Skip to main content

Circulating Tumour Cell Isolation and Molecular Profiling; Potential Therapeutic Intervention

  • Chapter
  • First Online:
Circulating Tumor Cells

Abstract

Comprehensive tumour characterisation is indispensable for patients to receive targeted therapy. The use of liquid biopsy, particularly circulating tumour cells (CTC), has shown great promise in the treatment and management of cancer patients. An in-depth understanding of CTCs at the cellular and molecular level can provide clues as to the mechanisms of cancer dissemination and the pathways responsible for conferring intrinsic and acquired resistance to therapeutic agents. Herein, we discuss the current methods of CTC isolation and analysis at the single-cell resolution for therapeutic applications in the management of cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hanahan, D. and R.A. Weinberg, Hallmarks of cancer: the next generation. Cell, 2011. 144(5): p. 646–74.

    Google Scholar 

  2. Marusyk, A. and K. Polyak, Cancer. Cancer cell phenotypes, in fifty shades of grey. Science, 2013. 339(6119): p. 528–9.

    Google Scholar 

  3. Sabnis, A.J. and T.G. Bivona, Principles of Resistance to Targeted Cancer Therapy: Lessons from Basic and Translational Cancer Biology. Trends in Molecular Medicine, 2019. 25(3): p. 185–197.

    Google Scholar 

  4. Cortes-Hernandez, L.E., et al., Molecular and Functional Characterization of Circulating Tumor Cells: From Discovery to Clinical Application. Clin Chem, 2019.

    Google Scholar 

  5. Sabnis, A.J. and T.G. Bivona, Principles of Resistance to Targeted Cancer Therapy: Lessons from Basic and Translational Cancer Biology. Trends Mol Med, 2019. 25(3): p. 185–197.

    Google Scholar 

  6. Ignatiadis, M., M. Lee, and S.S. Jeffrey, Circulating Tumor Cells and Circulating Tumor DNA: Challenges and Opportunities on the Path to Clinical Utility. Clinical Cancer Research, 2015. 21: p. 4786–4800.

    Google Scholar 

  7. Aktas, B., et al., Stem cell and epithelial-mesenchymal transition markers are frequently overexpressed in circulating tumor cells of metastatic breast cancer patients. Breast Cancer Res, 2009. 11(4): p. R46.

    Google Scholar 

  8. Markiewicz, A., et al., Aggressive Phenotype of Cells Disseminated via Hematogenous and Lymphatic Route in Breast Cancer Patients. Transl Oncol, 2018. 11(3): p. 722–731.

    Google Scholar 

  9. Ting, D.T., et al., Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep, 2014. 8(6): p. 1905–1918.

    Google Scholar 

  10. Hou, H.W., et al., Isolation and retrieval of circulating tumor cells using centrifugal forces. Scientific Reports, 2013. 3(1): p. 1259.

    Google Scholar 

  11. Warkiani, M.E., et al., Slanted spiral microfluidics for the ultra-fast, label-free isolation of circulating tumor cells. Lab Chip, 2014. 14(1): p. 128–37.

    Google Scholar 

  12. Warkiani, M.E., et al., Ultra-fast, label-free isolation of circulating tumor cells from blood using spiral microfluidics. Nat Protoc, 2016. 11(1): p. 134–48.

    Google Scholar 

  13. Kulasinghe, A., et al., Short term ex-vivo expansion of circulating head and neck tumour cells. Oncotarget, 2016. 7(37): p. 60101–60109.

    Google Scholar 

  14. Kulasinghe, A., L. Kenny, and C. Punyadeera, Circulating tumour cell PD-L1 test for head and neck cancers. Oral Oncol, 2017. 75: p. 6–7.

    Google Scholar 

  15. Kulasinghe, A., et al., PD-L1 expressing circulating tumour cells in head and neck cancers. BMC Cancer, 2017. 17(1): p. 333.

    Google Scholar 

  16. Kulasinghe, A., M.E. Warkiani, and C. Punyadeera, The Isolation and Characterization of Circulating Tumor Cells from Head and Neck Cancer Patient Blood Samples Using Spiral Microfluidic Technology. Methods Mol Biol, 2019. 2054: p. 129–136.

    Google Scholar 

  17. Follain, G., et al., Hemodynamic Forces Tune the Arrest, Adhesion, and Extravasation of Circulating Tumor Cells. Dev Cell, 2018. 45(1): p. 33–52 e12.

    Google Scholar 

  18. Yano, K., et al., Phenotypic heterogeneity is an evolutionarily conserved feature of the endothelium. Blood, 2007. 109(2): p. 613–5.

