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Fatty acid synthesis is required for breast cancer brain metastasis

Nature Cancer volume 2, pages 414–428 (2021)Cite this article

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An Author Correction to this article was published on 08 October 2021

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Abstract

Brain metastases are refractory to therapies that control systemic disease in patients with human epidermal growth factor receptor 2-positive breast cancer and the brain microenvironment contributes to this therapy resistance. Nutrient availability can vary across tissues, therefore metabolic adaptations required for brain metastatic breast cancer growth may introduce liabilities that can be exploited for therapy. Here we assessed how metabolism differs between breast tumors in brain versus extracranial sites and found that fatty acid synthesis is elevated in breast tumors growing in the brain. We determine that this phenotype is an adaptation to decreased lipid availability in the brain relative to other tissues, resulting in site-specific dependency on fatty acid synthesis for breast tumors growing at this site. Genetic or pharmacological inhibition of fatty acid synthase reduces human epidermal growth factor receptor 2-positive breast tumor growth in the brain, demonstrating that differences in nutrient availability across metastatic sites can result in targetable metabolic dependencies.

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Fig. 1: Evidence for increased fatty acid synthesis in breast cancer brain metastases.
Fig. 2: The brain tumor microenvironment contributes to increased de novo lipid synthesis in breast tumors.
Fig. 3: FASN is expressed in breast cancer brain metastases.
Fig. 4: Low levels of lipids are available to breast cancer cells in the brain microenvironment.
Fig. 5: FASN is required for breast tumor growth in the brain.
Fig. 6: Lipid availability determines response to pharmacological FASN inhibition.

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Data availability

Previously published microarray and RNA-seq data that were re-analyzed here are available in GEO under accession codes GSE86849 and GSE14020 and RNA-seq data that were re-analyzed here are available at https://github.com/npriedig/jnci_2018.

Metabolomics data have been deposited to the EMBL-EBI MetaboLights database73 with the identifier MTBLS2434. The complete dataset can be accessed here https://www.ebi.ac.uk/metabolights/MTBLS2434.

The data generated for this study are included in the manuscript, extended data, supplementary files and the provided source data files. Source data that have not been provided are available from the corresponding authors on reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank the members of the Vander Heiden and Jain Laboratory for helpful discussions. We specifically thank P. Kumar, A. Srinivasan Kumar, J. Willem van Wijnbergen., D. Staiculescu and J. A. Engelman for their input, as well as the MGH and MIT mouse facility and veterinary staff for technical support. We thank H. Lee (MGH Biostatistics) for his helpful input on statistical analysis. We thank Boehringer Ingelheim for providing the BI99179 compound via their opnMe program (https://opnme.com/molecules/fas-bi99179). This work was supported by a Koch Institute/DFHCC Bridge project grant to M.G.V.H. and R.K.J. G.F. received a fellowship from Susan G. Komen for the Cure. A.A. received support as an HHMI Medical Research Fellow. A.L., K.L.A. and S.M.D. were supported by the National Science Foundation and T32GM007287. A.L. was also supported by the Ludwig Center for Molecular Oncology Fund. R.F. was supported by the Novo Nordisk Foundation (NNF10CC1016517) and the Knut and Alice Wallenberg Foundation. L.C.C. acknowledges support from the National Institutes of Health (NIH) (R35CA197588). V.A.D. acknowledges support from shared instrumentation grants (S10OD018072 and S10OD023524). R.K.J. acknowledges support from the NIH (R35CA197742, R01CA208205, U01CA224173), National Foundation for Cancer Research; the Ludwig Center at Harvard; the Jane’s Trust Foundation; the Advanced Medical Research Foundation and by the US Department of Defense Breast Cancer Research Program Innovator Award W81XWH-10-1-0016. M.G.V.H. acknowledges support from a Faculty Scholar grant from the Howard Hughes Medical Institute, Stand Up to Cancer, the MIT Center for Precision Cancer Medicine, the Ludwig Center at MIT, the Emerald Foundation and the NIH (R35CA242379, R01CA201276, R01CA168653, P30CA14051).

