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Selection of metastasis competent subclones in the tumour interior

Abstract

The genetic evolutionary features of solid tumour growth are becoming increasingly well described, but the spatial and physical nature of subclonal growth remains unclear. Here, we utilize 102 macroscopic whole-tumour images from clear cell renal cell carcinoma patients, with matched genetic and phenotypic data from 756 biopsies. Utilizing a digital image processing pipeline, a renal pathologist marked the boundaries between tumour and normal tissue and extracted positions of boundary line and biopsy regions to X and Y coordinates. We then integrated coordinates with genomic data to map exact spatial subclone locations, revealing how genetically distinct subclones grow and evolve spatially. We observed a phenotype of advanced and more aggressive subclonal growth in the tumour centre, characterized by an elevated burden of somatic copy number alterations and higher necrosis, proliferation rate and Fuhrman grade. Moreover, we found that metastasizing subclones preferentially originate from the tumour centre. Collectively, these observations suggest a model of accelerated evolution in the tumour interior, with harsh hypoxic environmental conditions leading to a greater opportunity for driver somatic copy number alterations to arise and expand due to selective advantage. Tumour subclone growth is predominantly spatially contiguous in nature. We found only two cases of subclone dispersal, one of which was associated with metastasis. The largest subclones spatially were dominated by driver somatic copy number alterations, suggesting that a large selective advantage can be conferred to subclones upon acquisition of these alterations. In conclusion, spatial dynamics is strongly associated with genomic alterations and plays an important role in tumour evolution.

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Fig. 1: Study overview.
Fig. 2: Comparison between regions in the tumour centre versus margin.
Fig. 3: Phylogenetic trees and tumour maps showing the evolutionary routes of four example cases on a two-dimensional level.
Fig. 4: Computational modelling supports preferential localization of SCNA subclones in more necrotic regions of a tumour.
Fig. 5: Integrated analysis of genomic and spatial distances.
Fig. 6: Phylogenetic trees and tumour maps showing the inferred clonal expansion pattern and tumour evolutionary routes.

Data availability

Sequencing data that support this study have been deposited at the European Genome-phenome Archive (EGA), which is hosted by the European Bioinformatics Institute (EBI), under accession no. EGAS00001002793. Spatial X and Y coordinates and source data for all the figures are available at https://github.com/TIGI-Lab/NEE-spatial-analysis and https://github.com/yuezhao97.

Code availability

Code used for analyses is available at both https://github.com/yuezhao97 and https://github.com/TIGI-Lab/NEE-spatial-analysis.

