Skip to main content
SpringerLink
Log in

Cytomics and Predictive Medicine for Oncology

  • Chapter
  • First Online:
An Omics Perspective on Cancer Research

  • 1110 Accesses

Abstract

Cytomics combines the multimolecular cytometric analysis of cell and cell system (cytome, cytomes) heterogeneity on a single cell level with the exhaustive bioinformatic knowledge extraction from all analysis results (cytomics = system cytometry + bioinformatics). It therefore yields a maximum of information about the apparent molecular cell phenotype.

At present, in the typical hypothesis driven way the high amount of information collected by multiparameter single cell flow- or slide-based cytometry measurements is preferentially used to investigate the molecular behaviour of specific cell populations in the perspective of the hypothesis. The information outside the scope of the hypothesis remains frequently unused.

In contrast, under the predictive medicine by cytomics concept, the entire available information is processed (“sieved”) in a data driven way under the general data mining hypothesis that such data may contain useful information for clinical diagnosis and especially for therapy related predictions about disease progress in individual patients.

The present experience from clinical data sets of various malignant and other diseases suggests that this is a promising concept for cancer patients since it has amongst others the potential to identify high risk patients prior to an anticipated therapy as being unsusceptible with accuracies of greater 95% or 99%. This opens the way for early decision on alternative therapies by objective and molecularly standardised criteria. This has been traditionally difficult by current prognosis evaluation according to the widely used Kaplan-Meier statistics for patient groups.

The cytomics concept is also useful for cancer research in general because it favours the enrichment of informative parameters concerning disease outcome in individual organisms or cell cultures from an essentially unlimited number of parameters. The selected parameters are useful as a starting point for mathematical modelling in systems biology without requirement for detailed pre-existing knowledge about potential disease inducing mechanisms. It has therefore the potential for the discovery of new molecular cell pathways and for their subsequent molecular reverse engineering.

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

Access this chapter

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

  • Armstrong WB, Ridgway JM, Vokes DE et al (2006) Optical coherence tomography of laryngeal cancer. Laryngoscope 116:1107–1113

    Article  PubMed  Google Scholar 

  • Bibas AG, Podoleanu AG, Cucu RG et al (2004) 3-D optical coherence tomography of the laryngeal mucosa. Clin Otolaryngol 29:713–720

    Article  CAS  PubMed  Google Scholar 

  • Bland JM, Douglas GA (1998) Survival probabilities (the Kaplan–Meier method). BMJ 317: 1572–1580

    CAS  PubMed  Google Scholar 

  • Bollmann R, Bánkfalvi A, Griefingholt H et al (2005a) Validity of combined cytology and human papillomavirus (HPV) genotyping with adjuvant DNA-cytometry in routine cervical screening: results from 31.031 women from the Bonn-region in West Germany. Oncol Rep 13:915–922

    CAS  PubMed  Google Scholar 

  • Bollmann M, Heller H, Bánkfalvi A et al (2005b) Quantitative molecular urinary cytology by fluorescence in situ hybridisation: a tool for tailoring surveillance of patients with superficial bladder cancer? Br J Urol 95:1219–1225

    Google Scholar 

  • Bollmann M, Várnai AD, Griefingholt H et al (2006) Predicting treatment outcome in cervical diseases using liquid-based cytology, dynamic HPV genotyping and DNA cytometry. Anticancer Res 26:1439–1446

    PubMed  Google Scholar 

  • Caspersson T, Lomakka G, Svensson G et al (1955) A versatile ultramicrospectrograph for multiple-line and surface scanning high resolution measurements employing automized data analysis. Exp Cell Res 3:40–51

    Google Scholar 

  • Ecker RC, Steiner GE (2004) Microscopy-based multicolor tissue cytometry at the single-cell level. Cytometry A 59A:182–190

    Article  CAS  Google Scholar 

  • Ecker RC, Rogojanu R, Streit M et al (2006) An improved method for discrimination of cell populations in tissue sections using microscopy-based multicolor tissue cytometry. Cytometry A 69A:119–123

    Article  Google Scholar 

  • Farkas DL, Du C, Fisher GW et al (1998) Non-invasive image acquisition and advanced processing in optical bioimaging. Comput Med Imaging Graphics 22:89–102

    Article  CAS  Google Scholar 

  • Farkas DL, Becker D (2001) Applications of spectral imaging: detection and analysis of human melanoma and its precursors. Pigment Cell Res 14:2–8

    Article  CAS  PubMed  Google Scholar 

  • Feulgen K, Rossenbeck H (1924) Mikroskopisch-chemischer Nachweis einer Nucleinsäure vom Typus der Thymonucleinsäure und die darauf beruhende elektive Färbung von Zellkernen in mikroskopischen Präparaten. Hoppe Seyler Z Physiolog Chem 135:203–248

    CAS  Google Scholar 

  • Friedenberger M, Bode M, Krusche A et al (2007) Fluorescence detection of protein clusters in individual cells and tissue sections by using toponome imaging system: sample preparation and measuring procedures. Nat Protoc 2:2285–2294

    Article  CAS  PubMed  Google Scholar 

  • Gerstner AOH, Lenz D, Laffers W et al (2002) Near-infrared dyes for six-color immunophenotyping by laser scanning cytometry. Cytometry 48:115–123

    Article  PubMed  Google Scholar 

  • Gerstner AOH, Müller AK, Machlitt J et al (2003) Slide-based cytometry for predicting malignancy in solid salivary gland tumors by fine needle aspirate biopsies. Cytometry B (Clin Cytometry) 53B:20–25

    Article  Google Scholar 

  • Gerstner AOH, Trumpfheller C, Rasc P et al (2004) Quantitative histology by multicolor slide-based cytometry. Cytometry A 59A:210–219

    Article  Google Scholar 

  • Gerstner AOH, Thiele A, Tárnok A et al (2005) Preoperative detection of laryngeal cancer in mucosal swabs by slide-based cytometry. Eur J Cancer 41:445–452

    Article  PubMed  Google Scholar 

  • Gerstner AOH, Thiele A, Tárnok A et al (2006) Prediction of upper aerodigestive tract cancer by slide-based cytometry. Cytometry A 69A:582–587

    Article  CAS  Google Scholar 

  • Göhde W, Dittrich W (1970) Simultane Impulsfluorimetrie des DNS- und Proteingehaltes von Tumorzellen. Z Anal Chem 252:328–330

    Article  Google Scholar 

  • Harnett MM (2007) Laser scanning cytometry: understanding the immune system in situ. Nat Rev Immunol 7:897–904

    Article  CAS  PubMed  Google Scholar 

  • Harris AT (2006) Spectral mapping tools from the earth sciences applied to spectral microscopy data. Cytometry A 69A:872–879

    Article  Google Scholar 

  • Hemmer J, Schön E (1993) Cytogenetic progression and prognosis in oral carcinoma: a DNA flow cytometric study in 317 cases. Int J Oncol 3:635–640

    Google Scholar 

  • Hemmer J, Prinz W (1997) Comparison of DNA flow cytometry and fluorescence in situ hybridization with a set of 10 chromosome-specific DNA probes in four head and neck carcinomas. Cancer Gent Cytogenet 97:35–38

    Article  CAS  Google Scholar 

  • Hemmer J, Thein T, van Heerden WFP (1997) The value of DNA flow cytometry in predicting the development of lymph node metastasis and survival in patients with locally recurrent oral squamous cell carcinoma. Cancer 79:2309–2313

    Article  CAS  PubMed  Google Scholar 

  • Hemmer J, Nagel E, Kraft K (1999) DNA aneuploidy by flow cytometry is an independent prognostic factor in squamous cell carcinoma of the oral cavity. Anticancer Res 19:1419–1422

    CAS  PubMed  Google Scholar 

  • Kim KH, Ragan T, Previte MJR et al (2007) Three-dimensional tissue cytometer based on high-speed multiphoton microscopy. Cytometry A 71A:991–1002

    Article  Google Scholar 

  • Kraft M, Lerßen K, Lubatschowski H et al (2007) Technique of optical coherence tomography of the larynx during microlaryngoscopy. Laryngoscope 117:950–952

    Article  PubMed  Google Scholar 

  • Laffers W, Schlenkhoff C, Pieper K et al (2007) Concepts for absolute immunophenosubtyping by slide-based cytometry. Transfus Med Hemother 34:188–195

    Article  Google Scholar 

  • Levenson RM, Mansfield JR (2006) Multispectral imaging in biology and medicine: slices of life. Cytometry A 69A:748–758

    Article  Google Scholar 

  • Lobodasch K, Fröhlich F, Rengsberger M et al (2007) Quantification of circulating tumor cells for the monitoring of adjuvant therapy in breast cancer: an increase in cell number at completion of therapy is a predictor of early relapse. Breast 16:211–218

    Article  PubMed  Google Scholar 

  • Meistrich ML, Göhde W, White RA (1978) Resolution of x and y spermatids by pulse cytophotometry. Nature 274:821–823

    Article  CAS  PubMed  Google Scholar 

  • Mittag A, Lenz D, Gerstner AOH et al (2006) Hyperchromatic cytometry principles for cytomics using slide based cytometry. Cytometry A 69A:691–703

    Article  CAS  Google Scholar 

  • Mittag A, Bocsi J, Laffers M et al (2009) Technical and methodological basics of slide-based cytometry. In: Sack U, Tárnok A, Rothe G (eds) Cellular diagnostics. Basics, methods and clinical applications of flow cytometry. Karger, Basel

    Google Scholar 

  • Mosch B, Mittag A, Lenz D et al (2006) Laser scanning cytometry in human brain slices. Cytometry A 69A:135–138

    Article  Google Scholar 

  • Pan YT, Wu ZL, Yuan ZJ et al (2007) Subcellular imaging of epithelium with time-lapse optical coherence tomography. J Biomed Opt 12:050504

    Article  PubMed  Google Scholar 

  • Perfetto SP, Chattopadhyay PK, Roederer M (2004) Seventeen-colour flow cytometry: unravelling the immune system. Nat Rev Immunol 4:648–655

    Article  CAS  PubMed  Google Scholar 

  • Remmerbach TW, Weidenbach H, Pomjanski N et al (2001) Cytologic and DNA-cytometric early diagnosis of oral cancer. Anal Cell Pathol 22:211–221

    CAS  PubMed  Google Scholar 

  • Remmerbach TW, Weidenbach H, Hemprich A et al (2004) Earliest detection of oral cancer using non-invasive brush biopsy including DNA-image-cytometry: report of four cases. Anal Cell Pathol 25:159–166

    Google Scholar 

  • Repp R, Schaekel U, Helm G et al (2003) Immunophenotyping is an independent factor for risk stratification in AML. Cytometry B (Clin Cytom) 53B:11–19

    Google Scholar 

  • Rimm DL (2006) What brown cannot do for you. Nat Biotechnol 24:914–916

    Article  CAS  PubMed  Google Scholar 

  • Rosenwald A, Wright G, Chan WC et al (2002) The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 346:1937–1947

    Article  PubMed  Google Scholar 

  • Rothe G, Kellermann W, Valet G (1990) Flow cytometric parameters of neutrophil function as early indicators of sepsis- or trauma-related pulmonary or cardiovascular organ failure. J Lab Clin Invest 115:52–61

    CAS  Google Scholar 

  • Rubin MA, Zerkowski MP, Camp RL et al (2004) Quantitative determination of expression of the prostate cancer protein α-methylacyl-CoA racemase using automated quantitative analysis (AQUA). Am J Pathol 164:831–840

    CAS  PubMed  Google Scholar 

  • Schubert W (2003) Topological proteomics, toponomics, MELK-technology. Adv Biochem Eng Biotechnol 83:189–209

    CAS  PubMed  Google Scholar 

  • Schubert W (2006) Cytomics in characterizing toponomes: towards the biological code of the cell. Cytometry A 69A:209–211

    Article  CAS  Google Scholar 

  • Schwemmler W (1982) The endocytobiotic cell theory and the periodic system of cells. Acta Biotheor 31:45–68

    Article  CAS  PubMed  Google Scholar 

  • The International Non-Hodgkin’s Lymphoma Prognostic Factors Project (1993) A predictive model for aggressive non-Hodgkin’s lymphoma. N Engl J Med 329:987–94

    Article  Google Scholar 

  • Valet G, Warnecke HH, Kahle H (1986) Automated diagnosis of malignant and other abnormal cells by flow-cytometry using the newly developed DIAGNOS1 program system. In: Burger G, Ploem B, Goerttler K (eds) Proceedings of the international symposium on histometry. Academic, London, pp 58–67

    Google Scholar 

  • Valet G, Valet M, Tschöpe D et al (1993) White cell and thrombocyte disorders: Standardized, self-learning flow cytometric list mode data classification with the CLASSIF1 program system. Ann NY Acad Sci 677:233–251

    Article  CAS  PubMed  Google Scholar 

  • Valet G (1997) Cytometry, a biomedical key discipline. 3. System cytometry, a new research strategy. In: Robinson JP (ed) Purdue cytometry CD series, vol. 4 (ISBN: 1-890473-03-0), http://www.cyto.purdue.edu/cdroms/flow/vol4/8_websit/valet/keyvirt1.htm#system

  • Valet GK, Roth G, Kellermann W (1998) Risk assessment for intensive care patients by automated classification of flow cytometric data. In: Robinson JP, Babcock GF (eds) Phagocyte function. Wiley-Liss, New York, pp 289–306

    Google Scholar 

  • Valet G, Kahle H, Otto F et al (2001) Prediction and precise diagnosis of diseases by data pattern analysis in multiparameter flow cytometry: Melanoma, juvenile asthma and human immunodeficiency virus infection. Methods Cell Biol 64:487–508

    Article  CAS  PubMed  Google Scholar 

  • Valet G (2002) Predictive medicine by cytomics: potential and challenges. J Biol Regul Homeost Agents 16:164–167

    CAS  PubMed  Google Scholar 

  • Valet G, Arland M, Franke A et al (2002) Discrimination of chronic lymphocytic leukemia of B-cell type by computerized analysis of 3-color flow cytometric immunophenotypes of bone marrow aspirates and peripheral blood. Lab Hematol 8:134–142

    Google Scholar 

  • Valet G, Repp R, Link H et al (2003) Pretherapeutic identification of high-risk acute myeloid leukemia (AML) patients from immunophenotype, cytogenetic and clinical parameters. Cytometry B Clin Cytom 53B:4–10

    Article  Google Scholar 

  • Valet G, Höffkes HG (2004) Data pattern analysis for the individualised pretherapeutic identification of high-risk diffuse large B-cell lymphoma (DLBCL) patients by cytomics. Cytometry A 59A:232–236

    Article  CAS  Google Scholar 

  • Valet G (2005a) Human cytome project: a new potential for drug discovery. In: Real Academia Nacional de Farmacia (ed) Las Omicas genomica, proteomica, citomica y metabolomica: modernas tecnologias para desarrollo de farmacos. Madrid, pp 207–228

    Google Scholar 

  • Valet G (2005b) Cytomics, human cytome project and systems biology: top-down resolution of the molecular biocomplexity of organisms by single cell analysis. Cell Prolif 38:171–174

    Article  CAS  PubMed  Google Scholar 

  • Valet G (2006) Cytomics as a new potential for drug discovery. Drug Discov Today 11:785–791

    Article  CAS  PubMed  Google Scholar 

  • Virchow R (1858) Die Cellularpathologie in ihrer Begründung auf physiologische und pathologische Gewebelehre. August Hirschwald, Berlin

    Google Scholar 

  • Wang Z, Lee CS, Waltzer WC et al (2007) In vivo bladder imaging with microelectromechanical-systems-based endoscopic spectral domain optical coherence tomography. J Biomed Opt 12:034009

    Article  PubMed  Google Scholar 

  • Wirdefeldt K, Gatz M, Pawitan Y et al (2005) Risk and protective factors for Parkinson’s disease: a study in swedish twins. Ann Neurol 57:27–33

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department for Otorhinolaryngology/Head and Neck Surgery, University of Bonn, Bonn, Germany

    A. O. H. Gerstner

  2. Max-Planck-Institut für Biochemie, Martinsried, Germany

    G. Valet

Authors
  1. A. O. H. Gerstner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Valet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. O. H. Gerstner .

Editor information

Editors and Affiliations

  1. Dept. Clinical Oncology, Queen Elizabeth Hospital, Gascoigne Road 30, Kowloon, Hong Kong/PR China

    William C.S. Cho

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Gerstner, A.O.H., Valet, G. (2010). Cytomics and Predictive Medicine for Oncology. In: Cho, W. (eds) An Omics Perspective on Cancer Research. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2675-0_10

Download citation

Publish with us

Policies and ethics

Search

Navigation