molecular classification of breast cancer · 2015. 11. 27. · ck8/18 ck8/18 er, pr basal luminal...

Molecular classification of breast cancer – hormonal

receptors and beyond

Aleš Ryška

The Fingerland Department of Pathology

Hradec Králové

Can we do better???

Czech Republic

Hradec Králové

Concept of targeted therapy

Heinemann V, et al. Cancer Treat Rev 2013 DOI 10.1016/j.ctrv.2012.12.011.

“The ultimate goal of personalised medicine

is to define a disease sufficiently to enable

identification and treatment of only those

patients most likely to respond“

ONCOLOGIST PATHOLOGIST

Pathologist – integral part of multidisciplinary team

• morphology

• immunohistochemistry

• molecular biology

HER-2/neu & CEP 17

Diagnosis

TNM

Grade

Predictive markers

Breast cancer

• Heterogous group of tumors

– variable etiology, pathogenesis, prognosis, morphology, behaviour, response to treatment

progenitor cell

intermediary glandular cell

luminal cell

intermediary basal cell

myoepithelial cell

CK5/6, CK14 p63

CK5/6, CK14 P63, SMA

CK5/6, CK14 P63, SMA,

CD10

CK5/6, CK8/18

CK8/18 ER, PR

Luminal Basal

CK8/18 HER2

HER2+

ER

PR

HER2

Luminal A

DUCTAL LOBULAR

HER2+

Triple negative

Luminal B

ER+

PR+ Luminal (A or B)

HER2-

artefER-

artefPR- Triple negative !!!

HER2-

Sources of variability in tissue testing

Time to fixation Tissue processing

Type of fixation

Equipment

Quality of the

lab

Length of

fixation

Type of test

Experience

Demasking

Reagents

Controls

Computer

image

analysis

Interpretation

Scoring

Variation of

results

Post-analytic

phaze

Pre-analytic

phase

Analytic

phase

Wolff AC et al. J Clin Oncol 2007; 25: 118-145

Am J Clin Pathol 2010;134:813-819

Fixation

Operation

Modern Pathology (2009) 22, 1457–1467

30‘

120‘

Am J Clin Pathol 2010;134:594-596

… only cases known from core biopsy testing to exhibit 3+ overexpression of HER2/neu. Also, our estrogen receptor study3 included only high expressors …

Impact of fixation on Ki67 demonstration

Hitchman E et al. Histopathology. 2011 Dec;59(6):1261-3

Heterogeneity of neoplastic population!

Dowsett M, et al. J Natl Cancer Inst 2011;103:1656–1664

Varga Z, et al. (2012) PLoS ONE 7(5): e37379. doi:10.1371/journal.pone.0037379

time

Success of a biomarker

St. optimisticum

St. criticum

St. tragicum

Use common sense!

6980 patients 4651 ER+PR+ (66.6%) 1758 ER-PR- (25.2%) 481 ER+PR- (6.9%) 90 ER-PR+ (1.3%)

2.1%

D’Alfonso T: Am J Surg Pathol 2010

D’Alfonso T: Am J Surg Pathol 2010

HERretest project

Reference lab ISH

Reference lab IHC Negative Borderline Positive Total Discrepant

0,1+ (negative) 514 (83.3%) 13 (2.1%)2 8 (1.3%)2 535 (86.7%) IHC-/ISH+

N=21 (3.4%)

2+ (borderline) 57 (9.3%)3 5 (0.8%) 7 (1.1%) 69 (11.2%) IHC+/ISH-

N=59 (9.6%) 3+ (positive) 2 (0.3%)3 4 (0.6%) 7 (1.1%) 13 (2.1%)

Total 573 (92.9%) 22 (3.6%) 22 (3.6%) 617 (100%) Discrepant total

N=80 (13.0%)

617 cases – originally HER2 IHC negative (0, 1+) false negativity of primary testing = 4 % IHC-/ISH+ discordant phenotype = 3,4%