Author information

Author notes

  1. David P. Kodack

    Present address: Novartis Institutes for BioMedical Research, Cambridge, MA, USA

  2. Kamila Naxerova

    Present address: Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

  3. Costas A. Lyssiotis

    Present address: University of Michigan, Ann Arbor, MI, USA

  4. Lewis C. Cantley

    Present address: Weill Cornell Medicine and New York Presbyterian Hospital, New York, NY, USA

  5. Shawn M. Davidson

    Present address: Lewis Sigler Institute, Princeton University, Princeton, NJ, USA

  6. Véronique A. Dartois

    Present address: Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA

  7. These authors contributed equally: Gino B. Ferraro, Ahmed Ali, Alba Luengo.

Authors and Affiliations

  1. Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Gino B. Ferraro, David P. Kodack, Divya Bezwada, Jessica M. Posada, Jiang Chen, Zohreh Amoozgar, Ivy X. Chen, Kamila Naxerova, Mark Duquette, Sylvie Roberge, Dan G. Duda, Dai Fukumura & Rakesh K. Jain

  2. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ahmed Ali, Alba Luengo, Keene L. Abbott, Christopher R. Chin, Raphael Ferreira, Christopher Ng, Anna M. Westermark, David E. Housman, Shawn M. Davidson & Matthew G. Vander Heiden

  3. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA

    Ahmed Ali, Amy Deik, Xin Jin, Todd R. Golub, Shawn M. Davidson, Clary B. Clish & Matthew G. Vander Heiden

  4. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alba Luengo, Keene L. Abbott, David E. Housman, Shawn M. Davidson & Matthew G. Vander Heiden

  5. The Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA

    Landry Blanc, Brendan Prideaux & Véronique A. Dartois

  6. Institut de Chimie & Biologie des Membranes & des Nano-objets, CNRS UMR 5248, Bordeaux, France

    Landry Blanc

  7. Department of Neuroscience, Cell Biology, and Anatomy, University of Texas Medical Branch, Galveston, TX, USA

    Brendan Prideaux

  8. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Jessica M. Posada & Neal I. Lindeman

  9. Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden

    Raphael Ferreira & Jens Nielsen

  10. Division of Signal Transduction, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Costas A. Lyssiotis, Lewis C. Cantley & John M. Asara

  11. Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Elena Brachtel

  12. Dana-Farber Cancer Institute, Boston, MA, USA

    Matthew G. Vander Heiden

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Contributions

G.B.F., A.A. and A.L. performed experiments and helped analyze all data with supervision from R.K.J. and M.G.V.H. D.P.K., D.B. and D.F. contributed to identifying metabolic signatures of brain metastasis; K.L.A. and A.W. contributed to in vitro work and GCMS analysis. A.D. and C.B.C. performed LCMS lipidomics and helped with analysis; L.B., B.P. and V.A.D. performed IMS imaging and analysis; X.J. and T.R.G. contributed to extracellular fluid isolation and provided input on data interpretation. J.M.P., N.I.L. and E.B. collected clinical samples and contributed to analysis of patient tumor sections; J.C. and D.G.D. performed ultrasound imaging of liver tumors; C.R.C. and S.M.D. contributed to in vivo glucose tracing studies. Z.A. performed flow cytometry analysis. R.F. and J.N. helped with analysis of lipidomics data. I.X.C., C.N. and D.E.H. analyzed human expression databases. K.N. performed analysis of Affymetrix array. M.D. and S.R. contributed to CRISPR-Cas9 methodology and animal implantations. C.A.L., L.C.C. and J.M.A. performed targeted metabolomics; G.F., A.A., A.L., R.K.J. and M.G.V.H. designed the study and wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Rakesh K. Jain or Matthew G. Vander Heiden.

