Skip to main content
SpringerLink
Log in
Book cover

Ovarian Cancer: Molecular & Diagnostic Imaging and Treatment Strategies pp 113–123Cite as

Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges

  • Chapter
  • First Online:

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1330))

Abstract

Ovarian cancer remains the leading cause of death from gynecologic malignancy in the Western world. Tumors are comprised of heterogeneous populations of various cancer, immune, and stromal cells; it is hypothesized that rare cancer stem cells within these subpopulations lead to disease recurrence and treatment resistance. Technological advances now allow for the analysis of tumor genomes and transcriptomes at the single-cell level, which provides the resolution to potentially identify these rare cancer stem cells within the larger tumor.

In this chapter, we review the evolution of next-generation RNA sequencing techniques, the methodology of single-cell isolation and sequencing, sequencing data analysis, and the potential applications in ovarian cancer. We also summarize the current published work using single-cell sequencing in ovarian cancer.

By utilizing this novel technique to characterize the gene expression of rare subpopulations, new targets and treatment pathways may be identified in ovarian cancer to change treatment paradigms.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
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

Learn about institutional subscriptions

References

  1. Siegel, R. L., Miller, K. D., & Jemal, A. (2018). Cancer statistics, 2018. CA: A Cancer Journal for Clinicians, 68(1), 7–30.

    Google Scholar 

  2. Howlader, N., Noone, A. M., Krapcho, M., Miller, D., Bishop, K., Kosary, C. L., Yu, M., Ruhl, J., Tatalovich, Z., Mariotto, A., Lewis, D. R., Chen, H. S., Feuer, E. J., & Cronin, K. A. (Eds.). SEER cancer statistics review, 1975–2014. Bethesda, MD: National Cancer Institute. Retrieved from https://seer.cancer.gov/csr/1975_2014/, based on November 2016 SEER data submission, posted to the SEER web site, April 2017.

  3. Ng, J. S., Low, J. J., & Ilancheran, A. (2012). Epithelial ovarian cancer. Best Practice & Research Clinical Obstetrics & Gynaecology, 26, 337–345.

    Article  Google Scholar 

  4. Garraway, L. A., Verweij, J., & Ballman, K. V. (2013). Precision oncology: An overview. Journal of Clinical Oncology, 31(15), 1803–1805.

    Article  PubMed  Google Scholar 

  5. Werner, H. M., Mills, G. B., & Ram, P. T. (2014). Cancer systems biology: A peek into the future of patient care? Nature Reviews. Clinical Oncology, 11(3), 167–176.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Rubin, E. H., et al. (2014). Developing precision medicine in a global world. Clinical Cancer Research, 20(6), 1419–1427.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Schwaederle, M., et al. (2015). Impact of precision medicine in diverse cancers: A meta-analysis of phase II clinical trials. Journal of Clinical Oncology, 33(32), 3817–3825.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cancer Genome Atlas Research Network. (2011). Integrated genomic analyses of ovarian carcinoma. Nature, 474, 609–615.

    Article  CAS  Google Scholar 

  9. Patch, A. M., Christie, E. L., Etemadmoghadam, D., Garsed, D. W., George, J., Fereday, S., Nones, K., Cowin, P., Alsop, K., Bailey, P. J., et al. (2015). Whole-genome characterization of chemoresistant ovarian cancer. Nature, 521, 489–494.

    Article  CAS  PubMed  Google Scholar 

  10. Verhaak, R. G., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010). Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell, 17, 98–110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Verhaak, R., Tamayo, P., Yang, J. Y., Hubbard, D., Zhang, H., Creighton, C. J., Fereday, S., Lawrence, M., Carter, S. L., Mermel, C. H., et al. (2013). Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. Journal of Clinical Investigation, 123, 517–525.

    CAS  PubMed  Google Scholar 

  12. Tothill, R. W., Tinker, A. V., George, J., et al. (2008). Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clinical Cancer Research, 14, 5198–5208.

