Indian Journal of Clinical Biochemistry

, Volume 34, Issue 1, pp 3–18 | Cite as

Single Cell Omics of Breast Cancer: An Update on Characterization and Diagnosis

  • Shailendra Dwivedi
  • Purvi Purohit
  • Radhieka Misra
  • Malavika Lingeswaran
  • Jeewan Ram Vishnoi
  • Puneet Pareek
  • Sanjeev Misra
  • Praveen SharmaEmail author
Review Article


Breast cancer is recognized for its different clinical behaviors and patient outcomes, regardless of common histopathological features at diagnosis. The heterogeneity and dynamics of breast cancer undergoing clonal evolution produces cells with distinct degrees of drug resistance and metastatic potential. Presently, single cell analysis have made outstanding advancements, overshadowing the hurdles of heterogeneity linked with vast populations. The speedy progression in sequencing analysis now allow unbiased, high-output and high-resolution elucidation of the heterogeneity from individual cell within a population. Classical therapeutics strategies for individual patients are governed by the presence and absence of expression pattern of the estrogen and progesterone receptors and human epidermal growth factor receptor 2. However, such tactics for clinical classification have fruitfulness in selection of targeted therapies, short-term patient responses but unable to predict the long-term survival. In any phenotypic alterations, like breast cancer disease, molecular signature have proven its implication, as we aware that individual cell’s state is regulated at diverse levels, such as DNA, RNA and protein, by multifaceted interplay of intrinsic biomolecules pathways existing in the organism and extrinsic stimuli such as ambient environment. Thus for complete understanding, complete profiling of single cell requires a synchronous investigations from different levels (multi-omics) to avoid incomplete information produced from single cell. In this article, initially we briefed on novel updates of various methods available to explore omics and then we finally pinpointed on various omics (i.e. genomics, transcriptomics, epigenomics, proteomics and metabolomics) data and few special aspects of circulating tumor cells, disseminated tumor cells and cancer stem cells, so far available from various studies that can be used for better management of breast cancer patients.


Single cell omics Genomics Transcriptomics Proteomics Molecular sub-typing of breast cancer 



Funding was provided by Science and Engineering Research Board (Grant No. PDF/2015/000322).

Compliance with Ethical Standards

Conflict of interest

All Authors declare that there is no conflict of interest.

Ethical Approval

This article is review article, so does not contain any studies with animals performed by any of the authors.


