current concepts in breast cancer- beyond tnm professor ravi kant frcs (england), frcs (ireland),...

Current concepts in Breast Cancer- Beyond TNM

Professor Ravi KantFRCS (England), FRCS (Ireland), FRCS (Edinburgh),

FRCS(Glasgow), MS, DNB, FAMS, FACS, FICS, Professor of Surgery

Applications of Genes Assay in CA Breast

• To subclassify breast cancer• To estimate prognosis• To predict response to therapy

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Applications of Genes Assay in CA Breast

• To subclassify breast cancer• To estimate prognosis• To predict response to therapy

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Gene Expression Patterns of Breast Carcinomas Distinguish Tumor Subclasses With Clinical

Implications

PNAS 2001;98;10869-10874

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Molecular classification & Prognosis:

• Luminal A= Best prognosis• Luminal B• Luminal C• Normal breast like• Her 2+• Basal like= Worst= Triple Negative

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SubtypeType ImportanceLuminal A ER +, Best overall

survival, Best DFS

Luminal B ER,Her2+,Intermediate

Her 2 +ve ER-, Intermediate

Basal like ER-,PR-, Her2 - Worst 7

Tumor based Gene assayTest # of Genes Tissue

Mammaprint(Amsterdam)

70 Fresh

Oncotype Dx 21 Fixed

76 gene 76 Fresh

Wound response Fresh

Two gene ratio 2 Fixed

Intrinsic subtype Fresh9

Tumor based Gene assayTest Aim

Mammaprint 70 To predict risk of distant mets in N-, To identify who will benefit from Chemo

Oncotype Dx 21 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

76 gene 76 To predict DFS & OS in N-, early stage

Wound response To predict risk of mets & death

Two gene ratio 2 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

Intrinsic subtype To predict clinical outcome

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Tumor based Gene assayTest Aim

Mammaprint 70 To predict risk of distant mets in N-, To identify who will benefit from Chemo

Oncotype Dx 21 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

76 gene 76 To predict DFS & OS in N-, early stage

Wound response To predict risk of mets & death

Two gene ratio 2 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

Intrinsic subtype To predict clinical outcome

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• 70 gene classifier developed further by the company Agendia (www.agendia.com) under the name MammaPrint.

• MammaPrint was approved by the FDA in February 2007 for node negative women under 61 years of age and with a tumor < 5cm.

Tumor based Gene assayTest Aim

Mammaprint 70 To predict risk of distant mets in N-, To identify who will benefit from Chemo

Oncotype Dx 21 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

76 gene 76 To predict DFS & OS in N-, early stage

Wound response To predict risk of mets & death

Two gene ratio 2 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

Intrinsic subtype To predict clinical outcome

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Tumor based Gene assayTest Aim

Mammaprint 70 To predict risk of distant mets in N-, To identify who will benefit from Chemo

Oncotype Dx 21 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

76 gene 76 To predict DFS & OS in N-, early stage

Wound response

To predict risk of mets & death

Two gene ratio 2 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

Intrinsic subtype To predict clinical outcome

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Tumor based Gene assayTest Aim

Mammaprint 70 To predict risk of distant mets in N-, To identify who will benefit from Chemo

Oncotype Dx 21 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

76 gene 76 To predict DFS & OS in N-, early stage

Wound response To predict risk of mets & death

Two gene ratio

2 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

Intrinsic subtype To predict clinical outcome

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Tumor based Gene assayTest Aim

Mammaprint 70 To predict risk of distant mets in N-, To identify who will benefit from Chemo

Oncotype Dx 21 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

76 gene 76 To predict DFS & OS in N-, early stage

Wound response To predict risk of mets & death

Two gene ratio 2 To identify N-, ER+ who will benefit by addition of Chemo to Tamoxifen

Intrinsic subtype

To predict clinical outcome

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Applications of Genes Assay in CA Breast

• To subclassify breast cancer• To estimate prognosis/

prediction• To predict response to therapy

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Conventional classification

• Convential Classification assumes that women with a tumor bigger than 2 cm have a high risk to develop distant metastasis.

Size is an insufficient indiactor of metastasis risk

• Molecular studies shows that size alone is not an indicator of high or low metastasis risk.

