Skip to main content

Thank you for visiting nature.com. 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.

Liquid Biopsy

Correlation between targeted RNAseq signature of breast cancer CTCs and onset of bone-only metastases

Abstract

Background

Bone is the most frequent site of metastases from breast cancer (BC), but no biomarkers are yet available to predict skeletal dissemination.

Methods

We attempted to identify a gene signature correlated with bone metastasis (BM) onset in circulating tumour cells (CTCs), isolated by a DEPArray-based protocol from 40 metastatic BC patients and grouped according to metastasis sites, namely “BM” (bone-only), “ES” (extra-skeletal) or BM + ES (bone + extra-skeletal).

Results

A 134-gene panel was first validated through targeted RNA sequencing (RNAseq) on sub-clones of the MDA-MB-231 BC cell line with variable organotropism, which successfully shaped their clustering. The panel was then applied to CTC groups and, in particular, the “BM” vs “ES” CTC comparison revealed 31 differentially expressed genes, including MAF, CAPG, GIPC1 and IL1B, playing key prognostic roles in BC.

Conclusion

Such evidence confirms that CTCs are suitable biological sources for organotropism investigation through targeted RNAseq and might deserve future applications in wide-scale prospective studies.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Targeted RNAseq of CTCs reveals metastasis site-related GEP.
Fig. 2: Functional enrichment analysis of deregulated genes resulting from the comparison of “BM” vs “ES” CTCs.
Fig. 3: Survival probability analysis based on METABRIC BC dataset.

Data availability

The datasets generated during this study are available at Gene Expression Omnibus (GEO) data repository, with GEO accession number GSE160314.

References

  1. 1.

    Coleman RE. Clinical features of metastatic bone disease and risk of skeletal morbidity. Clin Cancer Res. 2006;12:6243s–6249s.

    PubMed  Article  Google Scholar 

  2. 2.

    Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501:338–45.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Leone BA, Vallejo CT, Romero AO, Machiavelli MR, Pérez JE, Leone J, et al. Prognostic impact of metastatic pattern in stage IV breast cancer at initial diagnosis. Breast Cancer Res Treat. 2017;161:537–48.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Wang R, Zhu Y, Liu X, Liao X, He J, Niu L. The clinicopathological features and survival outcomes of patients with different metastatic sites in stage IV breast cancer. BMC Cancer. 2019;19:1091.

    PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    D’Oronzo S, Gregory W, Nicholson S, Khong Chong Y, Brown J, Coleman R. Natural history of stage II/III breast cancer, bone metastasis and the impact of adjuvant zoledronate on distribution of recurrence. J Bone Oncol. 2021;28:100367.

    PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Waks AG, Winer EP. Breast cancer treatment: a review. J Am Med Assoc. 2019;321:288–300.

    CAS  Article  Google Scholar 

  7. 7.

    Kang Y, Siegel PM, Shu W, Drobnjak M, Kakonen SM, Cordón-Cardo C, et al. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell. 2003;3:537–49.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Markopoulos C, Hyams DM, Gomez HL, Harries M, Nakamura S, Traina T, et al. Multigene assays in early breast cancer: insights from recent phase 3 studies. Eur J Surg Oncol. 2020;46:656–66.

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Thery L, Meddis A, Cabel L, Proudhon C, Latouche A, Pierga JY, et al. Circulating tumor cells in early breast cancer. JNCI Cancer Spectr. 2019;3:pkz026.

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Cristofanilli M, Pierga JY, Reuben J, Rademaker A, Davis AA, Peeters DJ, et al. The clinical use of circulating tumor cells (CTCs) enumeration for staging of metastatic breast cancer (MBC): international expert consensus paper. Crit Rev Oncol Hematol. 2019;134:39–45.

    PubMed  Article  Google Scholar 

  11. 11.

    Alimirzaie S, Bagherzadeh M, Akbari MR. Liquid biopsy in breast cancer: a comprehensive review. Clin Genet. 2019;95:643–60.

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Palmirotta R, Lovero D, Cafforio P, Felici C, Mannavola F, Pellè E, et al. Liquid biopsy of cancer: a multimodal diagnostic tool in clinical oncology. Ther Adv Med Oncol. 2018;10:1758835918794630.

    PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Schochter F, Friedl TWP, deGregorio A, Krause S, Huober J, Rack B, et al. Are circulating tumor cells (CTCs) ready for clinical use in breast cancer? An overview of completed and ongoing trials using CTCs for clinical treatment decisions. Cells. 2019;8:1412.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  14. 14.

    Klotz R, Thomas A, Teng T, Han SM, Iriondo O, Li L, et al. Circulating tumor cells exhibit metastatic tropism and reveal brain metastasis drivers. Cancer Discov. 2020;10:86–103.

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Braun M, Markiewicz A, Kordek R, Sadej R, Romanska H. Profiling of invasive breast carcinoma circulating tumour cells-are we ready for the ‘liquid’ revolution? Cancers. 2019;11:143.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  16. 16.

    Lang JE, Ring A, Porras T, Kaur P, Forte VA, Mineyev N, et al. RNA-seq of circulating tumor cells in stage II-III breast cancer. Ann Surg Oncol. 2018;25:2261–70.

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Wang J, Xu R, Yuan H, Zhang Y, Cheng S. Single-cell RNA sequencing reveals novel gene expression signatures of trastuzumab treatment in HER2+ breast cancer: a pilot study. Medicine. 2019;98:e15872.

    PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Chen W, Hoffmann AD, Liu H, Liu X. Organotropism: new insights into molecular mechanisms of breast cancer metastasis. npj Precis Oncol. 2018;2:4.

    PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    D’Oronzo S, Brown J, Coleman R. The role of biomarkers in the management of bone-homing malignancies. J Bone Oncol. 2017;9:1–9.

    PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Wang H, Molina J, Jiang J, Ferber M, Pruthi S, Jatkoe T, et al. Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer. Mol Clin Oncol. 2013;1:1031–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Boral D, Vishnoi M, Liu HN, Yin W, Sprouse ML, Scamardo A, et al. Molecular characterization of breast cancer CTCs associated with brain metastasis. Nat Commun. 2017;8:196.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Aceto N, Bardia A, Wittner BS, Donaldson MC, O’Keefe R, Engstrom A, et al. AR expression in breast cancer CTCs associates with bone metastases. Mol Cancer Res. 2018;16:720–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Savci-Heijink CD, Halfwerk H, Koster J, van de Vijver MJ. A novel gene expression signature for bone metastasis in breast carcinomas. Breast Cancer Res Treat. 2016;156:249–59.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Cosphiadi I, Atmakusumah TD, Siregar NC, Muthalib A, Harahap A, Mansyur M. Bone metastasis in advanced breast cancer: analysis of gene expression microarray. Clin Breast Cancer. 2018;18:e1117–22.

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Nutter F, Holen I, Brown HK, Cross SS, Evans CA, Walker M, et al. Different molecular profiles are associated with breast cancer cell homing compared with colonisation of bone: evidence using a novel bone-seeking cell line. Endocr Relat Cancer. 2014;21:327–41.

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Coleman R, Hadji P, Body JJ, Santini D, Chow E, Terpos E, et al. Bone health in cancer: ESMO clinical practice guidelines. Ann Oncol. 2020;31:1650–63.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    D’Oronzo S, Lovero D, Palmirotta R, Stucci LS, Tucci M, Felici C, et al. Dissection of major cancer gene variants in subsets of circulating tumor cells in advanced breast cancer. Sci Rep. 2019;9:17276.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Yoneda T, Williams PJ, Hiraga T, Niewolna M, Nishimura R. A bone-seeking clone exhibits different biological properties from the MDA-MB-231 parental human breast cancer cells and a brain-seeking clone in vivo and in vitro. J Bone Miner Res. 2001;16:1486–95.

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Gene Ontology C. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015;43:D1049–56.

    Article  CAS  Google Scholar 

  31. 31.

    Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–61.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: network analysis and visualization of proteomics data. J. Proteome Res. 2019;18:623–32.

