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Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer

Abstract>

Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring1. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete2,3,4,5,6,7,8,9,10,11,12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.

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Fig. 1: High-resolution ctDNA and metastatic tissue whole genomes.
Fig. 2: Cancer clonal composition of ctDNA.
Fig. 3: Population and genomic discordance among synchronous metastatic tissue biopsy and cfDNA pairs.
Fig. 4: Genome evolution in ctDNA during systemic treatment with AR signalling inhibitors.
Fig. 5: ctDNA nucleosome architecture of mCRPC.

Data availability

The human reference genome hg38 was downloaded from UCSC. For cfDNA nucleosome depletion analyses, we used exon and TSS coordinates from the RefSeq Matched Annotation from NCBI and EMBL-EBI (MANE) database (genes not annotated in this database were omitted from analysis). Metastatic tissue RNA-sequencing data (cohort B) were obtained from previously published work (dbGaP study accession: phs001648.v2.p1)20. Gene sets representing housekeeping genes and highly expressed genes in haematopoietic lineages were derived from previously published work66 (Supplementary Tables 9 and 10). The 3,224 ARBSs were from previous chromatin immunoprecipitation followed by sequencing of 13 primary prostate cancer tissue samples41,42 (Supplementary Table 9). All de-identified WGS data have been deposited in the European Genome-Phenome Archive (EGA) under the accession code EGAS00001005783, and are available for download by contacting the corresponding authors. All other data supporting the findings of this study (source data) are available within the Article (including its Supplementary Information and Supplementary Tables).

Code availability

Custom computer code that was used for analysis is available on GitHub at https://github.com/annalam/cfdna-wgs-manuscript-code.

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Acknowledgements

This work was funded by the Canadian Institutes of Health Research, the Canadian Cancer Society Research Institute, the Prostate Cancer Foundation, Prostate Cancer Canada, the Movember Foundation, the Jane and Aatos Erkko Foundation, the Academy of Finland Center of Excellence programme (project no. 312043), the Terry Fox New Frontiers Program and the BC Cancer Foundation. No funding sources were involved in the design or execution of the study. We thank S. Taavitsainen and R. Nätkin for their assistance, and are grateful to all participating patients and their families.

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C.H.: conceptualization, data curation, software construction, formal analysis, visualization, methodology, writing (original draft), project administration, writing (review and editing). M.A.: conceptualization, data curation, software construction, formal analysis, visualization, methodology, writing (original draft), project administration, writing (review and editing). J.S.: conceptualization, data curation, software construction, formal analysis, visualization, methodology, writing (original draft), project administration, writing (review and editing). S.W.S.N.: data curation, software construction, formal analysis, visualization, methodology, writing (review and editing). X.E.C.: data curation, software construction, formal analysis, visualization, methodology, writing (review and editing). A.N.: data curation, formal analysis, software construction, writing (review and editing). O.V.K.: data curation, formal analysis, writing (review and editing). A.D.M.: software construction, formal analysis, visualization, methodology, writing (review and editing). K.B.: data curation, validation, methodology, writing (review and editing). E.S.: data curation, validation, methodology, writing (review and editing). C.Q.B.: data curation, validation, methodology, writing (review and editing). E.R.: software construction, methodology, writing (review and editing). J.V.W.B.: data curation, validation, writing (review and editing). N.A.L.: resources, writing (review and editing). M.N.: resources, writing (review and editing). R.A.: resources, writing (review and editing). E.J.S.: resources, writing (review and editing). M.E.G.: resources, writing (review and editing). D.A.Q.: resources, project administration, writing (review and editing). F.Y.F.: resources, project administration, supervision, writing (review and editing). K.N.C.: conceptualization, resources, project administration, supervision, funding acquisition, writing (review and editing). A.W.W.: conceptualization, resources, project administration, supervision, funding acquisition, writing (original draft), writing (review and editing). All authors reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Alexander W. Wyatt.