    Google Scholar 

  19. Yasmin-Karim, S., et al., E-selectin ligand-1 controls circulating prostate cancer cell rolling/adhesion and metastasis. Oncotarget, 2014. 5(23): p. 12097–110.

    Google Scholar 

  20. Tichet, M., et al., Tumour-derived SPARC drives vascular permeability and extravasation through endothelial VCAM1 signalling to promote metastasis. Nat Commun, 2015. 6: p. 6993.

    Google Scholar 

  21. Er, E.E., et al., Pericyte-like spreading by disseminated cancer cells activates YAP and MRTF for metastatic colonization. Nat Cell Biol, 2018. 20(8): p. 966–978.

    Google Scholar 

  22. Esposito, M., et al., Bone vascular niche E-selectin induces mesenchymal-epithelial transition and Wnt activation in cancer cells to promote bone metastasis. Nat Cell Biol, 2019. 21(5): p. 627–639.

    Google Scholar 

  23. Stewart, C.A., et al., Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancer. Nature Cancer, 2020. 1(4): p. 423–436.

    Google Scholar 

  24. Baccelli, I., et al., Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nat Biotechnol, 2013. 31(6): p. 539–44.

    Google Scholar 

  25. Boral, D., et al., Molecular characterization of breast cancer CTCs associated with brain metastasis. Nat Commun, 2017. 8(1): p. 196.

    Google Scholar 

  26. Wu, Z., et al., TPO-Induced Metabolic Reprogramming Drives Liver Metastasis of Colorectal Cancer CD110+ Tumor-Initiating Cells. Cell Stem Cell, 2015. 17(1): p. 47–59.

    Google Scholar 

  27. Alix-Panabieres, C., et al., Molecular Portrait of Metastasis-Competent Circulating Tumor Cells in Colon Cancer Reveals the Crucial Role of Genes Regulating Energy Metabolism and DNA Repair. Clin Chem, 2017. 63(3): p. 700–713.

    Google Scholar 

  28. Hodgkinson, C.L., et al., Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer. Nat Med, 2014. 20(8): p. 897–903.

    Google Scholar 

  29. Abbosh, C., et al., Corrigendum: Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature, 2018. 554(7691): p. 264.

    Google Scholar 

  30. Gorges, T.M., et al., Accession of Tumor Heterogeneity by Multiplex Transcriptome Profiling of Single Circulating Tumor Cells. Clinical Chemistry, 2016. 62(11): p. 1504–1515.

    Google Scholar 

  31. Girotti, M.R., et al., Application of Sequencing, Liquid Biopsies, and Patient-Derived Xenografts for Personalized Medicine in Melanoma. Cancer Discov, 2016. 6(3): p. 286–99.

    Google Scholar 

  32. Aguirre-Ghiso, J.A., On the theory of tumor self-seeding: implications for metastasis progression in humans. Breast Cancer Res, 2010. 12(2): p. 304.

    Google Scholar 

  33. Kim, M.Y., et al., Tumor self-seeding by circulating cancer cells. Cell, 2009. 139(7): p. 1315–26.

    Google Scholar 

  34. Turajlic, S. and C. Swanton, Metastasis as an evolutionary process. Science, 2016. 352(6282): p. 169–75.

    Google Scholar 

  35. Gao, Y., et al., Single-cell sequencing deciphers a convergent evolution of copy number alterations from primary to circulating tumor cells. Genome Res, 2017. 27(8): p. 1312–1322.

    Google Scholar 

  36. Joosse, S.A., et al., Chromosomal Aberrations Associated with Sequential Steps of the Metastatic Cascade in Colorectal Cancer Patients. Clin Chem, 2018. 64(10): p. 1505–1512.

    Google Scholar 

  37. Markiewicz, A., et al., Spectrum of Epithelial-Mesenchymal Transition Phenotypes in Circulating Tumour Cells from Early Breast Cancer Patients. Cancers (Basel), 2019. 11(1).

    Google Scholar 

  38. Lambros, M.B., et al., Single-Cell Analyses of Prostate Cancer Liquid Biopsies Acquired by Apheresis. Clin Cancer Res, 2018. 24(22): p. 5635–5644.

    Google Scholar 

  39. Mishima, Y., et al., The Mutational Landscape of Circulating Tumor Cells in Multiple Myeloma. Cell Rep, 2017. 19(1): p. 218–224.

    Google Scholar 

  40. Lohr, J.G., et al., Whole-exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer. Nat Biotechnol, 2014. 32(5): p. 479–84.

    Google Scholar 

  41. Paoletti, C., et al., Comprehensive Mutation and Copy Number Profiling in Archived Circulating Breast Cancer Tumor Cells Documents Heterogeneous Resistance Mechanisms. Cancer Res, 2018. 78(4): p. 1110–1122.

