Minimally invasive approaches to detect residual disease after surgery are needed to identify patients with cancer who are at risk for metastatic relapse. Circulating tumour DNA (ctDNA) holds promise as a biomarker for molecular residual disease and relapse1. We evaluated outcomes in 581 patients who had undergone surgery and were evaluable for ctDNA from a randomized phase III trial of adjuvant atezolizumab versus observation in operable urothelial cancer. This trial did not reach its efficacy end point in the intention-to-treat population. Here we show that ctDNA testing at the start of therapy (cycle 1 day 1) identified 214 (37%) patients who were positive for ctDNA and who had poor prognosis (observation arm hazard ratio = 6.3 (95% confidence interval: 4.45–8.92); P < 0.0001). Notably, patients who were positive for ctDNA had improved disease-free survival and overall survival in the atezolizumab arm versus the observation arm (disease-free survival hazard ratio = 0.58 (95% confidence interval: 0.43–0.79); P = 0.0024, overall survival hazard ratio = 0.59 (95% confidence interval: 0.41–0.86)). No difference in disease-free survival or overall survival between treatment arms was noted for patients who were negative for ctDNA. The rate of ctDNA clearance at week 6 was higher in the atezolizumab arm (18%) than in the observation arm (4%) (P = 0.0204). Transcriptomic analysis of tumours from patients who were positive for ctDNA revealed higher expression levels of cell-cycle and keratin genes. For patients who were positive for ctDNA and who were treated with atezolizumab, non-relapse was associated with immune response signatures and basal–squamous gene features, whereas relapse was associated with angiogenesis and fibroblast TGFβ signatures. These data suggest that adjuvant atezolizumab may be associated with improved outcomes compared with observation in patients who are positive for ctDNA and who are at a high risk of relapse. These findings, if validated in other settings, would shift approaches to postoperative cancer care.
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All clinical, ctDNA and raw RNA-seq data for IMvigor010 are deposited to the European Genome-Phenome Archive under accession number EGAS00001004997. All clinical and ctDNA data for the ABACUS trial are deposited to the European Genome-Phenome Archive under accession number EGAS00001004445. Public datasets that were used during data processing included 1000 Genomes Project (https://www.internationalgenome.org/), ExAC (http://exac.broadinstitute.org/), ESP (https://esp.gs.washington.edu/drupal/), dbSNP (https://www.ncbi.nlm.nih.gov/snp/) and Molecular Signature Database (http://www.gsea-msigdb.org/gsea/msigdb/)33. Qualified researchers may request access to individual patient-level data through the clinical study data request platform (https://vivli.org/). Further details on Roche’s criteria for eligible studies are available at https://vivli.org/members/ourmembers. For further details on Roche’s Global Policy on the Sharing of Clinical Information and how to request access to related clinical study documents, see https://www.roche.com/research_and_development/who_we_are_how_we_work/clinical_trials/our_commitment_to_data_sharing.htm.
Chin, R. I. et al. Detection of solid tumor molecular residual disease (MRD) using circulating tumor DNA (ctDNA). Mol. Diagn. Ther. 23, 311–331 (2019).
Christensen, E. et al. Early detection of metastatic relapse and monitoring of therapeutic efficacy by ultra-deep sequencing of plasma cell-free DNA in patients with urothelial bladder carcinoma. J. Clin. Oncol. 37, 1547–1557 (2019).
Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).
Thomsen, M. B. H. et al. Comprehensive multiregional analysis of molecular heterogeneity in bladder cancer. Sci. Rep. 7, 11702 (2017).
Snyder, A., Morrissey, M. P. & Hellmann, M. D. Use of circulating tumor DNA for cancer immunotherapy. Clin. Cancer Res. 25, 6909–6915 (2019).
Corcoran, R. B. & Chabner, B. A. Application of cell-free DNA analysis to cancer treatment. N. Engl. J. Med. 379, 1754–1765 (2018).
