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. 2010;11(2):R18.
doi: 10.1186/gb-2010-11-2-r18. Epub 2010 Feb 15.

A fuzzy gene expression-based computational approach improves breast cancer prognostication

Benjamin Haibe-Kains  1 , Christine Desmedt, Françoise Rothé, Martine Piccart, Christos Sotiriou, Gianluca Bontempi
Affiliations

A fuzzy gene expression-based computational approach improves breast cancer prognostication

Benjamin Haibe-Kains et al. Genome Biol. 2010.

Abstract

Early gene expression studies classified breast tumors into at least three clinically relevant subtypes. Although most current gene signatures are prognostic for estrogen receptor (ER) positive/human epidermal growth factor receptor 2 (HER2) negative breast cancers, few are informative for ER negative/HER2 negative and HER2 positive subtypes. Here we present Gene Expression Prognostic Index Using Subtypes (GENIUS), a fuzzy approach for prognostication that takes into account the molecular heterogeneity of breast cancer. In systematic evaluations, GENIUS significantly outperformed current gene signatures and clinical indices in the global population of patients.

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Figures

Figure 1
Figure 1
Risk prediction model design (GENIUS). Design of the fuzzy approach used to build the new risk prediction model, called GENIUS (Gene Expression progNostic Index Using Subtypes): (a) training phase to build GENIUS; (b) validation phase to test GENIUS in the independent dataset of untreated breast cancer patients. For the sake of clarity, we denoted P(ER-/HER2-), P(HER2+) and P(ER+/HER2-) by P(1), P(2) and P(3), respectively.
Figure 2
Figure 2
Proportion of subtypes in primary breast tumors. Venn diagram of proportions of the three molecular subtypes identified in a database of 3,537 breast cancer patients. We considered a threshold of 1% for the uncertainty of a patient belonging to a specific subtype. Therefore, patients have a tumor of a unique subtype if the posterior probability of belonging to that subtype exceeds 99%.
Figure 3
Figure 3
Time-dependent ROC curves at 5 years for the risk score predictions computed by GENIUS and AURKA. Training set: in the (a) global population of breast cancer patients, to illustrate the cutoff selected for risk group prediction (green lines). Validation set: in the (b) global population, the (c) ER+/HER2-, (d) ER-/HER2- and (e) HER2+ subtypes. AUC, area under the curve.
Figure 4
Figure 4
Survival curves for GENIUS risk group predictions. Kaplan-Meier survival curves for GENIUS risk group predictions in the (a) global population, the (b) ER+/HER2-, (c) ER-/HER2- and (d) HER2+ subtypes of the validation set.
Figure 5
Figure 5
Forest plot of the concordance indices for GENIUS and GENIUS CRISP. Forest plot of the concordance indices for GENIUS and GENIUS CRISP risk predictions, with respect to the subtypes in the validation set: (a) risk score predictions; (b) risk group predictions. The P-values at the right-hand side of the forest plot were computed from the statistical test of superiority of GENIUS.
Figure 6
Figure 6
Forest plot of the concordance indices for GENIUS and the state-of-the-art prognostic signatures. Forest plot of the concordance indices for GENIUS and the current prognostic signatures (AURKA, GGI, STAT1, PLAU, IRMODULE and SDPP) risk predictions, with respect to the subtypes in the validation set: (a) risk score predictions; (b) risk group predictions. The P-values at the right-hand side of the forest plot were computed from the statistical test of superiority of GENIUS.
Figure 7
Figure 7
Forest plot of the concordance indices for GENIUS and SUBCLASSIF. Forest plot of the concordance indices for GENIUS and GENIUS CRISP risk predictions, with respect to the subtypes in the validation set: (a) risk score predictions; (b) risk group predictions. The P-values at the right-hand side of the forest plot were computed from the statistical test of superiority of GENIUS.
Figure 8
Figure 8
Forest plot of the concordance indices for GENIUS and the clinical prognostic indices. Forest plot of the concordance indices for GENIUS and the clinical prognostic indices (AOL and NPI) risk predictions with respect to the subtypes in the validation set: (a) risk score predictions; (b) risk group predictions. The P-values at the right-hand side of the forest plot were computed from the statistical test of superiority of GENIUS.
Figure 9
Figure 9
Survival curves for the combination of GENIUS and the clinical prognostic indices risk group predictions. Kaplan-Meier survival curves for the combination of GENIUS and AOL/NPI predictions in the (a) AOL and (b) NPI global population, the (c) AOL and (d) NPI ER+/HER2-, the (e) AOL and (f) AOL ER-/HER2- and the (g) AOL and (h) NPI HER2+ subtypes of the validation set.
Figure 10
Figure 10
Study of six breast cancer cases highlighting benefits of using GENIUS. (a-f) Six cases of breast cancer patients from TBG dataset (breast cancer microarray dataset introduced by Desmedt et al. [34]) where prognostic clinical indices and gene signatures are compared to GENIUS. The boxes contain relevant comments highlighting the benefits of using GENIUS
Figure 11
Figure 11
Study of two breast cancer cases highlighting drawbacks of using GENIUS. (a,b) Two cases of breast cancer patients from the TBG dataset (breast cancer microarray dataset introduced by Desmedt et al. [34]) where prognostic clinical indices and gene signatures are compared to GENIUS. The boxes contain relevant comments highlighting the drawbacks of using GENIUS.
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