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Nature Genetics volume 54, pages 459–468 (2022)
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The persistence of cancer cells resistant to therapy remains a major clinical challenge. In triple-negative breast cancer, resistance to chemotherapy results in the highest recurrence risk among breast cancer subtypes. The drug-tolerant state seems largely defined by nongenetic features, but the underlying mechanisms are poorly understood. Here, by monitoring epigenomes, transcriptomes and lineages with single-cell resolution, we show that the repressive histone mark H3K27me3 (trimethylation of histone H3 at lysine 27) regulates cell fate at the onset of chemotherapy. We report that a persister expression program is primed with both H3K4me3 (trimethylation of histone H3 at lysine 4) and H3K27me3 in unchallenged cells, with H3K27me3 being the lock to its transcriptional activation. We further demonstrate that depleting H3K27me3 enhances the potential of cancer cells to tolerate chemotherapy. Conversely, preventing H3K27me3 demethylation simultaneously to chemotherapy inhibits the transition to a drug-tolerant state, and delays tumor recurrence in vivo. Our results highlight how chromatin landscapes shape the potential of cancer cells to respond to initial therapy.
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All sequencing files were deposited to GEO under the SuperSeries GSE164716. Source data are provided with this paper.
All statistical analyses were performed in R (v4.1) using custom R scripts. Codes for data analysis are available at the following repositories: https://github.com/vallotlab/ChemoPersistance, release v1.0.0 https://doi.org/10.5281/zenodo.6010802 and https://github.com/TeamPerie/lentiviral_barcode_detection_in10X_data/.
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Single-cell experiments were performed with the Single-Cell platform of Institut Curie. We thank A. Morillon for critical reading of the manuscript. This work was supported by the ATIP-Avenir program, by Plan Cancer, by the SiRIC-Curie program SiRIC grants no. INCa-DGOS-4654 and no. INCa-DGOS-Inserm_12554, by a starting European Research Council (ERC) grant from the H2020 program no. 948528-ChromTrace (to C.V.) and by Fondation de France no. 00107944 (to J.M.). The work was supported by an ATIP-Avenir grant from Centre national de la recherche scientifique (CNRS) and the Bettencourt Schueller Foundation, by the Labex CelTisPhyBio no. ANR-11-LABX-0038 and by a starting ERC grant from the H2020 program no. 758170-Microbar (to L.P.). High-throughput sequencing was performed by the ICGex NGS platform of Institut Curie, supported by Equipex grant no. ANR-10-EQPX-03, by the France Génomique Consortium from Agence nationale de la recherche no. ANR-10-INBS-09-08 (‘Investissements d’avenir’ program), by the ITMO-Cancer Aviesan – Plan Cancer III and by the SiRIC-Curie program SiRIC grant no. INCa-DGOS- 4654.
Kevin Grosselin
Present address: Broad Institute of MIT and Harvard, Cambridge, MA, USA
These authors contributed equally: Justine Marsolier, Pacôme Prompsy.
These authors jointly supervised this work: Leïla Perié, Céline Vallot.
