The evolutionary history of lethal metastatic prostate cancer

Gunes Gundem, Peter Van Loo, Barbara Kremeyer, Ludmil B Alexandrov, Jose M C Tubio, Elli Papaemmanuil, Daniel S Brewer, Heini M L Kallio, Gunilla Högnäs, Matti Annala, Kati Kivinummi, Victoria Goody, Calli Latimer, Sarah O'Meara, Kevin J Dawson, William Isaacs, Michael R Emmert-Buck, Matti Nykter, Christopher Foster, Zsofia Kote-Jarai, Douglas Easton, Hayley C Whitaker, ICGC Prostate Group, David E Neal, Colin S Cooper, Rosalind A Eeles, Tapio Visakorpi, Peter J Campbell, Ultan McDermott, David C Wedge, G Steven Bova, Colin S Cooper, Rosalind A Eeles, G Steven Bova, Douglas Easton, Christopher Foster, Andrew Futreal, Ultan McDermott, David E Neal, Michael Stratton, Daniel S Brewer, Adam P Butler, Gunes Gundem, Freddie Hamdy, Yong-Jie Lu, Andrew G Lynch, Charlie E Massie, Anthony Ng, Peter Van Loo, David C Wedge, Hayley C Whitaker, Yongwei Yu, Hongwei Zhang, Ludmil B Alexandrov, Elizabeth Bancroft, Dan Berney, Niedzica Camacho, Cathy Corbishley, Tokhir Dadaev, Nening Dennis, Tim Dudderidge, Sandra Edwards, Cyril Fisher, Jilur Ghori, Vincent J Gnanapragasam, Christopher Greenman, Steve Hawkins, Steven Hazell, Will Howat, Katalin Karaszi, Jonathan Kay, Zsofia Kote-Jarai, Barbara Kremeyer, Pardeep Kumar, Adam Lambert, Daniel Leongamornlert, Naomi Livni, Hayley Luxton, Lucy Matthews, Erik Mayer, Susan Merson, David Nicol, Christopher Ogden, Sarah O'Meara, Gill Pelvender, John Radcliffe, Nimish C Shah, Simon Tavare, Sarah Thomas, Alan Thompson, Claire Verrill, Anne Warren, Jorge Zamora, Gunes Gundem, Peter Van Loo, Barbara Kremeyer, Ludmil B Alexandrov, Jose M C Tubio, Elli Papaemmanuil, Daniel S Brewer, Heini M L Kallio, Gunilla Högnäs, Matti Annala, Kati Kivinummi, Victoria Goody, Calli Latimer, Sarah O'Meara, Kevin J Dawson, William Isaacs, Michael R Emmert-Buck, Matti Nykter, Christopher Foster, Zsofia Kote-Jarai, Douglas Easton, Hayley C Whitaker, ICGC Prostate Group, David E Neal, Colin S Cooper, Rosalind A Eeles, Tapio Visakorpi, Peter J Campbell, Ultan McDermott, David C Wedge, G Steven Bova, Colin S Cooper, Rosalind A Eeles, G Steven Bova, Douglas Easton, Christopher Foster, Andrew Futreal, Ultan McDermott, David E Neal, Michael Stratton, Daniel S Brewer, Adam P Butler, Gunes Gundem, Freddie Hamdy, Yong-Jie Lu, Andrew G Lynch, Charlie E Massie, Anthony Ng, Peter Van Loo, David C Wedge, Hayley C Whitaker, Yongwei Yu, Hongwei Zhang, Ludmil B Alexandrov, Elizabeth Bancroft, Dan Berney, Niedzica Camacho, Cathy Corbishley, Tokhir Dadaev, Nening Dennis, Tim Dudderidge, Sandra Edwards, Cyril Fisher, Jilur Ghori, Vincent J Gnanapragasam, Christopher Greenman, Steve Hawkins, Steven Hazell, Will Howat, Katalin Karaszi, Jonathan Kay, Zsofia Kote-Jarai, Barbara Kremeyer, Pardeep Kumar, Adam Lambert, Daniel Leongamornlert, Naomi Livni, Hayley Luxton, Lucy Matthews, Erik Mayer, Susan Merson, David Nicol, Christopher Ogden, Sarah O'Meara, Gill Pelvender, John Radcliffe, Nimish C Shah, Simon Tavare, Sarah Thomas, Alan Thompson, Claire Verrill, Anne Warren, Jorge Zamora

Abstract

Cancers emerge from an ongoing Darwinian evolutionary process, often leading to multiple competing subclones within a single primary tumour. This evolutionary process culminates in the formation of metastases, which is the cause of 90% of cancer-related deaths. However, despite its clinical importance, little is known about the principles governing the dissemination of cancer cells to distant organs. Although the hypothesis that each metastasis originates from a single tumour cell is generally supported, recent studies using mouse models of cancer demonstrated the existence of polyclonal seeding from and interclonal cooperation between multiple subclones. Here we sought definitive evidence for the existence of polyclonal seeding in human malignancy and to establish the clonal relationship among different metastases in the context of androgen-deprived