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Serial single-cell genomics reveals convergent subclonal evolution of resistance as patients with early-stage breast cancer progress on endocrine plus CDK4/6 therapy

Nature Cancer volume 2, pages 658–671 (2021)Cite this article

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Abstract

Combining cyclin-dependent kinase (CDK) inhibitors with endocrine therapy improves outcomes for patients with metastatic estrogen receptor-positive breast cancer but its value in earlier-stage patients is unclear. We examined evolutionary trajectories of early-stage breast cancer tumors, using single-cell RNA sequencing of serial biopsies from the FELINE clinical trial of endocrine therapy (letrozole) alone or combined with the CDK inhibitor ribociclib. Despite differences in subclonal diversity evolution across patients and treatments, common resistance phenotypes emerged. Resistant tumors treated with combination therapy showed accelerated loss of estrogen signaling with convergent upregulation of JNK signaling through growth factor receptors. In contrast, cancer cells maintaining estrogen signaling during mono- or combination therapy showed potentiation of CDK4/6 activation and ERK upregulation through ERBB4 signaling. These results indicate that combination therapy in early-stage estrogen receptor-positive breast cancer leads to emergence of resistance through a shift from estrogen to alternative growth signal-mediated proliferation.

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Fig. 1: Landscape of tumor and macroenvironment of patients with early-stage ER+ breast cancer in FELINE trial.
Fig. 2: Evolution of genomic mutations in response to endocrine or combination therapy.
Fig. 3: Accelerated evolution of estrogen independence during combination therapy.
Fig. 4: JNK pathway activation occurs during the emergence of combination therapy resistance and is associated with estrogen independence and increased CDK6 expression.
Fig. 5: Activation of ERBB4 and FGFR2 as resistance mechanisms to endocrine and combination therapy.
Fig. 6: Cell cycle reactivates during combination therapy follows the loss of estrogen receptor expression, activation of JNK1, repression of the cell cycle inhibitor cyclin-dependent kinase 2A and upregulation of CDK6 during the G1 checkpoint phase.

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Data availability

Raw single-cell RNA-seq data are available through Gene Expression Omnibus under accession code GSE158724. DNA-seq data are available from dbGaP at phs002287.v1.p1. Source data are provided with this manuscript as individual Excel files (one per figure) and listed in the Inventory. Code for Figs. 1–6 is available on our GitHub repository at https://github.com/U54Bioinformatics/FELINE_project. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

Custom code used in analyses are available on GitHub at https://github.com/U54Bioinformatics/FELINE_project.

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Acknowledgements

We thank the anonymous patients from the trial that made this study possible. A.H.B., J.G., J.C., J.T.C., P.C. and F.A. were supported by the National Cancer Institute of the National Institutes of Health (NIH) under award number U54CA209978. The content is solely the authors responsibility and does not necessarily represent the official views of the NIH. The High-Throughput Genomics Shared Resource was supported by the NIH award number P30CA042014. The Integrative Genomics Core was supported by NIH award number P30CA33572. J.T.C. was supported by a Cancer Prevention Research Institute of Texas Core Facility Support Award (RP170668).

Author information

Author notes

  1. Jinfeng Chen

    Present address: State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

  2. These authors contributed equally: Jason I. Griffiths, Jinfeng Chen.

  3. These authors jointly supervised this work: Andrea H. Bild, Qamar J. Khan.

Authors and Affiliations

  1. Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA

    Jason I. Griffiths, Jinfeng Chen, Patrick A. Cosgrove & Andrea H. Bild

  2. Department of Mathematics, University of Utah, Salt Lake City, UT, USA

    Jason I. Griffiths & Frederick R. Adler

  3. Division of Medical Oncology, University of Kansas Medical Center, Westwood, KS, USA

    Anne O’Dea, Priyanka Sharma & Qamar J. Khan

  4. Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA

    Cynthia Ma

  5. Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA

    Meghna Trivedi & Kevin Kalinsky

  6. Department of Medicine, University of Wisconsin School of Medicine and Public Health, Carbone Cancer Center, Madison, WI, USA