CNRS UMR3244, Institut Curie, PSL University, Paris, France
Justine Marsolier, Pacôme Prompsy, Adeline Durand, Camille Landragin, Amandine Trouchet, Léa Baudre & Céline Vallot
Translational Research Department, Institut Curie, PSL University, Paris, France
Justine Marsolier, Pacôme Prompsy, Adeline Durand, Camille Landragin, Léa Baudre, Ahmed Dahmani, Laura Sourd, Elisabetta Marangoni & Céline Vallot
CNRS UMR168, Institut Curie, PSL University, Sorbonne University, Paris, France
Anne-Marie Lyne, Sabrina Tenreira Bento, Almut Eisele & Leïla Perié
Single Cell Initiative, Institut Curie, PSL University, Paris, France
Amandine Trouchet, Mylène Bohec & Sylvain Baulande
CNRS UMR8231, ESPCI Paris, PSL University, Paris, France
Sophie Foulon & Kevin Grosselin
HiFiBio SAS, Paris, France
Kevin Grosselin
Genomics of Excellence (ICGex) Platform, Institut Curie, PSL University, Paris, France
Mylène Bohec & Sylvain Baulande
Functional Genomics of Solid Tumors laboratory, Centre de Recherche des Cordeliers, Sorbonne University, Inserm, USPC, Paris Descartes University, Paris Diderot University, Paris, France
Eric Letouzé
Department of Pathology-Genetics and Immunology, Institut Curie, PSL Research University, Paris, France
Anne-Vincent Salomon
INSERM U934, Institut Curie, PSL Research University, Paris, France
Anne-Vincent Salomon
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J.M., A.D., C.L., L.B., S.T.B., A.E. and A.T. performed the experiments. A.D. and A.-M.L. contributed equally to the paper. S.F. and K.G. helped conduct scChIP-seq experiments. E.M., L.S. and A.D. performed PDX experiments. A.-V.S. selected and annotated patient samples. M.B. and S.B. performed sequencing. P.P. and C.V. performed omics data analysis. A.-M.L., C.V. and L.P. analyzed lineage barcoding data. E.L. helped analyze whole-exome sequencing data. C.V., L.P. and J.M. conceived and designed the experiments. C.V., J.M., P.P. and L.P. wrote the manuscript with input from all authors.
Correspondence to Céline Vallot.
C.V. is a founder and equity holder of One Biosciences. The remaining authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a. Graph of the relative tumor volumes over time (days) for PDX_95 for eight mice treated with a first round of Capecitabine. b. (Left) UMAP representation of scRNA-seq datasets, colored according to RNA-inferred cluster IDs. (Right) Histogram of the frequency of each expression cluster in the indicated samples. c. Barplot displaying the top 5 pathways activated in persister cells. d. UMAP representation of scRNA-seq datasets, colored according to log2 expression signals for persister genes, log2FC and q-values are indicated above the graph. e. Histogram of the proportion of cells in the different cell cycle phases based on expression of cell cycle in the scRNA-seq datasets. Proportions in each sample were compared to untreated sample using two-sided Fisher’s exact test, p-values are indicated. f. Graph of the relative tumor volumes over time (days) for PDX_39 for fourteen mice treated with capecitabine and three untreated mice. g. UMAP representation of scRNA-seq datasets, colored according to sample ID. h. UMAP representation of scRNA-seq datasets, colored according to expression clusters. i. Barplot displaying the top 5 pathways activated in persister cells. j. UMAP representation of scRNA-seq datasets, colored according to log2 expression signals for persister genes, log2FC and q-values are indicated above the graph. k. Histogram of the proportion of cells in the different cell cycle phases based on expression of cell cycle in the scRNA-seq datasets. Proportions in persister sample were compared to untreated sample using a two-sided Fisher exact test, p-value are indicated. l-q as (f-k) for PDX_172.
Source data
All the experiments were performed in MDA-MB-468 cells. a. (Left) Histogram representing the percentage of the untreated population that tolerates 5-FU. (Right) Histogram representation of the percentage of persister cells that can proliferate actively under chemotherapy treatment. b. (Left) Histogram representation of the 5-FU IC50 of untreated and chemoresistant populations. (Right) Histogram representation of the doubling time (in days) of MDA-MB-468 untreated, persister and resistant cells. (n = 3, Mean ± sd, Anova test). c. (Left) UMAP representation of scRNA-seq datasets, colored according to RNA-inferred cluster ID. (Right) Histogram representing the frequency of each cluster in the indicated samples. d. Barplot displaying the top 5 pathways activated in MM468 persister cells. e. Dot plot representing -log10(q-value) of gene enrichment studies in PDX_95 versus MDA-MB-468 (MM468). Linear regression, associated correlation score and q-value are indicated. f. UMAP plot representing scRNA-seq datasets, points are colored according to log2 gene expression signals for differentially expressed genes between persister cells from cluster R2 and untreated cells from cluster R10, log2FC and q-values are indicated above the graph. g. Histogram of the proportion of cells in the different cell cycle phases based on expression of cell cycle in the scRNA-seq datasets. For each experiment, proportions in each sample were compared to the corresponding DMSO sample using two sided Fisher’s exact test, p-value are indicated.