• Small and large tumors can be either low or high risk as determined by Molecular studies

Molecularclassification

Molecular studies provides additional data to assess the risk of distant

metastasis

Molecular studiesclassification

Patients re-characterized as Low-RiskMammaPrint: 34%

MammaPrint Discovers 34% Low Risk MammaPrint Discovers 34% Low Risk Patients in Adjuvant! High-Risk GroupPatients in Adjuvant! High-Risk Group

Buyse et al., Journal of the National Cancer Institute. 2006;98(17):1183-92.

Adjuvant! High Risk

MammaPrint Low Risk

MammaPrint High Riskn = 20987%

n = 13766%

n = 7234%

N = 209

Gene Assay prediction > Adjuvant Online

• Buyse M. Validation and clinical utility of 70 gene prognostic signatures for women with node negative breast cancer.

• J Natl Cancer Inst 2006;98:1183-92.

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Different proportion of low and high risk patients

• MammaPrint profiles accurately 40% as low risk compared to only 15% with St. Gallen criteria.

MammaPrint identifies correctly

MammaPrint identifies correctly

• 40% of patients with low risk in comparison to the 15% that are identified with conventional methods, thus preventing many unnecessary chemotherapies.

• More precise in predicting the outcome of disease than St. Gallen when comparing survival rates.

Mets free / Survival

LN + or -

Gene Expression vs. Clinical

St Gallen vs NIH

TRANSBIG Validation:302 Patients, Node-Neg, T1/2, Age <

61

Buyse et al., Journal of the National Cancer Institute. 2006;98(17):1183-92.

RNA

Target n=400

Achieved n=307

High or Low Gene

Signature Risk

<<local>> pathological dataClinical Data

Tissue Samples

UK (Guy’s, Oxford)1984 – 1996

France (IGR, CRH)1978 – 1998

Sweden (Karolinska)1980 – 1990

Node negative, untreated

<60 years > 5 years follow-up T1, T2 Tumor cell % > 50%

Tissue Samples

UK (Guy’s, Oxford)1984 – 1996

France (IGR, CRH)1978 – 1998

Sweden (Karolinska)1980 – 1990

Node negative, untreated

<60 years > 5 years follow-up T1, T2 Tumor cell % > 50%

BrusselsComparison of

clinicalv gene signature assessment ofprognostic risk

EndpointsTime to Distant MetastasisOverall SurvivalDistant Metastasis-Free Survival, Disease-Free Survival

BrusselsComparison of

clinicalv gene signature assessment ofprognostic risk

EndpointsTime to Distant MetastasisOverall SurvivalDistant Metastasis-Free Survival, Disease-Free Survival

AmsterdamGene expression profiling

Agilent platform70-gene prognostic custom designed chip

AmsterdamGene expression profiling

Agilent platform70-gene prognostic custom designed chip

Audited clinical

data

Centrally reviewed path data (Milan)

Agendia 70-gene

prognostic signatureN=78

N=151

Level 5 and 4Level 5 and 4

IndependentIndependentvalidation study on validation study on archival materialarchival material

NN300• The signature isThe signature is robustrobust• The technology is The technology is reproducible reproducible

Level 2-3Level 2-3

Levels of evidence for biomarkers studiesLevels of evidence for biomarkers studies

Prospective clinical trial specifically addressing the

gene signature’s utility

N6000

Level 1Level 1

Three step validation strategyThree step validation strategy

Buyse et al., Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer, Journal of the National Cancer Institute, Vol 98, No. 17, 2006

MammaPrint® identifies early metastases risk with highest accuracy

29%39%

50%62%

75%83%

96% 100%

4.52

7.54

4.683.24 3.5

9.14

2.132.33

2 3 4 5 7 10 15 none

Censoring time (in years)

0.1

1

10

Adjusted hazard ratio for gene signature

Cumulative

proportion of events

Time to distant metastasis

HR: all endpoints strongest in first 5 years after diagnosis

FDA Clearance of MammaPrint® Study Purpose Details Comments

1NatureDevelopment 70-gene profile

200278 patients, LN0, <55yrs6.4% adjuvant treatment

Within 5 year metastasis risk by profile multivariate OR 18

2NEJM Validation 70-gene profile

2002151 patients5.2% adjuvant treatment

Metastasis-free at 10 yrs: low risk 87%,high risk: 44%5 yrs: low risk 93%,high risk 56%