    Google Scholar 

  42. Werner, S., et al., Suppression of early hematogenous dissemination of human breast cancer cells to bone marrow by retinoic Acid-induced 2. Cancer Discov, 2015. 5(5): p. 506–19.

    Google Scholar 

  43. Nong, J., et al., Circulating tumor DNA analysis depicts subclonal architecture and genomic evolution of small cell lung cancer. Nat Commun, 2018. 9(1): p. 3114.

    Google Scholar 

  44. Powell, A.A., et al., Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS One, 2012. 7(5): p. e33788.

    Google Scholar 

  45. Allam, M., S. Cai, and A.F. Coskun, Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics. npj Precision Oncology, 2020. 4(1): p. 11.

    Google Scholar 

  46. Chambers, A., A. Groom, and I. MacDonald, Dissemination and growth of cancer cells in metastatic sites. 2002. p. 563–72.

    Google Scholar 

  47. Wang, W., et al., Survival mechanisms and influence factors of circulating tumor cells. 2018. p. 6304701.

    Google Scholar 

  48. Allard, W., et al., Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases. 2004. p. 6897–904.

    Google Scholar 

  49. Maheswaran, S., et al., Detection of mutations in EGFR in circulating lung-cancer cells. The New England journal of medicine, 2008. 359(4): p. 366–377.

    Google Scholar 

  50. Payne, K., et al., Circulating Tumour Cell Biomarkers in Head and Neck Cancer: Current Progress and Future Prospects. Cancers, 2019. 11(8): p. 1115.

    Google Scholar 

  51. Chen, X.-X. and F. Bai, Single-cell analyses of circulating tumor cells. Cancer biology & medicine, 2015. 12(3): p. 184.

    Google Scholar 

  52. Ferreira, M.M., V.C. Ramani, and S.S. Jeffrey, Circulating tumor cell technologies. Molecular Oncology, 2016. 10(3): p. 374–394.

    Google Scholar 

  53. Yu, M., et al., Circulating Breast Tumor Cells Exhibit Dynamic Changes in Epithelial and Mesenchymal Composition. Science, 2013. 339(6119): p. 580–584.

    Google Scholar 

  54. Yu, M., et al., Cancer therapy. Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science, 2014. 345(6193): p. 216–20.

    Google Scholar 

  55. Arechederra, M., M.A. Ávila, and C. Berasain, Liquid biopsy for cancer management: a revolutionary but still limited new tool for precision medicine. 2020(0): p. 20200009.

    Google Scholar 

  56. Andree, K.C., G. van Dalum, and L.W.M.M. Terstappen, Challenges in circulating tumor cell detection by the CellSearch system. Molecular Oncology, 2016. 10(3): p. 395–407.

    Google Scholar 

  57. Gee, A.P. and A.G. Durett, Cell sorting for therapeutic applications -- points to consider. Cytotherapy, 2002. 4(1): p. 91–92.

    Google Scholar 

  58. Talasaz, A.H., et al., Isolating highly enriched populations of circulating epithelial cells and other rare cells from blood using a magnetic sweeper device. Proceedings of the National Academy of Sciences, 2009. 106(10): p. 3970–3975.

    Google Scholar 

  59. Lu, Y.-T., et al., NanoVelcro Chip for CTC enumeration in prostate cancer patients. Methods (San Diego, Calif.), 2013. 64(2): p. 144–152.

    Google Scholar 

  60. de Wit, S., G. van Dalum, and L.W.M.M. Terstappen, Detection of Circulating Tumor Cells. Scientifica, 2014. 2014: p. 819362.

    Google Scholar 

  61. Liquid Biopsy Technologies Diversify in Expanding CTC Field. Genetic Engineering & Biotechnology News, 2019. 39(7): p. 42–45.

    Article  Google Scholar 

  62. Vona, G., et al., Isolation by Size of Epithelial Tumor Cells: A New Method for the Immunomorphological and Molecular Characterization of Circulating Tumor Cells. The American Journal of Pathology, 2000. 156(1): p. 57–63.

    Google Scholar 

  63. Ignatiadis, M. and M. Reinholz, Minimal residual disease and circulating tumor cells in breast cancer. Breast Cancer Research, 2011. 13(5): p. 222.

    Google Scholar 

  64. Pinzani, P., et al., Isolation by size of epithelial tumor cells in peripheral blood of patients with breast cancer: correlation with real-time reverse transcriptase–polymerase chain reaction results and feasibility of molecular analysis by laser microdissection. Human Pathology, 2006. 37(6): p. 711–718.