    Article  CAS  PubMed  Google Scholar 

  13. Lee, J. Y., Yoon, J. K., Kim, B., Kim, S., Kim, M. A., Lim, H., et al. (2015). Tumor evolution and intratumor heterogeneity of an epithelial ovarian cancer investigated using next-generation sequencing. BMC Cancer, 15, 85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Donati, G. (2016). The niche in single-cell technologies. Immunology and Cell Biology, 94, 250–255.

    Article  CAS  PubMed  Google Scholar 

  15. Van Dijk, E. L., Auger, H., Jaszczyszyn, Y., & Thermes, C. (2014). Ten years of next-generation sequencing technology. Trends in Genetics, 30, 418–426.

    Article  PubMed  CAS  Google Scholar 

  16. Li, G. W., & Xie, X. S. (2011). Central dogma at the single-molecule level in living cells. Nature, 475, 308–315.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Raj, A., & van Oudenaarden, A. (2008). Nature, nurture, or chance: Stochastic gene expression and its consequences. Cell, 135, 216–226.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Van Gelder, R. N., von Zastrow, M. E., Yool, A., Dement, W. C., Barchas, J. D., & Eberwine, J. H. (1990). Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proceedings of the National Academy of Sciences of the United States of America, 87, 1663–1667.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Dean, F. B., Hosono, S., Fang, L., Wu, X., Faruqi, A. F., Bray-Ward, P., Sun, Z., Zong, Q., Du, Y., Du, J., et al. (2002). Comprehensive human genome amplification using multiple displacement amplification. Proceedings of the National Academy of Sciences of the United States of America, 99, 5261–5266.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Telenius, H., Carter, N. P., Bebb, C. E., Nordenskjold, M., Ponder, B. A., & Tunnacliffe, A. (1992). Degenerate oligonucleotide-primed PCR: General amplification of target DNA by a single degenerate primer. Genomics, 13, 718–725.

    Article  CAS  PubMed  Google Scholar 

  21. Mardis, E. R. (2011). A decade’s perspective on DNA sequencing technology. Nature, 470, 198–203.

    Article  CAS  PubMed  Google Scholar 

  22. Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009). mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods, 6, 377–382.

    Article  CAS  PubMed  Google Scholar 

  23. Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., et al. (2011). Tumour evolution inferred by single-cell sequencing. Nature, 472, 90–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lake, B. B., Codeluppi, S., Yung, Y. C., Gao, D., Chun, J., Kharchenko, P. V., et al. (2017). A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Scientific Reports, 7, 6031.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Gawad, C., Koh, W., & Quake, S. R. (2016). Single-cell genome sequencing: Current state of the science. Nature Reviews. Genetics, 17, 175–188.

    Article  CAS  PubMed  Google Scholar 

  26. Navin, N. E. (2014). Cancer genomics: One cell at a time. Genome Biology, 15(8), 452.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Gierahn, T. M., Wadsworth, M. H., et al. (2017). Seq-Well: Portable, low cost RNA sequencing of single cells at high throughput. Nature Methods, 14, 395–398.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vitak, S. A., Torkenczy, K. A., Rosenkrantz, J. L., et al. (2017). Sequencing thousands of single-cell genomes with combinatorial indexing. Nature Methods, 14, 302–308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cao, J., Packer, J. S., Ramani, V., et al. (2017). Comprehensive single-cell transcriptional profiling of a multicellular organism. Science, 357(6352), 661–667.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sheng, K., Cao, W., Niu, Y., Deng, Q., & Zong, C. (2017). Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nature Methods, 14, 267–270.

    Article  CAS  PubMed  Google Scholar 

  31. Fan, X., Zhang, X., Wu, X., Guo, H., Hu, Y., Tang, F., et al. (2015). Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biology, 16, 148.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Kivioja, T., Vähärautio, A., Karlsson, K., Bonke, M., Enge, M., Linnarsson, S., et al. (2011). Counting absolute numbers of molecules using unique molecular identifiers. Nature Methods, 9, 72–74.

