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Open Access Research

Formation of translational risk score based on correlation coefficients as an alternative to Cox regression models for predicting outcome in patients with NSCLC

Wolfgang Kössler1, Anette Fiebeler2, Arnulf Willms3, Tina ElAidi4, Bernd Klosterhalfen5 and Uwe Klinge6*

Author Affiliations

1 Institute of Computer Science, Humboldt University, Berlin, Germany

2 Department of Nephrology and Hypertension, Medical School Hannover, Germany

3 Surgical Department of the Military Hospital, Koblenz, Germany

4 Experimental Medicine and Immunotherapy, Institute for Applied Medical Technology, University Hospital RWTH Aachen, Germany

5 Department of Pathology, Hospital Düren, Germany

6 Department of Surgery, University Hospital RWTH Aachen, Germany

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Theoretical Biology and Medical Modelling 2011, 8:28  doi:10.1186/1742-4682-8-28

Published: 27 July 2011

Abstract

Background

Personalised cancer therapy, such as that used for bronchial carcinoma (BC), requires treatment to be adjusted to the patient's status. Individual risk for progression is estimated from clinical and molecular-biological data using translational score systems. Additional molecular information can improve outcome prediction depending on the marker used and the applied algorithm. Two models, one based on regressions and the other on correlations, were used to investigate the effect of combining various items of prognostic information to produce a comprehensive score. This was carried out using correlation coefficients, with options concerning a more plausible selection of variables for modelling, and this is considered better than classical regression analysis.

Methods

Clinical data concerning 63 BC patients were used to investigate the expression pattern of five tumour-associated proteins. Significant impact on survival was determined using log-rank tests. Significant variables were integrated into a Cox regression model and a new variable called integrative score of individual risk (ISIR), based on Spearman's correlations, was obtained.

Results

High tumour stage (TNM) was predictive for poor survival, while CD68 and Gas6 protein expression correlated with a favourable outcome. Cox regression model analysis predicted outcome more accurately than using each variable in isolation, and correctly classified 84% of patients as having a clear risk status. Calculation of the integrated score for an individual risk (ISIR), considering tumour size (T), lymph node status (N), metastasis (M), Gas6 and CD68 identified 82% of patients as having a clear risk status.

Conclusion

Combining protein expression analysis of CD68 and GAS6 with T, N and M, using Cox regression or ISIR, improves prediction. Considering the increasing number of molecular markers, subsequent studies will be required to validate translational algorithms for the prognostic potential to select variables with a high prognostic power; the use of correlations offers improved prediction.