30. Current Approaches to Identify and Evaluate Cancer Biomarkers for Patient Stratification

  1. W. John W. Morrow PhD, DSc, FRCPath4,
  2. Nadeem A. Sheikh PhD5,
  3. Clint S. Schmidt PhD6 and
  4. D. Huw Davies PhD7
  1. Robert Rees1,
  2. Stephanie Laversin2,
  3. Cliff Murray PhD3 and
  4. Graham Ball1

Published Online: 20 JUN 2012

DOI: 10.1002/9781118345313.ch30

Vaccinology: Principles and Practice

Vaccinology: Principles and Practice

How to Cite

Rees, R., Laversin, S., Murray, C. and Ball, G. (2012) Current Approaches to Identify and Evaluate Cancer Biomarkers for Patient Stratification, in Vaccinology: Principles and Practice (eds W. J. W. Morrow, N. A. Sheikh, C. S. Schmidt and D. H. Davies), Wiley-Blackwell, Oxford, UK. doi: 10.1002/9781118345313.ch30

Editor Information

  1. 4

    Seattle, WA, USA

  2. 5

    Dendreon Corporation, Seattle, WA, USA

  3. 6

    NovaDigm Therapeutics, Inc., Grand Forks, ND, USA

  4. 7

    University of California at Irvine, Irvine, CA, USA

Author Information

  1. 1

    The John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, and CompanDX Ltd, Nottingham, UK

  2. 2

    The John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, UK

  3. 3

    Source BioScience, Nottingham, UK

Publication History

  1. Published Online: 20 JUN 2012
  2. Published Print: 3 AUG 2012

ISBN Information

Print ISBN: 9781405185745

Online ISBN: 9781118345313

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Keywords:

  • breast cancer;
  • patient stratification;
  • genomics;
  • proteomics;
  • bioinformatics;
  • biomarkers;
  • personalized medicine

Summary

The requirement for more effective treatments for cancer is highlighted by the failure rate of many therapies, thereby necessitating more “targeted” approaches, based on an understanding of the molecular features of the individual cancer and patient status at time of diagnosis and treatment. Whether treatment is based on surgery, radiotherapy, or chemotherapy, or gene or immunotherapy, a proportion of patients will fail to respond and progress their disease. In this review we have focused on breast cancer as a heterogeneous disease where a need to stratify “low” from “high” risk patients and therapy “responders” from “nonresponders” is apparent. The application of high-throughput technologies results in a complexity of molecular features that requires bioinformatic data mining to identify important genotypic and proteomic traits associated with clinical utility. The potential combination of “omic” platforms provides an attractive means of gaining knowledge of the main characteristics associated with patient status.