Transcript Document

Novel Image Analysis Algorithms for
Quantifying Expression of Nuclear Proteins
assessed by Immunohistochemistry
Elton Rexhepaj , MSc
UCD School of Biomolecular and Biomedical Science
UCD Conway Institute,
University College Dublin,
Ireland.
[email protected]
Biomarker Validation: Application of Tissue Microarrays
DISCOVERY
VALIDATION
BIOMARKER
PANEL
BIOMARKER DEVELOPMENT
Interpretation of IHC
Manual
•
•
•
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Subjective, time consuming
Inherent intra-observer variability
Semi-quantitative data
Pathologist-based analysis remains
the current standard
Automated
•
•
•
•
Objective quantification of IHC staining
Reproducible data
Continuous output
A new tool in the hand of the pathologist
Application of Image Analysis Approaches
to assess IHC
•Altered nuclear-cytoplasmic ratio of survivin is a prognostic indicator
in breast cancer
Brennan et al resubmitted, Clinical cancer research, 2007
•Automated quantification of ER/PR expression in breast cancer
patients
Rexhepaj et al, manuscript in preparation
Altered Nuclear-Cytoplasmic Ratio of Survivin is a
Prognostic Indicator in Breast Cancer
• Promising tumour marker
• Located in the cytoplasm and the nucleus
• Nuclear and cytoplasmic fractions of survivin have different
biological roles
• Manual interpretation of survivin is challenging
• Conflicting data exists on its prognostic impact in breast cancer
• Need for new automated scoring models
• Can automated scores lead to discovery of new prognostic
subgroups
Automated image analysis of survivin
H&E
•
•
IHC x10
IHC x40
Staining
Intensity
Low
Breast Cancer TMA
– 102 patients
– 0.6mm cores arrayed in duplicate
– Full clinicopathological data
– Median follow-up 77 months
Image acquistion
– Aperio Scanscope CS Autoscanner
Medium
High
Brennan et al submitted 2007
Separating nuclear from cytoplasmic stain
Positive
pixel count
algorithm
Cytoplasm
HIGH
Cytoplasm
& nuclear
We were able to separate cytoplasm from nuclear staining and
independently quantify the IHC staining intensity
LOW
Random Forest Clustering Survivin
cytoplasmic to nuclear ratio
RFC dim 2
3
4
1
2
RFC dim 1
• By applying RFC we were able to find 4 cluster of patients
• Cytoplasm to nuclear ratio was differently expressed in each cluster
Brennan et al submitted 2007
CNR and patient survival
High CNR
CNR < 5
High CNR
CNR < 5
CNR > 5
Low CNR
CNR > 5
P = 0.05
Overall Survival
BC Specific Survival
Low CNR
P = 0.005
Time (Months)
Time (Months)
• Clusters with high CNR showed a increase of both BCS and OS survival
Brennan et al submitted 2007
Cox Regression Analysis of OS
Univariate
Multivariate*
HR
95% CI
p
value
HR
95% CI
p
value
CNR
(<5 v’s > 5
0.1
0.01 - 0.73
0.023
0.09
0.01 - 0.72
0.024
Nodal status
(pos v’s neg)
3.03
1.48 - 6.20
0.002
2.74
1.21 - 6.19
0.015
Grade
(1 & 2 v’s 3)
2.52
1.32 - 4.81
0.005
0.63
0.27 - 1.48
0.29
ER status
(pos v’s neg)
0.38
0.20 - 0.73
0.004
0.61
0.22 - 1.67
0.334
Her2
(1 & 2+ v’s 3+)
2.19
1.06 - 4.52
0.034
2.0
0.84 - 4.78
0.119
PR status
(pos v’s neg)
0.41
0.21 - 0.80
0.009
0.86
0.36 - 2.08
0.737
Tumor size
(continuous)
1.04
1.02 - 1.06
0.001
1.05
1.02 - 1.08
0.002
Ki-67
(0 – 10% v’s 11-100%)
2.60
1.01 – 6.67
0.047
0.98
0.31 – 3.10
0.975
Univariate and Multivariate analysis revealed that the CNR was a significant
predictor of OS in this cohort along with tumour size and nodal status
Brennan et al submitted 2007
Low CNR a new prognostic subgroup
Tumor Size
Median (Range)
0-20mm
>21mm
ER status1
ER ER +
PR status1
PR PR +
NHG
NHG I & II
NHG III
p53 Status2
p53 p53 +
Myc Amplification3
Low
Intermediate/High
Missing
Cytoplasmic:Nuclear
Ratio <5
(n = 78)
Cytoplasmic:Nuclear
Ratio >5
(n = 18)
22(10-100)
33 (42)
45 (58)
24 (11-60)
6 (33)
12 (67)
26(33)
52(67)
1 (6)
17 (94)
0.0195
35 (45)
43 (55)
3 (17)
15 (83)
0.0335
37 (47)
41 (53)
18 (100)
0
*
53 (68)
25 (42)
17 (94)
1 (6)
0.0055
43 (55)
16 (21)
22
17(94)
1 (6)
0.0165
P value
0.6014
A low Survivin CNR is associated with a mitotic/proliferative phenotype
Survivin - conclusions
• Image analysis applied to Survivin IHC
• Image analysis of IHC can produce new automated quantitative scoring
models
• RFC was used to identify new prognostic subgroups
• Previously unidentified prognostic subgroups can be uncovered
• A low Survivin CNR is associated with a mitotic/proliferative phenotype
Brennan et al submitted 2007
What can be improved
MACHINE
LEARNING
•The supervised approach
- not reproducible and can’t be extended to other
tissue types
- requires a domain expert for the selection of
validation and test cohort of patients
• The manual calibration :
- It is time consuming
- Need to be repeated for each new slide/cohort/type
of tissue
MANUAL
CALIBRATION
PATTERN
• Size
• Shape
• Distance
...