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Competing interests

A.L. is a current employee of a Flagship Pioneering biotechnology start-up company. I.X.C. is a current employee of Stimit Corporation. D.G.D. received consultant fees from Bayer, Simcere and BMS and research grants from Bayer, Exelixis and BMS. L.C.C. is a founder and member of the board of directors of Agios Pharmaceuticals and is a founder and receives research support from Petra Pharmaceuticals. R.K.J received honorarium from Amgen; consultant fees from Chugai, Merck, Ophthotech, Pfizer, SPARC, SynDevRx; owns equity in Accurius, Enlight, Ophthotech, SynDevRx; and serves on the Boards of Trustees of Tekla Healthcare Investors, Tekla Life Sciences Investors, Tekla Healthcare Opportunities Fund, Tekla World Healthcare Fund. Neither any reagent nor any funding from these organizations was used in this study. M.G.V.H. is a scientific advisory board member for Agios Pharmaceuticals, Aeglea Biotherapeutics, Auron Therapeutics, Faeth Therapeutics and iTeos Therapeutics.

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Peer review information Nature Cancer thanks Almut Schulze and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Histological characterization of MFP and brain BT474 tumors.

a, HER2 immunohistochemistry (IHC) of a BT474 mammary fat pad (MFP) tumor section. b, Immunofluorescence (IF) of ionized calcium binding adaptor molecule 1 (Iba1) and α-smooth muscle actin (SMA) of a consecutive tissue section from the tumor presented in a. c, HER2 IHC of a BT474 brain tumor section. d, IF of Glial fibrillary acidic protein (GFAP) and Iba1 of a consecutive tissue section from the tumor presented in c. For all panels, scale bar = 1 mm.

Extended Data Fig. 2 Assessment of glucose carbon fate in breast tumors growing in different tissue sites.

a, Relative gamma-aminobutyric acid (GABA) levels were measured by liquid chromatography–mass spectrometry (LCMS) in BT474 tumors isolated from the brain or mammary fat pad (MFP) of Nude mice. Data are from the dataset presented in Fig. 1a and Supplementary Data 1. **P = 0.0038 by two-tailed t-test (Brain tumors, n = 7; MFP tumors n = 6; tumors from independent mice). b, Western blot analysis of acetyl-CoA carboxylase (ACC1), fatty acid synthase (FASN), and stearoyl-CoA desaturase-1 (SCD1) expression in MDAMB361 brain and MFP tumor tissue. β-actin expression was assessed as a loading control, and relative densitometry values (normalized to β-actin expression) were used for quantitation with values presented beneath each blot. Differences in protein expression between brain and MFP tumors was compared using a two-tailed t-test. c, The percent of fully labeled glucose (m + 6) in blood plasma following a 12 h 30 mg/kg/min 13C-glucose infusion into Nude female mice bearing BT474 lesions in the brain or in the MFP as assessed by gas chromatography–mass spectrometry (GCMS). **P = 0.0094 by two-tailed t-test. (Plasma from mice bearing BT474 brain tumors, n = 5; Plasma from mice bearing BT474 MFP tumors, n = 6). d-m, The distribution of 13C-labeling in the indicated metabolites as measured by GCMS from BT474 tumors in the brain and MFP, and from noncancerous brain and MFP adipose tissue (WAT) after a 12 h 30 mg/kg/min 13C-glucose infusion into Nude female mice. The data for each isotopologue presented was normalized to 13C-glucose labeling in plasma. (Brain tumors, n = 5; MFP tumors, n = 6; Cortex tissue, n = 12; WAT, n = 5). n,o, BT474 brain and MFP tumors were collected following a 12 h 30 mg/kg/min 13C-glucose infusion and saponified palmitate levels (n) and the distribution of 13C-label in even isotopologues of saponified palmitate (o) were assessed by GCMS. Palmitate levels were normalized to tissue weight and compared using a two-tailed t-test (n.s. denotes not significant). Each isotopologue was normalized to 13C-glucose labeling in plasma and to palmitate total ion counts. These data are from a separate experiment as that shown in Fig. 1e, and were collected to enable normalization based on palmitate total ion counts. (Brain Tumors, n = 3; MFP Tumors, n = 5). Data in panels a, and c–o represent means ± s.e.m.

