Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Cycling cancer persister cells arise from lineages with distinct programs


Non-genetic mechanisms have recently emerged as important drivers of cancer therapy failure1, where some cancer cells can enter a reversible drug-tolerant persister state in response to treatment2. Although most cancer persisters remain arrested in the presence of the drug, a rare subset can re-enter the cell cycle under constitutive drug treatment. Little is known about the non-genetic mechanisms that enable cancer persisters to maintain proliferative capacity in the presence of drugs. To study this rare, transiently resistant, proliferative persister population, we developed Watermelon, a high-complexity expressed barcode lentiviral library for simultaneous tracing of each cell’s clonal origin and proliferative and transcriptional states. Here we show that cycling and non-cycling persisters arise from different cell lineages with distinct transcriptional and metabolic programs. Upregulation of antioxidant gene programs and a metabolic shift to fatty acid oxidation are associated with persister proliferative capacity across multiple cancer types. Impeding oxidative stress or metabolic reprogramming alters the fraction of cycling persisters. In human tumours, programs associated with cycling persisters are induced in minimal residual disease in response to multiple targeted therapies. The Watermelon system enabled the identification of rare persister lineages that are preferentially poised to proliferate under drug pressure, thus exposing new vulnerabilities that can be targeted to delay or even prevent disease recurrence.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Persister cells contain a rare proliferative subpopulation.
Fig. 2: Cycling and non-cycling persisters arise from different cell lineages that express distinct transcriptional programs.
Fig. 3: Persister cells shift their metabolism to FAO.
Fig. 4: Metabolic shift in tumours treated with oncogene-targeted therapy.

Data availability

RNA-seq data have been deposited in the NCBI Genome Expression Omnibus (GEO) under the accession code GSE150949. The Watermelon library and plasmid are available on Addgene (Addgene IDs 155257 and 155258). Source data are provided with this paper.

Code availability

Code used in this study is available from (additional code is available upon request).


  1. Salgia, R. & Kulkarni, P. The genetic/non-genetic duality of drug ‘resistance’ in cancer. Trends Cancer 4, 110–118 (2018).

    Article  CAS  Google Scholar 

  2. Vallette, F. M. et al. Dormant, quiescent, tolerant and persister cells: four synonyms for the same target in cancer. Biochem. Pharmacol. 162, 169–176 (2019).

    Article  CAS  Google Scholar 

  3. Sharma, S. V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 69–80 (2010).

    Article  CAS  Google Scholar 

  4. Hata, A. N. et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat. Med. 22, 262–269 (2016).

    Article  CAS  Google Scholar 

  5. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  ADS  CAS  Google Scholar 

  6. Taniguchi, H. et al. AXL confers intrinsic resistance to osimertinib and advances the emergence of tolerant cells. Nat. Commun. 10, 259 (2019).

    Article  ADS  Google Scholar 

  7. Dhimolea, E. et al. An embryonic diapause-like adaptation with suppressed Myc activity enables tumor treatment persistence. Cancer Cell 39, 240–256 (2021).

    Article  CAS  Google Scholar 

  8. Rehman, S. K. et al. Colorectal cancer cells enter a diapause-like DTP state to survive chemotherapy. Cell 184, 226–242 (2021).

    Article  CAS  Google Scholar 

  9. Raoof, S. et al. Targeting FGFR overcomes EMT-mediated resistance in EGFR mutant non-small cell lung cancer. Oncogene 38, 6399–6413 (2019).

    Article  CAS  Google Scholar 

  10. Zhu, X., Chen, L., Liu, L. & Niu, X. EMT-Mediated Acquired EGFR-TKI Resistance in NSCLC: Mechanisms and Strategies. Front. Oncol. 9, 1044 (2019).

    Article  Google Scholar 

  11. Garon, E. B. et al. Antiestrogen fulvestrant enhances the antiproliferative effects of epidermal growth factor receptor inhibitors in human non-small-cell lung cancer. J. Thorac. Oncol. 8, 270–278 (2013).

    Article  CAS  Google Scholar 

  12. Hangauer, M. J. et al. Drug-tolerant persister cancer cells are vulnerable to GPX4 inhibition. Nature 551, 247–250 (2017).