All the experiments were performed in MDA-MB-468 cells. a. Experimental design showing the infection of cells with a lentivirus produced from the plasmid barcode library (pRRL-CMV-GFP-BCv2AscI). Cells were then treated with indicated drugs and scRNA-seq was performed. b. Histogram of the fraction of cells with detected lineage barcodes in scRNA-seq data for each sample. Numbers above bars are the number of cells with a lineage barcode. c. Clustering of lineage barcode frequencies – detected by bulk and scRNA-seq – using Spearman’s correlation score. The size of the dots is proportional to the correlation score. d. Heatmap showing the frequency of individual lineage barcodes (rows), measured by bulk sequencing in different samples for experiment #3 (columns) and color coded as indicated. Normalized frequencies are clustered with hierarchical clustering, with Spearman’s correlation score and Ward method. e. Dotplot of Shannon diversity indexes calculated from bulk datasets at each different time points under 5-FU or DMSO treatment, diversities were compared using a two-sided Wilcoxon rank test. f. Scatter plot representation comparing normalized barcode frequency in simulated population versus initial population (D0), based on bulk data from experiment #3. Correlation scores and associated p-value are indicated. g. Scatter plot comparing normalized barcode frequency in the DMSO-treated cells at D50 and D147 (Left) or 5-FU-treated cells at D77 and D147 (Right) compared to the initial population at D0, based on bulk data from experiment #3. Spearman’s correlation scores and associated p-value are indicated. h. UMAP representation of lineage-barcoded cells. Cells in orange are untreated cells having a lineage barcode found in at least one persister cell. Cells in red are matched persister cells. Cells in grey are cells having a lineage barcode which is not common between persister and untreated cells. i. Volcano plot of differential analysis between ‘persisting’ and ‘non persisting’ untreated cells. j. Distribution of correlation scores between barcode frequencies of two replicates.
All the experiments were performed in MDA-MB-468 cells. a. (Left) Schematic view of the experimental design used to analyze the whole exome of untreated, persister and resistant cells. (Right) Graph of the coverage of bases per sample for MDA-MB-468 untreated, persister and resistant cells (n = 4). b. Venn diagram of the number of total mutations identified in chemoresistant populations (n = 4). c. Histogram representations of the proportions of mutations associated to each cosmic mutational signature in the untreated, persister and resistant populations (n = 2 experiments), proportions were compared with a Chi-squared test. d.Histogram representing the cancer cell fraction for untreated, persister and resistant cells (n = 2 experiments).
All the experiments were performed in MDA-MB-468 cells. a. Schematic view of the experimental design used to analyze chromatin landscapes of persister and resistant cells. All samples were analyzed at the single cell level except 5-FU-D77-#3 and 5-FU-D113-#4. Samples used for bulk ChIP analysis are indicated with a black asterix. b. Histogram representing the frequency of epigenomic clusters within each sample. c. Scatterplot representing for each differentially enriched H3K27me3 peak, log2 expression FC versus log2 enrichment FC for the associated gene. Pearson’s correlation scores and associated p-value are indicated. d. Cumulative scH3K27me3 profiles over TGFB1 and FOXQ1 in resistant cells. e. Venn diagram representation of the region-based differential analysis performed to extract regions depleted in H3K27me3 jointly in E2/E1 compared to E4 (scChIP-seq dataset) f. Scatter plot representing log2 expression fold-change induced by 5-FU in resistant cells versus EZH2i-1 induced changes, and compared to the untreated population (D0). Pearson’s correlation scores and associated p-value are indicated. g. Doughnut plot displaying the fraction of 5-FU persister genes potentially regulated or not by H3K27me3 and expressed upon EZH2i-1 treatment. h. Heatmap representation of the targets of the three master TF among persister genes. Blue color stands for target genes while white means the gene is not a target. i. Mean rank of TF enrichment among persister genes obtained by ChEA3 for FOXQ1, FOSL1 and NR2F2 (red line) compared to the average mean rank in a 100 sets of random genes (green curve). j. Cumulative scH3K27me3 and scH3K4me3 enrichment profiles over FOSL1 and NR2F2 in untreated and persister MDA-MB-468 cells (D33 – H3K27me3 and D60 – H3K4me3). Log2FC for H3K27me3 and scRNA between persister and untreated populations are indicated.