    Article  PubMed  CAS  Google Scholar 

  33. Bacher, R., & Kendziorski, C. (2016). Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biology, 17, 63.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Wagner, A., Regev, A., & Yosef, N. (2016). Revealing the vectors of cellular identity with single-cell genomics. Nature Biotechnology, 34, 1145–1160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Miragaia, R. J., Teichmann, S. A., & Hagai, T. (2017). Single-cell insights into transcriptomic diversity in immunity. Current Opinion in Systems Biology, 5, 63–71.

    Article  Google Scholar 

  36. Poirion, O. B., Zhu, X., Ching, T., & Garmire, L. (2016). Single-cell transcriptomics bioinformatics and computational challenges. Frontiers in Genetics, 7, 163.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Rostom, R., Svensson, V., Teichmann, S. A., & Kar, G. (2017). Computational approaches for interpreting scRNA-seq data. FEBS Letters, 591(15), 2213–2225.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Freytag, S., Tian, L., Lönnstedt, I., Ng, M., & Bahlo, M. (2018). Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res, 7, 1297.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Ramskold, D., Luo, S., Wang, Y. C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012). Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnology, 30, 777–782.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Aceto, N., Bardia, A., Miyamoto, D. T., Donaldson, M. C., Wittner, B. S., Spencer, J. A., Yu, M., Pely, A., Engstrom, A., Zhu, H., et al. (2014). Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell, 158, 1110–1122.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Patel, A. P., Tirosh, I., Trombetta, J. J., et al. (2014). Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 344(6190), 1396–1401.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Yachida, S., et al. (2010). Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature, 467, 1114–1117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Eppert, K., et al. (2011). Stem cell gene expression programs influence clinical outcome in human leukemia. Nature Medicine, 17, 1086–1093.

    Article  CAS  PubMed  Google Scholar 

  44. Parsons, D. W., et al. (2008). An integrated genomic analysis of human glioblastoma multiforme. Science, 321, 1807–1812.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gerlinger, M., et al. (2012). Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England Journal of Medicine, 366, 883–892.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Driessens, G., et al. (2012). Defining the mode of tumour growth by clonal analysis. Nature, 488, 527–530.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Schepers, A. G., et al. (2012). Lineage tracing reveals Lgr5+ stem cell activity in mouse intestinal adenomas. Science, 337, 730–735.

    Article  CAS  PubMed  Google Scholar 

  48. Winterhoff, B. J., Maile, M., Mitra, A. K., Sebe, A., Bazzaro, M., Geller, M. A., et al. (2017). Single cell sequencing reveals heterogeneity within ovarian cancer epithelium and cancer associated stromal cells. Gynecologic Oncology, 144, 598–606.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Konecny, G. E., Wang, C., Hamidi, H., Winterhoff, B., Kalli, K. R., Dering, J., et al. (2014). Prognostic and therapeutic relevance of molecular subtypes in high-grade serous ovarian cancer. Journal of the National Cancer Institute, 106, dju249.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Cai, D. L., & Jin, L. P. (2017). Immune cell population in ovarian tumor microenvironment. Journal of Cancer, 8, 2915–2923.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Navin, N. E. (2014). Tumor evolution in response to chemotherapy: Phenotype versus genotype. Cell Reports, 6, 417–419.

    Article  CAS  PubMed  Google Scholar 

  52. Almendro, V., Cheng, Y. K., Randles, A., Itzkovitz, S., Marusyk, A., Ametller, E., et al. (2014). Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Reports, 6, 514–527.

    Article  CAS  PubMed  Google Scholar 

  53. Shah, M. M., & Landen, C. N. (2014). Ovarian cancer stem cells: Are they real and why are they important? Gynecologic Oncology, 132, 483–489.

    Article  PubMed  Google Scholar 

  54. Burgos-Ojeda, D., Rueda, B. R., & Buckanovich, R. J. (2012). Ovarian cancer stem cell markers: Prognostic and therapeutic implications. Cancer Letters, 322, 1–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Chen, W., Dong, J., Haiech, J., Kilhoffer, M. C., & Zeniou, M. (2016). Cancer stem cell quiescence and plasticity as major challenges in cancer therapy. Stem Cells International, 2016, 1740936.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Nassar, D., & Blanpain, C. (2016). Cancer stem cells: Basic concepts and therapeutic implications. Annual Review of Pathology, 11, 47–76.