    Article  ADS  CAS  Google Scholar 

  13. Friedmann Angeli, J. P., Krysko, D. V. & Conrad, M. Ferroptosis at the crossroads of cancer-acquired drug resistance and immune evasion. Nat. Rev. Cancer 19, 405–414 (2019).

    Article  CAS  Google Scholar 

  14. Fox, D. B. et al. NRF2 activation promotes the recurrence of dormant tumour cells through regulation of redox and nucleotide metabolism. Nat. Metab. 2, 318–334 (2020).

    Article  CAS  Google Scholar 

  15. Foggetti, G. et al. Genetic determinants of EGFR-driven lung cancer growth and therapeutic response in vivo. Cancer Discov. 11, 1736–1753 (2021).

    Article  Google Scholar 

  16. Reczek, C. R. & Chandel, N. S. The two faces of reactive oxygen species in cancer. Ann. Rev. Cancer Biol. 1, 79–98 (2017).

    Article  Google Scholar 

  17. Zaugg, K. et al. Carnitine palmitoyltransferase 1C promotes cell survival and tumor growth under conditions of metabolic stress. Genes Dev. 25, 1041–1051 (2011).

    Article  CAS  Google Scholar 

  18. Kinker, G. S. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat. Genet. 52, 1208–1218 (2020).

    Article  CAS  Google Scholar 

  19. McFarland, J. M. et al. Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action. Nat. Commun. 11, 4296 (2020).

    Article  ADS  CAS  Google Scholar 

  20. Maynard, A. et al. Therapy-induced evolution of human lung cancer revealed by single-cell RNA sequencing. Cell 182, 1232–1251 (2020).

    Article  CAS  Google Scholar 

  21. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    Article  ADS  CAS  Google Scholar 

  22. Kwong, L. N. et al. Co-clinical assessment identifies patterns of BRAF inhibitor resistance in melanoma. J. Clin. Invest. 125, 1459–1470 (2015).

    Article  Google Scholar 

  23. Viswanathan, V. S. et al. Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway. Nature 547, 453–457 (2017).

    Article  CAS  Google Scholar 

  24. Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).

    Article  ADS  CAS  Google Scholar 

  25. Shah, K. N. et al. Aurora kinase A drives the evolution of resistance to third-generation EGFR inhibitors in lung cancer. Nat. Med. 25, 111–118 (2019).

    Article  CAS  Google Scholar 

  26. Havas, K. M. et al. Metabolic shifts in residual breast cancer drive tumor recurrence. J. Clin. Invest. 127, 2091–2105 (2017).

    Article  Google Scholar 

  27. Liau, B. B. et al. Adaptive chromatin remodeling drives glioblastoma stem cell plasticity and drug tolerance. Cell Stem Cell 20, 233–246 (2017).

    Article  CAS  Google Scholar 

  28. Guler, G. D. et al. Repression of stress-induced LINE-1 expression protects cancer cell subpopulations from lethal drug exposure. Cancer Cell 32, 221–237 (2017).

    Article  CAS  Google Scholar 

  29. Yang, C., Tian, C., Hoffman, T. E., Jacobsen, N. K. & Spencer, S. L. Melanoma subpopulations that rapidly escape MAPK pathway inhibition incur DNA damage and rely on stress signalling. Nat. Commun. 12, 1747 (2021).

    Article  ADS  CAS  Google Scholar 

  30. Reyes, J. et al. Fluctuations in p53 signaling allow escape from cell-cycle arrest. Mol. Cell 71, 581–591 (2018).

    Article  CAS  Google Scholar 

  31. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  Google Scholar 

  32. Jerby-Arnon, L. et al. A cancer cell program promotes t cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 (2018).

    Article  CAS  Google Scholar 

  33. Chong, J., Yamamoto, M. & Xia, J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites 9, E57 (2019).

    Article  Google Scholar 

  34. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article  CAS  Google Scholar 