a. Heatmap representation of single-cell H3K4me3 enrichment at H3K27me3-regulated persister genes, non-expressed protein coding genes and housekeeping genes in untreated cells (D0) and persister cells (D60). b. Violin plots representing the distribution of percentage of cells with H3K4me3 signal across H3K27me3-regulated persister genes, non-expressed protein coding genes and housekeeping genes, compared with a one-sided Wilcoxon rank text. c. Violin plot representing the distribution of percentage of H3K27me3-regulated persister genes with H3K4me3 signal in untreated cells (D0) and persister cells (D60). One-sided Wilcoxon rank test is used for the comparison between the two conditions d. (Up) Cumulative scH3K4me3 profiles over the TGFB1 locus between untreated cells (D0) and persister cells (D60). (Down) H3K27me3->H3K4me3 and H3K27me3->IgG sequential ChIP-seq profiles of TGFB1 in the untreated population. Comparative tracks show enrichment over IgG control with associated odd ratio and q-value. e-f. Dot plot of the number of false positive peaks detected (with IgG) for each number of bivalent peaks detected at various enrichment and q-values, assessed with one-sided Fisher’s exact test, adjusted for multiple testing. Used thresholds are indicated in red. g. Venn diagram of MDA-MB-468 bivalent genes found by sequential ChIP-seq in the H3K4me3-H3k27me3 way, the H3K27me3-H3K4me3 way. The enrichment of the intersection between the two ways is tested using a Fisher’s exact test. h. Dotplot of the -log10(q-value) of bivalent pathways (as in g.) in the H3K4me3-H3K27me3 and the H3K27me3-H3K4me3 ways. i. (Left) Barplot displaying the top 5 pathways enriched in H3K27me3/H3K4me3 bivalent genes identified in untreated cells in MDA-MB-468, BT20 and HCC38. X-axis corresponds to -log10 q-values. (Right) Venn diagram displaying the intersection of the pathways enriched in H3K27me3/H3K4me3 bivalent genes identified in the untreated cells. P-value corresponds to the significativity of the overlap calculated with Exact Test of Multi-set Intersections.
a-c. Barplot displaying the top pathways enriched in H3K27me3/H3K4me3 bivalent genes identified in the human tumor sample from Patient_95 or in the corresponding PDX model PDX_95 (a), Patient_39/PDX_39 (b) or Patient_172/PDX_172 (c). d-f. Venn diagram displaying the intersection of the pathways enriched in H3K27me3/H3K4me3 bivalent genes identified in the untreated cells in the human sample from Patient_95 and its corresponding PDX model (d), Patient_39/PDX_39 (e) or Patient_172/PDX_172 (f). g. H3K27me3 and H3K4me3 chromatin profiles of KLF4 in 8 human tumor samples. The percentage of tumoral cells are indicated for each sample. h. Dotplot showing the top pathways enriched in genes displaying a dual H3K27me3 and H3K4me3 enrichment in the human tumor samples from MSigDB c2_curated KEGG and c5_GO annotations. Color of the dot corresponds to adjusted p-values, calculated with hypergeometric test adjusted for multiple testing, and the size of the dot corresponds to the gene ratio.