    Google Scholar 

  65. Hofman, V., et al., Preoperative Circulating Tumor Cell Detection Using the Isolation by Size of Epithelial Tumor Cell Method for Patients with Lung Cancer Is a New Prognostic Biomarker. Clinical Cancer Research, 2011. 17(4): p. 827–835.

    Google Scholar 

  66. Khoja, L., et al., A pilot study to explore circulating tumour cells in pancreatic cancer as a novel biomarker. British journal of cancer, 2012. 106(3): p. 508–516.

    Google Scholar 

  67. De Giorgi, V., et al., Application of a Filtration- and Isolation-by-Size Technique for the Detection of Circulating Tumor Cells in Cutaneous Melanoma. Journal of Investigative Dermatology, 2010. 130(10): p. 2440–2447.

    Google Scholar 

  68. Mazzini, C., et al., Circulating tumor cells detection and counting in uveal melanomas by a filtration-based method. Cancers, 2014. 6(1): p. 323–332.

    Google Scholar 

  69. Peters, C.E., et al., 160-P: EFFECT OF TIME AFTER BLOOD DRAW AND ANTI-COAGULANT ON LYMPHOCYTE SUBSET AND MYELOID CELL ENRICHMENT WITH RosetteSep™ AND SepMate™. Human Immunology, 2012. 73: p. 148.

    Google Scholar 

  70. Lagoudianakis, E.E., et al., Detection of Epithelial Cells by RT-PCR Targeting CEA, CK20, and TEM-8 in Colorectal Carcinoma Patients Using OncoQuick Density Gradient Centrifugation System. Journal of Surgical Research, 2009. 155(2): p. 183–190.

    Google Scholar 

  71. Gertler, R., et al., Detection of circulating tumor cells in blood using an optimized density gradient centrifugation, in Molecular Staging of Cancer. 2003, Springer. p. 149–155.

    Google Scholar 

  72. Garcia-Cordero, J.L. and S.J. Maerkl, Microfluidic systems for cancer diagnostics. Current Opinion in Biotechnology, 2020. 65: p. 37–44.

    Google Scholar 

  73. Di Carlo, D., et al., Continuous inertial focusing, ordering, and separation of particles in microchannels. Proceedings of the National Academy of Sciences, 2007. 104(48): p. 18892.

    Google Scholar 

  74. Bhagat, A.A.S., S.S. Kuntaegowdanahalli, and I. Papautsky, Continuous particle separation in spiral microchannels using dean flows and differential migration. Lab on a Chip, 2008. 8(11): p. 1906–1914.

    Google Scholar 

  75. Lee, Y., G. Guan, and A.A. Bhagat, ClearCell® FX, a label-free microfluidics technology for enrichment of viable circulating tumor cells. Cytometry Part A, 2018. 93(12): p. 1251–1254.

    Google Scholar 

  76. Lim, S.B., C.T. Lim, and W.-T. Lim, Single-Cell Analysis of Circulating Tumor Cells: Why Heterogeneity Matters. Cancers, 2019. 11(10): p. 1595.

    Google Scholar 

  77. Ozkumur, E., et al., Inertial Focusing for Tumor Antigen–Dependent and –Independent Sorting of Rare Circulating Tumor Cells. Science Translational Medicine, 2013. 5(179): p. 179ra47–179ra47.

    Google Scholar 

  78. Khoo, B.L., et al., Advancing techniques and insights in circulating tumor cell (ctc) research, in Ex Vivo Engineering of the Tumor Microenvironment. 2017, Springer. p. 71–94.

    Chapter  Google Scholar 

  79. Keller, L. and K. Pantel, Unravelling tumour heterogeneity by single-cell profiling of circulating tumour cells. Nature Reviews Cancer, 2019. 19(10): p. 553–567.

    Google Scholar 

  80. Rossi, E. and R. Zamarchi, Single-Cell Analysis of Circulating Tumor Cells: How Far Have We Come in the -Omics Era? Frontiers in genetics, 2019. 10: p. 958–958.

    Google Scholar 

  81. Wang, W.-C., et al., Survival Mechanisms and Influence Factors of Circulating Tumor Cells. BioMed Research International, 2018. 2018: p. 6304701.

    Google Scholar 

  82. Nguyen, A., et al., Single Cell RNA Sequencing of Rare Immune Cell Populations. Frontiers in Immunology, 2018. 9(1553).

    Google Scholar 

  83. Nelep, C. and J. Eberhardt, Automated rare single cell picking with the ALS cellcelector™. Cytometry Part A, 2018. 93(12): p. 1267–1270.