Source data

Extended Data Fig. 3 Higher lipid synthesis in breast cancer lesions when compared to surrounding brain tissue.

a, Negative mode matrix assisted laser desorption/ionization–mass spectrometry imaging (MALDI-MSI) of brain tissue from a separate NSG mouse bearing a BT474 brain tumor than that shown in Fig. 1f,g that had been given 4 daily bolus intraperitoneal injections of 2 g/kg 13C-glucose. The spatial distribution of the indicated isotopologues of palmitate, stearate, oleate, and lysophosphatidylinositol (Lyso PI, 18:0) normalized to total ion counts are shown. b,c, HER2 immunohistochemistry (IHC) staining of brain tissue sections collected from the tumors analyzed by MALDI-MSI in Fig. 1g (b) and in Extended Data Fig. 3a (c). d, Negative mode MALDI-MSI of the m + 2 palmitate isotopologue presented in a normalized to m + 0 palmitate. For all panels, scale bar = 1 mm.

Extended Data Fig. 4 FASN expression is high in breast cancer brain metastases.

a-c, ACC1, ACLY and SCD1 mRNA expression levels from a patient-matched metastatic breast cancer RNAseq dataset30. Matched primary versus brain metastasis (Brain Met.) samples were analyzed separately for the major clinical molecular subtypes. *P = 0.0254 (a), ***P = 0.0003 (b), ****P = 0.0005 (c), by two-tailed paired sample t-test. (Triple negative (TN) tumors, n = 8; Human epidermal growth factor receptor 2 + (HER2 + ) tumors, n = 8; Hormone receptor + (HR + ) tumors, n = 5). d-e, Immunohistochemistry (IHC) and immunofluorescence (IF) assessment of brain metastasis tissue sections derived from 2 different patients with HER2 + breast cancer. IHC was performed to assess HER2 and FASN expression, and IF together with DAPI staining was used to assess glial fibrillary acidic protein (GFAP) and ionized calcium binding adaptor molecule 1 (Iba1) expression on consecutive tissue sections. (scale bar, 1 mm). f, Analysis of SCD1 mRNA expression from a metastatic breast cancer gene expression database comprised of unmatched human tumor samples31,32. Data presented represent mean expression ± s.e.m. *P = 0.0212 (Brain vs Bone), *P = 0.0172 (Brain vs Liver), ***P = 0.0006 by one-way ANOVA followed by Dunnett´s multiple comparisons test (Brain tumors, n = 22; Lung tumors, n = 20; Bone tumors, n = 18; Liver tumors, n = 5).

Extended Data Fig. 5 Impact of extracellular lipids on lipid synthesis enzyme expression and lipid abundance.

a,b, Western blot analysis of FASN, ACC1 and SCD1 in BT474 (a) and MDAMB361 cells (b) cultured in standard (+Lipids) or delipidated (-Lipids) media for 6 d. Lysates were generated from 3 independent samples. β-actin expression was assessed as a loading control. Relative densitometry values (normalized to β-actin expression) were used for quantitation and are presented below each blot. Differences in expression between conditions were compared by a two-tailed t-test. c,d, Relative levels of saponified palmitate measured by GCMS in BT474 (c) and MDAMB361 (d) cells that were cultured in standard (+Lipids) or delipidated (-Lipids) media for 72 h. The samples analyzed are the same as those presented in Fig. 5c. Palmitate levels are normalized to the standard media condition *P = 0.0307 by two-tailed t-test. The data shown represent means ± s.e.m. (n = 3 cell culture biological replicates). e,f, Ratio of complex lipid levels measured by LCMS of BT474 (e) and MDAMB361 (f) cells cultured in standard (+Lipids) or delipidated (-Lipids) media for 6 d. Lipid levels were normalized to protein content as quantified by sulforhodamine b and are presented as a ratio (-/+ Lipids) to show how levels differ based on media lipid availability. A black dotted line indicates a ratio of 1, representing no difference in lipid levels between -Lipids and + Lipids culture conditions. *q < 0.1 by Multiple t-test, False Discovery Rate corrected (n = 3 cell culture biological replicates).

