Tracking the origins and drivers of subclonal metastatic expansion in prostate cancer

Matthew K H Hong, Geoff Macintyre, David C Wedge, Peter Van Loo, Keval Patel, Sebastian Lunke, Ludmil B Alexandrov, Clare Sloggett, Marek Cmero, Francesco Marass, Dana Tsui, Stefano Mangiola, Andrew Lonie, Haroon Naeem, Nikhil Sapre, Pramit M Phal, Natalie Kurganovs, Xiaowen Chin, Michael Kerger, Anne Y Warren, David Neal, Vincent Gnanapragasam, Nitzan Rosenfeld, John S Pedersen, Andrew Ryan, Izhak Haviv, Anthony J Costello, Niall M Corcoran, Christopher M Hovens, Matthew K H Hong, Geoff Macintyre, David C Wedge, Peter Van Loo, Keval Patel, Sebastian Lunke, Ludmil B Alexandrov, Clare Sloggett, Marek Cmero, Francesco Marass, Dana Tsui, Stefano Mangiola, Andrew Lonie, Haroon Naeem, Nikhil Sapre, Pramit M Phal, Natalie Kurganovs, Xiaowen Chin, Michael Kerger, Anne Y Warren, David Neal, Vincent Gnanapragasam, Nitzan Rosenfeld, John S Pedersen, Andrew Ryan, Izhak Haviv, Anthony J Costello, Niall M Corcoran, Christopher M Hovens

Abstract

Tumour heterogeneity in primary prostate cancer is a well-established phenomenon. However, how the subclonal diversity of tumours changes during metastasis and progression to lethality is poorly understood. Here we reveal the precise direction of metastatic spread across four lethal prostate cancer patients using whole-genome and ultra-deep targeted sequencing of longitudinally collected primary and metastatic tumours. We find one case of metastatic spread to the surgical bed causing local recurrence, and another case of cross-metastatic site seeding combining with dynamic remoulding of subclonal mixtures in response to therapy. By ultra-deep sequencing end-stage blood, we detect both metastatic and primary tumour clones, even years after removal of the prostate. Analysis of mutations associated with metastasis reveals an enrichment of TP53 mutations, and additional sequencing of metastases from 19 patients demonstrates that acquisition of TP53 mutations is linked with the expansion of subclones with metastatic potential which we can detect in the blood.

Figures

Figure 1. Diagrams of patient sampling and…
Figure 1. Diagrams of patient sampling and patterns of metastasis for four patients.
In each of the panels, the diagram to the left depicts the timing and direction of metastatic spread for patients 299 (a), 498 (b), 177 (c), 001 (d). For patients 299 (a) and 498 (b), the multiple regions of the primary tumour that were sampled are indicated in the 3D models of the prostate reconstructed from prostate cross-sections. In the centre of each panel is an evolutionary tree depicting the distinct clones identified during the evolution of the tumour. The branch length is approximately proportional to the number of structural variations and SNVs. The matrix plots at the right of each panel represent the percentage of each clone in a given sample. The plot below this represents the disease progression for each patient measured by levels of prostate-specific antigen (PSA) along with an indication of when tumour or blood specimens were sampled and information on treatment phases.
Figure 2. Evolution of mutational signatures and…
Figure 2. Evolution of mutational signatures and driver mutations.
The trees in this diagram depict the genomic evolution of the tumours for patient (a) 299, (b) 498, (c) 177 and (d) 001. The branch lengths are approximately proportional to the number of structural variations and single-nuclotide variations. Dashed branches occur when the number of mutations could not be estimated (no WGS performed on the clone depicted in the branch). Branches are colour coded—black representing trunk (clonal) mutations, green representing the branch leading to the bulk primary tumour and red representing the branches leading to metastasis. The mutational signatures detected across the mutations in each branch are indicated via the pie charts. Signatures 1A (purple), 5 (aqua), 8 (cream), 6 (green, MSI), 20 (orange, MSI), 26 (yellow, MSI), 2 (blue, APOBEC), 3 (pink, BRCA). Chromoplexy chains are indicated via circles and crosses and mutations in known cancer drivers are listed.
Figure 3. Mutations in known cancer drivers.
Figure 3. Mutations in known cancer drivers.
The matrix indicates mutations identified in known cancer drivers for the primary tumours from four patients (left) and the metastases for seven patients (right).
Figure 4. TP53 mutations identified via TAm-Seq.
Figure 4. TP53 mutations identified via TAm-Seq.
(a) Pie charts representing the number of patients with detected TP53 mutations across the metastatic and localized cohorts. (b) A schematic indicating the presence of TP53 mutations and their allele frequency for all patients in the metastatic cohort that had matched primary and metastatic samples. Surg bed, surgical bed.