    Article  CAS  PubMed  Google Scholar 

  57. Takeishi, S., & Nakayama, K. I. (2016). To wake up cancer stem cells, or to let them sleep, that is the question. Cancer Science, 107, 875–881.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Bast, R. C., Hennessy, B., & Mills, G. B. (2009). The biology of ovarian cancer: New opportunities for translation. Nature Reviews. Cancer, 9, 415–428.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Fidler, I. J. (2003). The pathogenesis of cancer metastasis: The ‘seed and soil’ hypothesis revisited. Nature Reviews. Cancer, 3, 453–458.

    Article  CAS  PubMed  Google Scholar 

  60. Micalizzi, D. S., Maheswaran, S., & Haber, D. A. (2017). A conduit to metastasis: Circulating tumor cell biology. Genes & Development, 31, 1827–1840.

    Article  CAS  Google Scholar 

  61. Ramsköld, D., Luo, S., Wang, Y. C., Li, R., Deng, Q., Faridani, O. R., et al. (2012). Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature Biotechnology, 30, 777–782.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Ting, D. T., Wittner, B. S., Ligorio, M., Vincent Jordan, N., Shah, A. M., Miyamoto, D. T., et al. (2014). Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Reports, 8, 1905–1918.

    Article  CAS  PubMed  Google Scholar 

  63. Théry, C., Ostrowski, M., & Segura, E. (2009). Membrane vesicles as conveyors of immune responses. Nature Reviews. Immunology, 9, 581–593.

    Article  PubMed  CAS  Google Scholar 

  64. Liu, Y., Gu, Y., & Cao, X. (2015). The exosomes in tumor immunity. Oncoimmunology, 4, e1027472.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. James Hurley, L. H., Brock, G., Sinclair, I., Sullivan, R. J., O’Neill, V. J., Skog, J., & Flaherty, K. (2015). Profiling exosomal mRNAs in patients undergoing immunotherapy for malignant melanoma. Journal of Clinical Oncology, 33, e22159.

    Article  Google Scholar 

  66. Shih, A. J., Menzin, A., Whyte, J., Lovecchio, J., Liew, A., Khalili, H., et al. (2018). Identification of grade and origin specific cell populations in serous epithelial ovarian cancer by single cell RNA-seq. PLoS One, 13, e0206785.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Izar, B., Tirosh, I., Stover, E. H., Wakiro, I., Cuoco, M. S., et al. (2020). A single-cell landscape of high-grade serous ovarian cancer. Nature Medicine, 26(8), 1271–1279.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. HERCULES Project: Comprehensive characterization and effective combinatorial targeting of high-grade serous ovarian cancer via single-cell analysis. Retrieved from http://www.project-hercules.eu.

Download references

Author information

Authors and Affiliations

  1. Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Women’s Health, University of Minnesota School of Medicine, Minneapolis, MN, USA

    Shobhana Talukdar, Zenas Chang, Boris Winterhoff & Timothy K. Starr

  2. Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA

    Boris Winterhoff & Timothy K. Starr

  3. Institute of Health Informatics, University of Minnesota, Minneapolis, MN, USA

    Timothy K. Starr

Authors
  1. Shobhana Talukdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zenas Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Boris Winterhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Timothy K. Starr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Timothy K. Starr .

Editor information

Editors and Affiliations

  1. Dept. Veterinary Pathobiology, University of Missouri-Columbia Dept. Veterinary Pathobiology, COLUMBIA, MO, USA

    Heide Schatten

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Talukdar, S., Chang, Z., Winterhoff, B., Starr, T.K. (2021). Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges. In: Schatten, H. (eds) Ovarian Cancer: Molecular & Diagnostic Imaging and Treatment Strategies. Advances in Experimental Medicine and Biology, vol 1330. Springer, Cham. https://doi.org/10.1007/978-3-030-73359-9_7

Download citation

Publish with us

Policies and ethics

Search

Navigation