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Acknowledgements

The authors thank the TRACERx Renal trial team and the Skin and Renal Unit Research Team at The Royal Marsden NHS Foundation Trust, including E. Carlyle, L. Del Rosario, K. Edmonds, K. Lingard, M. Mangwende, S. Sarker, C. Lewis, F. Williams, H. Ahmod, T. Foley, D. Kabir, J. Korteweg, A. Murra, N. Shaikh, K. Peat, S. Vaughan and L. Holt. The results published here are in whole or part based on data generated by the TCGA Research Network (https://www.cancer.gov/tcga). This work was supported by the Francis Crick Institute that receives its core funding from Cancer Research UK (FC001003, FC001144, FC001169 and FC001988), the UK Medical Research Council (FC001169) and the Wellcome Trust (FC001003, FC001144, FC001169 and FC001988). This research was funded in whole, or in part, by the Wellcome Trust (FC001003, FC001144, FC001169 and FC001988). For the purpose of open access, the authors have applied a CC BY public copyright licence to any author-accepted manuscript version arising from this submission. K.L. is funded by the UK Medical Research Council (MR/P014712/1 and MR/V033077/1), the Rosetrees Trust and Cotswold Trust (A2437), the Royal Marsden Cancer Charity (thanks to the Ross Russell family and Macfarlanes donations), Melanoma Research Alliance and Cancer Research UK (C69256/A30194). J.I.L. is funded by MINECO, Spain (grant SAF2016-79847-R). X.F. and P.A.B. are funded by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001003 and FC001144), the UK Medical Research Council (FC001003 and FC001144) and the Wellcome Trust (FC001003 and FC001144). E.S. is funded by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC010144), the UK Medical Research Council (FC010144) and the Wellcome Trust (FC010144). S.T. is funded by Cancer Research UK (grant reference number C50947/A18176); the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC10988), the UK Medical Research Council (FC10988) and the Wellcome Trust (FC10988); the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and Institute of Cancer Research (grant reference number A109); the Royal Marsden Cancer Charity; The Rosetrees Trust (grant reference number A2204), Ventana Medical Systems Inc (grant reference numbers 10467 and 10530); the National Institutes of Health (Bethesda) and Melanoma Research Alliance. C.S. is Royal Society Napier Research Professor (RP150154). C.S. is funded by Cancer Research UK (TRACERx, PEACE and CRUK Cancer Immunotherapy Catalyst Network), Cancer Research UK Lung Cancer Centre of Excellence, the Rosetrees Trust, Butterfield and Stoneygate Trusts, NovoNordisk Foundation (ID16584), Royal Society Professorship Enhancement Award (RP/EA/180007), the National Institute for Health Research (NIHR) Biomedical Research Centre at University College London Hospitals, the CRUK-UCL Centre, Experimental Cancer Medicine Centre and the Breast Cancer Research Foundation (BCRF, USA). This research is supported by a Stand Up To Cancer‐LUNGevity–American Lung Association Lung Cancer Interception Dream Team translational research grant (no. SU2C-AACR-DT23-17). Stand Up To Cancer is a program of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the scientific partner of SU2C. C.S. receives funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007-2013) consolidator grant (FP7-THESEUS-617844), European Commission ITN (FP7-PloidyNet 607722), an ERC advanced grant (PROTEUS) from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 835297), and Chromavision from the European Union’s Horizon 2020 research and innovation programme (grant agreement 665233). A.F. receives funding from the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 892360. L.A., S.T.C.S., D.N., L.P. and J.L. are supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and Institute of Cancer Research. L.A. is funded by the Royal Marsden Cancer Charity. TRACERx Renal is funded by NIHR BRC at the Royal Marsden NHS Foundation Trust and Institute of Cancer Research (A109). The Francis Crick Institute receives its core funding from CRUK (FC010110), the UK Medical Research Council (FC010110) and the Wellcome Trust (FC010110).

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Contributions

K.L., J.I.L., E.S., P.A.B., C.S. and S.T. conceived and designed the experiments. J.I.L., A.R., L.A., S.T.C.S., F.B. and G.S. performed the experiments. K.L., Y.Z., X.F., J.I.L., A.F., H.X. and M.A. analysed the data. K.L., Y.Z., X.F., J.I.L., A.R., S.Ha., S.Ho., S.T.C.S., L.S., T.O., D.N., A.C., S.R., A.T., L.P., J.L., P.A.B., C.S. and S.T. contributed materials/analysis tools. All authors contributed data and to the interpretation. K.L., Y.Z., X.F., P.A.B. and S.T. wrote the paper.

Corresponding authors

Correspondence to Paul A. Bates or Charles Swanton or Samra Turajlic or Kevin Litchfield.