  35. Erik Garrison, G. M. Haplotype-based variant detection from short-read sequencing. Preprint at (2012).

Download references


We thank the Broad Cytometry Facility (P. Rogers, S. Saldi, C. Otis and N. Pirete); L. Gaffney and A. Hupalowska for help with figure preparation and artwork; A. Martinez Gakidis for scientific editing; the Yale Center for Genome Analysis (F. Lopez-Giraldez and D. Zhao); and the Yale Center for Research Computing for data processing, guidance, RNA-seq and use of the research computing infrastructure, specifically the Ruddle Cluster. Y.O. is supported by the Hope Fund for Cancer Research, Grillo-Marxuach Postdoctoral Fellowship and the Rivkin Scientific Scholar Award. M. Tsabar was supported by the American Cancer Society–New England Pay-if Group Postdoctoral Fellowship, PF-18-126-01-DMC. H.F.C. was supported by a V Foundation Scholar Award (D2015-027). P.I.T. is supported by an NIH F32 Postdoctoral Fellowship from National Institute of Allergy and Infectious Disease (1F32AI138458-01). Funding for breast cancer patient study came from grants from Sanofi and GlaxoSmithKline. A.N.H. was supported by NIH/NCI K08 CA197389. S.A.H. was supported in part by NCI/NIH CA016042 as well as the Marni Levine Memorial Research Award. B.H. received support from a Lo Graduate Fellowship for Excellence in Stem Cell Research. Additional support came from R01CA121210, R01CA120247 and P50CA196530. Yale Cancer Center Shared Resources used in this article were in part supported by NIH/NCI Cancer Center Support Grant P30 CA016359. The work was supported by the Klarman Cell Observatory, the NHGRI Center for Cell Circuits, and Howard Hughes Medical Institute (A.R.) as well as by the Breast Cancer Research Foundation- BCRF-16-020 and the Sheldon and Miriam Adelson Medical Research Foundation (J.S.B.).

Author information

Authors and Affiliations



Study design, data interpretation and preparation of the manuscript: Y.O., J.S.B. and A.R. Execution of experiments: Y.O., M. Tsabar, H.F.C., M.S.C., L.A.-Z., E.Z., P.I.T., B.H., A.D., K.A.P. and B.M.C. Computational and statistical analysis: Y.O., J.-C.H., M.S.C., W.C., M. Tabaka and M. Tsabar . Resources: M. Tabaka, C.P.F., S.A.H., D.J.S., G.L., C.C., A.N.H. and K.P.

Corresponding authors

Correspondence to Joan S. Brugge or Aviv Regev.

Ethics declarations

Competing interests

A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and was a scientific advisory board member of ThermoFisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov until 31 July 2020. From 1 August 2020, A.R. has been an employee of Genentech. J.S.B. is a consultant for Agios Pharmaceuticals, eFFECTOR Therapeutics, and Frontier Medicines. Y.O., A.R. and J.S.B. are inventors on US patent application 16/563,450 filed by the Broad Institute to expressed barcode libraries as described in this manuscript. C.P.F. is now an employee of Bristol Myers Squibb. K.P. is co-inventor on a patent licensed to Molecular MD for EGFR(T790M) mutation testing (through MSKCC). K.P. has received Honoraria/Consulting fees from Takeda, NCCN, Novartis, Merck, AstraZeneca, Tocagen, Maverick Therapeutics, Dynamo Therapeutics, Halda and research support from AstraZeneca, Kolltan, Roche Boehringer Ingelheim and Symphogen. A.N.H. a consultant for Nuvalent and is supported by Novartis, Pfizer, Amgen, Blueprint Medicines, Lilly, Roche/Genetech, Nuvalent, Relay Therapeutics. S.A.H. has contracted research with Ambrx, Amgen, Astra Zeneca, Arvinas, Bayer, Daiichi-Sankyo, Genentech/Roche, Gilead, GSK, Immunomedics, Lilly, Macrogenics, Novartis, Pfizer, OBI Pharma, Pieris, PUMA, Radius, Samumed, Sanofi, Seattle Genetics, Dignitana, Zymeworks, Phoenix Molecular Designs and Lilly, and stock options in NK Max.

