Quantitative Analysis of Circulating Tumor Cells Using RNA-Based Digital Scoring

  • Mark Kalinich
  • Tanya T. Kwan
  • Mehmet Toner
  • Daniel A. Haber
  • Shyamala MaheswaranEmail author
Part of the Recent Results in Cancer Research book series (RECENTCANCER, volume 215)


Circulating tumor cells (CTCs) provide valuable information about the molecular evolution of cancers, as they may initially respond and ultimately progress on therapy. As intact tumor cells isolated from the bloodstream, CTCs also enable assessment of heterogeneous subpopulations, and their analysis may include DNA, RNA, and protein biomarkers. New microfluidic cell isolation strategies greatly facilitate the challenge of enriching viable tumor cells from the billions of hematopoietic cells within a standard blood specimen. While counting and characterization of enriched CTCs have primarily relied on immunostaining for tumor cell-specific antigens, new RNA-based analytic platforms are providing new insight into the identity of CTCs and providing new tools for clinical applications. Single-cell RNA sequencing of CTCs reveals a high degree of heterogeneity among cancer cells from a single individual, while new digital RNA-based amplification platforms may now allow high-sensitivity and high-throughput quantitative scoring of CTCs for clinical applications. Here, we focus on transcriptomic analysis of CTCs and its relevance in understanding metastatic cancer progression and in developing diagnostic assays to monitor cancer.