    Kari B. Wisinski & Ruth O’Regan

  7. Division of Internal Medical Oncology, University of Arkansas for Medical Sciences, Fayetteville, AR, USA

    Issam Makhoul

  8. Department of Medicine, Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA, USA

    Laura M. Spring & Aditya Bardia

  9. School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

    Frederick R. Adler

  10. Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA

    Adam L. Cohen

  11. Department of Integrative Biology and Pharmacology, School of Medicine, School of Biomedical Informatics, UT Health Sciences Center at Houston, Houston, TX, USA

    Jeffrey T. Chang

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Contributions

J.I.G. contributed to study design and coordination, evaluated patient response to therapies, analyzed tumor heterogeneity, identified response-related phenotypes using scRNA-Seq ssGSEA pathway analysis, quantified resistance phenotypes using mathematical models, linked genetic copy-number alterations to phenotypes, reconstructed cancer cell cycle transition and gene expression and wrote the manuscript. J.C. conducted the bioinformatics pipelines to process DNA and scRNA-seq data, performed normalization and cell type classification, conducted structural variation analysis of whole-genome sequencing, determined subclonal tumor structure, analyzed WES data and contributed to writing the manuscript. P.C. performed scRNA and whole-genome sequencing experiment, and managed the project’s datasets. A.O.D., P.S., C.M., M.T., K.K., K.B.W., R.O.R., I.M., L.M.S. and A.B. contributed patient samples and contributed to writing the manuscript. F.R.A. developed analyses and models and contributed to writing the manuscript. J.T.C. developed bioinformatics pipelines, performed data management and curation, conducted data analysis and wrote the manuscript. A.L.C. contributed to data analysis and study design, provided clinical insight and contributed to writing the manuscript. Q.K. conceived and coordinated the clinical trial, contributed clinical support and infrastructure and provided clinical data and patient samples as well as contributed to writing the manuscript. A.H.B. designed the research project and analyses, performed scRNA experiments and data analysis, coordinated genomic and mathematical/statistical analyses and wrote the manuscript.

Corresponding authors

Correspondence to Qamar J. Khan or Andrea H. Bild.

Ethics declarations

Competing interests

R.O.R. participates on the advisory board for Cyclacel, PUMA, Biotheranostics, Lilly, Pfizer, Genentech and Novartis; and declares research funding from Pfizer, Novartis, Seattle Genetics and PUMA. P.S. declares research funding from Novartis, Merck and Bristol Myers Squibb; and consulting for Seattle Genetics, Merck, Novartis, Astra Zeneca, Immunomedics and Exact Biosciences. L.M.S. participates on the advisory board for Novartis, Lumicell, Puma Biotechnology and Avrobio. C.M. declares research funding from Pfizer and Puma; and consulting for Eisai, Athenex, OncoSignal, Agendia, Biovica, Astra Zeneca and Seattle Genetics. K.B.W. declares research funding and clinical trial involvement with Novartis, Eli Lilly, Astra Zeneca, Sanofi and Pfizer. He participated on an advisory board for Eisai, Pfizer and Astra Zeneca. K.K. is a medical advisor to Immunomedics, Pfizer, Novartis, Eisai, Eli Lilly, Amgen, Merck, Seattle Genetics and Astra Zeneca; receives institutional support from Immunomedics, Novartis, Incyte, Genentech/Roche, Eli Lilly, Pfizer, Calithera Biosciences, Acetylon, Seattle Genetics, Amgen, Zentalis Pharmaceuticals and CytomX Therapeutics; and his spouse is employed by Grail and previously by Array Biopharma and Pfizer. A.O.D. consults for Pfizer, PUMA Biotechnology, Astra Zeneca and Daiichi Sankyo. Q.J.K. declares research funding from Novartis. All other authors have no conflicts of interest to disclose.

Additional information

Peer review information Nature Cancer thanks Paolo Tarantino 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

Extended Data Fig. 1 Classification of patient tumors as sensitive or resistant to treatment, reflecting changes in tumor size observed at pathology relative to baseline.

Reconstructed trajectories of tumor burden are consistent with results of RECIST 1.1 MRI assessment at day 90 and allow sensitive and resistant tumors to be distinguished at end of treatment (day 180). a, Changes in tumor size during therapy for tumors classified as sensitive or resistant. Tumor growth (y-axis) calculated directly from data as the proportion tumor remaining at end of trial (final observed tumor size at pathology/baseline MRI tumor measurement). Values <1 indicate tumor shrinkage, whilst values>1 indicate an increase in size (Dashed horizontal line = no change in size during trial). A detailed biological response classification was determined by classifying tumors with similar trajectories using a Gaussian mixture model (colors). Sustained or partial responses were grouped and defined as sensitive tumors, whilst those with stable, progressive or rebound disease were classified as resistant tumors. The changes in tumor size are highly significantly different between resistance categories (two-sided ANOVA test: t=4.45, p<0.001). Violins show the distinct distribution of tumor growth observed across patients. Heatmap shows the strong agreement in the end of treatment classification obtained by classifying trajectories of tumor growth vs simple pathology/baseline MRI RECIST assessment of change in size during trial. Number patients (P) with sensitive (S) versus resistant (R) tumors by arm = Letrozole alone:P = 11,(S = 6, R = 5); Intermittent high dose ribociclib: P = 12 (S = 6, R = 6) Continuous low-dose ribociclib: P = 11 (S = 4, R = 7). b, Spiderplots show the reconstructed trajectories of tumor size (relative to day 0) during the trial, as inferred using all available clinical measurements of patients’ tumor size. Predicted tumor sizes at day 90 match the RECIST assessments of tumor response (top panels) whilst trajectories of tumor burden distinguish sensitive (shrinking) and resistant (persistent) tumor through to the end of the trial (bottom panels). Number patients (P) with sensitive (S) versus resistant (R) tumors by arm = Letrozole alone:P = 11,(S = 6, R = 5); Intermittent high dose ribociclib: P = 12 (S = 6, R = 6) Continuous low-dose ribociclib: P = 11 (S = 4, R = 7). c, Inferred change in tumor size between the start- midpoint (left panel) or start-end (right panel) of the trial, in patient response groups classified by either RECIST assessment at trial midpoint (top row) or the biological response classification from tumor trajectories (bottom row). RECIST assessments distinguish response/non-response at day 90 but not day 180, whilst the biological response classification does distinguish resistance or sensitivity at day 180 (two-sided ANOVA test: MRI day 180 p-value= 0.38 and Biological response day 90 p-value= 0.34). Number patients (P) with sensitive (S) versus resistant (R) tumors by arm = Letrozole alone:P = 11,(S = 6, R = 5); Intermittent high dose ribociclib: P = 12 (S = 6, R = 6) Continuous low-dose ribociclib: P = 11 (S = 4, R = 7).

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Extended Data Fig. 2 Landscape of tumor and microenvironment of 10 patients with single nucleus isolated by ICELL8 platform.

a, t-SNE plot of 3,484 cells. Cells were classified into cancer cells, normal epithelial cells, immune cells, stromal cells, and unclassified cells, which are indicated by colors and labels. The 3,484 cells are from 7 patients (3 from the Intermittent high dose arm and 4 from the Continuous low dose ribociclib arm. b, Gene copy number profile in cancer cells and neighboring normal cells. Blue color indicates copy number loss and red color indicates copy number gain. c, Expression of marker genes of cancer cells and normal epithelial cells (KRT19, CDH1), stromal cells (FAP, HTRA1), and immune cells (PTPRC). d, Proportion of cancer cells and neighboring normal cells in each patient.

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Extended Data Fig. 3 Mutational signature in 24 patients with whole-exome sequencing data.

a, Relative contribution of trinucleotide changes to three de novo mutational signatures identified in 24 patients. b, Relative contribution of each mutational signature to mutations in each patient.

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Extended Data Fig. 4 Mutated genes in three frequently altered oncogenic pathways.

Genes are grouped by oncogenic pathway. Presence of gene mutations in each patient is colored as indicated in the legend. Treatment arm and clinical response (Response: sensitive, resistant) are indicated in final two rows of the plot (colors indicated in legend).

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Extended Data Fig. 5 Intrinsic subtype of 35 patients with single nucleus isolated by 10x genomics platform and reduced subclonal estrogen receptor (ESR1) expression at end of therapy as correlated with increased basal-like pathway and Creighton endocrine therapy resistance signatures, independent of treatment.

a, Intrinsic subtyping. Each row represents a patient and each column represents an intrinsic subtype at three timepoints. The proportion of cancer cells in each intrinsic subtype was indicated by colors ranging from 0 to 85. Patient samples without cancer cells were indicated by gray. b, Reduced subclonal estrogen receptor (ESR1) expression. Top row shows the ESR1 expression and basal-like (left) and endocrine resistance (right) pathway signatures across subclonal cancer populations with differing MAPK activation (points) and the coloration signifies the treatment received. Fitted lines show the overall trend between ESR1 expression and pathway activity (shaded regions show 95% confidence bands). Bottom row shows the correlation between ESR1 expression and basal-like (left) and endocrine resistance (right) pathway signatures for each cancer subclone present at end of trial, in patients treated with different therapies (colors). Black points and error bars signifies the mean and confidence interval for the correlation between ESR1 and pathway activity under each treatment. Number of cells (n) and patients (P) with sensitive (S) versus resistant (R) tumors by arm = Letrozole alone: n = 46986, P = 11, S = 6, R = 5; Intermittent high dose ribociclib: n = 27790, P = 12, S = 6, R = 6; Continuous low dose ribociclib: n = 34543, P = 11, S = 4, R = 7.

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Extended Data Fig. 6 Divergence of JNK and ERK signaling pathway activity during treatment with combination therapy, especially in resistant tumors and heatmaps of the correlation between MAPK gene expression in each treatment arm (columns), showing the dichotomy between JNK and ERK activating genes across treatments.

A, JNK and ERK expression (coloR = pathway) during treatment (columns) in sensitive and resistant tumors (rows). Pathway trends determined across patients using hierarchical regression (solid lines). Inter-patient variability in pathway activity shown by dashed lines indicating patient specific responses and shaded regions showing confidence intervals of model estimates (JNK ssGSEA pathway=St JNK MAPK and ERK pathway=Biocarta ERK). Number of cells (n) and patients (P) with sensitive (S) versus resistant (R) tumors by arm = Letrozole alone: n = 46986, P = 11, S = 6, R = 5; Intermittent high dose ribociclib: n = 27790, P = 12, S = 6, R = 6; Continuous low dose ribociclib: n = 34543, P = 11, S = 4, R = 7. B, Dendrograms show the collinearity of MAPK gene expression following each endocrine or combination therapies (columns).

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Extended Data Fig. 7 Construction of the overall JNK activation phenotype score, utilizing this collinearity of gene expression between ERK and JNK genes.

a, UMAP dimension reduction of MAPK genes, showing the bivariate Gaussian distribution of UMAP values, centered around the major axis of phenotypic variation (black line). The frequency of cells found in different parts of the UMAP phenotype space is shown by the color gradient. The major axis of phenotypic variation (the JNK activation phenotype) is identified as the first principle component in the UMAP phenotype space. b, Relationship between the JNK activation phenotype and expression of MAPK genes that are known a JNK activators (red) or ERK activators (blue) across subclonal cancer populations. Loess smooths are added showing the positive relationship between the JNK phenotype score and key JNK activators and the negative association between ERK activators and the JNK phenotype. Number of cells (n) and patients (P) with sensitive (S) versus resistant (R) tumors by arm = Letrozole alone: n = 46986, P = 11, S = 6, R = 5; Intermittent high dose ribociclib: n = 27790, P = 12, S = 6, R = 6; Continuous low dose ribociclib: n = 34543, P = 11, S = 4, R = 7.