Turner, N. C. et al. Circulating tumour DNA analysis to direct therapy in advanced breast cancer (plasmaMATCH): a multicentre, multicohort, phase 2a, platform trial. Lancet Oncol. 21, 1296–1308 (2020).
Nuzzo, P. V. et al. Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes. Nat. Med. 26, 1041–1043 (2020).
Tie, J. et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci. Transl. Med. 8, 346ra92 (2016).
Garcia-Murillas, I. et al. Assessment of molecular relapse detection in early-stage breast cancer. JAMA Oncol. 5, 1473–1478 (2019).
Bratman, S. V. et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat. Cancer 1, 873–881 (2020).
Nabet, B. Y. et al. Noninvasive early identification of therapeutic benefit from immune checkpoint inhibition. Cell 183, 363–376.e13 (2020).
Zhang, Q. et al. Prognostic and predictive impact of circulating tumor DNA in patients with advanced cancers treated with immune checkpoint blockade. Cancer Discov. 10, 1842–1853 (2020).
Chan, A. T. C. et al. Analysis of plasma Epstein–Barr virus DNA in nasopharyngeal cancer after chemoradiation to identify high-risk patients for adjuvant chemotherapy: a randomized controlled trial. J. Clin. Oncol. 36, 3091–3100 (2018).
Taieb, J. et al. Analysis of circulating tumour DNA (ctDNA) from patients enrolled in the IDEA-FRANCE phase III trial: prognostic and predictive value for adjuvant treatment duration. Ann. Oncol. 30, v867 (2019).
Bellmunt, J. et al. Adjuvant atezolizumab versus observation in muscle-invasive urothelial carcinoma (IMvigor010): a multicentre, open-label, randomised, phase 3 trial. Lancet Oncol. 22, 525–537 (2021).
Stein, J. P. et al. Radical cystectomy in the treatment of invasive bladder cancer: long-term results in 1,054 patients. J. Clin. Oncol. 19, 666–675 (2001).
Balar, A. V. et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet 389, 67–76 (2017).
Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016).
Horn, L. et al. First-line atezolizumab plus chemotherapy in extensive-stage small-cell lung cancer. N. Engl. J. Med. 379, 2220–2229 (2018).
Powles, T. et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet 391, 748–757 (2018).
Powles, T. et al. Clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial. Nat. Med. 25, 1706–1714 (2019).
Hellmann, M. D. et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N. Engl. J. Med. 378, 2093–2104 (2018).
Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171, 540–556.e25 (2017).
Bajorin, D. F. et al. First results from the phase 3 CheckMate 274 trial of adjuvant nivolumab vs placebo in patients who underwent radical surgery for high-risk muscle-invasive urothelial carcinoma (MIUC). J. Clin. Oncol. 39, 391 (2021).
Tan, L. et al. Prediction and monitoring of relapse in stage III melanoma using circulating tumor DNA. Ann. Oncol. 30, 804–814 (2019).
Reinert, T. et al. Analysis of plasma cell-free DNA by ultradeep sequencing in patients with stages I to III colorectal cancer. JAMA Oncol. 5, 1124–1131 (2019).
Coombes, R. C. et al. Personalized detection of circulating tumor DNA antedates breast cancer metastatic recurrence. Clin. Cancer Res. 25, 4255–4263 (2019).
Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).
Wu, T. D., Reeder, J., Lawrence, M., Becker, G. & Brauer, M. J. GMAP and GSNAP for genomic sequence alignment: enhancements to speed, accuracy, and functionality. Methods Mol. Biol. 1418, 283–334 (2016).