3MammaPrintDevelopment MammaPrint

2006reproducibility of (1) and (2) on MammaPrint

Highly reproducible MammaPrint as a diagnostic tool

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TRANSBIGIndependent European validation

2006302 patientsno adjuvant treatment

Metastasis-free at 10 yrs: low risk 88%,high risk: 71%5 yrs: low risk 96%,high risk 83%

TRANSBIG, the Translational Research Network of the Breast International Group (BIG), conducted anindependent validation study of both the Amsterdam and Rotterdam gene signatures in a series of 302 patients

TRANSBIG, the Translational Research Network of the Breast International Group (BIG), conducted anindependent validation study of both the Amsterdam and Rotterdam gene signatures in a series of 302 patients

Although there was only a 3-gene overlap between the two signatures, both were validated on the same patient cohort

So time to learn basics again

Catabolism and tumorhypoxia related metabolism

Cell cycle and cytoskeleton related biogenesis

Extracellular matrixadhesion and remodeling

General signal transductionand intracellular transport

Growth factor

Immune response

Cellular mobility or actin filament related

MammaPrint interrogates all of the criticalgenomic pathways associated with tumor

progression and the metastatic cascade

MammaPrint interrogates critical genomic pathways

Applications of Genes Assay in CA Breast

• To subclassify breast cancer• To estimate prognosis/

prediction• To predict response to therapy

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71 Gene assay predictive value

• Van de Vijver MJ,He YD, van’t Veer IJ, et al. A gene expression signature as predictor of survival in breast cancer. Amsterdam

• N Engl J Med 2002; 347:1999-2009

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Oncotype Dx-21 gene predictive value

• Palk S, Tang G, Shak S et al. Gene expression and benefit of chemotherapy in women with node negative, ER + breast cancer. J Clin Oncol 2006;24:3726-34.

• Can be done on fixed tissue.

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21 gene analysis-Oncotype Dx

• Independent of –Tumor size–AgePark S. NEMJ 2004;351:2817-26.

• > Accurate than Adjuvant OnlineGoldstein RP. Abstract #63. San Antonio 2007

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21 gene analysis can predict

• breast cancer related mortalityHabel LA. Breast Cancer Res 2006

• NSABP- B14 trial

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21 gene Predictive value

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Newer prognostic Indicators

• Wound response gene– risk of mets and death

• 2 gene recurrence score –adding chemotherapy to tamoxifen in ER+ ve, N- ve

• Chang HY . Gene signature of fibroblast serum predicts cancer progression:similAarities between tumors and wound.

PloS Biol 2004; 2:206-14.

Wound response gene expression profile• Activation of fibroblasts• Active wound healing predicts ▲ risk of

–metastases–Death

( in Breast, Lung & Gastric cancer)– Cong HY. PLoS Biol 2004;2:206-14

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2 gene expression profile

• 60, ER+, Early stage (MaXJ.Cancer Cell 2004)

• Expression of–Homeobox 13–IL-17B

• ▲ ratio= poor outcome• ≡Tamoxifen alone will not do in such

patients50

Six models

• All are different• Great concordance in five out of six gene

expression profile models• 21 gene (Oncotype Dx) and 70 gene

(Mammaprint) are popular• 81% concordance between 21 & 70 gene.

• Perou, Fan C. NEMJ 2006

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Courtesy: Martine Piccart

Breast Cancer: The Treatment Dilemma

Choices of 40 experts world-wide

61 y-old, fit,postmenopausal

Node negativepT = 0.9 cm ductal cancerER and PR negativeHER2 negativeGrade 2

Of 100 women with breast cancer

Only 25% will develop distant metastases

But we treat over 75% of all patientswith chemotherapy

Which means that 50% of all breast cancer patients get a toxic chemotherapy that they did not need!