  1. Aceto N, Bardia A, Miyamoto DT et al (2014) Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 158:1110–1122CrossRefGoogle Scholar
  2. Alix-Panabières C, Pantel K (2014) Challenges in circulating tumour cell research. Nat Rev Cancer 14:623–631CrossRefGoogle Scholar
  3. Allard WJ, Matera J, Miller MC, Repollet M, Connelly MC, Rao C, Tibbe AGJ, Uhr JW, Terstappen LWMM (2004) Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases. Clin Cancer Res 10:6897–6904CrossRefGoogle Scholar
  4. Arenberger P, Arenbergerova M, Vohradnikova O, Kremen J (2008) Early detection of melanoma progression by quantitative real-time RT-PCR analysis for multiple melanoma markers. Keio J Med 57:57–64CrossRefGoogle Scholar
  5. Cristofanilli M, Budd GT, Ellis MJ et al (2004) Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 351:781–791CrossRefGoogle Scholar
  6. Cristofanilli M, Budd GT, Ellis MJ, et al (2009) Circulating tumor cells, disease progression, and survival in metastatic breast cancer 351:781–791. Scholar
  7. Easwaran H, Tsai H-C, Baylin SB (2014) Cancer epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol Cell 54:716–727CrossRefGoogle Scholar
  8. Gazzaniga P, Gradilone A, Petracca A, Nicolazzo C, Raimondi C, Iacovelli R, Naso G, Cortesi E (2010) Molecular markers in circulating tumour cells from metastatic colorectal cancer patients. J Cell Mol Med 14:2073–2077CrossRefGoogle Scholar
  9. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674CrossRefGoogle Scholar
  10. Hong X, Sullivan RJ, Kalinich M et al (2018) Molecular signatures of circulating melanoma cells for monitoring early response to immune checkpoint therapy. PNAS 114:1123–1128Google Scholar
  11. Ignatiadis M, Lee M, Jeffrey SS (2015) Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin Cancer Res 21:4786–4800CrossRefGoogle Scholar
  12. Kalinich M, Bhan I, Kwan TT et al (2017) An RNA-based signature enables high specificity detection of circulating tumor cells in hepatocellular carcinoma. PNAS 114:1123–1128CrossRefGoogle Scholar
  13. Kalinina O, Lebedeva I, Brown J, Silver J (1997) Nanoliter scale PCR with TaqMan detection. Nucleic Acids Res 25:1999–2004CrossRefGoogle Scholar
  14. Kar S, Carr BI (1995) Detection of liver cells in peripheral blood of patients with advanced-stage hepatocellular carcinoma. Hepatology 21:403–407PubMedGoogle Scholar
  15. Kwan, TT, Bardia, A, Spring, LM et al (2018) A digital RNA signature of circulating tumor cells predicting early therapeutic response in localized and metastatic breast cancer. Cancer Discov 8(10):1286–1299CrossRefGoogle Scholar
  16. Luo X, Mitra D, Sullivan RJ et al (2014) Isolation and molecular characterization of circulating melanoma cells. Cell Rep 7:645–653CrossRefGoogle Scholar
  17. Ma Y, Luk A, Young FP, Lynch D, Chua W, Balakrishnar B, de Souza P, Becker TM (2016) Droplet digital PCR Based Androgen Receptor Variant 7 (AR-V7) detection from prostate cancer patient blood biopsies. Int J Mol Sci 17:1264CrossRefGoogle Scholar
  18. McGranahan N, Swanton C (2017) Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168:613–628CrossRefGoogle Scholar
  19. Miyamoto DT, Lee RJ, Stott SL et al (2012) Androgen receptor signaling in circulating tumor cells as a marker of hormonally responsive prostate cancer. Cancer Discov 2:995–1003CrossRefGoogle Scholar
  20. Miyamoto DT, Zheng Y, Wittner BS et al (2015) RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349:1351–1356CrossRefGoogle Scholar
  21. Miyamoto DT, Lee RJ, Kalinich M et al (2018) An RNA-based digital circulating tumor cell signature is predictive of drug response and early dissemination in prostate cancer. Cancer Discov CD-16-1406Google Scholar
  22. Nagrath S, Sequist LV, Maheswaran S et al (2007) Isolation of rare circulating tumour cells in cancer patients by microchip technology. Nature 450:1235–1239CrossRefGoogle Scholar
  23. Nardi V, Azam M, Daley GQ (2004) Mechanisms and implications of imatinib resistance mutations in BCR-ABL. Curr Opin Hematol 11:35–43CrossRefGoogle Scholar
  24. Ozkumur E, Shah AM, Ciciliano JC et al (2013) Inertial focusing for tumor antigen–dependent and –independent sorting of rare circulating tumor cells. Sci Transl Med 5:179ra47–179ra47CrossRefGoogle Scholar
  25. Parkin B, Londoño-Joshi A, Kang Q, Tewari M, Rhim AD, Malek SN (2017) Ultrasensitive mutation detection identifies rare residual cells causing acute myelogenous leukemia relapse. J Clin Invest 127:3484–3495CrossRefGoogle Scholar
  26. Payne RE, Wang F, Su N, Krell J, Zebrowski A, Yagüe E, Ma X-J, Luo Y, Coombes RC (2012) Viable circulating tumour cell detection using multiplex RNA in situ hybridisation predicts progression-free survival in metastatic breast cancer patients. Br J Cancer 106:1790–1797CrossRefGoogle Scholar
  27. Pfitzner C, Schröder I, Scheungraber C, Dogan A, Runnebaum IB, Dürst M, Häfner N (2014) Digital-Direct-RT-PCR: a sensitive and specific method for quantification of CTC in patients with cervical carcinoma. Nature Publishing Group 4:3970Google Scholar
  28. Pierga J-Y, Bidard F-C, Denis MG, de Cremoux P (2007) Prognostic value of peripheral blood double detection of CK19 and MUC1 mRNA positive cells detected by RT-quantitative PCR in 94 breast cancer patients with a follow up of 9 years. Mol Oncol 1:267–268CrossRefGoogle Scholar
  29. Poste G, Fidler IJ (1980) The pathogenesis of cancer metastasis. Nature 283:139–146CrossRefGoogle Scholar
  30. Powell AA, Talasaz AH, Zhang H et al (2012) Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS ONE 7:e33788CrossRefGoogle Scholar
  31. Seiden MV, Kantoff PW, Krithivas K, Propert K, Bryant M, Haltom E, Gaynes L, Kaplan I, Bubley G, DeWolf W (1994) Detection of circulating tumor cells in men with localized prostate cancer. J Clin Oncol 12:2634–2639CrossRefGoogle Scholar
  32. Shen C, Hu L, Xia L, Li Y (2009) The detection of circulating tumor cells of breast cancer patients by using multimarker (Survivin, hTERT and hMAM) quantitative real-time PCR. Clin Biochem 42:194–200CrossRefGoogle Scholar
  33. Stott SL, Lee RJ, Nagrath S et al (2010) Isolation and characterization of circulating tumor cells from patients with localized and metastatic prostate cancer. Sci Transl Med 2:25ra23–25ra23CrossRefGoogle Scholar
  34. Ting DT, Wittner BS, Ligorio M et al (2014) Single-Cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep 8:1905–1918CrossRefGoogle Scholar
  35. Tirosh I, Venteicher AS, Hebert C et al (2016) Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature Publishing Group 539:309–313Google Scholar
  36. Vogelstein B, Kinzler KW (1999) Digital PCR. PNAS 96:9236–9241CrossRefGoogle Scholar
  37. Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N (2017) Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nature Publishing Group 17:223–238Google Scholar
  38. Wu S, Liu S, Liu Z, Huang J, Pu X, Li J, Yang D, Deng H, Yang N, Xu J (2015) Classification of circulating tumor cells by epithelial-mesenchymal transition markers. PLoS ONE 10:e0123976CrossRefGoogle Scholar
  39. Yu M, Stott S, Toner M, Maheswaran S, Haber DA (2011) Circulating tumor cells: approaches to isolation and characterization. J Cell Biol 192:373–382CrossRefGoogle Scholar
  40. Yu M, Ting DT, Stott SL et al (2012) RNA sequencing of pancreatic circulating tumour cells implicates WNT signalling in metastasis. Nature Publishing Group 487:510–513Google Scholar
  41. Yu M, Bardia A, Wittner BS et al (2013) Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science 339:580–584CrossRefGoogle Scholar
  42. Zhang X, Yuan X, Shi H, Wu L, Qian H, Xu W (2015) Exosomes in cancer: small particle, big player. J Hematol Oncol 8:83CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mark Kalinich
    • 1
  • Tanya T. Kwan
    • 1
  • Mehmet Toner
    • 2
  • Daniel A. Haber
    • 1
    • 3
  • Shyamala Maheswaran
    • 1
    Email author
  1. 1.Massachusetts General Hospital Cancer Center and Harvard Medical SchoolCharlestownUSA
  2. 2.Massachusetts General Hospital Center for Engineering in Medicine, Department of Surgery, and Shriner’s Children HospitalHarvard Medical SchoolCharlestownUSA
  3. 3.Howard Hughes Medical InstituteChevy ChaseUSA

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