Novel Image Analysis Algorithms for Quantifying Expression of Nuclear Proteins

assessed by Immunohistochemistry

Elton Rexhepaj , MScUCD School of Biomolecular and Biomedical Science

UCD Conway Institute, University College Dublin,

Ireland.elton.rexhepaj@ucd.ie

Biomarker Validation: Application of Tissue Microarrays

BIOMARKER DEVELOPMENT

Interpretation of IHC

• Subjective, time consuming

• Inherent intra-observer variability

• Semi-quantitative data

• Pathologist-based analysis remains the current standard

• Objective quantification of IHC staining

• Reproducible data

• Continuous output

• A new tool in the hand of the pathologist

Manual Automated

Application of Image Analysis Approaches to assess IHC

Brennan et al resubmitted, Clinical cancer research, 2007

Rexhepaj et al, manuscript in preparation

• 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

Altered Nuclear-Cytoplasmic Ratio of Survivin is a Prognostic Indicator in Breast Cancer

Automated image analysis of survivin

• 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

H&E IHC x10 IHC x40

Low

Medium

High

Staining Intensity

Brennan et al submitted 2007

Separating nuclear from cytoplasmic stain

Cytoplasm

Cytoplasm & nuclear

We were able to separate cytoplasm from nuclear staining and independently quantify the IHC staining intensity

LOW

HIGH

Positive pixel count algorithm

Random Forest Clustering Survivin cytoplasmic to nuclear ratio

• By applying RFC we were able to find 4 cluster of patients• Cytoplasm to nuclear ratio was differently expressed in each cluster

12

3

4

RFC dim 1

RF

C d

im 2

Brennan et al submitted 2007

CNR and patient survival

• Clusters with high CNR showed a increase of both BCS and OS survival O

vera

ll S

urv

ival

Time (Months)

P = 0.005

CNR < 5

CNR > 5

CNR < 5

CNR > 5

Time (Months)

P = 0.05

BC

Sp

ecif

ic S

urv

ival Low CNR

High CNRHigh CNR

Low CNR

Brennan et al submitted 2007

Cox Regression Analysis of OS

Univariate and Multivariate analysis revealed that the CNR was a significant predictor of OS in this cohort along with tumour size and nodal status

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

Brennan et al submitted 2007

Low CNR a new prognostic subgroup

Cytoplasmic:NuclearRatio <5(n = 78)

Cytoplasmic:NuclearRatio >5(n = 18)

P value

Tumor SizeMedian (Range) 22(10-100) 24 (11-60)0-20mm 33 (42) 6 (33) 0.6014

>21mm 45 (58) 12 (67)ER status1

ER - 26(33) 1 (6) 0.0195

ER + 52(67) 17 (94)PR status1

PR - 35 (45) 3 (17) 0.0335

PR + 43 (55) 15 (83)NHGNHG I & II 37 (47) 18 (100) *NHG III 41 (53) 0p53 Status2

p53 - 53 (68) 17 (94) 0.0055

p53 + 25 (42) 1 (6)Myc Amplification3

Low 43 (55) 17(94) 0.0165

Intermediate/High 16 (21) 1 (6)Missing 22

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

• Size• Shape• Distance . . .

Apply the learned or calibrated patterns to the rest of the cohort.

PATTERN

MACHINE LEARNING

MANUAL CALIBRATION

Alternative : Application of non-supervised learning algorithms to learn the patterns in a case by case basis

•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

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

COHORT I

Data

• more then 1000 patients• full clinico-pathological follow up

- 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

Application of IHC nuclear algorithm on tissue cores examples

Algorithm validation

- 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 scoreM

anu

al path

olo

gist asses

smen

t

Automated percentage

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

AcknowledgementsSupervisor UCD School of Medicine and Medical ScienceProf. William Gallagher Dr Amanda McCann

Dr Dermot LeahyUCD School of Medicine and Medical Science Dr. Donal Brennan

Gallagher Lab Dept of Pathology Lund University SwedenDr. Darran O’Connor Prof Goran LandbergDr. Linda Whelan Dr Karin JirstromDr. Annette Byrne Asa KronbladDr. Mairin RaffertyDr. Richard TalbotDr. Shauna Hegarty Dr. Helen CooneyCaroline CurridSharon McGeeElaine McSherry TARP Laboratory NCI, NIH, Washington Liam Faller Dr Stephen Hewitt Ian MillerDenise RyanFiona LaneganBen CollinsTom LauKaren PowerStephen Madden AperioSarah Penny Aisling O RiordanDr Catherine KellyDr Sallyann O’Brien

EMBO practical course on TissueMicroarray construction and image analysis

June 2008 – THE RETURN !!!

http://coursewiki.embo.org/doku.php?id=tissue_microarrays:microarray_course