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Competing interests

M.A. is a shareholder in Fluivia. R.A. has received honoraria from Clovis Oncology and has served in advisory or consulting roles for Dendreon, Advanced Accelerator Applications, Clovis Oncology, Axiom Biotechnologies, AstraZeneca, Pfizer, Merck, Amgen, Jubilant Pharmaceuticals and Alessa Therapeutics. R.A. also has research funding from Zenith Epigenetics, Novartis, Xynomic Pharma, Cancer Targeted Technology, Janssen, Merck, Abbvie, Amgen, AstraZeneca and BioXCel Therapeutics. E.J.S. has stock in Fortis, Harpoon and Teon, and has received honoraria for speaking engagements for Janssen and Johnson & Johnson. E.J.S. has also served in compensated advisory or consulting roles for Janssen, Fortis, Teon and Ultragenyx. M.E.G. is the founder of OncoGenex Technologies, Sustained Therapeutics and Sitka Pharmaceuticals, and has consulted for Astellas, AstraZeneca, Bayer, Janssen, Sanofi, Pfizer, Tersera, Roche and Genova Diagnostics. D.A.Q. has consulted for Varian and Circle Pharmaceuticals. F.Y.F. has served as a consultant or advisory board member for Janssen, Celgene, Blue Earth Diagnostics, Astellas, Myovant, Roivant, Bayer, Novartis and Foundation Medicine, has stock options in Artera and has stock options and is a scientific advisory board member for SerImmune and BlueStar Genomics. K.N.C. has research funding from Janssen, Astellas and Sanofi. K.N.C. also reports research funding and personal fees from Janssen, Astellas, AstraZeneca and Sanofi, as well as personal fees from Constellation Pharmaceuticals, Daiichi Sankyo, Merck, Novartis, Pfizer, Point Biopharma and Roche outside the submitted work. A.W.W. has served on advisory boards and/or received honoraria from AstraZeneca, Astellas, Janssen, and Merck. The laboratory of A.W.W. has a contract research agreement with ESSA Pharma. C.H., J.S., S.W.S.N., X.E.C., A.N., O.V.K., A.D.M., K.B., E.S., C.Q.B., E.R., J.V.W.B., N.A.L. and M.N. declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Genomic features of ctDNA and metastatic tissue whole genomes.

(a) Per-sample overview of genomic information (grouped by same-patient sample pairs). Figure content is otherwise identical to Fig. 1d. (b) Fragment-length distribution of plasma cell-free DNA (cfDNA) of nuclear (left) and mitochondrial (right) origin. (c) Structures of E26 transformation-specific (ETS) family oncogenic fusions detected in the cohort. Solid colours denote intragenic regions of fusion partner genes (including untranslated regions) and lighter hues represent flanking intergenic 5’ upstream or 3’ downstream regions. The intergenic region between SLC45A3 and ELK4 is shown in grey. (d) Box plots illustrating higher microhomology-containing deletion counts (deletion length 3–5 bp with at least 2 bp of microhomology) (left) and structural variant counts (right) in samples with BRCA2 defects relative to wild-type (WT) samples.

Extended Data Fig. 2 AR gene and enhancer amplification mechanisms and amplicon structure in cfDNA and metastatic tissue.

(a) AR-neighbourhood copy number structure in same-day patient-matched cfDNA and metastatic tissue. Upper and lower plots display absolute copy number (i.e. corrected for tumour content) and uncorrected coverage log ratios, respectively. In this patient, the tissue sample shows a focal amplification of the AR enhancer region and a broad copy gain of the entire locus, whereas the cfDNA sample shows a more complex pattern of apparent nested copy gains. Despite these contrasting structures, the overall AR enhancer and gene body copy numbers are relatively similar (considering that the range of AR gene body absolute copy number across all samples was 1-91). (b) Absolute copy number across the AR gene and enhancer neighbourhood for three cfDNA samples and one tissue sample. Pileup of all per-sample structural arrangements shown below (indicating dominant mechanisms of copy amplification). Note evidence of local enhancer copy loss in patients, suggestive of enhanced cfDNA degradation owing to nucleosome depletion38. (c) Average copy number change at the AR locus across serial ctDNA collected during systemic treatment with AR signalling inhibitors. Structural variant breakpoints and tandem duplication events from all cfDNA and tissue samples (regardless of time point) are shown below, illustrating a convergence toward amplification of the AR gene and enhancer.

