Microarray bias and data
integration for breast cancer
meta-analysis
Andy Sims
ABC (Applied Bioinformatics of Cancer) Research Group
Gene Expression Data Integration
•
Ability to measure level of thousands (‘all’) transcripts simultaneously
•
Holistic approaches to complex problems – ‘data driven’
•
Huge amount of publicly available data – great resource, under used…
Thousands of transcripts on each array, many datasets, hundreds+
tumours – huge cost!
•
Meta-analysis could indentify findings that are not study-specific, leading
to consensus and provide guidelines
•
Problems of bias and challenges towards data integration
…Even with same platform and same tissue
Batch sample processing
Batch A – 8 GenChips
Batch B – 9 GeneChips
A2
A8
A4
A3
A5
A7
A6
C9
C10
Batch A
C6
B9
C8
A2
A8
A4
C9
C10
C3
Uncorrected
x mean overall
mean batch
B7
B8
C1
A7
C7
C5
C2
A3
B2
A5
C4
B6
C6
C5
C7
C2
C4
C3
C1
C8
B3
B5
B6
B7
B4
B9
Batch C
Corrected =
B4
B5
B1
A6
A1
Arrghh..!
Batch C – 10 GeneChips
G2 assay
Batch B
B3
B8
B1
B2
A1
Roberts et al. (1999) Am. J. Hum. Genet. 65: 784-94
Sims et al. (2007) International Journal of Radiation Biology 13: 617-628
Amplification protocols introduce systematic but
reproducible errors into gene expression studies
amplified MCF7 vs. amplified MCF10A
Between
Unamplified MCF7 vs. Unamplified MCF10A
amplified MCF7 vs. Unamplified MCF10A
Wilson et al. (2004). Biotechniques 36:498-506
Across
Unamplified MCF7 vs. amplified MCF10A
• Fold changes generated using different protocols are comparable(ish!)
• However, raw intensity levels are NOT comparable
• Different protocols introduce a systematic reproducible component
and comparison between different protocols may be dangerous
Mean correction & Amplification
Raw expression
Across
amplified MCF10A replicates
amplified MCF7 vs. Unamplified MCF10A
Across
Unamplified MCF7 vs. amplified MCF10A
Corrected =
Unamplified MCF10A replicates
Effect of correction
CORRECTED Unamplified MCF7 vs. Unamplified MCF10A
Fold changes
Between
Unamplified MCF7 vs. Unamplified MCF10A
Uncorrected
x mean overall
mean batch
Bias is introduced at many stages
of microarray processing…
Generation of GeneChip (Exon v 3’)
Amplification method (NuGen v MA)
Scanners (GeneArray 2500 / 3000)
Amount of RNA (Amp v unamp)
Protocol (original v new)
…but it can be removed
Why it works… the maths bit!
For logged data
Measured
Intensity
=
‘Real’
x
Intensity +
Systematic
bias
If systematic bias is specific to a group of chips;
Mean
Measured
Intensity
=
Mean
‘Real’
Intensity
+
Systematic
bias
Combining equations and rearranging for ‘real intensity’ cancels out the systematic bias
RELATIVE
‘Real’
Intensity
=
Mean
Measured - Measured
Intensity
Intensity
-
Systematic + Systematic
bias
bias
To get absolute ‘real’ intensity values that compare with the measured values, add the
overall mean
Mean
Overall Mean
Uncorrected
‘Real’
Measured
Measured
Measured
Corrected =
x- mean
overall +
For unlogged data
Intensity
Intensity
mean batch
Intensity
Intensity
Integrating published datasets
Richardson et al. 2006
Farmer et al. 2005
Cancer Cell 9:121-132
Oncogene 24:4660-4671
U133 plus2, standard labelling
18 ‘basal-like carcinomas’
20 ‘non-basal-like carcinomas’
2 BRCA carcinomas
U133A, amplified labelling
16 basal tumours
27 luminal tumours
6 ‘molecular apocrine tumours’
≈
≈
Identified 640 Affymetrix probesets representing the 534 ‘intrinsic geneset’
Luminal A
Luminal B
ERBB2+
Basal
Normal-like
Sorlie et al. PNAS (2003) 100:8418-23
Integrating published datasets
UC
MC
Richardson et al.