References

    1. Berger M. F. et al.. The genomic complexity of primary human prostate cancer. Nature 470, 214–220 (2011) .
    1. Taylor B. S. et al.. Integrative genomic profiling of human prostate cancer. Cancer Cell 18, 11–22 (2010) .
    1. Barbieri C. E. et al.. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012) .
    1. 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) .
    1. 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) .
    1. Grasso C. S. et al.. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012) .
    1. Baca S. C. et al.. Punctuated evolution of prostate cancer genomes. Cell 153, 666–677 (2013) .
    1. Yachida S. et al.. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010) .
    1. Campbell P. J. et al.. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010) .
    1. de Bruin E. C. et al.. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346, 251–256 (2014) .
    1. Zhang J. et al.. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346, 256–259 (2014) .
    1. Nik-Zainal S. et al.. The life history of 21 breast cancers. Cell 149, 994–1007 (2012) .
    1. Karakiewicz P. I. et al.. Prognostic impact of positive surgical margins in surgically treated prostate cancer: multi-institutional assessment of 5831 patients. Urology 66, 1245–1250 (2005) .
    1. Coopman P. J. & Mueller S. C. The Syk tyrosine kinase: a new negative regulator in tumor growth and progression. Cancer Lett. 241, 159–173 (2006) .
    1. Dong S. W. et al.. Research on the reactivation of Syk expression caused by the inhibition of DNA promoter methylation in the lung cancer. Neoplasma 58, 89–95 (2011) .
    1. Ogane S. et al.. Spleen tyrosine kinase as a novel candidate tumor suppressor gene for human oral squamous cell carcinoma. Int. J. Cancer 124, 2651–2657 (2009) .
    1. Behjati S. et al.. Recurrent PTPRB and PLCG1 mutations in angiosarcoma. Nat. Genet. 46, 376–379 (2014) .
    1. Alexandrov L. B. & Stratton M. R. Mutational signatures: the patterns of somatic mutations hidden in cancer genomes. Curr. Opin. Genet. Dev. 24, 52–60 (2014) .
    1. Alexandrov L. B. et al.. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013) .
    1. Nik-Zainal S. et al.. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012) .
    1. Zhang C. Z., Leibowitz M. L. & Pellman D. Chromothripsis and beyond: rapid genome evolution from complex chromosomal rearrangements. Genes Dev. 27, 2513–2530 (2013) .
    1. Shen M. M. Chromoplexy: a new category of complex rearrangements in the cancer genome. Cancer Cell 23, 567–569 (2013) .
    1. Briggs S. & Tomlinson I. Germline and somatic polymerase epsilon and delta mutations define a new class of hypermutated colorectal and endometrial cancers. J. Pathol. 230, 148–153 (2013) .
    1. Palles C. et al.. Germline mutations affecting the proofreading domains of POLE and POLD1 predispose to colorectal adenomas and carcinomas. Nat. Genet. 45, 136–144 (2013) .
    1. Pritchard C. C. et al.. Complex MSH2 and MSH6 mutations in hypermutated microsatellite unstable advanced prostate cancer. Nat. Commun. 5, 4988 (2014) .
    1. Forshew T. et al.. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci. Transl. Med. 4, 136ra168 (2012) .
    1. Carreira S. et al.. Tumor clone dynamics in lethal prostate cancer. Sci. Transl. Med. 6, 254ra125 (2014) .
    1. Lang G. A. et al.. Gain of function of a p53 hot spot mutation in a mouse model of Li-Fraumeni syndrome. Cell 119, 861–872 (2004) .
    1. Olive K. P. et al.. Mutant p53 gain of function in two mouse models of Li-Fraumeni syndrome. Cell 119, 847–860 (2004) .
    1. Cattoretti G., Rilke F., Andreola S., D'Amato L. & Delia D. P53 expression in breast cancer. Int. J. Cancer 41, 178–183 (1988) .
    1. Leroy B., Anderson M. & Soussi T. TP53 mutations in human cancer: database reassessment and prospects for the next decade. Hum. Mutat. 35, 672–688 (2014) .
    1. Barbieri C. E. & Tomlins S. A. The prostate cancer genome: perspectives and potential. Urol. Oncol. 32, 53 e15–e22 (2014) .
    1. Barbieri C. E. et al.. The mutational landscape of prostate cancer. Eur. Urol. 64, 567–576 (2013) .
    1. Schoenborn J. R., Nelson P. & Fang M. Genomic profiling defines subtypes of prostate cancer with the potential for therapeutic stratification. Clin. Cancer Res. 19, 4058–4066 (2013) .
    1. Boyd L. K., Mao X. & Lu Y. J. The complexity of prostate cancer: genomic alterations and heterogeneity. Nat. Rev. Urol. 9, 652–664 (2012) .
    1. Yoshimoto M. et al.. PTEN losses exhibit heterogeneity in multifocal prostatic adenocarcinoma and are associated with higher Gleason grade. Mod. Pathol. 26, 435–447 (2013) .
    1. Kim T. M. et al.. Regional biases in mutation screening due to intratumoral heterogeneity of prostate cancer. J. Pathol. 233, 425–435 (2014) .
    1. Hong M. K. et al.. Percutaneous image-guided biopsy of prostate cancer metastases yields samples suitable for genomics and personalised oncology. Clin. Exp. Metastasis 31, 159–167 (2014) .
    1. Larson D. E. et al.. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 28, 311–317 (2012) .
    1. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 42, D7–D17 (2014) .
    1. Drmanac R. et al.. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327, 78–81 (2010) .
    1. Schroder J. et al.. Socrates: identification of genomic rearrangements in tumour genomes by re-aligning soft clipped reads. Bioinformatics 30, 1064–1072 (2014) .
    1. Van Loo P. et al.. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010) .
    1. Howie B. N., Donnelly P. & Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009) .
    1. Clarke L. et al.. The 1000 Genomes Project: data management and community access. Nat. Methods 9, 459–462 (2012) .
    1. Li H. & Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009) .
    1. Lohse M. et al.. RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Res. 40, W622–W627 (2012) .
    1. Kim D. et al.. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013) .
    1. Langmead B., Trapnell C., Pop M. & Salzberg S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009) .
    1. Alexandrov L. B., Nik-Zainal S., Wedge D. C., Campbell P. J. & Stratton M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013) .
    1. Bolli N. et al.. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 5, 2997 (2014) .
    1. Futreal P. A. et al.. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004) .
    1. Tamborero D. et al.. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 3, 2650 (2013) .
    1. Wang K., Li M. & Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010) .

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir