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FGFR-inhibitor-mediated dismissal of SWI/SNF complexes from YAP-dependent enhancers induces adaptive therapeutic resistance

Nature Cell Biology volume 23, pages 1187–1198 (2021)Cite this article

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Abstract

How cancer cells adapt to evade the therapeutic effects of drugs targeting oncogenic drivers is poorly understood. Here we report an epigenetic mechanism leading to the adaptive resistance of triple-negative breast cancer (TNBC) to fibroblast growth factor receptor (FGFR) inhibitors. Prolonged FGFR inhibition suppresses the function of BRG1-dependent chromatin remodelling, leading to an epigenetic state that derepresses YAP-associated enhancers. These chromatin changes induce the expression of several amino acid transporters, resulting in increased intracellular levels of specific amino acids that reactivate mTORC1. Consistent with this mechanism, addition of mTORC1 or YAP inhibitors to FGFR blockade synergistically attenuated the growth of TNBC patient-derived xenograft models. Collectively, these findings reveal a feedback loop involving an epigenetic state transition and metabolic reprogramming that leads to adaptive therapeutic resistance and provides potential therapeutic strategies to overcome this mechanism of resistance.

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Fig. 1: Identification of vulnerabilities of FGFR-aberrant TNBC cells.
Fig. 2: Reactivation of mTORC1 signalling is required for adaptive resistance to FGFR inhibitors.
Fig. 3: Upregulation of amino acid transporters drives mTORC1 signalling upon FGFR inhibition.
Fig. 4: Active YAP/TEAD-dependent enhancers contribute to FGFR inhibitor resistance in TNBC.
Fig. 5: Loss of BRG1 derepresses YAP-meditated enhancer activation and promotes YAP-dependent transcription.
Fig. 6: Combinatorial strategies overcome resistance to infigratinib in FGFR-aberrant patient-derived TNBC tumours.
Fig. 7: Epigenetic state transition is associated with infigratinib treatment in vivo.

Data availability

CRISPR screen, RNA-seq, ATAC-seq and ChIP-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE168027. Mass spectrometry data have been deposited in ProteomeXchange with the dataset identifier PXD028549. Unprocessed blots and statistical source data are presented within the paper. 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

We did not use any custom code or mathematical algorithm that is deemed central to the conclusions. All software and packages used are listed in the Reporting Summary and are publicly available.

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Acknowledgements

This work was supported by a grant from the Breast Cancer Research Foundation (BCRF-21-019) to M.B. and support from the Ludwig Center at Harvard to M.B and A.T. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank members from the Brown and Liu laboratories for helpful discussions and technical help. We thank the Molecular Biology Core Facilities (MBCF) at the Dana–Farber Cancer Institute for help with next-generation sequencing. We thank S. Gygi and R. Rodrigues from Thermo Fisher Scientific Center for Multiplexed Proteomics at Harvard Medical School for multiplexed proteomic analysis.

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Author notes

  1. These authors contributed equally: Yihao Li, Xintao Qiu.

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Yihao Li, Xintao Qiu, Xiaoqing Wang, Alok K. Tewari, Tengfei Xiao, Alba Font-Tello, Klothilda Lim, Raga Vadhi, Yingtian Xie, Paloma Cejas, Henry W. Long & Myles Brown

  2. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA

    Yihao Li, Xintao Qiu, Xiaoqing Wang, Alok K. Tewari, Tengfei Xiao, Alba Font-Tello, Klothilda Lim, Raga Vadhi, Yingtian Xie, Paloma Cejas, Henry W. Long, X. Shirley Liu & Myles Brown

  3. Department of Pathology, and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Hui Liu, Renee C. Geck & Alex Toker

  4. Lurie Family Imaging Center, Center for Biomedical Imaging in Oncology, Dana-Farber Cancer Institute, Boston, Boston, MA, USA

    Kristen L. Jones, Murry Morrow & Quang-Dé Nguyen

  5. Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Pei-Lun Kao, Aliya Jaber, Smitha Yerrum & Kin-Hoe Chow

  6. Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, MA, USA

    Pei-Lun Kao, Aliya Jaber, Smitha Yerrum & Kin-Hoe Chow

  7. Department of Data Science, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    X. Shirley Liu

  8. Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA

    Alex Toker & Myles Brown

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  1. Yihao Li
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Contributions

Y.L. and M.B. designed the experiment. Y.L., X.Q., X.W., H.L., R.C.G., T.X., A.F.-T., K.L., P.-L.K., A.J., R.V., P.C., K.L.J. and M.M. performed methodology, investigations and validation. Y.L., X.Q., R.C.G. and Y.X. performed software and formal analysis. K.-H.C., P.C., Q.-D.N., H.W.L., X.S.L., A.T. and M.B. provided resources and supervision. Y.L., X.Q., A.K.T., A.T. and M.B. wrote the manuscript. All authors read and corrected the manuscript.

Corresponding author

Correspondence to Myles Brown.

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Competing interests

M.B. is a consultant to Novartis. He receives sponsored research support from Novartis and serves on the scientific advisory boards of Kronos Bio, H3 Biomedicine and GV20 Oncotherapy. A.T. is a consultant for Oncologie and Medicxi and is on the scientific advisory board for Bertis. X.S.L. is a cofounder, board member, SAB member and consultant of GV20 Oncotherapy and its subsidiaries, a stockholder of BMY, TMO, WBA, ABT, ABBV and JNJ, and received research funding from Takeda, Sanofi, Bristol Myers Squibb and Novartis. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Cell Biology thanks Paul Wade, Richard Grose and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 A genome-wide CRISPR knock out screen identifies genetic dependencies in FGFR-aberrant TNBC.

a, Left, immunoblot of FGFR1 and FGFR2 protein levels in indicated cell lines. Right, copy number variation (CNV) of indicated cell lines from Cancer Cell Line Encyclopedia (CCLE) dataset. b, Population doublings of CAL-120 cells treated with DMSO or 300 nM infigratinib for 35 days during genome-wide CRISPR screen. c, Gene ontology analysis for cluster 1 essential genes from Fig. 1c. The bar plots represent number of overlapped gene; the dots indicate FDR value. d, Principal-component analysis of essentiality scores in cluster 2 genes from Fig. 1c applied across 29 TNBC cell lines in the DepMap RNAi genetic dependency dataset. FGFR-aberrant TNBC cell lines are depicted in blue. e, GSEA normalized enrichment score (NES) of FGF signature for essential genes in the indicated cell line. f, Plot of significantly enriched hallmark gene sets based on essential genes in CAL-120 cells treated with infigratinib versus DMSO. g, Immunoblot of mTOR protein levels in MFM-223 cells after introduction of CRISPR guide RNAs targeting AAVS1 (control) or mTOR. h, The relative cell viability of CAL-120, Hs578T and MFM-223 cells harbouring CRISPR knock out of AAVS1 (control) or mTOR in presence of DMSO. Data were normalized to AAVS1 depleted condition (100%). n = 3 biological independent samples. Data are presented as mean ± s.d. i, The relative cell viability of MFM-223 cells in the presence of 50 nM, or 500 nM infigratinib for 6 days versus DMSO. MFM-223 cells were depleted of mTOR or AAVS1 (control) as indicated. Data were normalized to DMSO treated condition (100%). n = 3 biological independent samples. Data are presented as mean ± s.d. Statistics: unpaired, two-tailed t-test. **P = 0.0015, **P = 0.0013, **P = 0.0053 and **P = 0.007. j, Immunoblot of mTOR, RPTOR, RICTOR, TSC2 and GAPDH protein levels in CAL-120 cells expressing indicated shRNA. k, The relative viability of CAL-120 cells expressing shRNA targeting mTOR, RPTOR, RICTOR, TSC2 versus control in presence of DMSO or 1000 nM infigratinib for 6 days. Data were normalized to AAVS1 depleted condition (100%). n = 3 biological independent samples. Data are presented as mean ± s.d.

Source data

Extended Data Fig. 2 Role of mTORC1 signaling in adaptive resistance to FGFR inhibitors.

a, Immunoblot of FGFR2, ERK1/2, AKT, S6K, and S6 phosphorylation and total protein levels in MFM-223 cells treated with 50 nM infigratinib at the indicated time points. b, Immunoblot of FGFR1, ERK1/2, AKT, S6K, and S6 phosphorylation and total protein levels in CAL-120 cells treated with 300 nM dovitinib at the indicated time points. c, Immunoblot of FGFR1, S6K, S6, ERK1/2 and AKT phosphorylation and GAPDH protein levels in CAL-120 cells depleted with FGFR1 by shRNA at indicated time points. d, Tumor volumes of Hs578T mouse xenografts treated with vehicle or infigratinib for 35 days. n = 8 (vehicle), n = 10 (infigratinib). Data are presented as mean ± s.d. Statistics: unpaired, two-tailed t-test, ***P = 0.001. e, Relative cell viability of CAL-120 cells treated with 1000 nM infigratinib alone or in combination with 500 nM MK2206 or 200 nM trametinib for 6 days. Data were normalized to DMSO treated condition (100%). n = 3 biological independent samples. Data are presented as mean ± s.d. f, Bliss synergy model of combination infigratinib and everolimus treatment in MFM-223 cells. g, Relative cell viability of CAL-120 cells treated with infigratinib in a dose-dependent manner alone or in combination with 5 nM rapalink-1 for 6 days. n = 3 biological independent samples. Data are presented as mean ± s.d.

Source data

Extended Data Fig. 3 Increased expression of YAP target genes and amino acid transporters are driven by FGFR inhibition.

Heatmap of protein expression levels detected by multiplexed proteomics in CAL-120 cells upon DMSO, 1d, or 12d of 300 nM infigratinib treatment. Rows are clustered by k-means analysis. b, GSEA analysis reveals a YAP signature is significantly enriched in genes and proteins differentially regulated by 12d of infigratinib treatment in CAL-120 cells. c, Normalized RNA-seq reads counts (RPKM) of VPS34, MAP4K3, Rag family of GTPases and PLD1 in CAL-120 cells treated with DMSO or infigratinib for 12 days. n = 3 biological independent samples. Data are presented as mean ± s.d. d, Immunoblot of S6K phosphorylation and total protein levels in CAL-120 cells treated with DMSO or infigratinib (300 nM) at indicated time points, cultured in full medium (AA + ), amino acid-free medium (AA-), or amino acid-free medium containing glutamine, leucine, and arginine (Gln/Leu/Arg) for 6 hours.

Source data

Extended Data Fig. 4 FGFR inhibition regulates enhancer landscape in TNBC cells.

a, MA plot of differentially regulated H3K27ac ChIP-seq peaks in CAL-120 cells treated with 12d infigratinib versus DMSO. Mean of normalized counts of each peak are plotted on the x-axis and log2 fold changes of differential peaks are plotted on the y-axis. Significantly changed peaks (FDR < 0.05) are marked in red. b, Heatmaps of ATAC-seq, H3K27ac and YAP ChIP-seq signals at regions of decreased chromatin accessibility (FDR < 0.01, Log2 fold change < -1.5, n = 2798) upon 12d infigratinib treatment. c, Genomic distribution of regions of increased chromatin accessibility upon 12d infigratinib treatment. d, Venn diagram depicting overlap between H3K27ac peaks (green) and YAP (blue) DNA binding peaks after 12d infigratinib treatment. e, YAP (blue) and H3k27ac (green) ChIP-seq tracks around YAP target gene FSTL1 in CAL120 cells in the presence of DMSO or infigratinib at indicated time points.

Extended Data Fig. 5 Infigratinib treatment decreases BRG1 chromatin binding and mediates enhancer reactivation.

a, Representative confocal images of YAP (green) subcellular localization and DAPI (blue) in presence with DMSO or 300 nM infigratinib for 8 days. b, Immunoblot of YAP phosphorylation and total protein levels in CAL-120 cells treated with 300 nM infigratinib at the indicated time points. c, Plot of similarity (GIGGLE score) between published binding profiles of chromatin binding factors and regions of increased chromatin accessibility in CAL-120 cells upon 12d infigratinib treatment. SWI/SNF subunits are marked by *. d, MA plot of differential BRG1 ChIP-seq signals in CAL-120 cells treated with 48 h infigratinib versus DMSO. Mean of normalized counts of each peak are plotted on the x-axis and log2 fold changes of peaks are plotted on the y-axis. Significantly changed peaks (FDR < 0.05) are marked in red. e, MA plot of differential H3K27ac ChIP-seq signals in CAL-120 cells depleted of BRG1 versus AAVS1 (control). Significantly changed peaks (FDR < 0.05) are marked in red. f, Venn diagram illustrating the overlap in the nearby gene to lost BRG1 binding sites upon 48 h FGFR inhibition from Extended Data Fig. 5d and nearby gene to gained H3K27ac signal after BRG1 depletion from Fig. 5f. g, RNA-seq analysis of RNA expression levels of the SLC7A5, SLC1A5 and SLC3A2 in CAL-120 cells depleted with AAVS1 (control) or BRG1. n = 3 biological independent samples. Data are presented as mean ± s.d.

