Correlación entre la anatomía

patológica y las plataformas

genómicas

Federico RojoFundación Jiménez Díaz

Estimation of the risk of recurrence (prognosis) and of the benefit from systemic treatments (prediction) based on combinations of clinico-pathologic risk factors :

✓ Histologic grade (and tumor type)

✓ Measures of proliferation: mitotic index (and Ki67)

✓ Tumor size

✓ Lymph node staging

✓ Breast cancer biomarkers: ER, PR, HER2

And…

✓ Multigene signatures

How get clinical decisions for breast cancer patients?

Qué información adicionalproporcionan las plataformas

genómicas sobre los parámetrosclínico-patológicos?

Paik S, et al. J Clin Oncol 2006Badve, SS et al. J Clin Oncol 2008Stemmer S et al. Presented at SABCS 2015 (Poster P5-08-02)

Significant proportion of high-grade tumors (13-25%)

have low RS

ECOG2197 trial, n=776

prospective, randomized, clinical trial that included 2,872 assessable patients with HRbreast cancer and 0 to 3 positive lymph nodes.protocol specified treatment with four 3doxorubicin (60 mg/mcyclophosphamidemg/mmg/mtherapy if HR positive

NSABP B-20

1. Histological grade: Correlation between Grade and RS

194 (67%

)

90 (31%)

6 (2%)

524 (51%)

404 (39%)

98 (10%

)

85 (25%)

159 (47%)

94 (28%

)

Clalit, n=2028

Clark, BZ et al. Appl Imm Mol Morphol 2013Khoury, T et al. Appl Imm Mol Morph 2016

1. Histological grade: Correlation between Grade and RS

N=1074

Reproducibility performance of pre- and

post-educational evaluations

Paradiso, A et al. J Clin Pathol 2009

term

-

reproducibility versus the

shows the 25th and 75th

limits of the two whiskers

1. Histological grade: Correlation between Grade and RS

Educational evaluations improve Grade reproducibility

Gluz, O. J Clin Oncol 2016

PlanB: Grade assesment by local and

central pathology lab

Overall agreement in HR+ disease 66%

Re

pro

du

cib

ility

pe

rfo

rman

ce

Bomeisl, PE et al. Arch Pathol Lab Med 2015

n=184

2. Tumor type: Correlation between histology and RS

Conlon, N et al. Breast J 2015Tsai, ML et al. Clin Breast Can 2016

Lobular carcinoma

2. Tumor type: Correlation between histology and RS

NSABP B-20

A significant proportion of small tumors have high RS (6-16%)

3. Tumor size: Correlation between size and RS

Paik S, et al. J Clin Oncol 2006Stemmer S et al. Presented at SABCS 2015 (Poster P5-08-02)

Clalit, n=2028

Badve, SS et al. J Clin Oncol 2008

ECOG2197 trial

4. Hormonal receptors: Correlation between ER/PgR and RS

Good correlation between ER/PgR IHC and OncotypeDX

93% concordance 88% concordance

Cheang, MCU et al. Oncologist 2015

N=1557 (GEICAM9906, NCIC CTG MA.5, NCIC CTG MA.12)

ER and PgR staining (%) distributed by intrisic subtypes

4. Hormonal receptors: Correlation between ER/PgR and PAM50 subtype

ER: Pearson’s correlation: 0.68; 95%CI: 0.66–0.71; p=.0001PgR: Pearson’s correlation: 0.59;95%CI: 0.56–0.62; p=.0001

Prat, A et al. J Clin Oncol 2013Clark, BZ et al. Appl Imm Mol Morphol 2013

4. Hormonal receptors: Correlation between PgR and PAM50 subtype

20% of PgR IHC staining correlates with Luminal B phenotype

Perez, EA et al. Breast Can Res 2015

Alliance N9831 trial (Phase III adjuvant trastuzumab trial)

5. HER2 status: Correlation between HER2 amplification and OncotypeDX

Prat, A et al. JNCI 2013

5. HER2 status: Correlation between HER2 and PAM50

Distribution of intrinsic subtypes in HER2 clinical status

Reis-Filho, JS & Pusztai, L. Lancet 2011 Liebermann, N. et al. ESMO 2011

N=2477

6. Proliferation: Correlation between Ki67 and OncotypeDX and Mammaprint

Clalit (n=2477)

6. Proliferation: Poor reproducibility in Ki67 betweenobservers

Dowsett, M et al. JNCI 2011Polley, M et al. JNCI 2013De Nielsen, TO et al. SABCS 2013