Clinico-patological features analyzed in the series

Feature Discordance

IHC- ISH + p1 OR (95% IS) p2

Total 3.4%

Age (years) - - 1.054 (1.013; 1.096) 0.009

Age >65 years 5.6 % 0.105 2.093 (0.873; 5.019) 0.098

Grade 3 4.3 % 0.617 1.355 (0.536; 3.425) 0.521

Tumor type DCI, PAP, other

(all but LCI, MUC, TUB) 4.9 % 0.011 3.695 (1.438; 9.499) 0.006

Tumor size > 20 mm 6.6 % 0.045 2.502 (1.040; 6.018) 0.041

ER: 0 % 5.7 % 0.116 1.960 (0.741; 5.188) 0.175

PR: 0 % 5.7 % 0.074 2.168 (0.894; 5.258) 0.087

Ki67 > 10% 6.1% 0.360 1.668 (0.602; 4.625) 0.326

Primary IHC 1+ 4.3% 0.356 1.607 (0.671; 3.853) 0.287

ER – and/or PR - 5.4 % 0.036 2.481 (1.032; 5.966) 0.038

Selection of IHC- cases advisable for ISH testing

Score N Discrepancy OR (95% IS) p1

0 60 0.0% Reference value (OR=1)

1 317 2.2%

2 189 4.2% 2.336 (0.834; 6.543) 0.106

3 51 11.8% 7.048 (2.269; 21.893) 0.001

• Histology – tumot type other than LCI, MUC, PAP • Tumor size > 20 mm (pT2 and higher) • Hormonal (partial or entire) independence – ER- and/or PR-

Koninki K, et al. HER-2 positive breast cancer: decreasing proportion but stable incidence in Finnish population from 1982 to 2005. Breast Cancer Research 2009

HER2 positivity in Czech population - 2014

90,0

87,2

86,6

88,0

86,6

86,6

86,9

4,9

3,8

3,9

10,0

8,9

8,5

8,2

10,0

8,7

9,2

4,6

3,3

4,0

0% 20% 40% 60% 80% 100%

% of patients

Negative

IHC 0/1+/2+ & ISH ≥ 2,0

IHC 3+

FN Olomouc a LF UP - Ústav

patologie

FNHK - Fingerlandův ústav

patologie

VFN Praha - Ústav patologie

MOÚ - Oddělení onkolog. a

experiment. patologie

Plzeň - Bioptická laboratoř s.r.o.

BIOLAB Praha, k. s.

Total

N = 20

N = 936

N = 508

N = 814

N = 2371

N = 1227

N = 5876

N = 5876

patients

Why are our predictions not 100% perfect?

Wu VS et al. J Steroid Biochem & Molec Biol 153 (2015) 45–53.

Cortazar R, et al. Lancet 2014; 384: 164–72

The Breast 23 (2014) 188e192

HER2

HR (ER/PR)

HER2 + HR

Normal conformation of HER2 molecule

Corrupt conformation of HER2 molecule

Arribas et al. Clin Cancer Res, 2010

Sperinde et al, Clin Cancer Res 2010

Predictive value of p95 protein

Can we do even better?

Advances in genetics a proteomics

• High resolution SNP chips • array CGH • multiplex PCR • deep parallel sequencing • gene expression profiling • .....

• Shift from research to practice • Costs of these methods are dropping down • Great expectations

Class discovery /molecular taxonomy

• Identification of specific molecular subclasses, having biologic and clinical significance

– Perou et al., Nature, 2000

– Sørlie et al., Proc Natl Acad Sci USA, 2001

• 42 patients

– (36 DIC, 2 LIC, 1 DCIS, 1 FA, 3 x normal tissue)

• ,,Hierarchical clustering“ of samples based on similarity of gene expression

• 1753 genes studied

• expression in individual sample compared to median of expression of the same gene in all samples, color coded

• Subsequent analysis based on expression of ,,Intrinsic gene subset“ – 496 genes

• gens selected to ensure that paired samples of the same tumor would be always classified together and would differentiate as much as possible from the remaining tumors

The number of clearly different molecular phenotypes observed among the breast tumours suggests that we are far from having a complete picture of the diversity of breast tumours. When hundreds (instead of tens) of breast tumours have been characterized, a more precisely defined tumour classification is likely, and statistically significant relationships with clinical parameters should be uncovered….