Source data

Extended Data Fig. 6 FASN expression is important for breast tumor growth in the brain.

a, Representative ultrasound images used to assess the size of BT474 and MDAMB361 brain tumors (delineated in yellow) in cranial window bearing NSG mice. Summary data is presented in Fig. 5f. (scale bar, 2 mm). b, Western blot analysis of FASN and β-actin expression in control BT474 cells and in BT474 cell clone in which FASN expression is disrupted by CRISPR/Cas9 with sgFASN_2, a different sgRNA than was used to generate the BT474 sgFASN_1 clone presented in Fig. 5a-g. c, d, Growth over time of MFP (c) and brain (d) tumors generated in NSG mice from control or sgFASN_2 BT474 cells. Tumor volumes were measured by caliper or by ultrasound in cranial-window bearing mice, respectively. P values shown were determined using two-way ANOVA (Days × Group). (BT474 control MFP tumors, n = 6; BT474 sgFASN_2 MFP tumors, n = 4; BT474 control brain tumors, n = 7; BT474 sgFASN_2, brain tumors, n = 7). e, Western blot analysis of FASN and β-actin expression in control MDAMB361 lysates or in a MDAMB361 clone in which FASN expression is disrupted CRISPR/Cas9 with sgFASN_2, a different sgRNA than was used to generate the MDAMB361 sgFASN clone presented in Fig. 5a-g. f, Kaplan-Meier survival curve for NSG mice bearing brain tumors produced from control or sgFASN_2 MDAMB361 cells. Median survival was 123 and 190 days for mice bearing MDAMB361 control and sgFASN_2 tumors, respectively. Hazard ratio = 7.842; 95% confidence interval = 1.314 to 46.81. (MDAMB361 control tumors, n = 6; MDAMB361 sgFASN_2 tumors, n = 6). Data in panels c and d represent means ± s.e.m.

Source data

Extended Data Fig. 7 FASN expression is important for JIMT1 growth in the brain.

a, Western blot analysis of FASN and β-actin expression in JIMT1 cells in which CRIPSR interference (CRISPRi) was used to disrupt FASN. Cells were transduced with an sgRNA targeting FASN (sgFASN) or a non-targeting control (sgNTC). b, Firefly luciferase-expressing sgNTC or sgFASN JIMT1 cells described in a were implanted into the brains of NSG mice, and tumor burden assessment by whole-body luminescence imaging is show for multiple animals on the indicated days after cell implantation in the brain (n = 6 mice).

Source data

Extended Data Fig. 8 FASN knockdown alters cell lipid composition.

a, The indicated acylglycerol levels measured by LCMS from control and FASN-disrupted (sgFASN_1) BT474 tumors growing in the brain of NSG mice are presented as a ratio to show how levels differ based on FASN expression. A black dotted line indicates a ratio of 1, representing no difference in lipid levels between sgFASN_1 and control BT474 tumor tissue. *q < 0.1 by Multiple t-test, False Discovery Rate corrected (n = 4 brain tumors). b, Complex lipid levels measured by LCMS from control and FASN-disrupted (sgFASN_1) BT474 cells in culture (+Lipids) are presented as a ratio to show how levels differ based on FASN expression. A black dotted line indicates a ratio of 1, representing no difference in lipid levels between sgFASN_1 and control cells. Lipid levels were normalized to protein content as determined by sulforhodamine b. All comparisons are significant, q < 0.1 by Multiple t-test, False Discovery Rate corrected. (n = 3 cell culture biological replicates).

Extended Data Fig. 9 FASN is not required for breast tumor growth in the liver.