    Google Scholar 

  84. Hu, P., et al., Single Cell Isolation and Analysis. Frontiers in Cell and Developmental Biology, 2016. 4(116).

    Google Scholar 

  85. Babayan, A., et al., Comparative study of whole genome amplification and next generation sequencing performance of single cancer cells. Oncotarget, 2016. 8(34).

    Google Scholar 

  86. Müller, C., et al., Hematogenous dissemination of glioblastoma multiforme. Science Translational Medicine, 2014. 6(247): p. 247ra101–247ra101.

    Google Scholar 

  87. Valihrach, L., P. Androvic, and M. Kubista, Platforms for single-cell collection and analysis. International journal of molecular sciences, 2018. 19(3): p. 807.

    Google Scholar 

  88. Park, E.S., et al., Isolation and genome sequencing of individual circulating tumor cells using hydrogel encapsulation and laser capture microdissection. Lab on a Chip, 2018. 18(12): p. 1736–1749.

    Google Scholar 

  89. Dunham, M.J., Chapter 19 - Experimental Evolution in Yeast: A Practical Guide, in Methods in Enzymology. 2010, Academic Press. p. 487–507.

    Google Scholar 

  90. Gierahn, T.M., et al., Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nature Methods, 2017. 14(4): p. 395–398.

    Google Scholar 

  91. Di Trapani, M., N. Manaresi, and G. Medoro, DEPArray™ system: An automatic image-based sorter for isolation of pure circulating tumor cells. Cytometry Part A, 2018. 93(12): p. 1260–1266.

    Google Scholar 

  92. Schochter, F., et al., 53BP1 Accumulation in Circulating Tumor Cells Identifies Chemotherapy-Responsive Metastatic Breast Cancer Patients. Cancers, 2020. 12(4): p. 930.

    Google Scholar 

  93. Boyer, M., et al., Circulating Tumor Cell Detection and Polyomavirus Status in Merkel Cell Carcinoma. Scientific Reports, 2020. 10(1): p. 1612.

    Google Scholar 

  94. Fabisiewicz, A. and E. Grzybowska, CTC clusters in cancer progression and metastasis. Medical Oncology, 2016. 34(1): p. 12.

    Google Scholar 

  95. Schmitz, B., et al., Magnetic activated cell sorting (MACS)--a new immunomagnetic method for megakaryocytic cell isolation: comparison of different separation techniques. Eur J Haematol, 1994. 52(5): p. 267–75.

    Google Scholar 

  96. Matuła, K., F. Rivello, and W.T.S. Huck, Single-Cell Analysis Using Droplet Microfluidics. Advanced Biosystems, 2020. 4(1): p. 1900188.

    Google Scholar 

  97. Rakszewska, A., et al., One drop at a time: toward droplet microfluidics as a versatile tool for single-cell analysis. NPG Asia Materials, 2014. 6(10): p. e133–e133.

    Google Scholar 

  98. Teh, S.-Y., et al., Droplet microfluidics. Lab on a Chip, 2008. 8(2): p. 198–220.

    Google Scholar 

  99. Seemann, R., et al., Droplet based microfluidics. Rep Prog Phys, 2012. 75(1): p. 016601.

    Google Scholar 

  100. Zheng, X., et al., Cell population analysis using single nucleotide polymorphisms from single cell transcriptomes. 2017, Google Patents.

    Google Scholar 

  101. Heitzer, E., et al., Complex Tumor Genomes Inferred from Single Circulating Tumor Cells by Array-CGH and Next-Generation Sequencing. Cancer Research, 2013. 73(10): p. 2965–2975.

    Google Scholar 

  102. Lambros, M.B., et al., Single-Cell Analyses of Prostate Cancer Liquid Biopsies Acquired by Apheresis. Clinical Cancer Research, 2018. 24(22): p. 5635–5644.

    Google Scholar 

  103. Wang, Y., et al., Single nucleotide variant profiles of viable single circulating tumour cells reveal CTC behaviours in breast cancer. Oncology reports, 2018. 39(5): p. 2147–2159.

    Google Scholar 

  104. Yin, J., et al., Characterization of circulating tumor cells in breast cancer patients by spiral microfluidics. Cell Biology and Toxicology, 2019. 35(1): p. 59–66.

    Google Scholar 

  105. Kanwar, N., et al., Identification of genomic signatures in circulating tumor cells from breast cancer. International Journal of Cancer, 2015. 137(2): p. 332–344.

    Google Scholar 

  106. Katsonis, P., et al., Single nucleotide variations: biological impact and theoretical interpretation. Protein science: a publication of the Protein Society, 2014. 23(12): p. 1650–1666.