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Extended Data Fig. 8 Correlation of growth factor receptors expression with estrogen pathway activity (Hallmark estrogen response early) in cancer cells from sensitive and resistant tumors under each therapy.

Strong negative correlations identify genes that are upregulated as estrogen signaling is lost. Specifically, tumors resistant to intermittent high dose and continuous low dose show compensatory activation of FGFR2 and ERBB4 respectively. Number of cells (n) and patients (P) with sensitive (S) versus resistant (R) tumors by arm = Letrozole alone: n = 46986, P = 11, S = 6, R = 5; Intermittent high dose ribociclib: n = 27790, P = 12, S = 6, R = 6; Continuous low dose ribociclib: n = 34543, P = 11, S = 4, R = 7.

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Extended Data Fig. 9 Transcriptional heterogeneity of key resistant genes.

a, sensitive and b, resistant tumors. For each patient’s tumor cells, a single-cell phylogenetic tree is shown at the center of circos plot. Cell annotation (timepoint and subclone) as well as expression of key resistance genes (ESR1, CDK6, FGFR2, ERBB4, RORA) are shown as heatmap. Phylogenetic tree of cells were constructed based on the distance between cell gene copy number profile. Subclones were inferred based on gene copy number profile. Zinbwave normalized gene expression were centered and scaled.

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Extended Data Fig. 10 Reconstruction of cell cycle, fluctuations in gene expression during the cell cycle, distinct cell cycle phases, frequencies of cells throughout the cell cycle and shifts in gene expression within the cell cycle during therapy.

a, Single cell RNA seq gene expression profiles of cell cycle genes are extracted and used to perform dimension reduction with the UMAP algorithm. Cell cycle states (colors) with differing expression were identified using a Gaussian mixture model and the transitions between these states determined by the shortest distance to travel through each state and return to the original (Traveling salesman route=black line). b, Cells states ordered along the traveling salesman route. c, Example of fluctuations in gene expression of cells around the cell cycle (distance of points from origin = RB1 expression; colorS = cell cycle state) Reconstruction of the fluctuation in average gene expression is predicted using a cyclical generalized additive model (black line with shaded confidence bands). d, Reconstructed fluctuations (colored curves) in expression of genes around the cell cycle are used to classify distinct phases of the cell cycle (annotated by arrows around). Here we show four examples of key cell cycle genes, which influence the classification of cell cycle phases (G0, G1, S/G2). e, The frequency of cells in each stage of the cell cycle (height of bars) was counted and used to examine changes in the fraction of sampled cells in each phases cell cycle phase over time and between treatment and response groups. f, During treatment, the changes in gene expression fluctuations around the cell cycle were examined. Distance of the curve from the origin indicates gene expression and colored curves shows expression at different timepoints. g, Consistent cell cycle stages present acrosspatients. For each patient (subpanel), single cell RNAseq gene expression profiles for cell cycle genes were extracted and the fitted UMAP model used to project cells onto the lower dimensional cell phenotype space (UMAP dimensions 1 and 2). Cell cycle stages (colors) with differing expression, identified using the Gaussian mixture model, were overlaid, showing that all patients have cells that are distributed across the cell cycle phenotype space. The traveling salesman route (black line) shows the transitions between these stages, as determined by the shortest distance to travel through each state and return to the original.

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Supplementary information

Supplementary Information

Supplementary Datasets 1 (Figs. 1–24) and 2 (Figs. 25–56 and trial protocol).

Reporting Summary

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Supplementary Tables 1–15

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Griffiths, J.I., Chen, J., Cosgrove, P.A. et al. Serial single-cell genomics reveals convergent subclonal evolution of resistance as patients with early-stage breast cancer progress on endocrine plus CDK4/6 therapy. Nat Cancer 2, 658–671 (2021). https://doi.org/10.1038/s43018-021-00215-7

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