Additional information

Peer review information Nature thanks Navdeep Chandel, Ken Duffy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Lineage detection efficacy and fluorescent dilution capacity of the Watermelon library.

a, Viability of PC9 cells treated osimertinib. % of viable PC9 cells (y axis) after 72 h of treatment with osimertinib at different concentrations (x axis). b, Schematic of tracking. (1). Tracking of non-persister cells. Red arrows: cell that was tracked in the lineage. (2). Tracking of persister cells. Colonies detected at day 14, and the time lapse is viewed and tracked from day 14 backward to day 0 to detect the common initiator cell. After the initiator cell is detected, this initiator is tracked forward as in a (red line indicates the tracked cell). c, Watermelon library complexity. Distribution of number of unique lineage barcodes (y axis, red bars) in a Watermelon plasmid library sequenced at a depth of ~68x106 reads. Blue curve: cumulative wealth distribution of unique barcodes. d, Watermelon library sequence diversity. Sequence logo of nucleotide composition at each position (x axis) relative to the beginning of the barcode sequence of 5,472,944 unique lineage barcodes detected in the Watermelon library. e, PC9-Watermelon cell line grown in dox containing media. A merge of the green, red and bright field channels is shown. Scale bar 20μm. f, Fluorescence dilution of H2B-mCherry over time reports proliferative history. Distributions of mCherry fluorescence level (x axis) for n = 3000 cells analysed by flow cytometry at each time point (colour legend) from cells transduced with the Watermelon vector, exposed to dox for 48 h, sorted for red positive cells and seeded in separate wells at t = 0 (Methods). Data are representative of two independent experiments

Source data.

Extended Data Fig. 2 scRNA-seq along a time course of osimertinib treated PC9-Watermelon cells.

a, Sorting strategy. Distribution of mCherry fluorescence level (x axis) in Watermelon-PC9 cells gates at day 14 of osimertinib treatment, marked by representative sorting gates used to sort three persister subpopulations: cycling, moderate cyclers and non-cycling. b, Number of high-quality cells profiled in each sample. c, Changes in expression profiles following treatment. t-stochastic neighbourhood embedding (tSNE) of 56,419 PC9-Watermelon cell profiles (dots), coloured (red) by the labelled time point. d, Assignment of cells to lineages by lineage barcode. Percent of cells (y axis) at each time point/subpopulation (x axis) that have a detected lineage barcode. e, Identification of cycling cells. Percent of cells (y axis) at each time point/subpopulation (x axis) that express either the G2/M or S phase signature. f, Majority fate. Clone size on day 14 (y axis) of each persister lineage barcode inferred from scRNA-seq ordered by ascending rank order (x axis) and coloured by majority fate based on flow sample provenance of the captured cells

Source data.

Extended Data Fig. 3 Estimates of lineage diversity.

a, Difference in number of cells profiled per time point. Number of cells (y axis) with captured lineage barcode at each day (x axis). Day 14 cells are partitioned by the three mCherry populations (legend). be, Species diversity estimators can be biased by coverage. Estimated sample coverage (cumulative proportion of all lineages in the total population that were observed, top, y axis, Methods), estimated number of lineages in the population (middle, y axis, Methods), and estimated inverse Simpson Index, also known as Hill number of order 2 (bottom, y axis, Methods) at each time point (x axis), computed from all cells with barcodes (left) or subsampled without replacement to match the smallest number of cells per time point, 4,656 cells on day 7 (right). Confidence bands (shaded area) indicate the empirical pointwise 95% coverage confidence interval over 1,000 subsampling repetitions. Since standard species richness estimators are not suited for the analysis of estimated proportions from stratified sampling, we randomly subsampled 8,320, 1,949, and 1,276 day 14 cells without replacement from the cycling and moderate cyclers and non-cycling population, respectively (left panel, for unsorted population proportions see Extended Data Fig. 2a). P-values obtained by (asymptotic) two-sided Welch’s t-test with bootstrap estimated standard errors, Holm-corrected with level 5% (Methods, n = 5,087, day 0, n = 11,348, day 3, n = 4,656, day 7, n = 11,545, day 14 subsampled). c, Alternative estimates of number of lineages with rarefaction. Rarefaction curves for the expected observed number of different lineages (y axis) at varying hypothetical sample sizes (x axis) for each time point (coloured lines). Actual number of observed lineages: marker; Interpolated results: solid lines; Extrapolation beyond the observed number: dashed lines. Day 14 cells were subsampled as done for the estimation of the number of lineages in the right-hand side panels of b. Shaded areas: confidence bands at 95% confidence level. d, e, Estimated cumulative proportion (eCDF) of lineages in the total population (y axis) sorted in decreasing order of estimated lineage proportion (x axis) for each time point (coloured lines) when estimating the proportion from all sequenced cells with barcodes (d) or subsampled to 4,656 cells (e) as in b. Subsampling (b, e) and rarefaction (c) facilitate comparison between different time points since estimators of population diversity are strongly biased by sample size. Confidence bands indicate the empirical pointwise 95% coverage confidence interval over 1000 repetitions of the subsampling.

