Skip to main content

Single Cell Genomics for Tumor Heterogeneity

  • Chapter
  • First Online:
Translational Research in Breast Cancer

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1187))

Abstract

Single cell genomics became a universal and powerful tool to study cellular diversity at genomic levels in normal and disease conditions. Cancer is a disease of genomic instability which instigates clonal evolution and intra-tumoral heterogeneity. Cancer progression also accompanies gross alterations in the microenvironment, and the stromal or immune cell types comprising the tumor microenvironment can be explored by single cell genomics. So far, breast cancer has been analyzed by single cell genomic tools for the clonal evolution, inter- and intra-tumoral heterogeneity in molecular signatures, and tumor microenvironment. We will briefly go over those studies and discuss the potential application of single cell genomics for the diagnostics and 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 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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

Similar content being viewed by others

References

  1. Welch DR. Tumor heterogeneity--a ‘contemporary concept’ founded on historical insights and predictions. Cancer Res. 2016;76(1):4–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Schwartzberg L, Kim ES, Liu D, Schrag D. Precision oncology: who, how, what, when, and when not? Am Soc Clin Oncol Educ Book. 2017;37:160–9.

    Article  PubMed  Google Scholar 

  3. McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168(4):613–28.

    Article  CAS  PubMed  Google Scholar 

  4. Goldhirsch A, Ingle JN, Gelber RD, Coates AS, Thurlimann B, Senn HJ, et al. Thresholds for therapies: highlights of the St Gallen international expert consensus on the primary therapy of early breast cancer 2009. Ann Oncol. 2009;20(8):1319–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yersal O, Barutca S. Biological subtypes of breast cancer: prognostic and therapeutic implications. World J Clin Oncol. 2014;5(3):412–24.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Johnston SR, Dowsett M, Smith IE. Towards a molecular basis for tamoxifen resistance in breast cancer. Annals of oncology: official journal of the European society for. Med Oncol. 1992;3(7):503–11.

    CAS  Google Scholar 

  7. Mohd Sharial MS, Crown J, Hennessy BT. Overcoming resistance and restoring sensitivity to HER2-targeted therapies in breast cancer. Ann Oncol. 2012;23(12):3007–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502(7471):333–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Robinson DR, Wu YM, Vats P, Su F, Lonigro RJ, Cao X, et al. Activating ESR1 mutations in hormone-resistant metastatic breast cancer. Nat Genet. 2013;45(12):1446–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Toy W, Shen Y, Won H, Green B, Sakr RA, Will M, et al. ESR1 ligand-binding domain mutations in hormone-resistant breast cancer. Nat Genet. 2013;45(12):1439–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Shi W, Jiang T, Nuciforo P, Hatzis C, Holmes E, Harbeck N, et al. Pathway level alterations rather than mutations in single genes predict response to HER2-targeted therapies in the neo-ALTTO trial. Ann Oncol. 2017;28(1):128–35.

    Article  CAS  PubMed  Google Scholar 

  12. Loibl S, von Minckwitz G, Schneeweiss A, Paepke S, Lehmann A, Rezai M, et al. PIK3CA mutations are associated with lower rates of pathologic complete response to anti-human epidermal growth factor receptor 2 (her2) therapy in primary HER2-overexpressing breast cancer. J Clin Oncol. 2014;32(29):3212–20.

    Article  CAS  PubMed  Google Scholar 

  13. Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, et al. PyClone: statistical inference of clonal population structure in cancer. Nat Methods. 2014;11(4):396–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li B, Li JZ. A general framework for analyzing tumor subclonality using SNP array and DNA sequencing data. Genome Biol. 2014;15(9):473.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Ha G, Roth A, Khattra J, Ho J, Yap D, Prentice LM, et al. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res. 2014;24(11):1881–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Oesper L, Mahmoody A, Raphael BJ. THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data. Genome Biol. 2013;14(7):R80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Miller CA, White BS, Dees ND, Griffith M, Welch JS, Griffith OL, et al. SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput Biol. 2014;10(8):e1003665.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.

    Article  PubMed  CAS  Google Scholar 

  20. Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Navin N, Krasnitz A, Rodgers L, Cook K, Meth J, Kendall J, et al. Inferring tumor progression from genomic heterogeneity. Genome Res. 2010;20(1):68–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472(7341):90–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Krop I, Ismaila N, Andre F, Bast RC, Barlow W, Collyar DE, et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline focused update. J Clin Oncol. 2017;35(24):2838–47.

    Article  PubMed  Google Scholar 

  24. Henry NL, Bedard PL, DeMichele A. Standard and genomic tools for decision support in breast cancer treatment. Am Soc Clin Oncol Educ Book. 2017;37:106–15.

    Article  PubMed  Google Scholar 

  25. Mer AS, Klevebring D, Gronberg H, Rantalainen M. Study design requirements for RNA sequencing-based breast cancer diagnostics. Sci Rep. 2016;6:20200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Myers MB. Targeted therapies with companion diagnostics in the management of breast cancer: current perspectives. Pharmacogenom Person Med. 2016;9:7–16.

    CAS  Google Scholar 

  27. Chung W, Eum HH, Lee HO, Lee KM, Lee HB, Kim KT, et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat Commun. 2017;8:15081.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Erdogan B, Webb DJ. Cancer-associated fibroblasts modulate growth factor signaling and extracellular matrix remodeling to regulate tumor metastasis. Biochem Soc Trans. 2017;45(1):229–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14(10):1014–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Salgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an international TILs Working Group 2014. Ann Oncol. 2015;26(2):259–71.

    Article  CAS  PubMed  Google Scholar 

  31. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006;313(5795):1960–4.

    Article  CAS  PubMed  Google Scholar 

  32. Mao Y, Qu Q, Chen X, Huang O, Wu J, Shen K. The prognostic value of tumor-infiltrating lymphocytes in breast cancer: a systematic review and meta-analysis. PLoS One. 2016;11(4):e0152500.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Solinas C, Ceppi M, Lambertini M, Scartozzi M, Buisseret L, Garaud S, et al. Tumor-infiltrating lymphocytes in patients with HER2-positive breast cancer treated with neoadjuvant chemotherapy plus trastuzumab, lapatinib or their combination: a meta-analysis of randomized controlled trials. Cancer Treat Rev. 2017;57:8–15.

    Article  CAS  PubMed  Google Scholar 

  34. Krishnamurti U, Wetherilt CS, Yang J, Peng L, Li X. Tumor-infiltrating lymphocytes are significantly associated with better overall survival and disease-free survival in triple-negative but not estrogen receptor-positive breast cancers. Hum Pathol. 2017;64:7–12.

    Article  CAS  PubMed  Google Scholar 

  35. Alix-Panabieres C, Pantel K. Clinical applications of circulating tumor cells and circulating tumor DNA as liquid biopsy. Cancer Discov. 2016;6(5):479–91.

    Article  CAS  PubMed  Google Scholar 

  36. Allard WJ, Matera J, Miller MC, Repollet M, Connelly MC, Rao C, et al. Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases. Clin Cancer Res. 2004;10(20):6897–904.

    Article  PubMed  Google Scholar 

  37. Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004;351(8):781–91.

    Article  CAS  PubMed  Google Scholar 

  38. Cristofanilli M, Hayes DF, Budd GT, Ellis MJ, Stopeck A, Reuben JM, et al. Circulating tumor cells: a novel prognostic factor for newly diagnosed metastatic breast cancer. J Clin Oncol. 2005;23(7):1420–30.

    Article  PubMed  Google Scholar 

  39. Pestrin M, Salvianti F, Galardi F, De Luca F, Turner N, Malorni L, 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):749–57.

    Article  CAS  PubMed  Google Scholar 

  40. Jiang R, Lu YT, Ho H, Li B, Chen JF, Lin M, et al. A comparison of isolated circulating tumor cells and tissue biopsies using whole-genome sequencing in prostate cancer. Oncotarget. 2015;6(42):44781–93.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bingham C, Fernandez SV, Fittipaldi P, Dempsey PW, Ruth KJ, Cristofanilli M, et al. Mutational studies on single circulating tumor cells isolated from the blood of inflammatory breast cancer patients. Breast Cancer Res Treat. 2017;163(2):219–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lohr JG, Kim S, Gould J, Knoechel B, Drier Y, Cotton MJ, et al. Genetic interrogation of circulating multiple myeloma cells at single-cell resolution. Sci Transl Med. 2016;8(363):363ra147.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Li H, Courtois ET, Sengupta D, Tan Y, Chen KH, Goh JJL, et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat Genet. 2017;49(5):708–18.

    Article  CAS  PubMed  Google Scholar 

  44. Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352(6282):189–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344(6190):1396–401.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell. 2017;171(7):1611–24.e24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Suva ML, Rheinbay E, Gillespie SM, Patel AP, Wakimoto H, Rabkin SD, et al. Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells. Cell. 2014;157(3):580–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Krishnaswami SR, Grindberg RV, Novotny M, Venepally P, Lacar B, Bhutani K, et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat Protoc. 2016;11(3):499–524.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ofengeim D, Giagtzoglou N, Huh D, Zou C, Yuan J. Single-cell RNA sequencing: unraveling the brain one cell at a time. Trends Mol Med. 2017;23(6):563–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gao R, Kim C, Sei E, Foukakis T, Crosetto N, Chan LK, et al. Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer. Nat Commun. 2017;8(1):228.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Ferrante TC, Terry R, et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat Protoc. 2015;10(3):442–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ke R, Mignardi M, Pacureanu A, Svedlund J, Botling J, Wahlby C, et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat Methods. 2013;10(9):857–60.

    Article  CAS  PubMed  Google Scholar 

  53. Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C, Borowsky AD, et al. Multiplexed ion beam imaging of human breast tumors. Nat Med. 2014;20(4):436–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 2014;11(4):417–22.

    Article  CAS  PubMed  Google Scholar 

  55. Dey SS, Kester L, Spanjaard B, Bienko M, van Oudenaarden A. Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol. 2015;33(3):285–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Han KY, Kim KT, Joung JG, Son DS, Kim YJ, Jo A, et al. SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome Res. 2018;28(1):75–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Han L, Zi X, Garmire LX, Wu Y, Weissman SM, Pan X, et al. Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform. Sci Rep. 2014;4:6485.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hou Y, Guo H, Cao C, Li X, Hu B, Zhu P, et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 2016;26(3):304–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Macaulay IC, Haerty W, Kumar P, Li YI, Hu TX, Teng MJ, et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Methods. 2015;12(6):519–22.

    Article  CAS  PubMed  Google Scholar 

  60. Peterson VM, Zhang KX, Kumar N, Wong J, Li L, Wilson DC, et al. Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol. 2017;35(10):936–9.

    Article  CAS  PubMed  Google Scholar 

  61. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14(9):865–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Rozenblatt-Rosen O, Stubbington MJT, Regev A, Teichmann SA. The human cell atlas: from vision to reality. Nature. 2017;550(7677):451–3.

    Article  CAS  PubMed  Google Scholar 

  63. Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hae-Ock Lee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lee, HO., Park, WY. (2021). Single Cell Genomics for Tumor Heterogeneity. In: Noh, DY., Han, W., Toi, M. (eds) Translational Research in Breast Cancer. Advances in Experimental Medicine and Biology, vol 1187. Springer, Singapore. https://doi.org/10.1007/978-981-32-9620-6_10

Download citation

Publish with us

Policies and ethics