Farmer et al.
ERBB2 (216836_s_at)
GRB7 (210761_s_at)
ERBB2 (210930_s_at)
GATA3 (209604_s_at)
GATA3 (209602_s_at)
GATA3 (209603_s_at)
FBP1 (209696_at)
ESR1 (205225_at)
NAT1 (214440_at)
KRT17 (212236_at)
KRT17 (205157_at)
KRT5 (201820_at)
Richardson et al.
Farmer et al.
ERBB2 (210930_s_at)
ERBB2 (216836_s_at)
GRB7 (210761_s_at)
XBP1 (200670_at)
GATA3 (209603_s_at)
GATA3 (209604_s_at)
GATA3 (209602_s_at)
FBP1 (209696_at)
ESR1 (205225_at)
KRT5 (201820_at)
KRT17 (212236_at)
KRT17 (205157_at)
Combining 6 published datasets
Mean-centered
Uncorrected
Basal, ERBB2, Luminal A, Luminal B, Normal-like
Chin et al
Desmedt et al
Ivshina et al
Pawitan et al
Sotiriou et al
W ang et al
Does combining datasets improve
prognostic prediction?
Use single or combinations of
datasets as ‘training sets’ to
generate profiles based upon
patient follow up data
Evaluate predictive performance
of profile using other datasets as
‘test sets’
Supervised principle components analysis with time to recurrence or distant metastasis
Bair and Tibshirani PLoS Biol (2004) 2:E108
1 training dataset
(159 tumours)
5 training datasets
(821 tumours)
1 test dataset
(286 tumours)
p = 0.045
p = 0.0063
More data = ‘improved’ prediction
0.12
0.1
0.08
0.06
0.04
0.02
0
0
1
2
3
4
5
0.1
0.08
0.06
0.04
0.02
0
0
6
200
400
600
800
1000
1200
1000
1200
Number of tumours
p=0.02
p=0.0006
0.12
0.1
0.08
0.06
0.04
0.02
0
0
p=0.02
0.12
Number of datasets
Mean-centered
R2 Statistic (Cox proportional hazards model)
R2 Statistic (Cox proportional hazards model)
p=0.002
1
2
3
Number of datasets
4
5
6
R2 Statistic (Cox proportional hazards model)
R2 Statistic (Cox proportional hazards model)
Uncorrected
0.12
p=0.002
0.1
0.08
0.06
0.04
0.02
0
0
200
400
600
800
Number of tumours
Uneven dataset composition
1:1 ratio (12,12v12,12)
2:1 ratio (14,15v19,7)
5:1 ratio (3,15v19,3)
2:1 ratio (14,15v19,7)ER+
380
347
725
608
5:1 ratio (3,15v19,3)ER+
283
290
480*
684*
Between
Basal
Luminal
Farmer
Richardson
Across
Sam Common /1000
Between
UC
Across
MC
wMC
1:1 ratio (12,12v12,12)ER+
394
368
708
-
Summary
• Systematic bias introduced at many stages of microarray experiments
• Must be removed in order to combine datasets at raw expression level
• Combining datasets CAN improve prognosis prediction
BUT is better when systematic bias reduced
• Prognostic performance best when test-sets reflect training-sets
• Weighting correction based upon dataset composition may improve findings
Cannot compensate for all differences (many unknown)
• A resource to look at co-expression, subtle interactions (Cheaply!)
• More representative/accurate of population – identify/compare rare subtypes
• Consistent patient meta-data required
Chin
Desmedt
Ivshina
Pawitan
Sotoriou
Wang
Acknowledgements
Rob Clarke, Tony Howell
CR-UK Bioinformatics Group
Graeme Smethurst, Michal Okoniewski, Claire Wilson, Crispin Miller
CR-UK GeneChip Microarray Service
Stuart Pepper, Yvonne Hey, Gill Newton
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