Wu, D. & Smyth, G. K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
The study was sponsored by F. Hoffmann-La Roche Ltd/Genentech, Inc. We thank the patients who participated in the trial and the clinical site investigators, the Barts Health Experimental Cancer Medicine Centre (CRUK), A. Yiu and M. Ravasz from Fios Genomics for additional help with transcriptomic analysis, S. Flynn, K. Zvonar and D. Rishipathak of the Genentech Biomarker Operations Team for providing support in the generation and analysis of the data, and E. E. Kadel III from the Genentech Biomarker Development team for data integration. Medical writing assistance for this manuscript was provided by H. Grewal and Z. Augur of Anshin Biosolutions, M. Malhotra of Natera and A. J. Pratt of Health Interactions, Inc., and was funded by F. Hoffmann-La Roche Ltd. M. Jacobson and J. Ball contributed to research funding for the ABACUS cohort.
T.P. received honoraria from advisory/consultancy roles with AstraZeneca, BMS, Exelixis, Incyte, Ipsen, Merck, MSD, Novartis, Pfizer, Seattle Genetics, Merck Serono (EMD Serono), Astellas, Johnson & Johnson, Eisai and Roche; institutional research funding support from AstraZeneca, Roche, BMS, Exelixis, Ipsen, Merck, MSD, Novartis, Pfizer, Seattle Genetics, Merck Serono (EMD Serono), Astellas and Johnson & Johnson; and travel, accommodation and expenses support from Roche, Pfizer, MSD, AstraZeneca and Ipsen. Z.J.A. discloses employment with Genentech, previous employment with Natera and stock and other ownership interests with Roche. N.D., D.S.S., V.D. and K.C.Y. disclose employment with Genentech and stock and other ownership interests with Roche. R.B. and X.S. disclose employment with and own stock or other ownership interests in Genentech. B.E.S. discloses honoraria from Merck, Roche, Pfizer and Ellipses; advisory/consulting fees from Merck, Ellipses and Onc; expert testimony or speakers’ bureau fees from Merck and Pfizer; travel, accommodation or expenses support from Roche and Pfizer. P.G. received consulting fees from AstraZeneca, Bayer, BMS, Clovis Oncology, Driver, Dyania Health, EMD Serono, Exelixis, Foundation Medicine, GlaxoSmithKline, Genentech, Genzyme, Heron Therapeutics, Immunomedics, Janssen, Merck, Mirati Therapeutics, Pfizer, Roche, Seattle Genetics and QED Therapeutics; and institutional research funding support from AstraZeneca, Bavarian Nordic, Bayer, BMS, Clovis Oncology, Debiopharm, Genentech, GlaxoSmithKline, Immunomedics, Kure It Cancer Research, Merck, Mirati Therapeutics, OncogeneX, Pfizer and QED Therapeutics. M.H. received honoraria or advisory fees from Pfizer, AstraZeneca, Bayer, Genentech, PER, Projects in Knowledge, Astellas Pharma, Sanofi/Genzyme, Research to Practice, BMS and Daiichi Sankyo; research funding support from AstraZeneca, Genentech, Pfizer and Bayer; and travel, accommodation or expenses support from Pfizer, Bayer, Astellas Pharma and Genentech/Roche; and also has two pending patents and one licensed patent with Imbio. S.O. received advisory/consulting fees and honorarium from Astellas, Bayer, BMS, Eisai, Janssen, MSD, Novartis, Pfizer and Sanofi; research funding support from Ipsen and Sanofi; and travel, accommodation or expenses support from Bayer, BMS, Eisai, MSD, Novartis and Pfizer. J.E.G. received consulting fees or honoraria from Amgen, AstraZeneca, Bayer, Janssen-Cilag, Merck, MSD, Pfizer, Roche and BMS. P.A. received advisory/consulting fees and honoraria from MSD Oncology, Sanofi and Roche/Genentech. D.C. received consulting fees from Astellas Pharma, AstraZeneca, Bayer, Boehringer Ingelheim, BMS, Ipsen, Janssen Oncology, Lilly, MSD Oncology, Novartis, Pfizer, Pierre Fabre, Roche/Genentech and Sanofi; research funding support from Janssen Oncology; and travel, accommodation or expenses support from AstraZeneca Spain, BMS, Pfizer and Roche. H.N. received research funding support from Ono and Chugai; consulting fees from Bayer, Chugai, BMS, Astellas, MSD, Janssen and AstraZeneca; and participated in speakers’ bureaus for Chugai, MSD, Astellas and AstraZeneca. S.D. received honoraria from Ferring, Olympus, Pacific Edge, Photocure, QED, MDxHealth and Spectrum Pharmaceuticals; advisory fees from Ferring, Photocure, QED and Taris; and research funding, travel, accommodation or expenses support from Photocure. S.S., H.S. and A.A disclose employment with Natera. B.G.Z. discloses employment with and owns stock or other ownership interests in Natera. M.P. discloses employment with F. Hoffmann-La Roche. J.Z. discloses employment with Hoffmann-La Roche. C.C., C.B. and S.M. disclose employment with Genentech. J.B. received advisory/consulting fees from Astellas Pharma, AstraZeneca/MedImmune, BMS, Genentech, Merck, Novartis, Pfizer and Pierre Fabre; honoraria from UpToDate; research funding support from Millennium, Sanofi and Pfizer/EMD Serono; owns stock or other ownership interests in Rainer; and travel, accommodation or expenses support from Pfizer, MSD Oncology and Ipsen.
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Extended data figures and tables
a, Inclusion criteria for the ctDNA BEP. Of the 809 patients enrolled in IMvigor010, 581 passed the predetermined quality control criteria. b–e, Kaplan–Meier estimates comparing patients treated with atezolizumab (blue) to observation (red) for DFS in the ITT population (b), stratified for nodal status, PD-L1 status and tumour stage, DFS in the ctDNA BEP population (c), stratified for nodal status, PD-L1 status and tumour stage, OS in the ITT (d), stratified for nodal status, PD-L1 status and tumour stage, and OS in the ctDNA BEP population (e), stratified for nodal status, PD-L1 status and tumour stage. Formal testing in IMvigor010 of OS as the secondary end point was not permitted based on the hierarchical study design. Note that stratification factors in this analysis were chosen to match exactly those used in the IMvigor010 primary clinical analysis16. f, g, Kaplan–Meier analyses of DFS showing different ctDNA dynamics from C1D1 to C3D1 where ‘Pos’ indicates ctDNA(+) and ‘Neg’ indicates ctDNA(−), in the atezolizumab arm (blue colours) (f) and in the observation arm (red colours) (g).
a, b, Forest plots comparing atezolizumab to observation in all ctDNA-evaluable patients, including DFS (a) and OS (b). Subgroups are defined by baseline clinical features and tissue immune biomarkers including nodal status, tumour stage, the number of lymph nodes resected, previous neoadjuvant chemotherapy, PD-L1 status by tissue immunohistochemistry (IHC), TMB status by tissue whole-exome sequencing (WES), as well as transcriptomic signatures including tGE3, TBRS, angiogenesis and TCGA subtypes. Forest plots show HRs for recurrence or death estimated using a univariable Cox proportional-hazards model, and 95% confidence intervals of HRs are represented by horizontal bars. c, Bar plot depicting association of baseline prognostic factors with ctDNA-positive (red) and ctDNA-negative (grey) status, wherein nodal-positive patients were enriched for ctDNA-positive status (nodal-positive patients were 47.5% ctDNA positive, and nodal-negative patients were 25.2% ctDNA positive).
Extended Data Fig. 3 Forest plots for DFS and OS in ctDNA-positive and ctDNA-negative subgroups defined by clinical and immune features.
a–d, Forest plots comparing atezolizumab to observation including DFS in ctDNA(+) patients (a), OS in ctDNA(+) patients (b), DFS in ctDNA(−) patients (c) and OS in ctDNA(−) patients (d). Subgroups are defined by baseline clinical features and tissue immune biomarkers including nodal status, tumour stage, the number of lymph nodes resected, previous neoadjuvant chemotherapy, PD-L1 status by tissue IHC, TMB status by tissue WES, as well as transcriptomic signatures including tGE3, F-TBRS, angiogenesis and TCGA subtypes. Forest plots show HRs for death estimated using a univariable Cox proportional-hazards model, and 95% confidence intervals of HRs are represented by horizontal bars.
Extended Data Fig. 4 Continuous ctDNA metrics and association with clinical outcome within ctDNA(+) patients.
Only the observation arm ctDNA(+) patients are shown to demonstrate the prognostic value of baseline ctDNA. a, Scatter plot showing ctDNA concentration as measured by sample mean tumor molecules per mL of plasma (sample MTM/ml) versus DFS in months. The solid points indicate an event, and the empty points indicate censoring. b, Kaplan–Meier plot for DFS comparing patients with high ctDNA concentrations (dark red, greater than or equal to median sample MTM/ml) versus low ctDNA levels (light red, less than the median sample MTM/ml). c, Forest plot for DFS comparing patients with high versus low ctDNA levels using different quantile thresholds for splitting sample MTM/ml, including 10%, 25%, 50% (median), 75% and 90% quantiles. d, Scatter plot showing OS in months (x axis) versus ctDNA concentration as measured by sample MTM/ml. The solid points indicate an event, and the empty points indicate censoring. e, Kaplan–Meier plot for OS comparing patients with high ctDNA concentrations (dark red, greater than or equal to median sample MTM/ml) versus low ctDNA concentrations (light red, less than the median sample MTM/ml). f, Forest plot for OS comparing patients with high versus low ctDNA concentrations using different quantile thresholds for splitting ctDNA sample MTM/ml, including 10%, 25%, 50% (median), 75% and 90% quantiles. Forest plots show HRs for recurrence or death estimated using a univariable Cox proportional-hazards model, and 95% confidence intervals of HRs are represented by horizontal bars.
Extended Data Fig. 5 Reductions in ctDNA levels occurred at a higher rate in the atezolizumab arm than in the observation arm and were associated with improvements in clinical outcome in the atezolizumab arm.
a–e, Reduction is assessed in C1D1 ctDNA(+) patients in the C1/C3 BEP and is defined as a decrease in sample MTM/ml from C1D1 to C3D1. The proportion of patients who are ctDNA(+) at C1D1 who have reduced ctDNA by C3D1 for the atezolizumab arm (blue) and the observation arm (red) (a). Kaplan–Meier analyses comparing patients who have reduced ctDNA (‘reduction’, dark blue or dark red) compared with those who have ctDNA levels that increase (‘non-reduction’, light blue or light red) for DFS in the atezolizumab arm (b), DFS in the observation arm (c), OS in the atezolizumab arm (d) and OS in the observation arm (e). f–i, Patients shown are from the atezolizumab arm C1/C3 BEP and are ctDNA(+) at baseline and have different types of ctDNA dynamics based on the percent change in sample MTM/ml from C1 to C3. Note that the scale for the per cent change goes from −100% (clearance) to infinity, where negative values indicate reductions, and positive values indicate increases. Kaplan–Meier analysis of DFS (f) and OS (h) in which ctDNA reduction is split into patients who clear ctDNA (‘reduction with clearance’, dark blue, solid lines) and those who have decreased ctDNA without clearance (‘reduction without clearance’, dark blue, dashed lines). Patients with an increase in ctDNA are also shown (‘increase’, light blue, solid lines). Forest plots for DFS (g) and comparing patients with reduction (anywhere from clearance (−100% change) to minor decreases in ctDNA (<0% change)) (i) using different thresholds for percent change in sample MTM/ml, including −100% (reduction with clearance versus reduction without clearance), −50%, −25% and −10% changes. Forest plots show HRs for recurrence or death estimated using a univariable Cox proportional-hazards model, and 95% confidence intervals of HRs are represented by horizontal bars.
Extended Data Fig. 6 The ABACUS ctDNA study in the neoadjuvant setting supports the association of ctDNA with clinical outcomes.
a, ABACUS consort diagram depicting how patients in the ctDNA BEP (n = 40) were identified from the overall ABACUS study population (n = 95). ABACUS is a phase II prospective neoadjuvant ctDNA data from a prospective phase II study of neoadjuvant atezolizumab before cystectomy in muscle invasive urothelial cancer. b. Kaplan–Meier estimates comparing recurrence-free survival (RFS) of ctDNA-positive patients (red) to ctDNA-negative patients (blue) as assessed at the baseline (C1D1) time point before neoadjuvant treatment. c, Kaplan–Meier estimates for ctDNA-positive (red) versus ctDNA-negative patients (blue) at the post-neoadjuvant time point.
a–e, Kaplan–Meier analyses for ctDNA(+) patients in the atezolizumab (blue) and observation (red) arms in subgroups defined by high levels (dark colours) and low levels (light colours) of immune biomarkers of response to immunotherapy including PD-L1 by IHC (a), TMB from WES (b), tGE3 gene expression signature (c), and subgroups defined by immune biomarkers of resistance to immunotherapy including pan-TBRS (F-TBRS) gene expression signature (d) and angiogenesis gene expression signature (e). f, Hallmark gene set enrichment analysis in ctDNA(+) patients in the observation arm comparing non-relapsers (blue) to relapsers (red).
Kaplan–Meier analyses for ctDNA(−) patients in the atezolizumab (blue) and observation (red) arms for DFS (left column) and OS (right column) in subgroups defined by high levels (dark colours) and low levels (light colours) of immune biomarkers of response to immunotherapy including PD-L1 by IHC, TMB from WES, tGE3 gene expression signature, and subgroups defined by immune biomarkers of resistance to immunotherapy including the pan-TBRS (F-TBRS) gene expression signature and the angiogenesis gene expression signature.
a, Bar plot showing that TCGA subgroups are similarly distributed in ctDNA(+) (left) and ctDNA(−) (right) populations. b, Bar plot showing that TCGA subgroup distribution is associated with PD-L1 status (IC01, left; IC2/3; right). c–g, Kaplan–Meier analyses showing ctDNA(+) (dark colours) and ctDNA(−) (light colours) patients on atezolizumab (blue) and observation (red) for DFS in TCGA subgroups including luminal papillary (c), luminal infiltrated (d), luminal (e), basal/squamous (f) and neuronal (g). h, Kaplan–Meier analyses for OS in the neuronal TCGA subgroup.
Note that collection time analyses are shown for patients with muscle-invasive bladder cancer only, because patients with upper-tract urothelial carcinoma often received two surgeries. a, Scatter plot showing ctDNA levels (sample mean MTM) versus C1D1 collection time (days from surgery). b, Box plot showing the C1D1 collection time (y axis) for the ctDNA-negative (x axis, left box plot, n = 339) and ctDNA-positive (x axis, right box plot, n = 199) patients. No difference was found between the collection times for the ctDNA-negative patients and the ctDNA-positive patients (Wilcoxon P = 0.18, two sided). The box plot middle line is the median, the lower and upper hinges correspond to the first and third quartiles, the upper whisker extends from the hinge to the largest value no further than 1.5 × IQR from the hinge and the lower whisker extends from the hinge to the smallest value at most 1.5 × IQR of the hinge, while data beyond the end of the whiskers are outlying points that are plotted individually. c, Bar plot showing the fraction of patients who were ctDNA positive (dark grey fill) for patients with C1D1 collection times less than the median collection time (x axis, left bar plot) and greater than the median collection time (x axis, right bar plot). d, Histogram showing the distribution of times between surgery and C1D1. e, Histogram plot showing the distribution of durations between a C1D1 ctDNA(+) test and radiological relapse.
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Powles, T., Assaf, Z.J., Davarpanah, N. et al. ctDNA guiding adjuvant immunotherapy in urothelial carcinoma. Nature 595, 432–437 (2021). https://doi.org/10.1038/s41586-021-03642-9
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