netbiosig2014-talk by david amar

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Pathways as robust biomarkers for cancer

classification: the power of big expression data

David Amar, Tom Hait, and Ron Shamir

Blavatnik School of Computer Science

Tel Aviv University

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Motivation and introduction

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Comparative genomicsStandard expression experiments: cases vs.

controls -> differential genes -> interpretationProblems

Small number of samples Non-specific signal Interpretation of a gene set/ gene ranking

Goal: find specific changes for a tested diseaseE.g., an up-regulated pathwayCrucial for clinical studies

Previous integrative classification studies Huang et al. 2010 PNAS (9,160 samples); Schmid

et al. PNAS 2012 (3,030); Lee et al. Bioinformatics 2013 (~14,000) Multilabel classification Global expression patternsOnly 1-3 platformsMany datasets were removed from GEONo “healthy” class (Huang);No diseases (Lee)

Pathprint (Altschuler et al. 2013)Use pathwaysTissue classification (as in Lee et al.)

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Integrating pathways and molecular profilesEnrichment tests

Improves interpretability GSEA\GSA

Ranked based Higher statistical power

ClassificationExtract pathway features

Example: given a pathway remove non-differential genes

Not clear if prediction performance improves compared to using genes (Staiger et al. 2013)

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Pathway-based gene expression database

PathwaysKEGG Reactome

Biocarta NCI

Expression profiles

GSE

GDS

TCGA

Sample labelsDiseas

e

Dataset\sample

descriptionSingle sample - single pathway

analysis

For each

pathway

• Mean• SD

Y

Sam

ple

s

XP

Pathway features

Platform data

Single sample analysis

g1 , g2 ,g3 , … , gk

Ranked genes\

transcripts

Sample j

g1 , g2 ,g3 , … , gk

Weighted ranks

/i kiW ie

w 1 , w 2 ,w 3 , … , w k

Standardized profile

low expressio

n

highexpressio

n

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Single sample analysisInput: an expression profile of a sample

A vector of real values for each patientStep 1: rank the genesStep 2: calculate a score for each gene

Rank of gene g in sample s

Total number of ranked genes (Yang et al. 2012,2013)

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Pathway features1723 pathways in total

Covering 7842 genesMean size: 36.35 (median 15)

Score all genes that are in the pathway databases

Pathway statistics:Mean scoreStandard deviationSkewnessKS test

Pathway DBsKEGG

Reactome

Biocarta NCI

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Patient labelsUnite ~180 datasets, >14,000

samplesPublic databases contain ‘free

text’Problem: automatic mapping

fails, example:GDS4358:” lymph-node

biopsies from classic Hodgkins lymphoma HIV- patients before ABVD chemotherapy”

MetaMap top score: “HIV infections”

Solution: manual analysisRead descriptions and papers

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Current microarray dataData from GEO

13,314 samples17 platforms

Sample annotationIgnore terms with less than

100 samples 5 datasets 48 disease terms

Disease terms

XP

Sam

ple

s

Pathway features

Y

Disease terms {0,1}

Sam

ple

s11

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Analysis and results

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Multi-label classification algorithmsLearn a single classifier for each

disease Ignore class dependencies

Adaptation: Bayesian CorrectionLearn single classifiersCorrect errors using the DO DAG

Transformation: use the label power sets and learn a multiclass modelUsing RF: multi-label trees

Was better than most approaches in an experimental study (Madjarov et al. 2012)

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How to validate an classifier?Use leave-dataset out cross-validation

Global AUC scores: each prediction Pij vs the correct label Yij Disease based AUC scores: consider each column separately

Y

Disease terms {0,1}

Sam

ple

s

P

Probabilities [0,1]

Sam

ple

s

The output of a multi-label learner

Test set

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A problem (!)What is in the background?For a disease D define:

Positives: disease samplesNegatives: direct controlsBackground controls

Example: 500 positives

500 negatives

10000 BGCs

Y

P

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Multistep validationIt is recommended to use several scores (Lee et al.

2013)Measure global AUPRFor each disease we calculate three scores

Measure Used (additional) information

AUPR: check separation between positives and all others

Sick vs. not sick

ROC: test for separation between positives and negatives

Direct use of negatives

Meta analysis p-value: calculate the overall separation significance within the original datasets (a p-value)

Mapping of samples to datasets

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Performance results

Meta analysis q-value < 0.001 (filled boxes)

Positives vs. negatives ROC

AUPR

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Performance results

8.5% improvement in recall, 12% in precision, compared to Huang et al.

Validation on RNA-SeqData from TCGA: 1,699 samples

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Pathway-Disease networkSteps (for each of the selected diseases):

1. Disease-pathway edges1. RF importance: Select the top features2. Test for disease relevance

2. Add edges between diseases1. Use the DO structure

3. Add edges between pathways1. Based on significant overlap in genes

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Cancer network

DownUp

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Cardiovascular disease

DownUp

Gastric cancers

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SummaryLarge scale integrationMulti-label learningCareful validationPathway based features as biomarkersSummary of the results in a networkCurrently

Add genes: overcome missing valuesShows improvement in validation

AcknowledgementsRon ShamirTom Hait