sohrab shah department of computer science university of british columbia
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Detection of structural abnormalities in tumour genomes using model based approaches: application to 107 patients with follicular lymphoma. Sohrab Shah Department of Computer Science University of British Columbia. BC Cancer Research Centre : Doug Horsman K-John Cheung Jr. - PowerPoint PPT PresentationTRANSCRIPT
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