, 35 (2011).
The authors thank all members of the Boutros and Bristow laboratories for helpful suggestions. This study was conducted with the support of Movember funds through Prostate Cancer Canada and with the additional support of the Ontario Institute for Cancer Research, funded by the government of Ontario. This study was conducted with the support of the Ontario Institute for Cancer Research to P.C.B. through funding provided by the government of Ontario. This work has been funded by a Doctoral Fellowship from the Canadian Institutes of Health Research (CIHR) to E.L. The authors gratefully thank the Princess Margaret Cancer Centre Foundation and the Radiation Medicine Program Academic Enrichment Fund for support (to R.G.B.). R.G.B. is a recipient of a Canadian Cancer Society Research Scientist Award. This work was supported by Prostate Cancer Canada and is proudly funded by the Movember Foundation, grant RS2014-01. P.C.B. was supported by a Terry Fox Research Institute New Investigator Award and a CIHR New Investigator Award. This project was supported by Genome Canada through a Large-Scale Applied Project contract to P.C.B., S.P.S. and R. Morin.
The authors declare no competing financial interests.
Supplementary Figures 1–24. (PDF 3665 kb)
GeneWise 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 pairs, and the gene symbols from both HUGO and HGNC. (XLS 26652 kb)
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) versus 1/2 (event) is given. (XLS 80 kb)
GISTIC genes. Genes identified in recurrent GISTIC peaks are listed, along with their individual locations, Cytobands, q values and gene symbols are given. (XLS 709 kb)
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 (RNase P, component H) as a control gene. Overall validation rates are shown. (XLS 24 kb)
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 Mb. NCI-H510A non–small cell lung cancer cells were used as a positive control for MYCL1 amplification, as these cells contain a ~2.9-Mb amplification of chromosome 1p, including the entire region covered by these probes. PC3 prostate cancer cells were used as a negative control. (XLS 22 kb)
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. (XLS 19 kb)
Differential CNAs associated with MYCL1 amplification. For each gene, we compared its frequency of CNA in MYCL1-amplified tumors and in MYC-amplified tumors. This table shows gene ID (both Ensembl gene and transcript) along with gene symbols and genomic location. It lists the frequency of occurrence in MYCL1-amplified tumors, the frequency of occurrence in MYC-amplified tumors, the P value from a proportion test and the multiple testing–adjusted q value. (XLS 1686 kb)
MYCL1-associated transcriptome dysregulation. Comparison of tumors harboring MYCL1 amplifications (n = 8) and those without (n = 16) identified 294 genes showing differential abundance (q < 0.05, Bayesian-moderated t test; Online Methods). A list of gene symbols for these genes is given. (XLS 37 kb)
Patient annotation. Key clinical information about each patient, including age at time of treatment, diagnostic Gleason score, clinical T category, biochemical recurrence status and ERG fusion status. (XLS 37 kb)
Tumor cellularity analysis. For each tumor sample subjected to whole-genome sequencing, tumor cellularity was assessed both by a urological pathologist (CellularityPath) and the Qpure algorithm executed on SNP microarray data (CellularityQpure). (XLS 26 kb)
Sequencing statistics. Overview of whole-genome sequencing. For each tumor and region, the collapsed coverage values for blood (replicated for each region) and tumor are given, along with the input material type for the tumor sequencing and the numbers of SNVs (of various functional categories), CNAs and genomic rearrangements. The number of somatic events in FFPE samples is elevated, likely owing to artifacts of the FFPE procedure. (XLS 25 kb)
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. (XLS 275 kb)
Functional SNVs. All detected functional somatic SNVs, along with their genomic locations, base change and status in each sequenced tumor region. (XLS 398 kb)
WGA effects. Comparison of samples with and without WGA amplification based on the identity of SNPs detected by the OncoScan microarray platform. (XLS 17 kb)
Pathway analysis of MYCL1-associated mRNA differences. The GOEAST tool was used to assess functional enrichment among genes showing different mRNA abundance in MYCL1-amplified and MYCL1-neutral tumors. (XLS 80 kb)
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. (XLS 30 kb)
About this article
Cite this article
Boutros, P., Fraser, M., Harding, N. et al. Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat Genet 47, 736–745 (2015). https://doi.org/10.1038/ng.3315
Improved Prognostic Stratification Using Circulating Tumor Cell Clusters in Patients with Metastatic Castration-Resistant Prostate Cancer
Nature Reviews Urology (2021)
The clonal origin of multifocal papillary thyroid cancer: intrathyroidal spread or independent tumors?
Minerva Endocrinology (2021)
Cell Stem Cell (2021)
Nature Reviews Urology (2021)