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Competing interests

S.T. and C.S. have a patent on INDEL burden and checkpoint inhibitor response pending, and a patent on targeting of frameshift neoantigens for personalized immunotherapy pending. K.L. has a patent on INDEL burden and CPI response pending and outside of the submitted work, speaker fees from Roche tissue diagnostics, research funding from CRUK TDL/Ono/LifeArc alliance, and a consulting role with Monopteros Therapeutics. S.T. receives hohorariums from Jules Bordet Institute, Erasmus, Open Health and MD Anderson. S.T. has received speaking fees from Roche, Ventana, IDEA pharma, AstraZeneca, Novartis and Ipsen. S.T. has received expenses from WK Weiser, AACR, Research Degrees Team, Melanoma Focus, SITC, Jules Bordet Institute, ESMO, SMR, Broad, KCA, IFOM, EORTC, ASCO, Ventana, Roche, Institute of Molecular Medicine, KTH Sweden, Pfizer, Erasmus, Systems Biology and MD Anderson. S.T. has the following patents filed: Indel mutations as a therapeutic target and predictive biomarker PCTGB2018/051892 and PCTGB2018/051893 and Clear Cell Renal Cell Carcinoma Biomarkers P113326GB. C.S. acknowledges grant support from Pfizer, AstraZeneca, Bristol Myers Squibb, Roche-Ventana, Boehringer-Ingelheim, Archer Dx Inc (collaboration in minimal residual disease sequencing technologies) and Ono Pharmaceuticals. C.S. is an AstraZeneca Advisory Board member and Chief Investigator for the MeRmaiD1 clinical trial, has consulted for Pfizer, Novartis, GlaxoSmithKline, MSD, Bristol Myers Squibb, Celgene, AstraZeneca, Illumina, Amgen, Genentech, Roche-Ventana, GRAIL, Medicxi, Bicycle Therapeutics and the Sarah Cannon Research Institute, has stock options in Apogen Biotechnologies, Epic Bioscience and GRAIL, and has stock options and is co-founder of Achilles Therapeutics. C.S. holds patents relating to assay technology to detect tumour recurrence (PCT/GB2017/053289), targeting neoantigens (PCT/EP2016/059401), identifying patent response to immune checkpoint blockade (PCT/EP2016/071471), determining whether HLA LOH is lost in a tumour (PCT/GB2018/052004), predicting survival rates of cancer patients (PCT/GB2020/050221), treating cancer by targeting insertion/deletion mutations (PCT/GB2018/051893), identifying insertion/deletion mutation targets (PCT/GB2018/051892), detecting tumour mutations (PCT/US2017/028013) and identifying responders to cancer treatment (PCT/GB2018/051912). E.S. receives research funding from Merck Sharp Dohme and Astrazeneca and is on the scientific advisory board of Phenomic. J.L. receives honorariums from Roche, Novartis, iOnctura, BMS, Pfizer, Incyte, Dynavax, CRUK, GSK, Eisai, Merck, touchIME and touchExperts. J.L. has consulted for Iovance, Boston Biomedical, Pfizer, BMS, GSK, Novartis, Incyte, Immunocore, YKT Global, iOnctura and Apple Tree. J.L. has received speaker fees from BMS, Pfizer, Incyte, Roche, Pierre Fabre, AstraZeneca, Novartis, EUSA Pharma, MSD, Ervaxx, Merck, GSK, Ipsen, Aptitude, Eisai, Calithera, Ultimovacs and Seagen. J.L. has received expenses from BMS, iOnctura, Roche, Pfizer, Incyte, Merck, Novartis, Pierre Fabre, BUG, ESMO, AIM, AstraZeneca, NCRI, Syneos Health, EUSA Pharma, KCA, Bioevents, MedConcept, GSK and RVMais. J.L acknowledges institutional research support from BMS, MSD, Novartis, Pfizer, Achilles Therapeutics, Roche, Nektar Therapeutics, Covance, Immunocore, Pharmacyclics and Aveo. L.P. acknowledges institutional research support from NIHR, Rosetrees Trust and the Kidney and Melanoma cancer fund of RMH charity. L.P. has consulted for BMS, Eisai, Novartis, Pfizer and MSD.

Additional information

Peer review information Nature Ecology & Evolution thanks Liang Cheng and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Digital tumour maps of 79 TRACERx Renal slices.

Digital tumour maps of 79 TRACERx Renal slices. Different colours mark different terminal clones within each slice.

Extended Data Fig. 2 Relative ratio of wGII score, necrosis and proportion of regions containing grade 4 ccRCC using different cut-off values for defining tumour centre and margin.

Relative ratio of wGII score, necrosis and proportion of regions containing grade 4 ccRCC using different cut-off values for defining tumour centre and margin. Median wGII score of tumour centre divided by median wGII score of tumour margin was shown in green, frequency of regions containing necrosis in the tumour centre divided by frequency of regions containing necrosis in the tumour margin was shown in red, frequency of regions containing pathological grade 4 ccRCC in the tumour centre divided by frequency of regions containing pathological grade 4 ccRCC in the tumour margin was shown in purple, and frequency of regions containing metastasizing clones in the tumour centre divided by frequency of regions containing metastasizing clones in the tumour margin was shown in grey.