  1. 1.
    Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63:11–30.CrossRefGoogle Scholar
  2. 2.
    Raina V, Tyagi BB, Manoharan N. Two year report of the population based cancer registries, 2004–2005. Incidence and distribution of cancer. New Delhi: National Cancer Registry Programme, Indian Council of Medical Research; 2009. p. 63–5.
  3. 3.
    Dwivedi S, Chikara G, Samdariya S, Pareek P, Sharma P, Khattri S, et al. Molecular biotechnology for diagnostics. In: Khan MS, Khan IA, Barh D, editors. Applied molecular biotechnology: the next generation of genetic engineering. New Delhi: CRC Press, Taylor & Francis Group, Inc; 2016. p. 303–33.CrossRefGoogle Scholar
  4. 4.
    Cai L, Dalal CK, Elowitz MB. Frequency-modulated nuclear localization bursts coordinate gene regulation. Nature. 2008;455:485–90.CrossRefGoogle Scholar
  5. 5.
    Polyak K. Breast cancer: origins and evolution. J Clin Investig. 2007;117:3155–63.CrossRefGoogle Scholar
  6. 6.
    Dwivedi S, Sharma P. Prospects of molecular biotechnology in diagnostics: step towards precision medicine. Indian J Clin Biochem. 2017;32(2):121–3.CrossRefGoogle Scholar
  7. 7.
    Dwivedi S, Purohit P, Misra R, Pareek P, Goel A, Khattri S, et al. Diseases and molecular diagnostics: a step closer to precision medicine. Indian J Clin Biochem. 2017;32(4):374–98.CrossRefGoogle Scholar
  8. 8.
    Dwivedi S, Shukla KK, Gupta G, Sharma P. Non-invasive biomarker in prostate carcinoma: a novel approach. Indian J Clin Biochem. 2013;28(2):107–9.CrossRefGoogle Scholar
  9. 9.
    Dwivedi S, Goel A, Mandhani A, Khattri S, Sharma P, Misra S, et al. Functional genetic variability at promoters of pro-(IL-18) and anti-(IL-10) inflammatory affects their mRNA expression and survival in prostate carcinoma patients: five year follow-up study. Prostate. 2015;75(15):1737–46.CrossRefGoogle Scholar
  10. 10.
    Dwivedi S, Goel A, Khattri S, Mandhani A, Sharma P, Misra S, et al. Genetic variability at promoters of IL-18 pro- and IL-10 anti-inflammatory gene affects susceptibility and their circulating serum levels: an explorative study of prostate cancer patients in North Indian populations. Cytokine. 2015;74(1):117–22.CrossRefGoogle Scholar
  11. 11.
    Dwivedi S, Goel A, Khattri S, Mandhani A, Sharma P, Pant KK. Tobacco exposure by various modes may alter pro-inflammatory (IL-12) and anti-inflammatory (IL-10) levels and affects the survival of prostate carcinoma patients: an explorative study in North Indian population. Biomed Res Int. 2014;2014:158530.CrossRefGoogle Scholar
  12. 12.
    Sharma P, Dwivedi S. Nutrigenomics and nutrigenetics: new insight in disease prevention and cure. Indian J Clin Biochem. 2017;32(4):371–3.CrossRefGoogle Scholar
  13. 13.
    Dwivedi S, Shukla S, Goel A, Sharma P, Khattri S, Pant KK. Nutrigenomics in breast cancer. In: Barh D, editor. Omics approaches in breast cancer. New Delhi: Springer; 2014. p. 127–51.Google Scholar
  14. 14.
    Dwivedi S, Purohit P, Misra R, Pareek P, Vishnoi JR, Sharma P, et al. Methods for isolation of high quality and quantity of RNA and single cell suspension for flow-cytometry from cancer tissue: a comparative analysis. Indian J Clin Biochem. 2017. Scholar
  15. 15.
    Carter NP, Bebb CE, Nordenskjo M, Tunnacliffe A. Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. Genomics. 1992;13:718–25.CrossRefGoogle Scholar
  16. 16.
    Paunio T, Reima I, Syvänen A-C. Preimplantation diagnosis by whole-genome amplification, PCR amplification, and solid-phase minisequencing of blastomere DNA. Clin Chem. 1996;42:1382–90.PubMedGoogle Scholar
  17. 17.
    Dean FB, Hosono S, Fang L, Wu X, Faruqi AF, Bray-Ward P, et al. Comprehensive human genome amplification using multiple displacement amplification. Proc Natl Acad Sci. 2002;99:5261–6.CrossRefGoogle Scholar
  18. 18.
    Lasken RS. Single-cell sequencing in its prime. Nat Biotechnol. 2013;31:211–2.CrossRefGoogle Scholar
  19. 19.
    Zong C, Lu S, Chapman AR, Xie XS. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science. 2012;338:1622–6.CrossRefGoogle Scholar
  20. 20.
    Huang L, Ma F, Chapman A, Lu S, Xie XS. Single-cell whole-genome amplification and sequencing: methodology and applications. Annu Rev Genom Hum Genet. 2015;16:79–102.CrossRefGoogle Scholar
  21. 21.
    Chen C, Xing D, Tan L, Li H, Zhou G, Huang L, et al. Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI). Science. 2017;356:189–94.CrossRefGoogle Scholar
  22. 22.
    Tsoucas D, Yuan GC. Recent progress in single-cell cancer genomics. Curr Opin Genet Dev. 2017;42:22–32.CrossRefGoogle Scholar
  23. 23.
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome as-sembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CrossRefGoogle Scholar
  24. 24.
    Ni X, Zhuo M, Su Z, Duan J, Gao Y, Wang Z, et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer pa-tients. Proc Natl Acad Sci. 2013;110:21083–8.CrossRefGoogle Scholar
  25. 25.
    Demeulemeester J, Kumar P, Møller EK, Nord S, Wedge DC, Peterson A, et al. Tracing the origin of disseminated tumor cells in breast cancer using single-cell se-quencing. Genome Biol. 2016;17:250.CrossRefGoogle Scholar
  26. 26.
    Baslan T, Kendall J, Rodgers L, Cox H, Riggs M, Stepansky A, et al. Genome-wide copy number analysis of single cells. Nat Protoc. 2012;7:1024–41.CrossRefGoogle Scholar
  27. 27.
    Xu X, Hou Y, Yin X, Bao L, Tang A, Song L, et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell. 2012;148:886–95.CrossRefGoogle Scholar
  28. 28.
    Janiszewska M, Liu L, Almendro V, Kuang Y, Paweletz C, Sakr RA, et al. In situ single-cell analysis identifies heterogeneity for PIK3 CA mutation and HER2 amplification in HER2-positive breast cancer. Nat Genet. 2015;47:1212–9.CrossRefGoogle Scholar
  29. 29.
    Spits C, Le Caignec C, De Rycke M, Van Haute L, Van Steirteghem A, Liebaers I, et al. Whole genome multiple displacement amplification from single cells. Nat Protoc. 2006;1:1965–70.CrossRefGoogle Scholar
  30. 30.
    Van Loo P, Voet T. Single cell analysis of cancer genomes. Curr Opin Genet Dev. 2014;24:82–91.CrossRefGoogle Scholar
  31. 31.
    Dean FB, Hosono S, Fang L, Wu X, Faruqi AF, Bray-Ward P, et al. Comprehensive human genome amplification using multiple displacement amplification. Proc Natl Acad Sci USA. 2002;99:5261–6.CrossRefGoogle Scholar
  32. 32.
    Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61–70.CrossRefGoogle Scholar
  33. 33.
    Guo H, Zhu P, Wu X, Li X, Wen L, Tang F. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 2013;23:2126–35.CrossRefGoogle Scholar
  34. 34.
    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;26:304–19.CrossRefGoogle Scholar
  35. 35.
    Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods. 2014;11:817–20.CrossRefGoogle Scholar
  36. 36.
    Jin W, Tang Q, Wan M, Cui K, Zhang Y, Ren G, et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature. 2015;528:142–6.PubMedPubMedCentralGoogle Scholar
  37. 37.
    Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502:59–64.CrossRefGoogle Scholar
  38. 38.
    Widschwendter M, Berger J, Müller HM, Zeimet AG, Marth C. Epigenetic downregulation of the retinoic acid receptor-beta2 gene in breast cancer. J Mammary Gland Biol Neoplasia. 2001;6:193–201.CrossRefGoogle Scholar
  39. 39.
    Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82.CrossRefGoogle Scholar
  40. 40.
    Guo G, Huss M, Tong GQ, Wang C, Li Sun L, Clarke ND, et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev Cell. 2010;18:675–85.CrossRefGoogle Scholar
  41. 41.
    Dalerba P, Kalisky T, Sahoo D, Rajendran PS, Rothenberg ME, Leyrat AA, et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat Biotechnol. 2011;29:1120–7.CrossRefGoogle Scholar
  42. 42.
    Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348:aaa6090.CrossRefGoogle Scholar
  43. 43.
    Lovatt D, Ruble BK, Lee J, Dueck H, Kim TK, Fisher S, et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat Methods. 2014;11:190–6.CrossRefGoogle Scholar
  44. 44.
    Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Yang JL, Ferrante TC, et al. Highly multiplexed subcellular RNA sequencing in situ. Science. 2014;343:1360–3.CrossRefGoogle Scholar
  45. 45.
  46. 46.
    Batchelor E, Loewer A, Lahav G. The ups and downs of p53: understanding protein dynamics in single cells. Nat Rev Cancer. 2009;9:371–7.CrossRefGoogle Scholar
  47. 47.
    Tang X, Lin C-C, Spasojevic I, Iversen ES, Chi J-T, Marks JR. A joint analysis of metabolomics and genetics of breast cancer. Breast Cancer Res BCR. 2014;16:415. Scholar
  48. 48.
    Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–52.CrossRefGoogle Scholar
  49. 49.
    Sørlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003;100:8418–23.CrossRefGoogle Scholar
  50. 50.
    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14.CrossRefGoogle Scholar
  51. 51.
    Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, et al. Breast cancer classification and prognosis based on gene expression profiles from a populationbased study. Proc Natl Acad Sci USA. 2003;100:10393–8.CrossRefGoogle Scholar
  52. 52.
    Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J, et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst. 2009;101:736–50.CrossRefGoogle Scholar
  53. 53.
    Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science. 1987;235:177–82.CrossRefGoogle Scholar
  54. 54.
    Mukai H. Treatment strategy for HER2-positive breast cancer. Int J Clin Oncol. 2010;15:335–40.CrossRefGoogle Scholar
  55. 55.
    Carey LA. Breast cancer: HER2ea good addiction. Nat Rev Clin Oncol. 2012;9:196–7.CrossRefGoogle Scholar
  56. 56.
    Wetzels RH, Holland R, van Haelst UJ, Lane EB, Leigh IM, Ramaekers FC. Detection of basement membrane components and basal cell keratin 14 in noninvasive and invasive carcinomas of the breast. Am J Pathol. 1989;134:571–9.PubMedPubMedCentralGoogle Scholar
  57. 57.
    Carey LA, Dees EC, Sawyer L, Gatti L, Moore DT, Collichio F, et al. The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes. Clin Cancer Res. 2007;13:2329–34.CrossRefGoogle Scholar
  58. 58.
    Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 2010;12:R68.CrossRefGoogle Scholar
  59. 59.
    Dwivedi S, Purohit P, Mittal P, Goel A, Verma R, Khattri S, et al. Genetic engineering: towards gene therapy and molecular medicine. In: Barh D, Azevedo V, editors. Omics technologies and bio-engineering: towards improving quality of life. Cambridge: Academic Press; 2017. p. 507–30.Google Scholar
  60. 60.
    Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genom. 2006;27(7):96.CrossRefGoogle Scholar
  61. 61.
    Lacroix M, Leclercq G. About GATA3, HNF3A, and XBP1, three genes co-expressed with the oestrogen receptor-alpha gene (ESR1) in breast cancer. Mol Cell Endocrinol. 2004;219:1–7.CrossRefGoogle Scholar
  62. 62.
    Nielsen TO, Hsu FD, Jensen K, Cheang M, Karaca G, Hu Z, et al. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin Cancer Res. 2004;10:5367–74.CrossRefGoogle Scholar
  63. 63.
    Bertucci F, Finetti P, Cervera N, Charafe-Jauffret E, Mamessier E, Adélaïde J, et al. Gene expression profiling shows medullary breast cancer is a subgroup of basal breast cancer. Cancer Res. 2006;66:4636–44.CrossRefGoogle Scholar
  64. 64.
    Lacroix M, Leclercq G. Hereditary breast cancer: an update on genotype and phenotype. In: Yao PA, editor. New breast cancer research. New York: Nova Science Publishers; 2006. p. 27–51.Google Scholar
  65. 65.
    Charafe-Jauffret E, Ginestier C, Monville F. Gene expression profiling of breast cell lines identifies potential new basal markers. Oncogene. 2006;25:2273–84.CrossRefGoogle Scholar
  66. 66.
    Dwivedi S, Goel A, Sadashiv Verma A, Shukla S, Sharma P, et al. Molecular diagnosis of metastasizing breast cancer based upon liquid biopsy. In: Barh D, editor. Omics approaches in breast cancer. New Delhi: Springer; 2014. p. 425–59.Google Scholar
  67. 67.
    Dwivedi S, Purohit P, Misra R, Pareek P, Goel A, Khattri S, et al. Diseases and molecular diagnostics: a step closer to precision medicine. Indian J Clin Biochem. 2017;32:374–98.CrossRefGoogle Scholar
  68. 68.
    Rhim AD, Mirek ET, Aiello NM, Maitra A, Bailey JM, McAllister F, et al. EMT and dissemination precede pancreatic tumor formation. Cell. 2012;148(1–2):349–61.CrossRefGoogle Scholar
  69. 69.
    Hodgkinson CL, Morrow CJ, Li Y, Metcalf RL, Rothwell DG, Trapani F, et al. Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer. Nat Med. 2014;20(8):897–903.CrossRefGoogle Scholar
  70. 70.
    Yoon HJ, Kim TH, Zhang Z, Azizi E, Pham TM, Paoletti C, et al. Sensitive capture of circulating tumour cells by functionalized graphene oxide nanosheets. Nat Nanotechnol. 2013;8:735–41.CrossRefGoogle Scholar
  71. 71.
    Yao X, Choudhury AD, Yamanaka YJ, Adalsteinsson VA, Gierahn TM, Williamson CA, et al. Functional analysis of single cells identifies a rare subset of circulating tumor cells with malignant traits. Integr Biol (Camb). 2014;6:388–98.CrossRefGoogle Scholar
  72. 72.
    Levitin HM, Yuan J, Sims PA. Single-cell transcriptomic analysis of tumor heterogeneity. Trends Cancer. 2018;4(4):264–8.CrossRefGoogle Scholar
  73. 73.
    Wicha MS, Liu S, Dontu G. Cancer stem cells: an old idea a paradigm shift. Cancer Res. 2006;66:1883–90.CrossRefGoogle Scholar
  74. 74.
    Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA. 2003;100:3983–8.CrossRefGoogle Scholar
  75. 75.
    Ginestier C, Hur MH, Charafe-Jauffret E, Monville F, Dutcher J, et al. ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell. 2007;1:555–67.CrossRefGoogle Scholar
  76. 76.
    Dwivedi S, Sharma P. Stem cell biology: a new hope in regenerations and replenishments therapy. Ind J Clin Biochem. 2018;33(4):368–70. Scholar

Copyright information

© Association of Clinical Biochemists of India 2019

Authors and Affiliations

  • Shailendra Dwivedi
    • 1
  • Purvi Purohit
    • 1
  • Radhieka Misra
    • 2
  • Malavika Lingeswaran
    • 1
  • Jeewan Ram Vishnoi
    • 3
  • Puneet Pareek
    • 4
  • Sanjeev Misra
    • 3
  • Praveen Sharma
    • 1
    Email author
  1. 1.Department of BiochemistryAll India Institute of Medical SciencesJodhpurIndia
  2. 2.Under-graduate Medical ScholarEra’s Lucknow Medical College and HospitalLucknowIndia
  3. 3.Department of Surgical OncologyAll India Institute of Medical SciencesJodhpurIndia
  4. 4.Department of Radio-TherapyAll India Institute of Medical SciencesJodhpurIndia

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