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Pereira B, Chin SF, Rueda OM, Vollan HK, Provenzano E, Bardwell HA, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun. 2016;7:11479.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486:346–52.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Costa C, Muinelo-Romay L, Cebey-López V, Pereira-Veiga T, Martínez-Pena I, Abreu M, et al. Analysis of a real-world cohort of metastatic breast cancer patients shows circulating tumor cell clusters (CTC-clusters) as predictors of patient outcomes. Cancers. 2020;12:1111.

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  38. 38.

    Davis AA, Zhang Q, Gerratana L, Shah AN, Zhan Y, Qiang W, et al. Association of a novel circulating tumor DNA next-generating sequencing platform with circulating tumor cells (CTCs) and CTC clusters in metastatic breast cancer. Breast Cancer Res. 2019;21:137.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Westbrook JA, Cairns DA, Peng J, Speirs V, Hanby AM, Holen I, et al. CAPG and GIPC1: breast cancer biomarkers for bone metastasis development and treatment. J Natl Cancer Inst. 2016;108:djv360.

  40. 40.

    Pavlovic M, Arnal-Estapé A, Rojo F, Bellmunt A, Tarragona M, Guiu M, et al. Enhanced MAF oncogene expression and breast cancer bone metastasis. J. Natl Cancer Inst. 2015;107:djv256.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41.

    Pulido C, Vendrell I, Ferreira AR, Casimiro S, Mansinho A, Alho I, et al. Bone metastasis risk factors in breast cancer. Ecancermedicalscience. 2017;11:715.

    PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Early Breast Cancer Trialists’ Collaborative, G. Adjuvant bisphosphonate treatment in early breast cancer: meta-analyses of individual patient data from randomised trials. Lancet. 2015;386:1353–61.

    Article  CAS  Google Scholar 

  43. 43.

    D’Oronzo S, Silvestris E, Paradiso A, Cives M, Tucci M. Role of bone targeting agents in the prevention of bone metastases from breast cancer. Int J Mol Sci. 2020;21:3022.

  44. 44.

    Coleman R. Clinical benefits of bone targeted agents in early breast cancer. Breast. 2019;48:S92–6.

    PubMed  Article  Google Scholar 

  45. 45.

    Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature. 2013;501:355–64.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Bellahcène A, Bachelier R, Detry C, Lidereau R, Clézardin P, Castronovo V. Transcriptome analysis reveals an osteoblast-like phenotype for human osteotropic breast cancer cells. Breast Cancer Res Treat. 2007;101:135–48.

    PubMed  Article  CAS  Google Scholar 

  47. 47.

    Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014;24:496–510.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Ting DT, Wittner BS, Ligorio M, Vincent Jordan N, Shah AM, Miyamoto DT, et al. Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep. 2014;8:1905–18.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Rossi E, Zamarchi R. Single-cell analysis of circulating tumor cells: how far have we come in the -Omics Era? Front Genet. 2019;10:958.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Magbanua MJM, Rugo HS, Wolf DM, Hauranieh L, Roy R, Pendyala P, et al. Expanded genomic profiling of circulating tumor cells in metastatic breast cancer patients to assess biomarker status and biology over time (CALGB 40502 and CALGB 40503, alliance). Clin Cancer Res. 2018;24:1486–99.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Powell AA, Talasaz AH, Zhang H, Coram MA, Reddy A, Deng G, et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS ONE. 2012;7:e33788.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Wu MY, Li CJ, Yiang GT, Cheng YL, Tsai AP, Hou YT, et al. Molecular regulation of bone metastasis pathogenesis. Cell. Physiol. Biochem. 2018;46:1423–38.

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Zhuang X, Zhang H, Li X, Li X, Cong M, Peng F, et al. Differential effects on lung and bone metastasis of breast cancer by Wnt signalling inhibitor DKK1. Nat Cell Biol. 2017;19:1274–85.

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Kasoha M, Bohle RM, Seibold A, Gerlinger C, Juhasz-Böss I, Solomayer EF. Dickkopf-1 (Dkk1) protein expression in breast cancer with special reference to bone metastases. Clin Exp Metastasis. 2018;35:763–75.

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Johnson RW, Merkel AR, Page JM, Ruppender NS, Guelcher SA, Sterling JA. Wnt signaling induces gene expression of factors associated with bone destruction in lung and breast cancer. Clin Exp Metastasis. 2014;31:945–59.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Eyre R, Alférez DG, Santiago-Gómez A, Spence K, McConnell JC, Hart C, et al. Microenvironmental IL1beta promotes breast cancer metastatic colonisation in the bone via activation of Wnt signalling. Nat Commun. 2019;10:5016.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  57. 57.

    Holen I, Lefley DV, Francis SE, Rennicks S, Bradbury S, Coleman RE, et al. IL-1 drives breast cancer growth and bone metastasis in vivo. Oncotarget. 2016;7:75571–84.

    PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Tulotta C, Lefley DV, Freeman K, Gregory WM, Hanby AM, Heath PR, et al. Endogenous production of IL1B by breast cancer cells drives metastasis and colonization of the bone microenvironment. Clin Cancer Res. 2019;25:2769–82.

    CAS  PubMed  Article  Google Scholar 

  59. 59.

    Zhang Y, He W, Zhang S. Seeking for correlative genes and signaling pathways with bone metastasis from breast cancer by integrated analysis. Front Oncol. 2019;9:138.

    PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Lobbezoo DJ, van Kampen RJ, Voogd AC, Dercksen MW, van den Berkmortel F, Smilde TJ, et al. Prognosis of metastatic breast cancer: are there differences between patients with de novo and recurrent metastatic breast cancer? Br J Cancer. 2015;112:1445–51.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Wang T, Li B, Nelson CE, Nabavi S. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinforma. 2019;20:40.

    Article  Google Scholar 

  62. 62.

    Yang MQ, Weissman SM, Yang W, Zhang J, Canaann A, Guan R. MISC: missing imputation for single-cell RNA sequencing data. BMC Syst. Biol. 2018;12:114.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Li X, Rouchka EC, Brock GN, Yan J, O’Toole TE, Tieri DA, et al. A combined approach with gene-wise normalization improves the analysis of RNA-seq data in human breast cancer subtypes. PLoS ONE. 2018;13:e0201813.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  64. 64.

    Soneson C, Robinson MD. Bias, robustness and scalability in single-cell differential expression analysis. Nat Methods. 2018;15:255–61.

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Mou T, Deng W, Gu F, Pawitan Y, Vu TN. Reproducibility of methods to detect differentially expressed genes from single-cell RNA sequencing. Front Genet. 2019;10:1331.

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Holland CH, Tanevski J, Perales-Patón J, Gleixner J, Kumar MP, Mereu E, et al. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biol. 2020;21:36.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Zhu Z, Qiu S, Shao K, Hou Y. Progress and challenges of sequencing and analyzing circulating tumor cells. Cell Biol Toxicol. 2018;34:405–15.

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Luigia Stefania Stucci for her precious help in recruiting participants and to Claudia Felici for her valuable technical support. The authors would like to thank also “DIVELLA SpA” and particularly Enzo Divella, for the generous donation of the Ion Torrent used for NGS analyses.

Funding

This work was supported by the Apulia Region (“Oncogenomic”, “Jonico-Salentino”, “Precision Medicine” and “Tecnopolo per la Medicina di Precisione-GR Puglia 2117/2018” projects).

Author information

Affiliations

Authors

Contributions

Conception and design: SD, DL, RP and FS; development of methodology: PC, DL and RP; acquisition of the data: JB, PC, SD, EL, CP, FS and SW; analysis and interpretation of the data: SD and DL; writing, review, and/or revision of the manuscript: all authors; study supervision: FS.

Corresponding author

Correspondence to Franco Silvestris.

Ethics declarations

Ethics approval and consent to participate

The patients were enrolled after written informed consent. The study was approved by the Ethics Committee of the University Hospital “Policlinico of Bari” (Project identification code: 44100) and performed in accordance with the principles of the Declaration of Helsinki.

Consent to publish

Not applicable.

Competing interests

JB: speaker fees from Amgen and advisory board Novartis; DL, SD, RP, PC, SW, CP, EL, RC and FS declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lovero, D., D’Oronzo, S., Palmirotta, R. et al. Correlation between targeted RNAseq signature of breast cancer CTCs and onset of bone-only metastases. Br J Cancer (2021). https://doi.org/10.1038/s41416-021-01481-z

Download citation

Search

Quick links