gms | German Medical Science

49. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
19. Jahrestagung der Schweizerischen Gesellschaft für Medizinische Informatik (SGMI)
Jahrestagung 2004 des Arbeitskreises Medizinische Informatik (ÖAKMI)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Schweizerische Gesellschaft für Medizinische Informatik (SGMI)

26. bis 30.09.2004, Innsbruck/Tirol

Evaluating the prognostic as well as the predictive value of gene-signatures in cancer clinical trials

Meeting Abstract (gmds2004)

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  • corresponding author presenting/speaker Carina Ittrich - DKFZ, Heidelberg, Deutschland
  • Axel Benner - DKFZ, Heidelberg, Deutschland
  • Lutz Edler - DKFZ, Heidelberg, Deutschland

Kooperative Versorgung - Vernetzte Forschung - Ubiquitäre Information. 49. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 19. Jahrestagung der Schweizerischen Gesellschaft für Medizinische Informatik (SGMI) und Jahrestagung 2004 des Arbeitskreises Medizinische Informatik (ÖAKMI) der Österreichischen Computer Gesellschaft (OCG) und der Österreichischen Gesellschaft für Biomedizinische Technik (ÖGBMT). Innsbruck, 26.-30.09.2004. Düsseldorf, Köln: German Medical Science; 2004. Doc04gmds121

The electronic version of this article is the complete one and can be found online at:

Published: September 14, 2004

© 2004 Ittrich et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.



The advent of DNA microarray technology has recently enabled the quantitative measurement of complex multi-gene expression patterns in human cancer. The results can be used to increase the accuracy of the diagnosis of cancer and to identify new subtypes of tumors that can predict the clinical course of the cancer disease. This includes the risk of tumor progression and the duration of patient survival, but also the occurrence of adverse events to treatment. Therefore, genomic data are now increasingly collected in almost all stages of clinical research as well as in the development of new treatments. In particular, clinical studies were conducted to determine so-called gene expression signatures (GES) which are proposed to be used as a molecular marker for the prediction of clinical outcome either in addition to established clinical prognostic factors or in place of them. By gene expression signature we understand (synonymously with expression profile) a set of genes selected with a specific aim. The set of genes generates for each patient a multivariate feature vector of gene expression intensities. Usually the multivariate GES of a patient is summarized into a scalar to be used as a new prognostic index. As pointed out in Simon et al. [1] an appropriate validation of the GES in an independent trial is a prerequisite for the use of this information for the prognosis of the course of the disease.

To evaluate whether a molecular prognostic factor offers any predictive value, i.e. the response to treatment depends on this factor, one has to investigate the interaction between treatment and the molecular factor in a multi-armed trial. The role of the interaction between molecular biomarker and treatment is illustrated in Figure 1 [Fig. 1] which shows a scenario of possible outcomes of a two armed trial and a dichotomous GES:

a) GES is a prognostic factor for response,

b) GES has the potential to be used as a predictive factor of response for specific treatment schedules.

This contribution will inform how GES are determined from microarray experiments and how a specific GES can be validated. It will be demonstrated how the statistical interaction defined in regression models can be used for the assessment of the GES as predictive factor. Data analysis can then be performed in a two-factorial setting. Examples will be given for real and simulated data and questions of sample sizes will be addressed.


Simon, Radmacher, Dobbin, McShane. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. JNCI 2003; 95: 14 - 18.
Pepe, M.S. (2003) The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, Oxford.