Extended Data Fig. 3 Comparison of frequency of somatic mutations and somatic copy number alterations between tumour centre and tumour margin.

a, Comparison of somatic mutations between tumour centre and tumour margin. b, Comparison of somatic copy number alterations (SCNA) between tumour centre and tumour margin. Fisher’s exact test was used and an asterisk (*) was put alongside the gene name where p ≤ 0.05.

Extended Data Fig. 4 Comparison between regions containing metastasizing clones and regions not containing metastasizing clones.

a, Comparison of angiogenesis signature, hypoxia signature and proliferation rate (as measured by Ki67) using TCGA RNA sequencing data. Metastasizing/non-metastasizing subclones were inferred based on the presence/absence of 9p21.3 loss. b, Comparison of frequency of Ki67 between regions containing metastasizing clones (n = 92) and regions not containing metastasizing clones (n = 62). c, Comparison of weighted Genome Integrity Index (wGII) score between regions containing metastasizing clones (n = 182) and regions not containing metastasizing clones (n = 76).

Extended Data Fig. 5 Comparisons in computational simulations with various parameters and impact of tumour boundary shape on SCNA burden.

a, Necrotic fraction in central versus marginal biopsies in simulations with varying d_nec and p_adv values. b, Number of SCNAs in central versus marginal biopsies in simulations with varying d_nec and p_adv values. c, Number of SCNAs in less versus more necrotic regional biopsies in simulations with varying d_nec and p_adv values. d, Number of SCNAs in central (‘At.Centre’, n = 735) versus marginal (‘At.Margin’, n = 271) biopsies, in tumours with less versus more circularity at the tumour boundary. e, Average subclone birth time relative to the age of the tumour, in tumours with smaller versus larger circularity at the tumour boundary (Less circular: n = 989, more circular: n = 1106).

Extended Data Fig. 6 Comparison between simulations with or without necrosis.

a, Spatial pattern of subclones in a representative simulated tumour without necrosis. Founder clone is in grey while other subclones are in randomly assigned colours. b, Number of SCNAs in central (‘At.Centre’, n = 1922) versus marginal (‘At.Margin’, n = 572) biopsies. c, Spatial pattern of subclones in a representative simulated tumour with necrosis. Tumour areas in yellow are necrotic. Founder clone is in grey while other subclones are in randomly assigned colours. d, Spatial maps of the birth time of subclones relative to the age of the simulated tumour. Darker blue reflects later birth. (i) simulations without necrosis; (ii) simulations with necrosis.

Extended Data Fig. 7 Analyses of tumours with different circularity scores.

a, Definition and distribution of circularity score of the study cohort. Circularity score was calculated as 4*𝜋*area/periphery2. For a perfect circle, circularity score is 1, while for irregular shapes, circularity score is less than 1. The smaller the circularity score, the more irregular the shape. b, Circularity score for tumours harbouring different subclonal driver events in ccRCC. c, Progression-free survival for tumours with regular or irregular boundaries. d, Overall survival for tumours with regular or irregular boundaries.

Extended Data Fig. 8 Voronoi diagrams inferring the area occupied by each region.

Voronoi diagrams inferring the area occupied by each region. Light blue indicates higher Ki67, while dark blue indicates lower Ki67.

Extended Data Fig. 9 Pathological slides demonstrating regions with high- and low-Ki67, and regions with necrosis and without necrosis.

Pathological slides demonstrating regions (a) with high-Ki67, (b) with low-Ki67, (c) with necrosis and (d) without necrosis.

Extended Data Fig. 10 Spatial analysis of bilateral and multifocal tumours.

Tumour maps and phylogenetic trees showing the spatial positions and clonal information of 2 bilateral (K114 and K334) and 1 multifocal tumour (K265) in the study cohort.

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Zhao, Y., Fu, X., Lopez, J.I. et al. Selection of metastasis competent subclones in the tumour interior. Nat Ecol Evol 5, 1033–1045 (2021). https://doi.org/10.1038/s41559-021-01456-6

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