Extended Data Fig. 4 Lineage fate analysis.

a, Single cell-derived clone size by sample. In each sample, detected barcodes were sorted in descending order by the sum of their counts. Each unique lineage barcode was accounted as a separate clone. b, The number of observed multi-fate lineages is significantly smaller (P = 1x10−5) than expected by chance. Distribution of the number of multi-fate lineages (x axis) in simulated data. Red line: observed number of multi-fate lineages. c, d, Clone size reproducibility is significantly higher than expected by chance. c. Clone size on day 7 of each persister lineage barcode inferred from scRNA-seq (x, y axes) in each of two independent experiments seeded from the same barcoded founding cell population. Top: linear correlation coefficient. d, Distribution of r2 values (x axis) in simulated day 14 data. Red line: observed r2 between the two replicates at day 14.

Extended Data Fig. 5 Differences in transcriptional programs and drug response in cycling and non-cycling persisters.

a, EMT signature expression is similar in cycling and non-cycling persisters. Distribution of expression levels of EMT (y axis, log2(TPM+1)) across time points and subpopulations. Effect size (ES): difference between the mean signature score of cycling and non-cycling persisters. b, c, Higher expression of glutathione metabolism and NRF2 signatures in cycling vs. non-cycling persisters. Signature score (y axis) of glutathione metabolism (b) and NRF2 pathway (c) signatures in cells profiled at each time point (x axis) stratified by their lineage majority fate at day 14 (colour legend). Effect size (ES) indicates difference between the mean signature score of cycling and non-cycling persisters. df, Persister populations show differential sensitivity to fulvestrant. Effect of fulvestrant co-treatment (300nM fulvestrant during days 14-20 of 300nM osimertinib) on overall survival (d) non-cycling (e) and cycling cells (f). gi, Persister populations show differential sensitivity to RSL3. Effect of different doses of RSL3 co-treatment (during days 3-14 of 300nM osimertinib) on overall survival (g) non-cycling (h) and cycling cells (i). j, Co-treatment with 2 μM of RSL3 shifts surviving persister cells to cycling. Distributions of mCherry fluorescence level (x axis) for cells analysed by flow cytometry with and without RSL3 co-treatment (panel legend). Error bars are mean +/− s.d. of two (e, f) or three biologically independent experiments (d, h, i). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; two-tailed t-tests compared to osimertinib only condition (d-f, h-i)

Source data.

Extended Data Fig. 6 Correlation between clone size at day 14 and gene expression.

a, Gene expression is progressively more predictive of persister lineages over time. For each time point (x axis), maximum (blue) and minimum (orange) over the correlation coefficients (y axis) of each gene and the lineage size at day 14 (also see Fig. 2g). b, Choosing expanded and non-expanded lineages for gene expression comparisons. Cut-offs (vertical lines) for highly expanded and non-expanded lineages on day 14 based on the estimated proportion of each lineage in the population (y axis), sorted by decreasing proportion (x axis, log scale) for each time point (coloured lines). c, For each time point (x axis), maximum (blue line) and minimum (orange line) over the correlation coefficients (y axis) of each gene and the lineage size at day 14 as in a, but restricted to cells of highly expanded lineages as in b. d, Genes with top correlation to lineage expansion. Top five rows: distribution of gene expression of top correlated genes (log normalized counts, y axis) at each time point (x axis), comparing cells from non-expanded (red) and expanded (pink) lineages, as defined in b. Bottom row: numbers of cells (y axis) per time point in non-expanded, (dark grey) and expanded (light grey) lineages. Distributions are visualized as enhanced box plots indicating median (grey bar) and geometric progression of quantiles (progressively decreasing box widths for 75th, 87.5th, 93.75th, 96.875th, etc. percentiles, and analogously for 25th, 12.5th, 6.25th, 3.125th, etc. percentiles, labelling up to 1.5625% of the data as outliers). Bonferroni-Holm adjusted P values, determined by a two-sided Mann–Whitney U-test with continuity correction, or no significance (NS, P > 5%). e, Increase in correlation of top correlated genes as early as day 3. For each time point (x axis), rank of selected genes’ (coloured solid lines) correlation with the lineage size at Day 14 among all genes (y axis), normalized to lie between 0 and 1, and average relative correlation rank of genes with similar mean expression as determined by grouping genes by their mean log-normalized expression over all time points combined into 20 bins (coloured dashed lines) (Methods).