PlanB: RS by (central) Ki-67

6. Proliferation: Correlation between Ki67 and OncotypeDX

Gluz, O. J Clin Oncol 2016

n=2568

Stalhammar, G et al. Mod Pathol 2016

N=436

6. Proliferation: Correlation between Ki67 and PAM50

Concordance index (c index) for risk of recurrence for ROR, RS, IHC4 and clinical treatment score (CTS)

Cuzick, J et al. J Clin Oncol 2011Dowsett, M et al. J Clin Oncol 2013

Predicted time to distant recurrence for a node-negative patient age ≥ 65 years with a poorly differentiated 1- to 2-cm tumor treated with anastrozole

IHC4 = 94.7*(-0.100*ER10 – 0.079*PgR10 + 0.586*HER2 + 0.240*ln(1 +10*ki67)

7. Combined parameters: Correlation between IHC4 and PAM50 or RS

Magee equations:

✓ Recurrence score = 15.31385 + Nottingham score*1.4055 + ERIHC*(-0.01924) + PRIHC*(-0.02925) + (0 for HER2 negative, 0.77681 for equivocal, 11.58134 for HER2 positive) + tumorsize*0.78677 + Ki-67 index*0.13269

✓ Recurrence score = 18.8042 + Nottingham score*2.34123 + ERIHC*(-0.03749) + PRIHC*(-0.03065) + (0 for HER2 negative, 1.82921 for equivocal, 11.51378 for HER2 positive) + tumorsize*0.04267

✓ Recurrence score = 24.30812 + ERIHC*(-0.02177) + PRIHC*(-0.02884) + (0 forHER2 negative, 1.46495 for equivocal, 12.75525 for HER2 positive) + Ki-67*0.18649

Flanagan, MB et al. Mod Pathol 2008Klein, ME et al. Mod Pathol 2013Turner, BM et al. Mod Pathol 2015 http://path.upmc.edu/onlineTools/MageeEquations.html

7. Combined parameters: Correlation between Magee equationand RS

Flanagan, MB et al. Mod Pathol 2008Klein, ME et al. Mod Pathol 2013Turner, BM et al. Mod Pathol 2015

7. Combined parameters: Correlation between Magee equationand RS

✓ Pearson correlation coefficient (r) for the OncotypeDX and Magee recurrence scores = 0.6644 (n=283; P=0.0001)

✓ The PPV was 0.86 for low risk RS and 1.0 for high risk, and the NPV, 0.45 and 0.97, respectively

✓ Eliminating high and low risk cases, between 5% and 23% of cases would potentially not have been sent for OncotypeDXtesting, creating a potential cost savings between $56,550 and $282,750.

Elloumi, F. et al. BMC Med Genomics 2011

8. Potential limitations of multigene plataforms: Role of cancer stroma

Turner, BM et al. Mod Pathol 2015Acs, G et al. Mod Pathol 2012

8. Potential limitations of multigene plataforms: Role of cancer stroma

✓ Increased stromal cellularity and presence of inflammatory cells are associated with RS≥18

✓ Elevated Ki67 in stroma predicts RS≥18

Gyanchandani, R et al. Clin Can Res 2016

8. Potential limitations of multigene plataforms: Tumor heterogeneity

1-Intragroup correlation coefficient distribution by gene signature

✓ Non-significant correlation between whole section and representative cores

✓ OncotypeDX, Mammaprint and PAM50 show higher heterogeneity (25% missclasification) between whole section and core sample, associated with high Ki67 and low PgR

1. Qué correlación podemos esperar de los parámetros clínico-patológicos y el riesgo determinado por palaformas genómicas en cáncer de mama?

✓ Grado (y tipo tumoral): moderada (80-85%; tipo histológico, 65%), limitada por la alta proporción de G2 y la concordancia inter-observador

✓ Tamaño tumoral: moderada (67-84%)

✓ Receptores hormonales: moderada-alta (85-95%)

✓ HER2: variable (47-91%)

✓ Proliferación: baja (45-85%), limitada por baja reproducibilidad de Ki67

✓ Fenotipo: moderada (48-87%)

✓ Parámetros combinados (IHC4, Maggie): moderada-alta (70-95%)

2. Existen factores limitantes: Heterogeneidad y estroma tumoral

3. Y… la concordancia de resultados inter-plataforma es moderada*

Conclusiones

*0.33-0.56; Stein, RC et al. ECC 2015