Tamoxifen Resistance in Breast Cancer
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1 Gene Expression Analysis : Tamoxifen Resistance in Breast Cancer B. Haibe-Kains 1,2 G. Bontempi 2 C. Sotiriou 1 1 Unité Microarray, Institut Jules Bordet 2 Machine Learning Group, Université Libre de Bruxelles September 29, 2006 Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
2 Biological Question and Datasets Biological question : Can we predict which patients will resist to the Tamoxifen treatment in an adjuvant setting? Tamoxifen treated patients coming from 3 different institutions, can we pool the data? gene-expressions : normalization survival data : model fitting. 255 eligible patients (samples) probes (variables) Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
3 Survival Data Tamoxifen treated patients survival curves Tamoxifen treated patients hazard functions Probability of survival OXFT KIT GUYT 99 patients (19 events) 69 patients (20 events) 87 patients (28 events) Hazard Rate OXFT KIT GUYT 99 patients (19 events) 69 patients (20 events) 87 patients (28 events) Time Time Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
4 Tamoxifen Analysis Raw gene expession data cross-validation Normalization Data transformation Feature selection Model fitting In order to keep the design simple, we fix : the number of models to aggregate (see feature selection step) the cutoff selection (see the model fitting step). Performance assessment Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
5 Normalization Goal : reduce the systematic variability between samples. Method : normalization. Procedure 1 Background correction, expression quantification and normalization were performed using Robust Multichip Average [Irizarry et al., 2003, Bolstad et al., 2003]. 2 RMA performed separately per population 3 Gene median centering per population. No artifact highlighted by unsupervised clustering. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
6 Data Transformation Goal : reduce the dimensionality of the problem. Method : cluster highly correlated variables. Procedure 1 Use of an independent dataset of untreated patients (137 patients, probes) 2 Filtering of the less variant probes. 3 Hierarchical clustering (average linkage, uncentered Pearson correlation). 4 Cut the tree at height Keep only clusters with at least 5 known UniGenes. 110 clusters where half are significantly associated with survival (on the Tamoxifen dataset). Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
7 Data Transformation Feature Pairwise Correlation Histogram of pairwize correlations Frequency pairwize correlation mean: median: std: range: [ 0.651,1] Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
8 Feature Selection Goal : fast selection of the relevant features. Method : ranking based on univariate model significance. Procedure 1 For each feature, compute the likelihood ratio test of the univariate Cox model. 2 Perform the ranking based on the p-value. 3 Select the top n features (n is fixed). n features are selected. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
9 Model Fitting Goal : fit a simple model with low variance. Method : aggregation of n univariate classifiers. Procedure 1 The univariate models for the n top features were computed during the previous step. 2 Compute a linear combination of these classifiers with weight of 1. Continuous score representing the risk of a patient. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
10 Performance Assessment We do binary classification from survival data. We can not use traditional statistics (sensitivity, specificity, χ 2 test,... ) Adaptation of such estimators to deal with censoring. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
11 Survival Statistics for 2 Groups There exist several ways to assess difference in survival between 2 groups Kaplan-Meier estimator and Logrank test : KM method estimates survivor function such that # death at time t j Ŝ(t) = {}}{ d j [1 ]. n j j:t j t }{{} # at risk at time t j logrank method tests H 0 : S 1 (t) = S 2 (t) t 0. Hazard ratio (HR) : relative hazard between 2 groups using Cox model with one dummy variable (G = 0/1 for low and high-risk groups). Time-dependent ROC Curves and area under ROC curves : extension of traditional ROC curves dealing with censoring data. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
12 Performance in LOO Logrank and Time-Dependent ROC Curve Survival curves COXSM13 (LOO classification) Tamoxifen treated patients time dependent ROC curves (5 yrs) Probability of survival logrank = 1.041e 07 loglik = e 06 low risk 179(33) high risk 76(34) sensitivity COXSM13 (AUC=0.767) Time 1 specificity Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
