Evolutionary dynamics of 1,976 lymphoid malignancies predict clinical outcome

Cancer development, progression, and response to treatment are evolutionary processes, but characterising the evolutionary dynamics at sufficient scale to be clinically-meaningful has remained challenging. Here, we develop a new methodology called EVOFLUx, based upon natural DNA methylation barcodes fluctuating over time, that quantitatively infers evolutionary dynamics using only a bulk tumour methylation profile as input. We apply EVOFLUx to 1,976 well-characterised lymphoid cancer samples spanning a broad spectrum of diseases and show that tumour growth rates, malignancy age and epimutation rates vary by orders of magnitude across disease types. We measure that subclonal selection occurs only infrequently within bulk samples and detect occasional examples of multiple independent primary tumours. Clinically, we observe that tumour growth rates are higher in more aggressive disease subtypes, and in two series of chronic lymphocytic leukaemia patients, evolutionary histories are independent prognostic factors. Phylogenetic analyses of longitudinal CLL samples using EVOFLUx detect the seeds of future Richter transformation many decades prior to presentation. We provide orthogonal verification of EVOFLUx inferences using additional genetic and clinical data. Collectively, we show how widely-available, low-cost bulk DNA methylation data precisely measures cancer evolutionary dynamics, and provides new insights into cancer biology and clinical behaviour. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was primarily funded by an Accelerator award Cancer Research UK/AIRC/AECC joint funder-partnership (to JF, JIMS and TAG) and the US National Institutes of Health National Cancer Institute (U54 CA217376, to DS and TAG). Additional funding: Cancer Research UK (A19771 and DRCNPG-May21_100001) to TAG, the European Research Council under the European Union's Horizon 2020 Research and Innovation Program (810287, BCLLatlas, to EC and JIMS), La Caixa Foundation (CLLEvolution LCF/PR/HR17/52150017 [HR17- 00221LCF] and CLLSYSTEMS - LCF/PR/HR22/52420015 [HR22-00172] Health Research 2017 and 2022 Programs, to EC), Generalitat de Catalunya Suport Grups de Recerca AGAUR (2021-SGR-01343 to JIMS and 2021-SGR-01172 to EC). JN and OK were supported by the Swedish Research Council (2019-01976), Swedish Childhood Cancer Fund (PR2019-0046/PR2022-0082), and Swedish Cancer Society (CAN2022-2395). CG was further supported by the BBSRC London Interdisciplinary Doctoral Programme (LIDo) and Thorton Foundation funding to the Institute of Cancer Research. MDF was supported by a postdoctoral fellowship of the AECC Scientific Foundation. This research utilized the Cancer Research UK City of London High Performance Computing (HPC) facility, and was partially developed at the Centro Esther Koplowitz (CEK, Barcelona, Spain). We acknowledge the SciLifeLab National Genomics Infrastructure, SNP&SEQ Technology Platform in Upppsala, Sweden, funded by the Swedish Research Council and the Knut and Alice Wallenberg Foundation, for assistance with DNA methylation analyses. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: No new human data was generated in the course of this study. Previously published DNA methylation data re-analysed in this study can be found under accession codes: B cells, EGAS00001001196; ALL, GSE56602, GSE49032, GSE76585, GSE69229; MCL, EGAS00001001637, EGAS00001004165; CLL, EGAD00010000871, EGAD00010000948, EGAD00010001975; MM, EGAS00001000841; DLBCL, EGAD00010001974. CLL gene expression data is available EGAS00001000374 and EGAS00001001306. ChIP-seq datasets are available from Blueprint https://www.blueprint-epigenome.eu/ under the accession EGAS00001000326. Matched WES and WGS are available under accessions EGAS00000000092 and EGAD00001008954 respectively. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes No new data was generated in the course of this study. The harmonised and filtered methylation matrix is available (preferred browser Firefox) at: http://inb-cg.bsc.es/hcli\_priv/publications\_data/fluctuating\_cpgs\_gabbutt\_duran\_ferrer\_2023/QC\_2204_ssNob.tsv.7z - the password for this file is available upon request to the authors. Previously published DNA methylation data re-analysed in this study can be found under accession codes: B cells, EGAS00001001196; ALL, GSE56602, GSE49032, GSE76585, GSE69229; MCL, EGAS00001001637, EGAS00001004165; CLL, EGAD00010000871, EGAD00010000948, EGAD00010001975; MM, EGAS00001000841; DLBCL, EGAD00010001974. CLL gene expression data is available EGAS00001000374 and EGAS00001001306. ChIP-seq datasets are available from Blueprint https://www.blueprint-epigenome.eu/ under the accession EGAS00001000326. Matched WES and WGS are available under accessions EGAS00000000092 and EGAD00001008954 respectively. [http://inb-cg.bsc.es/hcli\_priv/publications\_data/fluctuating\_cpgs\_gabbutt\_duran\_ferrer\_2023/QC\_2204_ssNob.tsv.7z][1] [1]: http://inb-cg.bsc.es/hcli_priv/publications_data/fluctuating_cpgs_gabbutt_duran_ferrer_2023/QC_2204_ssNob.tsv.7z