Apply the learned or
calibrated patterns to
the rest of the cohort.
Alternative : Application of non-supervised learning algorithms to
learn the patterns in a case by case basis
Automated image analysis of ER and PR
• Members of the nuclear hormone family
• Expressed in around 70% of breast cancer cases
• Estrogen often induces a multiplication of progesterone
receptors
• Currently, hormone receptor status is manually assessed by a
pathologist
• an arbitrary cut off of 10% positive cells is used to decide
whether a patient should have adjuvant hormonal therapy
Data
COHORT I
- 564 pre-menopausal women with primary
breast cancer
- Patients were randomly assigned to either
two years of adjuvant tamoxifen
COHORT II
- 512 consecutive breast cancer cases
COHORT III
- 179 consecutive cases of invasive breast
cancer
• more then 1000 patients
• full clinico-pathological follow up
Application of IHC nuclear algorithm on tissue cores
examples
Algorithm validation
Manual pathologist assessment
Automated percentage
- Validation set -18 representative tissue cores stained with ER
- A trained pathologist was ask to blindly score each tissue core
- A very good correlation was observed between manual and automated
score
Correlation of manual with automated score of ER
• A good correlation was seen between manual and automated scores
Correlation of manual with automated score of PR
• A good correlation was seen between manual and automated scores
Selection of the threshold for ER status – cohort I
0.05
• 358 thresholds were generated in the range 0-100%
• For each cut-off
• BCS and OS of ER negative patients was compared to that of ER positive
patients
• The best cut-off for ER was 5% and for 7% for PR
A novel approach to automatically define the
threshold for ER status – cohort I
- Random forest clustering was used to automatically cluster patient in ER+/subgroups
A novel approach to automatically define the
threshold for PR status – cohort I
- Random forest clustering was used to automatically cluster patient in PR+/subgroups.
ER/PR status as defined by clusters and correlation
with manual scores – cohort I
• ER status as defined by RFC was correlated with manual scores.
• Spearman correlation coefficient was 0.8 for ER and 0.7 for PR
Correlation of RFC clusters with tamoxifen response
cohort I
• There was a significant effect of 2 years tamoxifen treatment on the ER+ and
PR+ cohort of patients as determined by RFC
• No treatment effect was evident in ER-, PR- patients as determined by RFC
Summary
- A novel non-supervised image analysis algorithm
- Excellent correlation of manual with automated scoring
- Univariate analysis of OS showed no significant difference in
the HRs between manual and automated scores
- A patient clustering approach to investigate patient stratification.
- A new automated approach to stratify patients in ER-/+
- The ability to predict tamoxifen response was similar in manual
and automated
Acknowledgements
Supervisor
Prof. William Gallagher
UCD School of Medicine and Medical Science
Dr Amanda McCann
Dr Dermot Leahy
UCD School of Medicine and Medical Science
Dr. Donal Brennan
Gallagher Lab
Dr. Darran O’Connor
Dr. Linda Whelan
Dr. Annette Byrne
Dr. Mairin Rafferty
Dr. Richard Talbot
Dr. Shauna Hegarty
Dr. Helen Cooney
Caroline Currid
Sharon McGee
Elaine McSherry
Liam Faller
Ian Miller
Denise Ryan
Fiona Lanegan
Ben Collins
Tom Lau
Karen Power
Stephen Madden
Sarah Penny
Aisling O Riordan
Dr Catherine Kelly
Dr Sallyann O’Brien
Dept of Pathology Lund University Sweden
Prof Goran Landberg
Dr Karin Jirstrom
Asa Kronblad
TARP Laboratory NCI, NIH, Washington
Dr Stephen Hewitt
Aperio
EMBO practical course on TissueMicroarray construction and
image analysis
http://coursewiki.embo.org/doku.php?id=tissue_microarrays:microarray_course
June 2008 – THE RETURN !!!