metastatic prostate cancer. Using whole-genome sequencing, we characterized multiple metastases arising from prostate tumours in ten patients. Integrated analyses of subclonal architecture revealed the patterns of metastatic spread in unprecedented detail. Metastasis-to-metastasis spread was found to be common, either through de novo monoclonal seeding of daughter metastases or, in five cases, through the transfer of multiple tumour clones between metastatic sites. Lesions affecting tumour suppressor genes usually occur as single events, whereas mutations in genes involved in androgen receptor signalling commonly involve multiple, convergent events in different metastases. Our results elucidate in detail the complex patterns of metastatic spread and further our understanding of the development of resistance to androgen-deprivation therapy in prostate cancer.

Figures

Extended Data Figure 1. Variants identified in…
Extended Data Figure 1. Variants identified in 51 whole-genome sequenced samples from 10 patients
Number of (a) insertion/deletions, (b) high-confidence substitutions and (c) chromosomal rearrangements are plotted across all the samples from the 10 patients that were whole-genome sequenced.
Extended Data Figure 2. Validation of the…
Extended Data Figure 2. Validation of the subclonal hierarchies in A22
The primary means of validation was a deep sequencing validation experiment that included selected substitutions and indels from each sample, as described in Extended Data Table 2 and Supplementary Information, section 2b. In addition, indels and rearrangements identified in WGS represent datasets orthogonal to the substitution data from which the subclones were identified. The subsets of samples in which validated substitutions, indels and rearrangements are found correlate strongly with the subclonal clusters identified from the clustering of substitutions from WGS, providing support for the existence of these subclones. For each patient, hierarchical clustering of the variant allele fraction (VAF) was performed separately for substitutions (a) and indels (b). VAFs are represented as a heatmap with deeper shades of red indicating a higher proportion of reads reporting the mutant allele. Above each heatmap, mutations are colour-coded according to the subclone they were assigned to by Dirichlet process clustering of WGS data in the case of substitutions or by VAF for indels. Indels that could not be assigned to any cluster are annotated with black. For A22, additional samples not subject to WGS were included in the validation experiment. For these patients the phylogenetic tree from Figure 2 was modified to incorporate these additional samples (c). Number of substitutions assigned to each subclone (d) and numbers of indels (e) and rearrangements (f) present in different subsets of samples are plotted as bar charts. VAFs from WGS and validation data, plotted as scatter plots (g), are very highly correlated. Subclone colour key (h).
Extended Data Figure 3. Validation of the…
Extended Data Figure 3. Validation of the subclonal hierarchies in A31 and A32
Validation strategy as described in Extended Data Figure 2. For A31 and A32, hierarchical clustering of the VAF was performed separately for substitutions (a) and (j) and indels (b) and (k). Heatmaps are annotated as described in Extended Data Figure 2. Additional samples for A31 and A32 are incorporated into the phylogenetic trees (c) and (l). Subclones for A31 CD and A32 CE are annotated in the corresponding 2d-DP plots (d) and (m). Numbers of substitutions in WGS data assigned to each subclone are plotted in (e) and (n). VAFs from WGS and validation data, plotted as scatter plots (f) and (o), are very highly correlated. Number of indels (g) and (p) and rearrangements (h) and (q) present in different subsets of samples are plotted as bar charts. Subclone Colour keys for A31 and A32 (i and r) respectively.