Applications of Genes Assay in CA Breast

• To subclassify breast cancer• To estimate prognosis• To predict response to therapy

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To predict response to therapy

• Selection of the therapy based on attributes of the –Tumor–Host

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21 gene analysis can predict

• Responsiveness to Chemo/ Hormone Gianni L. J Clin Oncol.2005Palk S. J Clin Oncol 2006;24:3726-34

• NSABP- B14 trial

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TAILORx=

Trial Assigning Individualized Options for Treatment

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TAILORx trial

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ASSESS clinical RISK AND MammaPrint RISKASSESS clinical RISK AND MammaPrint RISK(adjuvant!online & MammaPrint)(adjuvant!online & MammaPrint)

BOTH HIGH BOTH HIGH RISKRISK

DISCORDANTDISCORDANTRISKRISK

BOTH LOW BOTH LOW RISKRISK

RANDOMIZERANDOMIZEdecision-makingdecision-making

ChemotherapyChemotherapy No chemotherapyNo chemotherapy

Use MammaPrintUse MammaPrintUse clinical riskUse clinical risk

MINDACT study designMINDACT study design

6000 patients, <70 YRS, 1-3 POS NODES6000 patients, <70 YRS, 1-3 POS NODES

highhigh low

low

55% 35% 10%

MINDACTMicroarray in Node Negative Disease

may avoid Chemo

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Her2 positive MammaPrint low risk patients have an excellent survival

Knauer SABCC 2008

Knauer et al, 2008 unpublished

HR 0.28 (0.14- 0.56; p=0<0.001)

88% Endocrine & Chemo (n= 265)

69% Endocrine (n=184)

Distant metastasis-free survival (years)

MammaPrint low risk (n=126)

HR 1.99 (0.00- 6.3; p=non significant)

Median Follow-up 5.2 years

benefit

Benefit of adjuvant chemotherapy Benefit of adjuvant chemotherapy in MammaPrint high risk patientsin MammaPrint high risk patients

To predict response to therapy

• Selection of the therapy based on attributes of the –Tumor–Host

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To predict response to therapy based on attributes of the host

• Drugs metabolized by–CYP450 encoded enzymes

• CYP 2• CYP 3• CYP2D6• CYP2C19

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To predict response to therapy based on attributes of the host

• Drugs metabolized–AmpliChip CYP450 by Roche

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Prognostic Signature and Clinical Benefit-the chemotherapy choice-

MammaPrint prognosis signature

• Assigns patients to risk categories with high specificity and sensitivity

(low risk vs high risk for recurrence)

• Low risk sufficiently low to forego chemotherapy

• High risk identifies patients with early relapseand shows chemo benefit (predictiveness)

Who to treat:• Prognosis profiles as diagnostic tool

-> improved selection for adjuvant therapy

How to treat:• Predictive profiles for drug response -> selection of patients who will benefit most

Clinical applications of microarrays

WHO WHO NEEDS NEEDS

THERAPY?THERAPY?

WHICH WHICH THERAPY WILL WORK THERAPY WILL WORK

BEST?BEST?

Prognostic factorsPrognostic factors Predictive factorsPredictive factors

What does “low risk” mean? • MammaPrint® “Low Risk”: 90% metastasis-

free without any adjuvant treatment over the following 10 years (NEJM 2002/JNCI 2006)

• Most of the “Low Risk” patients are ER+• With ER+ patients receiving hormonal therapy,

a further 50% risk reduction can be achieved in the “Low Risk” group, thus MammaPrint “Low Risk” means >95% 10 year metastasis-free survival

Summary : Poor risk

• Basal like• Luminal B• HER2+/ER-• Poor 70 gene profile• High 21 gene recurrence score• Activated wound response

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Summary : New Decision aid

• 21 gene (Oncotype Dx) & 70 gene (Mammaprint) is better than Adjuvant Online

• Personalised treatment• Less toxicity, less cost

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Contrast of Appearance vs.Contrast of Appearance vs.Expression PhenotypingExpression Phenotyping

Microarray Low Risk High Risk

Microscope Low Grade High GradeTreatment

Advice

Triple Negative

• ER-, PR-, and HER2 –• Basal like on gene profiling• Adverse prognosis• → new Rx

Thanks