Extended Data Fig. 3 Expression-linked features and clinico-genomic correlates of nucleosome depletion.

(a–b) Correlation between transcription terminus site (TTS) nucleosome depletion and metastatic tissue mRNA expression decile for 61 cfDNA samples in our cohort, measured using relative read-depth depletion and WPS area under the curve. (c) Aggregate spatial read-depth profile of 61 cfDNA samples centred at the TTS, stratified into gene subsets by mRNA expression decile. (d) Aggregate spatial coverage log ratio profile of 61 cfDNA samples centred at gene bodies, with traces stratified into gene subsets representing median metastatic tissue mRNA expression deciles. Coverage log ratios are calculated relative to a pooled white blood cell normal. Traces are normalized to flanking regions (50 kb at ±2 Mb from the gene start/end) and are smoothed using locally weighted smoothing. (e) Examples of spatial gene body coverage depletion in cfDNA (left) but not patient-matched metastatic tumour tissue (right) in highly expressed genes (top). Gene bodies (TSS to TTS) are shaded grey. (f) In silico reverse dilution experiment illustrating the effect of ctDNA fraction on nucleosome depletion as measured by area under the WPS (±2 peaks surrounding the TSS). Vertical axis shows aggregated TSS nucleosome depletion (in 61 cfDNA samples) at 350 genes highly expressed in hematopoietic cells but lowly expressed in mCRPC tissue. Horizontal axis represents fraction of ctDNA sample sequencing coverage that has been randomly substituted for normal cfDNA not derived from prostate cancer (1 = complete substitution with healthy cfDNA). (g) Correlation between AR gene and enhancer copy number (horizontal axis) versus average read-depth depletion at ARBSs. (h) Correlation between time-matched PSA (ng ml−1) and WGS ctDNA fraction estimates versus average ARBS read-depth depletion.

Extended Data Fig. 4 Joint analysis of plasma cfDNA fragment locations and lengths around the TSSs of high- and low-expressed genes.

(a) cfDNA fragment density heat maps ('nucleograms') averaged across 63 mCRPC cfDNA samples in the cohort (left column), for four gene sets defined based on their expression quartile in mCRPC tissues. The middle column shows the same heat maps after eliminating interaction terms (i.e. outer product of the marginal distributions). In the right column heat maps, the interaction terms are isolated by subtracting the middle column heat maps from the left column heat maps. The analysis reveals enrichment of short cfDNA fragments at the TSS, and longer cfDNA fragments at immediate flanking regions. (b) Single gene nucleograms showing fragment localization and lengths near TSSs of four highly expressed genes (averaged across 63 mCRPC cfDNA samples). Nucleosome positions are well conserved and clearly visible, but the precise nucleosome localization pattern varies between genes. (c) cfDNA fragment length histograms within two 100 bp windows positioned relative to gene transcription start sites (TSS), grouped by gene expression level.

Supplementary information

Supplementary Information

This file contains a Supplementary Note (providing a list of Stand Up 2 Cancer (SU2C) / Prostate Cancer Foundation (PCF) West Coast Prostate Cancer Dream Team members and affiliations), as well as Supplementary Methods and associated Supplementary Figures 1–25. All technical validation experiments (and supporting data) are enclosed.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–11, detailing (1) overview of clinical samples (2), time between serial cfDNA collections, (3) validation of truncal copy number models using Battenberg and ASCAT, (4) genomic rearrangements affecting known putative prostate cancer driver genes, (5) validation of subclonal reconstruction via blinded analysis of simulated whole genomes, (6) per-subclone fitted mutational signature weights from DeconstructSigs, (7) genes with a high frequency of protein-altering mutations (overall and acquired after AR targeted therapy), (8) pathogenic androgen-receptor point mutations, (9) 3804 housekeeping genes, (10) 350 genes highly expressed in haematopoietic lineages but minimally expressed in mCRPC tissue, (11) coordinates of 3224 AR-binding sites derived from ChIP–seq of primary prostate cancer tissue (hg38).

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Herberts, C., Annala, M., Sipola, J. et al. Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer. Nature 608, 199–208 (2022). https://doi.org/10.1038/s41586-022-04975-9

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