Gene Expression Based Tumor Classification Using Biologically Informed Models ISI 2003 Berlin Claudio Lottaz und Rainer Spang Computational Diagnostics Group Max Planck Institute for Molecular Genetics, Berlin

Tumor Diagnosis/Prognosis with expression profiles Data: Tens of thousands of genes Tens to hundreds of patients Patients are labeled E.g. Disease (D) / Control (C) Problem: Predict the label of a new patient given his/her expression profile Patients Genes D C

Statistical Context: Learning Theory High dimensional models  overfitting problems Additional constraints  Regularized Multivariate Models What are these constraints? -A small maximal number of genes allowed in the model (Variable Selection, Sparse Models) - Likelihood penalties, - Informative priors, - Large Margins,

Frustrations Falsely predicted patients Questionable Labels Genes that make no sense in the context Secondary and tertiary effects are more prominent then causal molecular mechanisms

The patient groups are seen as molecular homogenous groups Genes are anonymous variables: x 1,…,x n Implicit assumptions of standard approaches

Our approach: 1. Sub-class finding instead of global class prediction 2. Use of functional annotations of genes Molecular Symptoms

Global class prediction vs. Subclass finding D C D‘ D\D‘ C Global class prediction: Find a molecular signature that separates D from C and generalizes to new patients Subclass finding: Find a Subclass D‘  D and a molecular signature that separates D‘ from C We call this signature a molecular symptom associated to D

Molecular symptoms High specificity and sub-optimal sensitivity partially supervised Molecular properties that are (almost) unique to the disease group, but do not need to be present in all patients having the disease Novel stratification of patients Hidden molecular sub-entities There are in general many molecular symptoms associated to a disease One patient can have several molecular symptoms

Exploiting functional annotations of genes A posteriori use of functional annotations A priori use of functional annotations ( suggested here ) Data Functional Annotations Statistical Analysis Data Functional Annotations Statistical Analysis

What are we looking for? C D‘ 1 D‘ 2 D‘ 3 D‘ 4 DNA Repair Apoptosis Cell Proliferation HOX Genes

Functional grouping of genes using Gene Ontologies (GO) n GO is a database of terms for genes n Known genes are annotated to the terms n Terms are connected as a directed acyclic graph n Levels represent specifity of the terms

The whole thing at a glance GO contains three different sub- ontologies: –Molecular function –Biological process –Cellular component

The Annotation Gene 1023 Gene Gene Gene 13 Gene Gene Gene 311 Gene 314 Gene Gene 6702 Gene Gene Genes are annotated to both leave and inner nodes Genes can have multiple annotations

Gene 1023 Gene Gene Gene 1023 Gene Gene Augmented Ontology

Structured Analysis of Microarrays (StAM) - Claudio Lottaz - Modular Grid of three components 1. Classification in Leave Nodes 2.Diagnosis Propagation 3.Regularization: Gene 1023 Gene Gene Gene 311 Gene 314 Gene 22666

1. Classification in the leave nodes by Shrunken centroid classification: PAM (Tibshirani et al 2002) DLDA-like Discrimination Discriminant function via the distance to the shrunken class centroids d(C), d(D) Regularization: Variable selection via centroid shrinkage Class Probabilities:

2. Propagation of Diagnosis by weighted averages w1w1 w2w2 w3w3 C1 C3C2 Pa Weights are proportional to CV-Performance measured by a weighted deviance The weight  is used to enforce high specificity and relaxed sensitivity. Typically:  =0.95

3. Regularization by graph shrinkage To get rid of uninformative branches of the Gene Ontology, we shrink the weights in the progression step by a constant   is chosen by crossvalidation

Redundancy of a shrunken graph For two nodes we define the distance:... which reflects the probability of an inconsistent diagnosis For a single node we define its redundancy after shrinkage:... where K  is the set of all remaining nodes in the graph after shrinkage For a shrunken GO graph, we define its redundancy:

Expression data from a leukemia study Study on acute lymphoblastic leukaemia (ALL) 327 patients genes (Affymetrix HG- U95Av2) Yeoh et al., Cancer Cell 2002 My focus in this talk: MLL – ( ) vs. Others Objective: Diagnosis of cytogenetic subtypes of ALLs: 20 MLL - ( ) 27 E2A-PBX1 15 BCR-ABL 79 TEL-AML1 87 Hyperdiploid 7 Hypodiploid 29 Pseudodiploid 18 nomal (B-cell ALL) 43 T-ALL

Training and Test Disease group: ( MLL positive ALL ) Control group : ( other types of ALL ) Trainings – Test data (2/3 – 1/3 of both Disease and Control cases, randomly split) All model selection steps are part of the training !!! They are performed using CV of only the trainings data. - Centroid shrinkage in the leave nodes - Deviances for the propagation weights - Graph shrinkage

Error Rate Redundancy

The GO graph before and after shrinkage AfterBefore The results on the next slides show the performance of the fully shrinked model on the test data

Rows: GO- Nodes Columns: 7 MLL-Patients (Testset) Color: MLL- Score Selectivity/ Specificity

Molecular Symptom III Molecular Symptom I Molecular Symptom II

Spermatogenesis ??? Cyclin A1 Even Skipped Homolog Transcription Factor like 5 Cell CycleOncogenesisCell Proliferation Molecular Symptom I

Apoptosis Lectin Protein Tyrosin Phosphotase Molecular Symptom I

Phosphorylation Molecular Symptom II

Cell-Cell Signalling Molecular Symptom III

Almost Global Nodes Signal Transduction The Root

Claudio Lottaz R-code StAM Structured Analysis of Microarrays Julie Floch Java Application for browsing StAM output

Summary Data Functional Annotations Statistical Analysis D‘ D\D‘ C a prori use of functional annotations gene ontologies StAM 1. Classification in Leave Nodes 2.Diagnosis Propagation 3.Regularization: subclass finding Molecular Symptoms