Extended Data Figure 4. Validation of the…
Extended Data Figure 4. Validation of the subclonal hierarchies in A24 and A34
Validation strategy as described in Extended Data Figure 2. For A24 and A34, hierarchical clustering of the VAF was performed separately for substitutions (a) and (i) and indels (b) and (j). Heatmaps are annotated as described in Extended Data Figure 2. Indels that could not be assigned to any cluster (if any) are annotated with black. Additional samples for A24 and A34 are incorporated into the phylogenetic tree (c) and (k). The additional cluster in A24, supported by rearrangements only, is indicated by a light green branch in the tree. Numbers of substitutions in WGS data assigned to each subclone are plotted in (d) and (l). VAFs from WGS and validation data, plotted as scatter plots (e) and (m), are very highly correlated. Number of indels (f) and (n) and rearrangements (g) and (o) present in different subsets of samples are plotted as bar charts. Subclone Colour keys for A24 and A34 (h and p) respectively.
Extended Data Figure 5. Validation of the…
Extended Data Figure 5. Validation of the subclonal hierarchies in A10 and A29
Validation strategy as described in Extended Data Figure 2. For A10 and A29, hierarchical clustering of the VAF was performed separately for substitutions (a) and (h) and indels (b) and (i). Heatmaps are annotated as described in Extended Data Figure 2. Indels that could not be assigned to any cluster (if any) are annotated with black. Loci with depth <20X are coloured in light blue. The additional sample (D) for A29 is incorporated into the phylogenetic tree (j). Validation experiment for A10-E, the prostate sample, gave very low coverage (d). Subclones for A29-A and A29-C are annotated in the 2d-DP plot (k). Numbers of substitutions in WGS data assigned to each subclone are plotted in (c) and (l). VAFs from WGS and validation data, plotted as scatter plots (d) and (m), are very highly correlated. Number of indels (e) and (n) and rearrangements (f) and (o) present in different subsets of samples are plotted as bar charts. Subclone Colour keys for A10 and A29 (g and p) respectively.
Extended Data Figure 6. Validation of the…
Extended Data Figure 6. Validation of the subclonal hierarchies in A17 and A12
Validation strategy as described in Extended Data Figure 2. For A17 and A12, hierarchical clustering of the VAF was performed separately for substitutions (a) and (i) and indels (b) and (j). Heatmaps are annotated as described in Extended Data Figure 2. Mutations that could not be assigned to any cluster are annotated with black. For A12, the C-specific cluster that is not present in substitutions is shown in very light green. Subclones for A17 AD are annotated in the 2d-DP plot (c). Numbers of substitutions in WGS data assigned to each subclone are plotted in (d) and (l). VAFs from WGS and validation data, plotted as scatter plots (e) and (m), are very highly correlated. Number of indels (f) and (n) and rearrangements (g) and (o) present in different subsets of samples are plotted as bar charts. Additional samples for A12 are incorporated into the phylogenetic tree (k). Subclone Colour keys for A17 and A12 (h and p) respectively.
Extended Data Figure 7. Validation of the…
Extended Data Figure 7. Validation of the subclonal hierarchies in A21
Validation strategy as described in Extended Data Figure 2. Hierarchical clustering of the VAF was performed separately for substitutions (a) and indels (b). Heatmaps are annotated as described in Extended Data Figure 2. Loci with depth <20X is coloured in light blue. Additional samples L, N, and Q from FFPE material had low coverage. The only loci present in these samples were all truncal. These samples are incorporated into the phylogenetic tree (c). Numbers of substitutions in WGS data assigned to each subclone are plotted in (d). Number of indels (e) and rearrangements (f) present in different subsets of samples are plotted as bar charts. VAFs from WGS and validation data, plotted as scatter plots (g), are very highly correlated. Subclone Colour key (h).
