Identification of Biomarkers and Expression Signatures

  • Patricia SeverinoEmail author
  • Elisa Napolitano Ferreira
  • Dirce Maria Carraro


Recently, molecular biology has been substantially improved by the development of new technologies that allow the assessment of the genome, transcriptome and proteome on a high-throughput scale and at reasonable costs. The translation of all the information generated by these technologies into new biomarkers is an enormous challenge for the biomedical community, and vast efforts have been made in this arena. The practice of personalized medicine based on DNA/RNA information used for clinical decision-making has led to considerable advances in different areas of medicine and is now a reality at several medical centers worldwide. The aspiration is that in the near future, the medical community will have more and more available biomarkers to properly classify patients and to allow them to offer efficient and tailored treatment for a broader range of diseases, resulting in a high cure rate and minimal side effects. In this chapter, we discuss the identification of biomarkers by primarily examining gene expression. Two of the most important approaches, microarrays and RNA sequencing (RNA-Seq), and strategies for defining gene expression signatures are addressed. We also present important aspects involved in the validation of gene expression signatures as biomarkers, the bottlenecks and difficulties for their broader use in clinical practice and some good examples of signatures representing aspects of human diseases.


Gene Expression Signature Mismatch Repair Pathway Monitoring Treatment Efficacy Potential Gene Expression MammaPrint Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Aaroe J, Lindahl T, Dumeaux V et al (2010) Gene expression profiling of peripheral blood cells for early detection of breast cancer. Breast Cancer Res 12(1):R7CrossRefGoogle Scholar
  2. Alizadeh AA, Eisen MB, Davis RE et al (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503–511PubMedCrossRefGoogle Scholar
  3. Arango BA, Rivera CL, Glück S (2013) Gene expression profiling in breast cancer. Am J Transl Res 5:132–138PubMedCentralPubMedGoogle Scholar
  4. Balko JM, Giltnane J, Wang K et al (2013) Molecular profiling of the residual disease of triple-negative breast cancers after neoadjuvant chemotherapy identifies actionable therapeutic targets. Cancer Discov 4:232–245PubMedCentralPubMedCrossRefGoogle Scholar
  5. Biomarkers Definitions Working Group (2001) Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 69:89–950CrossRefGoogle Scholar
  6. Bloom G, Yang IV, Boulware D et al (2004) Multi-platform, multi-site, microarray-based human tumor classification. Am J Pathol 164:9–16PubMedCentralPubMedCrossRefGoogle Scholar
  7. Cooper-Knock J, Kirby J, Ferraiuolo L et al (2012) Gene expression profiling in human neurodegenerative disease. Nat Rev Neurol 8:518–530PubMedCrossRefGoogle Scholar
  8. Drukker CA, van Tinteren H, Schmidt MK et al (2014) Long-term impact of the 70-gene signature on breast cancer outcome. Breast Cancer Res Treat 143:587–592PubMedCentralPubMedCrossRefGoogle Scholar
  9. Elashoff MR, Wingrove JA, Beineke P et al (2011) Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients. BMC Medical Genomics 4:26PubMedCentralPubMedCrossRefGoogle Scholar
  10. Gray RG, Quirke P, Handley K et al (2011) Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer. J Clin Oncol 29:4611–4619PubMedCrossRefGoogle Scholar
  11. Haferlach T, Kohlmann A, Wieczorek L et al (2010) Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group. Clin Oncol 28:2529–2537CrossRefGoogle Scholar
  12. Kern SE (2012) Why your new cancer biomarker may never work: recurrent patterns and remarkable diversity in biomarker failures. Cancer Res 72:6097–6101PubMedCentralPubMedCrossRefGoogle Scholar
  13. Klein EA (2013) A genomic approach to active surveillance: a step toward precision medicine. Asian J Androl. 15:340–341PubMedCentralPubMedCrossRefGoogle Scholar
  14. Lapointe J, Li C, Higgins JP et al (2004) Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA 101:811–816PubMedCentralPubMedCrossRefGoogle Scholar
  15. Maak M, Simon I, Nitsche U et al (2013) Independent validation of a prognostic genomic signature (ColoPrint) for patients with stage II colon cancer. Ann Surg 257:1053–1058PubMedCrossRefGoogle Scholar
  16. Morin R, Bainbridge M, Fejes A et al (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. BioTechniques 45:81–94PubMedCrossRefGoogle Scholar
  17. Mortazavi A, Williams BA, McCue K, Wold B et al (2008) Mapping and quantifying mammalian transcriptomes by RNA-SEq. Nat Methods 5:621–628PubMedCrossRefGoogle Scholar
  18. Paik S, Tang G, Shak S et al (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24:3726–3734PubMedCrossRefGoogle Scholar
  19. Rifai N, Gillette MA, Carr SA (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24:971PubMedCrossRefGoogle Scholar
  20. Sahin IH, Garrett C (2013) The heterogeneity of KRAS mutations in colorectal cancer and its biomarker implications: an ever-evolving story. Transl Gastrointestinal Cancer 2:164–166Google Scholar
  21. Schena M, Shalon D, Davis RW et al (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470PubMedCrossRefGoogle Scholar
  22. Shipp MA, Ross KN, Tamayo P et al (2002) Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 8:68–74PubMedCrossRefGoogle Scholar
  23. Su AI, Welsh JB, Sapinoso LM et al (2001) Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 61:7388–7393PubMedGoogle Scholar
  24. Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 25:511–515CrossRefGoogle Scholar
  25. van’t Veer LJ, Dai H, van de Vijver MJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536CrossRefGoogle Scholar
  26. Watson M (2006) CoXpress: differential co-expression in gene expression data. BMC Bioinformatics 7:509PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Patricia Severino
    • 1
    Email author
  • Elisa Napolitano Ferreira
    • 2
  • Dirce Maria Carraro
    • 2
  1. 1.Instituto Israelita de Ensino e Pesquisa Albert EinsteinSão PauloBrazil
  2. 2.A. C. Camargo Cancer CenterSão PauloBrazil

Personalised recommendations