Detection of structural abnormalities in tumour genomes using model based

approaches: application to 107 patients with follicular lymphoma

Sohrab ShahDepartment of Computer Science

University of British Columbia

UBC computer science:Kevin MurphyRaymond Ng

BC Cancer Research Centre:Doug HorsmanK-John Cheung Jr.

2Detection of structural abnormalities in tumour genomes using model based approaches

Structural abnormalities in cancer

3Detection of structural abnormalities in tumour genomes using model based approaches

Copy number alterations (CNA) can lead to disease

• CNAs are a hallmark of tumor genomes• CNAs can lead to adverse expression

changes of affected genes • Recurrent CNAs in patients with common

phenotype potentially represent molecular markers of disease

• Task: find recurrent CNAs for diagnostics, gene-disease association, disease susceptibility

Bayani et al, Cancer Research 2002

Amplification

Nature 437, 1084-1086

4Detection of structural abnormalities in tumour genomes using model based approaches

Goal: Classify each probe as loss, neutral, gain

Solution: fit a hidden Markov model (HMM) to the data

CNA labels?

Detect CNAs using array comparative genomic hybridization (aCGH)

aCGH data

Loss

Neutral Gain

27K probes Per patient

5Detection of structural abnormalities in tumour genomes using model based approaches

Why HMMs for aCGH?

1. measurement noise

2. spatial correlation

3. Classification (L,N,G)

Student-t mixture emission model

HMM transition matrix

Continuous data -> discrete biology

Advantages of an HMM:

Ground truth labeled data

6Detection of structural abnormalities in tumour genomes using model based approaches

Our HMM leads to improved accuracy

• Contribution: novel HMM adaptation for aCGH – Extension of Fridlyand et al (2004)

• 15% improvement over state of the art• 95% classification accuracy for 49 manually annotated

samples– Shah et al, Bioinformatics (2006)

7Detection of structural abnormalities in tumour genomes using model based approaches

Large-scale study of follicular lymphoma (FL)

• 107 patients, aCGH data: 27K probes per patient

• Manual annotation of all patients• Clinical data available

– Survival– Time to transformation to more

aggressive stage

• GOAL: provide a pattern of recurrent CNAs (called a profile) that characterize this disease– Pick specific probes for

validation– Determine affected genes

Multiple aCGH samples

CNA profile

8Detection of structural abnormalities in tumour genomes using model based approaches

Analysing 107 aCGH profiles of follicular lymphoma

Pati

en

ts

Probes

Neutral

Gain

Loss

9Detection of structural abnormalities in tumour genomes using model based approaches

Alteration frequency (AF) vs manual – Chr 1

• 1p36: region of interest

• Experimental validation rate of 79% using FISH

Manual

AF Loss

AF Gain

Loss

Gain

Where are the signals strongest?

10Detection of structural abnormalities in tumour genomes using model based approaches

A novel Hierarchical HMM (HHMM) for inferring recurrent CNAs

Borrow statistical strength across patients using raw data

Focus on consensus

Explicit modeling of ambiguity distinguishes ‘random’

effect from shared signals

Produce sparse output where signals are strongest

Shah et al Bioinformatics 2007

11Detection of structural abnormalities in tumour genomes using model based approaches

HHMM yields sparse output where shared signals are strongest

Manual

AF Loss

AF Gain

HHMM

HHMM-U

Loss

Gain

12Detection of structural abnormalities in tumour genomes using model based approaches

Future work1. Validation of HHMM predictions on an

independent cohort

2. Extend HHMM for clustering patients• Stratification of the population based on aCGH

may point to distinct molecular subtypes of FL • Correlation of sub-groups to clinical variables

may lead to prognostic profiles• Detect subgroup-specific markers that are

distinct from ‘background’• Clinically predictive markers?

13Detection of structural abnormalities in tumour genomes using model based approaches

Acknowledgements

Michael Smith Foundation for Health Research: Senior graduate scholarship

Genome Canada/Genome BC: Research grant for array CGH

http://www.cs.ubc.ca/~sshah

Advisors: Kevin Murphy and Raymond NgCollaborators: K-John Cheung Jr., Douglas Horsman