• 85 samples

• 78 carcinomas (including 40 from previous study)

• Overall and relapse-free survival analysis of the 49 breast cancer patients, uniformly treated in a prospective study

A

B

Class discovery /molecular taxonomy

Alternative (simpler and cheaper) approaches • molecular taxonomy may be simulated by e.g.

clustering analysis of immunohistochemically detected proteins

• immunohistochemical detection of 26 proteins

• 552 patients with early BC

• more than 1600 samples (tissue microarrays)

• analysis of interobserver agreement in data interpretation among five experts

• 3 cohorts of BC (799 cases)

• clustering analysis using 5 different “intrinsing gene“ sets

• consensus among 5 experts: κ= 0,435 – 0,757

• perfect agreement (5/5) in 42,4 -63,6 % cases

• leaving luminal type out (A,B,C) κ= 0.435–0.582

• perfect agreement (5/5) in 17.5- 46.1% cases

• κ=-1 perfect disagreement

• κ= 0 consensus fully random

• κ= 1 perfect agreement

• Κ=0.01–0.2 minor agreement

• κ= 0.21–0.4 slight agreement

• κ= 0.41–0.6 average agreement

• κ= 0.61–0.8 substantial agreement

• κ= 0.81–1.0 (nearly) perfect agreement

Clin Cancer Res 2006;12:781-790.

• 633 cases - expression of 5 “traditional markers“ (ER, PR, Ki67, HER2, p53) 4 clusters

Class prediction

• Several multigene predictors (gene signatures) exist, predicting behaviour and tratment response

– minimum of common genes

– similar performance – similar, but not identical prognostic groups

– majority built and verified on specific cohorts of BC (e.g. ER+/N0)

• OncotypeDX (21 genes) – ASCO / NCCN guidelines

• RT-PCR (FFPE material)

• Recurrence Score: low – intermediate - high

• ER+ a N0 treated by tamoxifen; other groups... • basically – objectification of proliferation in ER+ BC

• MammaPrint (70 genes) – FDA

• oligonucleotide microarray (fresh tissue), transport in medium within 5 days to central laboratory

• good prognosis / poor prognosis group

• N0 < 60 years < 5 cm TU

• BC Gene Expression ratio, Veridex LLC, Endopredict

• predictors of response to CHT, hormonal, targeted Th

Class discovery /molecular taxonomy

• Enormous expectations: taxonomy should bring dramatic improvement of clinical management

• Reality:

– Validation ???, reproducibility ???, sets of genes, number of classes clinical implications ???

• e.g. majority of luminal tumors are HR+ (routinely identified by IHC) • any other subclassification based on proliferation is arbitrary

• What about luminal cases which are HR- ?

• HER2+ BC is managed the same way irrespective of molecular subclass

• molecular taxonomy is in fact reflecting expresion of HR, HER2 and proliferation

Class prediction

• similar problems with standardization, quality control and clinical validation

• What is the ,,added value“ compared to classical molecular markers (ER,PR, HER2, Ki67) ?

• 40-60% clinically intermediate cases (grey zone) remains unresolved also after OncotypeDX

• first randomized clinical trials (OncotypeDX-

TAILORx, MammaPrint - MINDACT)

Conclusions

• Individual molecular markers (ER, PR, HER2...) are used for decades and represent currently a standard of classification and patients stratification for individual treatment

• advances in molecular genetics a proteomics enabled new possibilities of molecular classification and prediction in BC

• however, despite great expectations these methods do not replace traditional classification, they are oversimplifying complexity of the BC spectrum

• nevertheless, they can be a significant addition in prognosis determination and prediction of treatment effect, namely in selected groups and in „grey zone“ cases not resolved by classical approach

???

Hanahan D, Weinberg RA. Cell. 2011; 144(5): 646-74.

Brychtova S. et al.: Stromal Microenvironment Alterations in Malignant Melanoma, DOI: 10.5772/22912

Lawrence MS et al. Nature 2013; 499: (7457):214-8.

Impact of gene mutation on expression of encoded protein

Hornychova H, et al. Tumor-infiltrating lymphocytes predict response to neoadjuvant chemotherapy in patients with breast carcinoma. Cancer Invest. 2008; 26: 1024-31.

Denkert C, von Minckwitz G, et al. J Clin Oncol. 2015 ;33(9):983-91.