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Extended Data Fig. 6 Combinatorial strategies for treating FGFR-aberrant patient-derived mouse xenografts.

a, Growth curve of CAL-120 cells treated with DMSO, 1000 nM infigratinib, 10 μM V-9302 or in combinations at the indicated time points. n = 3 biological independent samples. Data are presented as mean ± s.d. Statistics: two-way analysis of variance (ANOVA). ****P < 0.0001 for infigratinib versus infigratinib +V9302. b, Immunoblot of S6K phosphorylation levels in CAL-120 cells treated with DMSO or infigratinib for 6 days, including the combination of V-9302. c, Immunoblot of S6K phosphorylation, YAP and GAPDH protein levels in CAL-120 cells depleted with CRISPR guide RNAs targeting AAVS1 (control) or YAP, and treated with DMSO or infigratinib for 6 days as indicated. d, RT-qPCR analysis of RNA expression levels of indicated amino acid transporters in CAL-120 cells depleted with AAVS1 (control) or YAP in the presence of DMSO or infigratinib for 6 days. n = 3 biological independent samples. Data are presented as mean ± s.d. e, Growth curve of CAL-120 cells depleted with YAP or AAVS1 (control) in the presence or absence of 1000 nM infigratinib at the indicated time points. n = 3 biological independent samples. n = 3 biological independent samples. Data are presented as mean ± s.d. statistics: two-way analysis of variance (ANOVA), ****P < 0.0001 for YAP depletion versus control in presence of infigratinib f, Genetic alteration and relative mRNA expression of FGFR1-3 in FGFR-aberrant PDX models from Fig. 6a. The orange line indicates the cut off between sensitive and resistant tumors. g, Kaplan–Meier curves of NIBRX5249 tumors progression (doubling) time. Tumors were treated with vehicle control, 12.5 mg/kg infigratinib, 0.5 mg/kg everolimus, 1 mg/kg CA3 or in combination as indicated. Statistics: Log-rank test. **P = 0.0029 for vehicle versus infigratinib +everolimus. h, Kaplan–Meier curves of NIBRX6047 tumors progression (doubling) time. Tumors were treated with vehicle control, 12.5 mg/kg infigratinib, 0.5 mg/kg everolimus, 1 mg/kg CA3 or in combination as indicated. Statistics: Log-rank test. **P = 0.0014 for vehicle versus infigratinib +everolimus. ***P = 0.0006 for vehicle versus infigratinib +CA3. i and j, Relative body weights of NIBRX-5249 (i) and NIBRX-6047 (j) implanted mice from Fig. 6b and d, respectively. Data are presented as mean ± s.d.

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Extended Data Fig. 7 Single cell profiling of chromatin accessibility in the NIBRX-5249 PDX model.

a, Quality control (QC) of scATAC-seq analysis in NIBRX-5249 PDX tumors treated with vehicle control or 12.5 mg/kg infigratinib from Fig. 6b. b, t-SNE plot showing cell clustering by sequencing depth. c, k-means clustering of cells from NIBRX-5249 PDX tumors treated with vehicle (left) or infigratinib (right) in t-SNE projection space. d, GSEA analysis reveals the YAP target gene signature is significantly enriched in NIBRX-5249 PDX treated with infigratinib versus vehicle. e, Heatmap represents relative mRNA expression levels of SLC7A5, SLC1A5 and SLC3A2 in NIBRX-5249 PDX samples treated with vehicle control, 12.5 mg/kg infigratinib, 1 mg/kg CA3 or in combination as indicated for 20 days.

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Li, Y., Qiu, X., Wang, X. et al. FGFR-inhibitor-mediated dismissal of SWI/SNF complexes from YAP-dependent enhancers induces adaptive therapeutic resistance. Nat Cell Biol 23, 1187–1198 (2021). https://doi.org/10.1038/s41556-021-00781-z

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