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Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types.

Starmans MH, Krishnapuram B, Steck H, Horlings H, Nuyten DS, van de Vijver MJ, Seigneuric R, Buffa FM, Harris AL, Wouters BG, Lambin P - Br. J. Cancer (2008)

Bottom Line: Stratifying patients in groups resulted in a clear difference in survival (P-values <0.05).Further patient stratification was compared to patient stratification with several well-known published signatures.Furthermore, evidence is provided that supports the idea that many published signatures track the same biological processes and that proliferation is one of them.

View Article: PubMed Central - PubMed

Affiliation: Maastricht Radiation Oncology (Maastro), GROW Research Institute, University of Maastricht, Uns 50/23, PO box 616, Maastricht 6200MD, The Netherlands.

ABSTRACT
Tumour proliferation is one of the main biological phenotypes limiting cure in oncology. Extensive research is being performed to unravel the key players in this process. To exploit the potential of published gene expression data, creation of a signature for proliferation can provide valuable information on tumour status, prognosis and prediction. This will help individualizing treatment and should result in better tumour control, and more rapid and cost-effective research and development. From in vitro published microarray studies, two proliferation signatures were compiled. The prognostic value of these signatures was tested in five large clinical microarray data sets. More than 1000 patients with breast, renal or lung cancer were included. One of the signatures (110 genes) had significant prognostic value in all data sets. Stratifying patients in groups resulted in a clear difference in survival (P-values <0.05). Multivariate Cox-regression analyses showed that this signature added substantial value to the clinical factors used for prognosis. Further patient stratification was compared to patient stratification with several well-known published signatures. Contingency tables and Cramer's V statistics indicated that these primarily identify the same patients as the proliferation signature does. The proliferation signature is a strong prognostic factor, with the potential to be converted into a predictive test. Furthermore, evidence is provided that supports the idea that many published signatures track the same biological processes and that proliferation is one of them.

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A model of the clinical factors with and without the signature was generated. Receiver–operator curves (ROC) were used to compare the two models in three data sets. ((A) Miller data set, (B) van de Vijver data set, (C) Zhao data set).
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fig2: A model of the clinical factors with and without the signature was generated. Receiver–operator curves (ROC) were used to compare the two models in three data sets. ((A) Miller data set, (B) van de Vijver data set, (C) Zhao data set).

Mentions: To quantify the gain in prognostic power obtained with this signature, a model of the clinical factors with and without the signature was generated and evaluated with the AUC. Part of the data set was used as training set, to generate the model, and the other part as a test set. Only the data sets with more than 1 clinical parameter and more than 150 patients are included. Different sizes of training and test sets were evaluated; the overall performance did not change significantly (data not shown). The results shown in Figure 2 were produced with 150 and 100 samples as training set for the breast cancer and the renal cancer data sets, respectively. In two out of three data sets, the AUC increased significantly when the proliferation signature was added to the model (Figure 2, P-values paired t-test ≪0.0001).


Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types.

Starmans MH, Krishnapuram B, Steck H, Horlings H, Nuyten DS, van de Vijver MJ, Seigneuric R, Buffa FM, Harris AL, Wouters BG, Lambin P - Br. J. Cancer (2008)

A model of the clinical factors with and without the signature was generated. Receiver–operator curves (ROC) were used to compare the two models in three data sets. ((A) Miller data set, (B) van de Vijver data set, (C) Zhao data set).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2600688&req=5

fig2: A model of the clinical factors with and without the signature was generated. Receiver–operator curves (ROC) were used to compare the two models in three data sets. ((A) Miller data set, (B) van de Vijver data set, (C) Zhao data set).
Mentions: To quantify the gain in prognostic power obtained with this signature, a model of the clinical factors with and without the signature was generated and evaluated with the AUC. Part of the data set was used as training set, to generate the model, and the other part as a test set. Only the data sets with more than 1 clinical parameter and more than 150 patients are included. Different sizes of training and test sets were evaluated; the overall performance did not change significantly (data not shown). The results shown in Figure 2 were produced with 150 and 100 samples as training set for the breast cancer and the renal cancer data sets, respectively. In two out of three data sets, the AUC increased significantly when the proliferation signature was added to the model (Figure 2, P-values paired t-test ≪0.0001).

Bottom Line: Stratifying patients in groups resulted in a clear difference in survival (P-values <0.05).Further patient stratification was compared to patient stratification with several well-known published signatures.Furthermore, evidence is provided that supports the idea that many published signatures track the same biological processes and that proliferation is one of them.

View Article: PubMed Central - PubMed

Affiliation: Maastricht Radiation Oncology (Maastro), GROW Research Institute, University of Maastricht, Uns 50/23, PO box 616, Maastricht 6200MD, The Netherlands.

ABSTRACT
Tumour proliferation is one of the main biological phenotypes limiting cure in oncology. Extensive research is being performed to unravel the key players in this process. To exploit the potential of published gene expression data, creation of a signature for proliferation can provide valuable information on tumour status, prognosis and prediction. This will help individualizing treatment and should result in better tumour control, and more rapid and cost-effective research and development. From in vitro published microarray studies, two proliferation signatures were compiled. The prognostic value of these signatures was tested in five large clinical microarray data sets. More than 1000 patients with breast, renal or lung cancer were included. One of the signatures (110 genes) had significant prognostic value in all data sets. Stratifying patients in groups resulted in a clear difference in survival (P-values <0.05). Multivariate Cox-regression analyses showed that this signature added substantial value to the clinical factors used for prognosis. Further patient stratification was compared to patient stratification with several well-known published signatures. Contingency tables and Cramer's V statistics indicated that these primarily identify the same patients as the proliferation signature does. The proliferation signature is a strong prognostic factor, with the potential to be converted into a predictive test. Furthermore, evidence is provided that supports the idea that many published signatures track the same biological processes and that proliferation is one of them.

Show MeSH
Related in: MedlinePlus