    Google Scholar 

  107. Mackay, H.J., et al., Prognostic value of microsatellite instability (MSI) and PTEN expression in women with endometrial cancer: Results from studies of the NCIC Clinical Trials Group (NCIC CTG). European Journal of Cancer, 2010. 46(8): p. 1365–1373.

    Google Scholar 

  108. Popat, S., R. Hubner, and R.S. Houlston, Systematic Review of Microsatellite Instability and Colorectal Cancer Prognosis. Journal of Clinical Oncology, 2005. 23(3): p. 609–618.

    Google Scholar 

  109. Thibodeau, S., G. Bren, and D. Schaid, Microsatellite instability in cancer of the proximal colon. Science, 1993. 260(5109): p. 816–819.

    Google Scholar 

  110. Goldstein, J., et al., Multicenter retrospective analysis of metastatic colorectal cancer (CRC) with high-level microsatellite instability (MSI-H). Annals of Oncology, 2014. 25(5): p. 1032–1038.

    Google Scholar 

  111. Cappuzzo, F., et al., Increased HER2 Gene Copy Number Is Associated With Response to Gefitinib Therapy in Epidermal Growth Factor Receptor–Positive Non–Small-Cell Lung Cancer Patients. Journal of Clinical Oncology, 2005. 23(22): p. 5007–5018.

    Google Scholar 

  112. Watkins, J.A., et al., Genomic scars as biomarkers of homologous recombination deficiency and drug response in breast and ovarian cancers. Breast Cancer Research, 2014. 16(3): p. 211.

    Google Scholar 

  113. Greene, S.B., et al., Chromosomal Instability Estimation Based on Next Generation Sequencing and Single Cell Genome Wide Copy Number Variation Analysis. PLOS ONE, 2016. 11(11): p. e0165089.

    Google Scholar 

  114. Baca, Sylvan C., et al., Punctuated Evolution of Prostate Cancer Genomes. Cell, 2013. 153(3): p. 666–677.

    Google Scholar 

  115. De Luca, F., et al., Mutational analysis of single circulating tumor cells by next generation sequencing in metastatic breast cancer. Oncotarget, 2016. 7(18): p. 26107.

    Google Scholar 

  116. Benezeder, T., et al., Multigene methylation analysis of enriched circulating tumor cells associates with poor progression-free survival in metastatic breast cancer patients. Oncotarget, 2017. 8(54): p. 92483.

    Google Scholar 

  117. Hwang, B., J.H. Lee, and D. Bang, Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine, 2018. 50(8): p. 96.

    Google Scholar 

  118. Haque, A., et al., A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Medicine, 2017. 9(1): p. 75.

    Google Scholar 

  119. Dey, S.S., et al., Integrated genome and transcriptome sequencing of the same cell. Nature Biotechnology, 2015. 33(3): p. 285–289.

    Google Scholar 

  120. Macaulay, I.C., et al., Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq. Nature Protocols, 2016. 11(11): p. 2081–2103.

    Google Scholar 

  121. Han, K.Y., et al., SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome research, 2018. 28(1): p. 75–87.

    Google Scholar 

  122. Kalinich, M., et al., An RNA-based signature enables high specificity detection of circulating tumor cells in hepatocellular carcinoma. Proceedings of the National Academy of Sciences, 2017. 114(5): p. 1123–1128.

    Google Scholar 

  123. D’Avola, D., et al., High-density single cell mRNA sequencing to characterize circulating tumor cells in hepatocellular carcinoma. Scientific Reports, 2018. 8(1): p. 11570.

    Google Scholar 

  124. Aceto, N., et al., AR Expression in Breast Cancer CTCs Associates with Bone Metastases. Molecular cancer research: MCR, 2018. 16(4): p. 720–727.

    Google Scholar 

  125. Hanash, S., Disease proteomics. Nature, 2003. 422(6928): p. 226–232.

    Google Scholar 

  126. Hanash, S. and A. Taguchi, Application of proteomics to cancer early detection. Cancer journal (Sudbury, Mass.), 2011. 17(6): p. 423–428.

    Google Scholar 

  127. Nusinow, D.P., et al., Quantitative Proteomics of the Cancer Cell Line Encyclopedia. Cell, 2020. 180(2): p. 387–402.e16.

    Google Scholar 

  128. Spitzer, Matthew H. and Garry P. Nolan, Mass Cytometry: Single Cells, Many Features. Cell, 2016. 165(4): p. 780–791.

    Google Scholar 

  129. Gerdtsson, E., et al., Multiplex protein detection on circulating tumor cells from liquid biopsies using imaging mass cytometry. Convergent science physical oncology, 2018. 4(1): p. 015002.