a. Histogram representing the number of MDA-MB-468 cells pretreated or not with EZH2i-1 (UNC1999) and after treatment over 21 days with 5-FU (n = 3, Mean ± sd, p-value correspond to Anova test). b. Representative images of immunoblotting of MDA-MB-468 cells treated for 21 days with DMSO or indicated EZH2 inhibitors. EZH2, Tubulin and H3K27me3 are represented. Results are representative of three independent experiments. c. Clustering of samples according to lineage barcode frequencies, detected by bulk analysis, using Spearman’s correlation score. MDA-MB-468 were co-treated with DMSO or 5-FU and EZH2i-1 for 21 days (Up) or pretreated with indicated EZH2i (‘EZH2i-1’: UNC1999, inactive EZH2i-1: ‘UNC2400’ or EZH2i-2: ‘GSK126’) for 10 days and then co-treated with DMSO or 5-FU for 21 days (Down). d/f. Histogram representing the number of BT20 (d) or HCC38 (f) cells pretreated with EZH2i inhibitors and after treatment over 21 days with 5-FU (n = 3, Mean ± sd, p-value correspond to Anova test). e/g. Representative images of immunoblotting of BT20 (e) or HCC38 (g) cells treated for 21 days with DMSO or indicated EZH2 inhibitors. EZH2, Tubulin and H3K27me3 are represented. Results are representative of three independent experiments. For gel source data, see Source Data Ext. Fig. 8.
Source data
a. Projection of MDA-MB-468 cells treated with KDM6i onto the UMAP scH3K27me3 space. b. Histogram representing the number of MDA-MB-468 cells after treatment over 21 days with DMSO or 5-FU alone or in combination with KDM6i (n = 3, Mean ± sd, p-value correspond to Anova test). c. Colony-forming assay of MDA-MB-468 treated over 60 days with DMSO or 5-FU alone or co-treated with GSK-J5, an inactive isomer of GSK-J4 (In KDM6i). d. Histogram representing the number of MDA-MB-468 cells after treatment over 50 days with DMSO or 5-FU alone or co-treatment with KDM6A/Bi (GSKJ-4) or its inactive isomer In KDM6i (GSK-J5) added at the indicated days (n = 3, Mean ± sd, p-value correspond to Anova test). e/f. Colony-forming assay of BT20 (e) or HCC38 (f) cells co-treated with DMSO or 5-FU and indicated concentrations of KDM6i (GSKJ-4) or its inactive isomer In KDM6i (GSK-J5). The data correspond to 1 of 3 biological replicates.
Supplementary Methods
A workbook with multiple tabs. Supplementary Tables 1_to_3_scRNA_PDX: Differential analysis of scRNA-seq expression between persister cells and untreated cells from PDX_95, PDX_39 and PDX_172: log2[fold change], adjusted P value (q-value) from two-sided Wilcoxon rank test adjusted for multiple testing and percentage of persister cells expressing the corresponding gene are indicated for all genes. Supplementary Table 4_Multiomic_gene_based_table_MM468: For each gene, the results of the following are indicated: (i) the differential analysis of scRNA-seq expression between persister and untreated cells (log2[fold change], adjusted P value from two-sided Wilcoxon rank test adjusted for multiple testing, percentage of persister cells expressing the corresponding gene), (ii) the motif analysis (TF_CheA3 mean rank and score), (iii) the differential analysis of ChIP-seq datasets (TRUE, significant depletion in H3K27me3 between persister and untreated cells), (iv) bivalent promoter analysis (odds ratios and q-value with Fisher’s exact test corrected for multiple testing for K4me3 → K27me3 and K27me3 → K4me3 IPs) and (v) the differential analysis of scRNA-seq expression between EZH2i (UNC1999) treated and untreated cells. Supplementary Table 5_Summary_models_and_technologies: Details of the models, samples and technologies used, as well as the output of each experiment. Supplementary Table 6_Primer sequences: Primers for lineage barcode detection, scChIP-seq beads sequence and chromatin indexing index sequences are indicated. Supplementary Table 7_Summary of single-cell ChIP-seq count tables: Analyzed samples and corresponding cell numbers over 1,000 reads are indicated.
Numerical source data files for Fig. 1.
Numerical source data files for Extended Data Fig. 1.
Numerical source data files for Fig. 5.
Image source data file: uncropped scans of all blots presented in Extended Data Fig. 8.
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Marsolier, J., Prompsy, P., Durand, A. et al. H3K27me3 conditions chemotolerance in triple-negative breast cancer. Nat Genet 54, 459–468 (2022). https://doi.org/10.1038/s41588-022-01047-6
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DOI: https://doi.org/10.1038/s41588-022-01047-6
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