Extended Data Fig. 7 Metabolite profiles of cycling persisters, non-cycling persisters and untreated parental cells and FAO measurements.

a, b, PCA loadings (x axis) for the top 46 metabolites (y axis) associated with PC1 (a) and PC2 (b). c, UMAP representation of metabolomics data. d, Mean FAO level (y axis, relative to mean of the untreated controls) measured by 3H-palmitic acid oxidation in PC9-Watermelon cells either untreated, treated only with 100 μM etomoxir for 3 days, or treated with 300nM osimertinib for 14 days. e, Mean FAO levels (y axis, relative to cells seeded at 300,000 per well, as used for the osimertinib time course) in PC9-Watermelon cells seeded at different densities (x axis) 24 h before measurement. two tailed t-tests; **P < 0.01; NS – not significant (compared to 300,000 cells per well). f, Mean confluence (y axis) of PC9-Watermelon cells treated with 100μM Etomoxir for 14 days (Methods). g, Mean fraction of cycling persisters for control and sgCPT1A PC9 cells. Error bars are mean +/− s.d. of two (f) or three biologically independent experiments (d, e, g). **P < 0.01; ***P < 0.001; ****P < 0.0001; NS, not significant (P > 0.05); two-tailed t-tests (eg)

Source data.

Extended Data Fig. 8 Single cell analysis of multiple Watermelon persister models.

a, b, UMAP representation of cells coloured by cell line (a) and cluster identity (b). ce, Fraction of cells (y axis) in each cluster (x axis) coloured by cell line (c), treatment (d) and experimental replicate (e). f, Proportion of cells (y axis) in each cell cycle phase (coloured stacked bars) based on cell cycle scores inferred from scRNA-seq data across cell line models.

Extended Data Fig. 9 Modelling minimal residual disease using an engineered transgenic mouse model.

a, Transgenic composition of the EGFRL858R genetically engineered mouse model used in this study. b, Tumour burden in a representative osimertinib-treated mouse as measured by MRI. c, IHC staining for EGFR L858R mutant and mKate in treatment-naive mouse lung tumours. d, Experimental schema for isolation and molecular profiling of mKate+ cells. e, Flow gating scheme for mKate+ cells. R4 cells from untreated and mice bearing minimal residual disease were sorted and subjected to sequencing. mKate negative cells (R6). Images in c are representative of three independent experiments.

Extended Data Fig. 10 Changes in expression of metabolic programs in patient tumours.

a, b, Increase in FAM and ROS pathway signatures in drug-treated human lung adenocarcinoma. Distribution of expression scores of FAM (a) and ROS (b) signatures in cells from individual EGFR-driven lung adenocarcinoma tumours (with more than 10 cells) across different treatment time points (x axis). Box plots are represented by centre line, median; box limits, upper and lower quartiles; whiskers extend at most 1.5 × interquartile range past upper and lower quartiles. \(\bar{{\rm{x}}}\): Mean signature level for time point. For number of cells per patient see Supplementary Table 3. ce, Correlation between ROS (y axis) and FAM (x axis) signature scores in (c) treatment-naive (TN), residual disease (RD) and progressive disease (PD) human lung adenocarcinoma, (d) treatment-naive melanoma, and (e) treatment-naive breast cancer. Significance based on bootstrap test (c, Methods) and t distribution (d, e). 95% confidence interval (d, e, shaded area).

Supplementary information

Supplementary Information

This file contains Supplementary Methods and Supplementary References.

Reporting Summary

Supplementary Table 1

Mean signatures expression of the three different persister subpopulations.

Supplementary Table 2

Pooled experiment cell number by model information.

Supplementary Table 3

Patient information

Supplementary Table 4

A list of primers used in this study.

Supplementary Table 5

Gene signature list

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Oren, Y., Tsabar, M., Cuoco, M.S. et al. Cycling cancer persister cells arise from lineages with distinct programs. Nature 596, 576–582 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing