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Molecular profiling of human cancer

Traditionally, tumours have been categorized on the basis of histology. However, the staining pattern of cancer cells viewed under the microscope is insufficient to reflect the complicated underlying molecular events that drive the neoplastic process. By surveying thousands of genes at once, using DNA arrays, it is now possible to read the molecular signature of an individual patient's tumour. When the signature is analysed with clustering algorithms, new classes of cancer emerge that transcend distinctions based on histological appearance alone. Using DNA arrays, protein arrays and appropriate experimental models, the ultimate goal is to move beyond correlation and classification to achieve new insights into disease mechanisms and treatment targets.

Key Points

  • Traditional approaches to cancer classification and diagnosis have been based on histological examination.

  • Extensive genome data and DNA array technology have provided opportunities to monitor gene expression in cancer cells for thousands of genes at once.

  • When preparing tissue samples, tissue fixation procedures must allow preservation of macromolecules.

  • Tissue heterogeneity is another important issue for research on cancerous cells. Approaches to tissue heterogeneity include global sampling, the use of cell lines derived from tumours, and laser capture microdissection. Several studies have reported transcriptional profiling of cancer using DNA arrays. These studies have uncovered classes of cancer that extend traditional classification on the basis of histology and morphology.

  • Clustering of genes by transcriptional profiling is also providing insights into gene function and cancer pathology, such as metastasis.

  • Technologies are being developed that will allow cellular protein and signal pathway profiling. This will further extend our understanding of the molecular pathology of cancer, and can lead to patient-tailored therapies.

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Figure 1: Global survey versus microdissection approaches to gene profiling from heterogeneous tumour tissue specimens.
Figure 2: Molecular profiling of pre-malignant progression.
Figure 3: Tumour tissue arrays.
Figure 4: Discovery of diffuse large B-cell lymphoma (DLBCL) clinical subtypes by DNA array profiling10.
Figure 5: Proteomic arrays.
Figure 6: Protein circuit profiling from breast cancer epithelial cells using a hypothetical multiplexed array format.

References

  1. Adams, M. D. Initial assessment of human gene diversity and expression patterns based upon 83 million nucleotides of cDNA sequence. Nature 377 , 3–20 (1999).

    Google Scholar 

  2. Aparicio, S. A. J. R. How to count human genes. Nature Genet. 25, 129–130 (2000).

    Article  CAS  PubMed  Google Scholar 

  3. Hancock, W. et al. Integrated genomic/proteomic analysis. Anal. Chem. 71, 743–748 ( 1999).

    Article  Google Scholar 

  4. Kohn, K. Molecular interaction map of the mammalian cell cycle control and DNA repair systems . Mol. Biol. Cell 10, 2703– 2734 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Pease, A. C. et al. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc. Natl Acad. Sci. USA 91, 5022–5026 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. DeRisi, J. et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genet. 14, 457– 460 (1996).

    Article  CAS  PubMed  Google Scholar 

  7. Lipshutz, R. et al. High density synthetic oligonucleotide arrays. Nature Genet. 21, S20–S24 (1999).

    Article  Google Scholar 

  8. Singh-Gasson, S. et al. Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array. Nature Biotechnol. 17, 974–978 (1999).

    Article  CAS  Google Scholar 

  9. Holland, J. F. et al. Cancer Medicine 5th Edn, Section I (Williams and Wilkins, New York, 1999).

    Google Scholar 

  10. Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).Describes the application of DNA arrays to molecular profiling of patients with lymphoma. Large-scale transcriptional profiling, coupled with pattern recognition algorithms, reveals previously unknown gene expression patterns that divide patients into two main groups — high and low ten-year survival.

    Article  CAS  PubMed  Google Scholar 

  11. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 , 531–537 (1999). A generic approach to cancer classification is proposed on the basis of gene expression monitoring using DNA arrays. Transcript profiling is used to distinguish acute myeloid leukaemia from acute lymphoblastic leukaemia.

    Article  CAS  PubMed  Google Scholar 

  12. Ross, D. T. et al. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genet. 24, 227 –235 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Perou, C. M. et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc. Natl Acad. Sci. USA 96, 9212–9217 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Perou, C. M. et al. Molecular portraits of human breast tumors. Nature 406, 747–752 ( 2000).DNA arrays are used to develop a molecular portrait of individual patient's breast cancers. The gene expression pattern of a tumour was correlated with the microscopic characteristics of the tumour and some were followed before and after treatment.

    Article  CAS  PubMed  Google Scholar 

  15. Bittner, M. et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536 –540 (2000).Global transcript analysis is used to show that subsets of human melanomas have distinct phenotypic characteristics.

    Article  CAS  PubMed  Google Scholar 

  16. Sgroi, D. et al. In vivo gene expression profile analysis. Cancer Res . 59, 5656–5661 ( 1999).

    CAS  PubMed  Google Scholar 

  17. Goldsworthy, S. M. et al. Effects of fixation on RNA extraction and amplification from laser capture microdissected tissue. Mol. Carcinogenesis 25, 86–91 (1999).

    Article  CAS  Google Scholar 

  18. Paweletz et al. (SELDI) Biomarker profiling of stages of cancer progression directly from human tissue using a protein biochip. Drug Devel. Res. 49, 34–42 (2000).

    Article  CAS  Google Scholar 

  19. Kononen, J. et al. Tissue microarrays for high throughput molecular profiling of tumor specimens. Nature Med. 4, 844– 847 (1998).Technology is described to array hundreds of small pieces of individual tumours on a single microscopic slide. The result is a high-throughput means to screen a marker on hundreds of tumour samples at once.

    Article  CAS  PubMed  Google Scholar 

  20. Ornstein, D. et al. Proteomic analysis of laser capture microdissected human prostate cancer and in vitro prostate cell lines. Electrophoresis 21, 2235–2242 ( 2000).

    Article  CAS  PubMed  Google Scholar 

  21. Emmert-Buck, M. R. et al. Laser capture microdissection. Science 274, 998–1001 (1996). Technology is described for sampling the macromolecules of tissue cells under microscopic visualization. The extracted DNA, RNA or protein of pure tissue cells can be applied to DNA, RNA or protein profiling arrays.

    Article  CAS  PubMed  Google Scholar 

  22. Simone, N. L. et al. Laser capture microdissection: Opening the microscopic frontier to molecular analysis. Trends Genet. 14, 272–276 (1998).

    Article  CAS  PubMed  Google Scholar 

  23. Luo, L. et al. Gene expression profiles of laser-captured adjacent neuronal subtypes . Nature Med. 5, 117–122 (1999).

    Article  CAS  PubMed  Google Scholar 

  24. Perrone, E. E. et al. Tissue microarray assessment of prostate cancer tumor proliferation in african american and white men. J. Natl Cancer Inst. 92, 937–939 (2000).

    Article  CAS  PubMed  Google Scholar 

  25. Clark, E. A., Golub, T., Lander, E. & Hynes, R. Genomic analysis of metastasis reveals an essential role for RhoC. Nature 406, 532–535 (2000). DNA arrays are used to study the switch from a locally growing tumour to a metastatic form. This approach revealed an important role for RhoC, a small GTPase, which may influence invasion.

    Article  CAS  PubMed  Google Scholar 

  26. Leethanakul, C. et al. Distinct pattern of expression of differentiation and growth–related genes in squamous cell carcinomas of the head and neck revealed by the use of laser capture microdissection and cDNA arrays. Oncogene 19, 3220–3224 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Cole, K. A. et al. The genetics of cancer — A 3D model. Nature Genet. 21, 38–41, ( 1999).

    Article  CAS  PubMed  Google Scholar 

  28. Humphery-Smith, I. Cordwell, S. J. & Blackstock, W. P. Proteome research: Complementarity and limitations with respect to the RNA and DNA worlds. Electrophoresis 18, 1217–1242 (1997).

    Article  CAS  PubMed  Google Scholar 

  29. Gygi, S. P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotechnol. 17, 994 –999 (1999).

    Article  CAS  Google Scholar 

  30. Buckholz, R. et al. Automation of yeast two-hybrid screening. J. Mol. Microbiol. Biotechnol. 1, 135–140 (1999).

    CAS  PubMed  Google Scholar 

  31. Page, M. J. et al. Proteomic definition of normal human luminal and myoepithelial breast cells purified from reduction mammoplasties. Proc. Natl Acad. Sci. USA 96, 12589–12594 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Banks, R. E. et al. The potential use of laser capture microdissection to selectively obtain distinct populations of cells for proteomic analysis. Electrophoresis 20, 689–700 ( 1999).

    Article  CAS  PubMed  Google Scholar 

  33. Emmert-Buck, M. et al. An approach to the proteomic analysis of human tumors. Mol. Carcinogen. 27, 158–165 (2000).

    Article  CAS  Google Scholar 

  34. Arenkov, P. et al. Protein microchips: Use for immunoassay and enzymatic reactions . Anal. Biochem. 278, 123– 131 (2000).Protein array technology is described in which antibodies, antigens and enzymes are immobilized in a miniature array. This is an example of technology that could be applied to the identification of protein profiles associated with cancer type or clinical response.

    Article  CAS  PubMed  Google Scholar 

  35. Ekins, R. & Chu, F. W. Multianalyte microspot immunoassay-microanalytical ‘compact disk’ of the future. Clin. Chem. 37, 1955–1967 (1991).

    CAS  PubMed  Google Scholar 

  36. Mendoza, L. G. et al. High throughput microarray based enzyme linked immunosorbant assay. BioTechniques 27, 778– 788 (1999).

    Article  CAS  PubMed  Google Scholar 

  37. Rowe, C. A. et al. Array biosensor for simultaneous identification of bacterial, viral, and protein analytes. Anal. Chem. 71, 3846–3852 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. Chiem, N. H. & Harrison, D. J. Microchip systems for immunoassay: an integrated immunoreactor with electrophoretic separation for serum theophylline determination. Clin. Chem. 44, 591– 598 (1998).

    CAS  PubMed  Google Scholar 

  39. Ekins, R. & Chu, R. W. in Principals and Practices of Immunoassays 2nd edn 625–646 (eds Price, C. P. & Newman, D. J.) (Stockton, New York, 1997).

    Google Scholar 

  40. Lueking, A. et al. Protein microarrays for gene expression and antibody screening . Anal. Biochem. 270, 103– 111 (1999).

    Article  CAS  PubMed  Google Scholar 

  41. Silzel, J. W. et al. Mass-sensing multianalyte microarray immunoassay with imaging detection. Clin. Chem. 44, 2036– 2043 (1998).

    CAS  PubMed  Google Scholar 

  42. Jones, V. W. et al. Microminiaturized immunoassays using atomic force microscopy and compositionally patterned antigen arrays. Anal. Chem. 70, 1233–1241 (1998).

    Article  CAS  PubMed  Google Scholar 

  43. Vasiliskov, V. et al. Fabrication of microarray of gel-immobilized compounds on a chip by copolymerization. BioTechniques 27, 592–606 (1999).

    Article  CAS  PubMed  Google Scholar 

  44. Wadkins, R. M. et al. Biosensors and bioelectronics: detection of multiple toxic agents using a planar array. Immunosensor 13, 407–415 (1998).

    CAS  Google Scholar 

  45. Shriver-Lake, L. C. Ogert, R. A. & Ligler, F. S. A fiber optic evanescent-wave immunosensor for large molecules. Sensors Actuators 11, 239– 243 (1993).

    Article  CAS  Google Scholar 

  46. Narang, U., Gauger, P. R., Kusterbeck, A. W. & Ligler, F. S. Multianalyte detection using a capillary-based flow immunosensor. Anal. Biochem. 255, 13–19 (1998).

    Article  CAS  PubMed  Google Scholar 

  47. Carson, R. T. & Vignali, D. A. Simultaneous quantitation of 15 cytokines using a multiplexed flow cytometric assay. J. Immunol. Methods 227, 41–52 ( 1999).

    Article  CAS  PubMed  Google Scholar 

  48. Fulton, R. J. et al. Advanced multiplexed analysis with the FlowMetrix system. Clin. Chem. 43, 1749–1756 (1997).

    CAS  PubMed  Google Scholar 

  49. Hunter, T. Signaling: 2000 and beyond. Cell 100, 113 –127 (2000).

    Article  CAS  PubMed  Google Scholar 

  50. Pawson, T. Protein modules and signalling networks. Nature 373 , 573–580 (1995).

    Article  CAS  PubMed  Google Scholar 

  51. Croix, B. et al. Genes expressed in human tumor endothelium. Science 289, 1197–1202 ( 2000).

    Article  Google Scholar 

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Authors and Affiliations

Authors

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Correspondence to Lance Liotta.

Related links

Related links

DATABASE LINKS

Annexin I

Apolipoprotein D

BCL2

BRCA1

DLCL

IGFBP1

non-Hodgkin lymphoma

NOTCH

RANTES

RhoC

Tissue factor

WNTs

FURTHER INFORMATION

NCI CANCER GENOME ANATOMY PROJECT

The Brown lab microarray guide

LASER CAPTURE MICRODISSECTION TECHNOLOGY

NCI ARRAY FACILITY

TISSUE ARRAY RESEARCH AND PRODUCTION

Glossary

CARCINOMA

Cancer originating from epithelial cells. Most human cancers that are not leukaemias or lymphomas are carcinomas.

SERUM MARKERS

Substances that are soluble in the serum (non-cellular portion of blood) and that are present at high levels in association with specific cancers.

STROMAL CELLS

Connective tissue cells such as fibroblasts.

CARCINOMA IN SITU

An early non-invasive form of carcinoma, confined to the epithelium.

LEUKAEMIA

A malignant disorder in which precursors of white blood cells proliferate and accumulate.

LYMPHOMA

A cancer of lymphoid cells, producing a distinct tumour mass. The many different types of lymphoma are thought to arise from different subtypes of immune defence cells.

GERMINAL CENTRES

Subregions of lymph nodes rich in B cells, which are formed or expand during the activation and differentiation of B cells.

ELISA

A sensitive antibody-based method for the detection of an antigen such as a protein.

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Liotta, L., Petricoin, E. Molecular profiling of human cancer. Nat Rev Genet 1, 48–56 (2000). https://doi.org/10.1038/35049567

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