    Google Scholar 

  130. Sinkala, E., et al., Profiling protein expression in circulating tumour cells using microfluidic western blotting. Nature communications, 2017. 8: p. 14622–14622.

    Google Scholar 

  131. Armitage, E.G. and C. Barbas, Metabolomics in cancer biomarker discovery: Current trends and future perspectives. Journal of Pharmaceutical and Biomedical Analysis, 2014. 87: p. 1–11.

    Google Scholar 

  132. Johnson, C.H., J. Ivanisevic, and G. Siuzdak, Metabolomics: beyond biomarkers and towards mechanisms. Nature Reviews Molecular Cell Biology, 2016. 17(7): p. 451–459.

    Google Scholar 

  133. Duncan, K.D., et al., Quantitative Mass Spectrometry Imaging of Prostaglandins as Silver Ion Adducts with Nanospray Desorption Electrospray Ionization. Analytical Chemistry, 2018. 90(12): p. 7246–7252.

    Google Scholar 

  134. Comi, T.J., et al., Categorizing Cells on the Basis of their Chemical Profiles: Progress in Single-Cell Mass Spectrometry. Journal of the American Chemical Society, 2017. 139(11): p. 3920–3929.

    Google Scholar 

  135. Abouleila, Y., et al., Live single cell mass spectrometry reveals cancer-specific metabolic profiles of circulating tumor cells. Cancer Science, 2019. 110(2): p. 697–706.

    Google Scholar 

  136. Aboulkheyr Es, H., et al., Personalized Cancer Medicine: An Organoid Approach. Trends Biotechnol, 2018. 36(4): p. 358–371.

    Google Scholar 

  137. Keller, L. and K. Pantel, Unravelling tumour heterogeneity by single-cell profiling of circulating tumour cells. Nat Rev Cancer, 2019. 19(10): p. 553–567.

    Google Scholar 

  138. Paolillo, C., et al., Detection of Activating Estrogen Receptor Gene (ESR1) Mutations in Single Circulating Tumor Cells. Clin Cancer Res, 2017. 23(20): p. 6086–6093.

    Google Scholar 

  139. Sundaresan, T.K., et al., Detection of T790M, the Acquired Resistance EGFR Mutation, by Tumor Biopsy versus Noninvasive Blood-Based Analyses. Clin Cancer Res, 2016. 22(5): p. 1103–10.

    Google Scholar 

  140. Dagogo-Jack, I. and A.T. Shaw, Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol, 2018. 15(2): p. 81–94.

    Google Scholar 

  141. Maheswaran, S., et al., Detection of mutations in EGFR in circulating lung-cancer cells. N Engl J Med, 2008. 359(4): p. 366–77.

    Google Scholar 

  142. Marchetti, A., et al., Assessment of EGFR mutations in circulating tumor cell preparations from NSCLC patients by next generation sequencing: toward a real-time liquid biopsy for treatment. PLoS One, 2014. 9(8): p. e103883.

    Google Scholar 

  143. Pailler, E., et al., Method for semi-automated microscopy of filtration-enriched circulating tumor cells. BMC Cancer, 2016. 16: p. 477.

    Google Scholar 

  144. Pailler, E., et al., High level of chromosomal instability in circulating tumor cells of ROS1-rearranged non-small-cell lung cancer. Ann Oncol, 2015. 26(7): p. 1408–15.

    Google Scholar 

  145. Pailler, E., et al., Circulating Tumor Cells with Aberrant ALK Copy Number Predict Progression-Free Survival during Crizotinib Treatment in ALK-Rearranged Non-Small Cell Lung Cancer Patients. Cancer Res, 2017. 77(9): p. 2222–2230.

    Google Scholar 

  146. Tan, C.L., et al., Concordance of anaplastic lymphoma kinase (ALK) gene rearrangements between circulating tumor cells and tumor in non-small cell lung cancer. Oncotarget, 2016. 7(17): p. 23251–62.

    Google Scholar 

  147. Liu, Y., et al., Meta-analysis of the mutational status of circulation tumor cells and paired primary tumor tissues from colorectal cancer patients. Oncotarget, 2017. 8(44): p. 77928–77941.

    Google Scholar 

  148. Schneck, H., et al., Analysing the mutational status of PIK3CA in circulating tumor cells from metastatic breast cancer patients. Mol Oncol, 2013. 7(5): p. 976–86.

    Google Scholar 

  149. Gasch, C., et al., Frequent detection of PIK3CA mutations in single circulating tumor cells of patients suffering from HER2-negative metastatic breast cancer. Mol Oncol, 2016. 10(8): p. 1330–43.