Extended Data Figure 8. Convergent evolution at…
Extended Data Figure 8. Convergent evolution at the AR locus
Rearrangements and copy number segments in the vicinity of the AR locus are shown for A31, A21, A29 and A10. (a) In A31, there are three different AR amplification events. In orange is a tandem duplication whose existence is supported by tumour reads in ADEF but not C. However PCR-gel validation confirms its existence in the prostate sample C - the faintness of the band suggesting that this rearrangement is present subclonally in A31-C - as well as the prostate sample I, which was not subject to WGS. One tandem duplication is common to both prostate samples (shown in green) while the other is specific to sample C (dark pink). (b) In A21, there are 4 different sets of complex rearrangements, one shared by ACDEGH and the remainder specific to F, I and J. (c) Rearrangements in the vicinity of the AR locus and inter-mutation distances for A29 plotted on a log10 scale for lesions specific to the metastasis (left) and specific to the prostate (middle). Each sample has a different set of complex rearrangements, which are associated with distinct kataegis events. (d) In A10, one tandem duplication is shared by CD while four others are each specific to a single sample.
Figure 1. n -D Dirichlet process clustering…
Figure 1. n-D Dirichlet process clustering reveals widespread polyclonal seeding in A22
(a) For pairs of metastases, cancer cell fractions (CCF), i.e. the fraction of cells within a sample containing a mutation, are plotted for all the substitutions detected in the WGS data. Red density areas off the axes and with CCF >0 and <1 reveal the existence of mutation clusters present at subclonal levels in more than one metastatic site. Mutation clusters for each sample are indicated with circles coloured according to the subclone they correspond to (Supplementary Table 3). The centre of each circle is positioned at the CCF values of the subclone in the two samples. The clusters at (1,1) correspond to the mutations present in all the cells in both sites (CCF=1) while those on axes refer to sample-specific subclones. For example, light blue and dark green clusters absent from sample A are positioned on the y-axis when H is compared to A but are moved to (0.60,0.08) and (0.60,0.88) when H is compared to K. (b) Each subclone detected in A22 is represented as a set of colour-coded ovals across all organ sites (Supplementary Table 3). Each row represents a sample, with ovals in the far left column nested if required by the pigeonhole principle (SI). The area of the ovals is proportional to the CCF of the corresponding subclone. Subclonal mutation clusters are shown with solid borders. Oval plots are divided into three types: trunk (CCF=1 in all samples), leaf (specific to a single sample) and branch (present in >1 sample and either not found in all samples or subclonal in at least one). (c) Phylogenetic tree showing the relationships between subclones in A22. Branch lengths are proportional to the number of substitutions in each cluster. Branches are annotated with samples in which they are present and with oncogenic/putative oncogenic alterations assigned to that subclone (LOH: Loss of Heterozygosity). (d) Subclone colour key.
Figure 2. Subclonal structure within 10 metastatic…
Figure 2. Subclonal structure within 10 metastatic lethal prostate cancers
All the subclones identified in the whole genome sequenced samples are shown as phylogenetic trees and oval plots (as described in Figure 1). Patients with polyclonal seeding (A34, A22, A31, A32 and A24) are on the right (amp: amplification).
Figure 3. Metastasis-to-metastasis seeding occurs either by…
Figure 3. Metastasis-to-metastasis seeding occurs either by a linear or a branching pattern of spread
Body maps show the seeding of all tumour sites from (a) A22, (b) A21 and (c) A24. Sites shown include samples subject to targeted sequencing (A22-L, A24-F, A24-G) in addition to WGS samples. Seeding events are represented with arrows colour-coded according to Supplementary Table 3 and with double-heads when seeding could be in either direction. When the sequence of events may be ordered from the acquisition of mutations, arrows are numbered chronologically. Subclones on branching clonal lineages are labelled with the same number but with different letters, e.g. 4a & 4b. See Supplementary Information Section 4e for a detailed discussion of the body map in these cases.
Figure 4. Drivers of tumorigenesis are truncal…
Figure 4. Drivers of tumorigenesis are truncal while drivers of castration resistance are convergent
(a) Proportion of trunk, branch and leaf mutations in each sample. (b) Heatmap of oncogenic alterations present on the trunk (top) or off the trunk, i.e. on branches or leaves (bottom). Alterations in oncogenes and tumour suppressors are shown in red and blue, respectively, with shade indicating the number of events in that patient. Focal deletions and substitutions/indels are shown with crosses and stars, respectively. Double crosses indicate homozygous deletions resulting from deletions of both alleles. (c) Continuous selective pressure on AR signalling is observed in the form of multiple rearrangements resulting in multiple copy number increases at the AR locus within the same patient. Chromosomal rearrangements are plotted on top of the genome-wide copy number for each of the 4 WGS samples from A24. Rearrangements are coloured according to the colour code in Supplementary Table 3. Arcs above and below the top vertical line indicate deletion and tandem duplication events, while arcs above and below the second vertical line are head-to-head and tail-to-tail inversions, respectively.

References

    1. Nowell PC. The clonal evolution of tumor cell populations. Science. 1976;194:23–28.
    1. Nik-Zainal S, et al. The life history of 21 breast cancers. Cell. 2012;149:994–1007.
    1. Greaves M, Maley CC. Clonal evolution in cancer. Nature. 2012;481:306–313.
    1. Gerlinger M, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–892.
    1. Gupta GP, Massague J. Cancer metastasis: building a framework. Cell. 2006;127:679–695.
    1. Poste G, Fidler IJ. The pathogenesis of cancer metastasis. Nature. 1980;283:139–146.
    1. Fidler IJ. The pathogenesis of cancer metastasis: the ’seed and soil’ hypothesis revisited. Nat Rev Cancer. 2003;3:453–458.
    1. Talmadge JE, Fidler IJ. AACR centennial series: the biology of cancer metastasis: historical perspective. Cancer Res. 2010;70:5649–5669.
    1. McFadden DG, et al. Genetic and clonal dissection of murine small cell lung carcinoma progression by genome sequencing. Cell. 2014;156:1298–1311.
    1. Cleary AS, Leonard TL, Gestl SA, Gunther EJ. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature. 2014;508:113–117.
    1. Bolli N, et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat Commun. 2014;5:2997.
    1. Karantanos T, Thompson TC. GEMMs shine a light on resistance to androgen deprivation therapy for prostate cancer. Cancer Cell. 2013;24:11–13.
    1. Harris WP, Mostaghel EA, Nelson PS, Montgomery B. Androgen deprivation therapy: progress in understanding mechanisms of resistance and optimizing androgen depletion. Nat Clin Pract Urol. 2009;6:76–85.
    1. Bernard D, Pourtier-Manzanedo A, Gil J, Beach DH. Myc confers androgen-independent prostate cancer cell growth. J Clin Invest. 2003;112:1724–1731.
    1. Sharma NL, et al. The androgen receptor induces a distinct transcriptional program in castration-resistant prostate cancer in man. Cancer Cell. 2013;23:35–47.
    1. Francis JC, Thomsen MK, Taketo MM, Swain A. beta-catenin is required for prostate development and cooperates with Pten loss to drive invasive carcinoma. PLoS Genet. 2013;9:e1003180.
    1. Marusyk A, et al. Non-cell-autonomous driving of tumour growth supports subclonal heterogeneity. Nature. 2014;514:54–58.
    1. Sun Y, et al. Treatment-induced damage to the tumor microenvironment promotes prostate cancer therapy resistance through WNT16B. Nat Med. 2012;18:1359–1368.
    1. Campbell PJ, et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature. 2010;467:1109–1113.
    1. Maley CC, et al. Selectively advantageous mutations and hitchhikers in neoplasms: p16 lesions are selected in Barrett’s esophagus. Cancer Res. 2004;64:3414–3427.
    1. Majumder PK, et al. A prostatic intraepithelial neoplasia-dependent p27 Kip1 checkpoint induces senescence and inhibits cell proliferation and cancer progression. Cancer Cell. 2008;14:146–155.
    1. Yachida S, et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature. 2010;467:1114–1117.
    1. Visakorpi T, et al. In vivo amplification of the androgen receptor gene and progression of human prostate cancer. Nat Genet. 1995;9:401–406.
    1. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet. 2003;33:49–54.
    1. Lee YF, et al. A gene expression signature associated with metastatic outcome in human leiomyosarcomas. Cancer Res. 2004;64:7201–7204.

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