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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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Related in: MedlinePlus

A signature score was calculated for each patient in the different data sets. These scores were used to cluster the patients in two groups, one with low expression and one with high expression of the signature. Kaplan–Meier survival curves for the two groups were compared ((A) Miller data set, (B) Wang data set, (C) van de Vijver data set, (D) Zhao data set, (E) Beer data set).
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fig1: A signature score was calculated for each patient in the different data sets. These scores were used to cluster the patients in two groups, one with low expression and one with high expression of the signature. Kaplan–Meier survival curves for the two groups were compared ((A) Miller data set, (B) Wang data set, (C) van de Vijver data set, (D) Zhao data set, (E) Beer data set).

Mentions: In every data set, a signature score (Equation (1)) was calculated for each patient. The patients were separated in two groups by clustering these signature scores, to obtain a natural separation rather than using an arbitrary value such as the median to split the patients. This clustering was repeated 1000 times to assess the stability of the group assignment. Results of the log-rank tests are given in Supplementary Information Table S2, and in Figure 1, the Kaplan–Meier curves for signature 2 are shown. Signature 2 gives clear risk stratification in all data sets, all P-values of the 1000 clustering runs <0.05. Results of the log-rank test show not only that signature 2 gives a better risk stratification than signature 1, also the overall robustness of the separation is stronger, indicated by the small standard deviations. Nevertheless, both signatures show very good prognostic value on the three breast cancer data sets. The range and standard deviations of the 1000 clustering runs also show that the results are robust for these data sets and that the splitting patients based on clustering of signature scores is stable.


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 signature score was calculated for each patient in the different data sets. These scores were used to cluster the patients in two groups, one with low expression and one with high expression of the signature. Kaplan–Meier survival curves for the two groups were compared ((A) Miller data set, (B) Wang data set, (C) van de Vijver data set, (D) Zhao data set, (E) Beer data set).
© Copyright Policy
Related In: Results  -  Collection

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

fig1: A signature score was calculated for each patient in the different data sets. These scores were used to cluster the patients in two groups, one with low expression and one with high expression of the signature. Kaplan–Meier survival curves for the two groups were compared ((A) Miller data set, (B) Wang data set, (C) van de Vijver data set, (D) Zhao data set, (E) Beer data set).
Mentions: In every data set, a signature score (Equation (1)) was calculated for each patient. The patients were separated in two groups by clustering these signature scores, to obtain a natural separation rather than using an arbitrary value such as the median to split the patients. This clustering was repeated 1000 times to assess the stability of the group assignment. Results of the log-rank tests are given in Supplementary Information Table S2, and in Figure 1, the Kaplan–Meier curves for signature 2 are shown. Signature 2 gives clear risk stratification in all data sets, all P-values of the 1000 clustering runs <0.05. Results of the log-rank test show not only that signature 2 gives a better risk stratification than signature 1, also the overall robustness of the separation is stronger, indicated by the small standard deviations. Nevertheless, both signatures show very good prognostic value on the three breast cancer data sets. The range and standard deviations of the 1000 clustering runs also show that the results are robust for these data sets and that the splitting patients based on clustering of signature scores is stable.

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