    Google Scholar 

  150. Pestrin, M., et al., Heterogeneity of PIK3CA mutational status at the single cell level in circulating tumor cells from metastatic breast cancer patients. Mol Oncol, 2015. 9(4): p. 749–57.

    Google Scholar 

  151. Markou, A., et al., Multiplex Gene Expression Profiling of In Vivo Isolated Circulating Tumor Cells in High-Risk Prostate Cancer Patients. Clin Chem, 2018. 64(2): p. 297–306.

    Google Scholar 

  152. Yu, M., et al., Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science, 2013. 339(6119): p. 580–4.

    Google Scholar 

  153. Carter, L., et al., Molecular analysis of circulating tumor cells identifies distinct copy-number profiles in patients with chemosensitive and chemorefractory small-cell lung cancer. Nat Med, 2017. 23(1): p. 114–119.

    Google Scholar 

  154. Pantel, K. and C. Alix-Panabieres, Liquid biopsy and minimal residual disease - latest advances and implications for cure. Nat Rev Clin Oncol, 2019. 16(7): p. 409–424.

    Google Scholar 

  155. Scher, H.I., et al., Assessment of the Validity of Nuclear-Localized Androgen Receptor Splice Variant 7 in Circulating Tumor Cells as a Predictive Biomarker for Castration-Resistant Prostate Cancer. JAMA Oncol, 2018. 4(9): p. 1179–1186.

    Google Scholar 

  156. Jolly, M.K., et al., Phenotypic Plasticity, Bet-Hedging, and Androgen Independence in Prostate Cancer: Role of Non-Genetic Heterogeneity. Front Oncol, 2018. 8: p. 50.

    Google Scholar 

  157. Antonarakis, E.S., et al., Clinical Significance of Androgen Receptor Splice Variant-7 mRNA Detection in Circulating Tumor Cells of Men With Metastatic Castration-Resistant Prostate Cancer Treated With First- and Second-Line Abiraterone and Enzalutamide. J Clin Oncol, 2017. 35(19): p. 2149–2156.

    Google Scholar 

  158. Beltran, H., et al., The Initial Detection and Partial Characterization of Circulating Tumor Cells in Neuroendocrine Prostate Cancer. Clin Cancer Res, 2016. 22(6): p. 1510–9.

    Google Scholar 

  159. Miyamoto, D.T., et al., RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science, 2015. 349(6254): p. 1351–6.

    Google Scholar 

  160. Tsao, S.C., et al., Characterising the phenotypic evolution of circulating tumour cells during treatment. Nat Commun, 2018. 9(1): p. 1482.

    Google Scholar 

  161. Jordan, N.V., et al., HER2 expression identifies dynamic functional states within circulating breast cancer cells. Nature, 2016. 537(7618): p. 102–106.

    Google Scholar 

  162. Chen, P.Y., et al., Adaptive and Reversible Resistance to Kras Inhibition in Pancreatic Cancer Cells. Cancer Res, 2018. 78(4): p. 985–1002.

    Google Scholar 

  163. Oser, M.G., et al., Transformation from non-small-cell lung cancer to small-cell lung cancer: molecular drivers and cells of origin. Lancet Oncol, 2015. 16(4): p. e165–72.

    Google Scholar 

  164. Koch, C., et al., Characterization of circulating breast cancer cells with tumorigenic and metastatic capacity. EMBO Molecular Medicine. n/a(n/a): p. e11908.

    Google Scholar 

  165. Cayrefourcq, L., et al., Establishment and characterization of a cell line from human circulating colon cancer cells. Cancer Res, 2015. 75(5): p. 892–901.

    Google Scholar 

  166. Franken, A., et al., Label-Free Enrichment and Molecular Characterization of Viable Circulating Tumor Cells from Diagnostic Leukapheresis Products. Clin Chem, 2019. 65(4): p. 549–558.

    Google Scholar 

Download references

Acknowledgements

M. E. W. would like to acknowledge the support of the Australian Research Council through Discovery Project Grants (DP170103704 and DP180103003) and the National Health and Medical Research Council through the Career Development Fellowship (APP1143377). JPT would like to acknowledged the financial support of Guangzhou Laboratory.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jean Paul Thiery .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Radfar, P., Es, H.A., Kulasinghe, A., Thiery, J.P., Warkiani, M.E. (2023). Circulating Tumour Cell Isolation and Molecular Profiling; Potential Therapeutic Intervention. In: Cote, R.J., Lianidou, E. (eds) Circulating Tumor Cells. Current Cancer Research. Springer, Cham. https://doi.org/10.1007/978-3-031-22903-9_14

Download citation

Publish with us

Policies and ethics