a, Heatmap showing complex lipid levels measured by LCMS in extracellular fluid (ECF) isolated from the MFP, brain, and liver of non-tumor bearing NSG mice. Data presented within each row were z-score normalized. The data for Brain and MFP ECF are the same as that shown in Fig. 4d. (Brain ECF, n = 2; MFP ECF, n = 2 ECF samples pooled from up to ~8-10 mice). b, Heatmap showing complex lipid levels measured by LCMS of MFP, brain and liver tissue from non-tumor bearing NSG mice. Data presented within each row were z-score normalized. The data for brain and MFP are the same as that shown in Fig. 4e. (Brain tissue, n = 2; MFP tissue, n = 2; Liver Tissue, n = 2). c, Western blot analysis of acetyl-CoA carboxylase (ACC1), ATP citrate lyase (ACLY), fatty acid synthase (FASN), and stearoyl-CoA desaturase-1 (SCD1) in BT474 brain and liver tumor tissue. β-actin expression was assessed as a loading control, and relative densitometry values (normalized to β-actin expression) were used for quantitation and are presented below each blot. Differences in protein expression between brain and liver BT474 lesions was compared by two-tailed t-test. d, The percent of fully labeled (m + 6) glucose in blood plasma following a 12 h 30 mg/kg/min 13C-glucose infusion into female Nude mice bearing BT474 lesions in the liver as assessed GCMS (n = 5 plasma from mice bearing BT474 liver tumors). e, The distribution of pyruvate labeling in BT474 tumors growing in the liver and in non-cancerous liver tissue was measured by GCMS following a 12 h 13C-glucose infusion into female Nude mice. Each isotopologue was normalized to 13C-glucose labeling in plasma (Liver tumor, n = 6; Liver tissue, n = 5). f, The distribution of 13C-label in even isotopologues of palmitate derived from saponified lipids in BT474 liver tumors and in non-cancerous liver tissue following a 12 h 13C-glucose infusion into female Nude mice was measured by GCMS and normalized 13C-glucose labeling in plasma (Liver tumor, n = 6; Liver tissue, n = 5). g, Tumor growth over time of liver tumors in NSG mice generated from control and FASN-disrupted (sgFASN_1) BT474 cells (presented in Fig. 5a-g). Tumor volumes were measured by ultrasound. (BT474 control tumors, n = 4; BT474 sgFASN_1 tumors, n = 4). MAG-monoglyceride; DAG-diglyceride; TAG-triglyceride; Cer-ceramide; LPC- lysophosphatidyl-choline; LPE-lysophosphotidyethanolamine; PC-phosphatidylcholine; PE-phosphatidylethanolamine; PI-phosphatidylinositol; PS-phosphatidylserine; SM-sphingomyelin; CE-cholesteryl ester; @-sphingosine, palmitoylethanolamide, 7−Dehydrodesmosterol, cholesterol, coenzyme Q9, coenzyme Q10. Data presented in panels d, e, f and g are means ± s.e.m.

Source data

Extended Data Fig. 10 Lipid availability determines response to pharmacological FASN inhibition.

a-c, BT474 brain tumor-derived organotypic slice cultures were treated for 6 d with vehicle or 500 nM TVB3166 in standard (+Lipids) or delipidated (-Lipids) media. The percent of apoptotic and viable HER2 positive BT474 cells was determined by measuring Annexin V (AV) and 7-amino actinomycin D (7AAD) uptake by flow cytometry. The flow cytometry gating strategy is presented in Supplementary Fig. 2. *P = 0.0111, ***P = 0.0003 by one-way ANOVA followed by Tukey´s test (n = 5 BT474 brain tumor slices). d,e, Mouse weights for the cohort presented in Fig. 6f,g that was treated daily with BI99179 (15 mg/kg) or vehicle control by oral gavage (n = 5 mice). Data presented in all panels are means ± s.e.m.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Tables 1–3

Supplementary Table 1: Gene array analysis. Supplementary Table 2: gRNA and oligonucleotide sequences. Supplementary Table 3: Commercial reagent information.

Supplementary Data 1

Metabolomics data. Used in Fig. 1.

Supplementary Data 2

Lipidomics data. Used in Fig. 4 and Extended Data Figs. 5, 8 and 9.

Supplementary Data 3

Raw data used for statistical analyses. Used in Figs. 3, 5 and 6 and Extended Data Figs. 2, 4–6 and 10

Source data

Source Data Fig. 1

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Source Data Extended Data Fig. 9

Unprocessed western blots.

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Ferraro, G.B., Ali, A., Luengo, A. et al. Fatty acid synthesis is required for breast cancer brain metastasis. Nat Cancer 2, 414–428 (2021). https://doi.org/10.1038/s43018-021-00183-y

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