13 Performance Hazard Ratio and Logrank Training set Test set Hazard ratio Log-rank p OXFT (99/19) KIT/GUYT (156/48) 2.17 [1.2,3.91] 8.46e-3 KIT (69/20) OXFT/GUYT (186/47) 4.07 [2.23,7.41] 7.98e-7 GUYT (87/28) OXFT/KIT (168/39) 5.93 [3,11.75] 1.24e-9 KIT/GUYT (156/48) OXFT (99/19) [5.38,39.52] 1.74e-11 OXFT/GUYT (186/47) KIT (69/20) 3.44 [1.36,8.67] 5.27e-3 OXFT/KIT (168/39) GUYT (87/28) 2.23 [1.05,4.71] 3.11e-2 Leave-one-out c.v [2.32;6.41] 1.04e-7 Multiple 10-fold c.v [2.66,3.84] 9.04e-7 Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
14 Performance wrt Signature Size Hazard ratio (CI) wrt signature size 10FOLD CV Logrank p value wrt signature size 10FOLD CV log2 hazard ratio log10 logrank p value signature.size signature.size Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
15 Performance vs Random At level of performance estimation : 1000 random permutations of the labels and perform the whole procedure only 1% of such classifications gives better discrimnation. At level of feature selection : random selection of n features most of the feature selection result in good classifiers because of the high proportion of relevant features. use of the anti -ranking very poor performance. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
16 Feature Selection Stability Signature stabilit COXSM13 (10FOLDCV) pclust.79 pclust.148 pclust.120 pclust.112 pclust.375 pclust.201 pclust.859 pclust.521 pclust.784 pclust.360 pclust.231 pclust.110 pclust.337 pclust.971 pclust.13 pclust.1008 pclust.606 pclust.289 pclust.435 pclust.88 pclust.34 pclust.425 pclust.752 pclust.851 pclust.714 pclust.26 pclust.207 pclust.350 pclust.968 pclust.692 pclust.709 pclust.1318 pclust.17 pclust.399 pclust.252 pclust.188 pclust.149 pclust.43 pclust.146 pclust.62 pclust.177 pclust.667 pclust.462 pclust.181 Over the multiple 10-fold cross-validations, several top n features are selected If the same set of features are selected every time, we see high relative frequency for these features relative frequency Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
17 Feature Selection Stability Signature stabilit COXSM13 (10FOLDCV) pclust.79 pclust.148 pclust.120 pclust.112 pclust.375 pclust.201 pclust.859 pclust.521 pclust.784 pclust.360 pclust.231 pclust.110 pclust.337 pclust.971 pclust.13 pclust.1008 pclust.606 pclust.289 pclust.435 pclust.88 pclust.34 pclust.425 pclust.752 pclust.851 pclust.714 pclust.26 pclust.207 pclust.350 pclust.968 pclust.692 pclust.709 pclust.1318 pclust.17 pclust.399 pclust.252 pclust.188 pclust.149 pclust.43 pclust.146 pclust.62 pclust.177 pclust.667 pclust.462 pclust.181 partial ranking area Over the multiple 10-fold cross-validations, several top n features are selected If the same set of features are selected every time, we see high relative frequency for these features. We can compute the area under partial ranking w.r.t. the number of selected features relative frequency Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
18 Stability wrt Signature Size Partial ranking stability wrt signature size 10FOLD CV Area Under Partial Ranking n = 13 seems to be a good trade-off between the number of selected features (signature) and its stability Signature Size Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
19 Final Model Use the same method with all the samples. The result is a model with a set of features. The model and the features are expected from previous results. pclust.120 pclust.360 pclust.201 pclust.148 pclust.521 pclust.784 pclust.375 pclust.231 pclust.110 pclust.337 pclust.112 pclust.79 pclust.859 pclust.120 pclust.360 pclust.201 pclust.148 pclust.521 pclust.784 pclust.375 pclust.231 pclust.110 pclust.337 pclust.112 pclust.79 pclust.859 Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
20 Future Works Study the stability of the initial clustering (data transformation step). Use of Gene Ontology to study the signature in a biological point of view. Objective comparison with the traditional clinical variables. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
21 Current Research Interests Meta-analysis. Ranking statistics. Input space transformation (using biological knowledge, such that gene list enrichment or GO). Optimization framework for binary classification of survival data. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
22 Thank you for your attention. Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
23 Bolstad, B. M., Irizarry, R. A., Astrand, M., and Speed, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19(2): Irizarry, R. A., Boldstad, B. M., Collin, F., Cope, L. M., Hobbs, B., and Speed, T. R. (2003). Summaries of affymetrix genechip probe level data. Nucleic Acids Research, 31(4). Benjamin Haibe-Kains (ULB) IRIDIA Microarray Meeting September 29, / 21
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