Journal ClubCremona 24 Maggio 2008

Genomica e proteomica: significato e utilità attuali

Alberto Ballestrero

Clinica di Medicina Interna a indirizzo Oncologico

DIMI - Università di Genova

Incorporation of genomic into breast cancer management

Gene expression analysisPrognosis Prediction

Classification/Gene discovery

Who treat? Which therapy?

Improve biological knowledge

Gene expression analysis assumptions

1) Expression analysis allows identifying the tumour transcriptional features (transcriptoma)

2) Transcriptoma contains the information required to predict tumour evolution and response to treatments

Technologies used for high-throughput gene expression analysis

Marchionni, L. et. al. Ann Intern Med 2008;148:358-369

78 t

um

ors

78 t

um

ors

70 significant prognosis genes in N- patients70 significant prognosis genes in N- patients

PoorPoorsignaturesignature

Van’t Veer et al. Nature, 2002Van’t Veer et al. Nature, 2002

GoodGoodsignaturesignature

Netherlands signature

Are results reproducible?

September 2006

Median coefficient of variation for:

within-laboratory replicates = 5-15%

between-laboratory replicates = 10-20%

Building a genomic classifier

Patients of interest

Training set

Classifier gene selection: discriminant analysis linear or not

linear

Classifier

Testing set

Internal validation

Independent patients

Esternal validation:

• Retrospective

• Prospective

70-gene prognostic signature(“Netherlands signature”)

1st Validation Study

van de Vijver et al. N Engl J Med, 2002

Van’t Veer et al. Nature, 2002

Mammaprint Agendia

Netherlands signature: 151 N- patients

Van de Vijver MJ et al. N Engl J Med 347:1999-2009, 2002

Netherlands signature: 144 N+ patients

Van de Vijver MJ et al. N Engl J Med 347:1999-2009, 2002

Reclassify St. Gallen and NIH subgroups according to 70-gene signature: 151 N- patients

The gene-expression profile is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard

systems based on clinical and histologic criteria.

Van de Vijver MJ et al. N Engl J Med 347:1999-2009, 2002

Risk assessments for metastases and death: 70-gene signature vs Adjuvant

N° of patients in risk groups

Mts within 5 yrs Deaths within 10 yrs

Risk classification

High Low Sensitivity Specificity Sensitivity Specificity

70-gene signature

194 1130.90

(0.78-0.95)0.42

(0.36-0.48)0.84

(0.73-0.92)0.42

(0.36-0.48)

Adjuvant! 222 800.87

(0.75-0.94)0.29

(0.24-0.35)0.82

(0.71-0.90)0.29

(0.23-0.35)

Both test correctly identify the high risk patients. Gene signature is superior in correctly identifying the low risk patients.

2nd Validation Study Buyse et al. J Natl Cancer Inst, 2006

90% (CI 85%-96%)

71% (CI 65%-78%)

Buyse et al. J Natl Cancer Inst 2006

30% discordant cases between 70-gene

signature and Adjuvant

DMF

MINDACT triala testing hypotesis for a key question

Key question for use of 70-gene to decide on Key question for use of 70-gene to decide on chemotherapy.chemotherapy.Evaluate the Evaluate the risk of undertreatingrisk of undertreating patients who would patients who would otherwise get chemotherapy per clinical-pathological criteria.otherwise get chemotherapy per clinical-pathological criteria.

Testing hypotesis.The patients who have a low risk gene prognosis signature The patients who have a low risk gene prognosis signature and high risk clinical-pathologic criteria, and who were and high risk clinical-pathologic criteria, and who were randomized to receive no chemotherapy has a 5-year DMFS randomized to receive no chemotherapy has a 5-year DMFS = 92% (null hypothesis).= 92% (null hypothesis).

EORTC-BIG MINDACT TRIAL DESIGN6,000 Node negative women

Clinical and Genomic

BOTH HIGH RISK

DISCORDANT Clinical and

Genomic RisksClinical and

GenomicBOTH LOW RISK

RANDOMIZEdecision-making

Chemotherapy No chemotherapy

Use genomic riskUse clinical riskHigh risk Low risk

High risk Low risk

Assess clinical risk and genomic risk

1717

Oncotype DX® 21-Gene Recurrence Score (RS) Assay

PROLIFERATIONKi-67

STK15Survivin

Cyclin B1MYBL2

ESTROGENERPR

Bcl2SCUBE2

INVASIONStromelysin 3Cathepsin L2

HER2GRB7HER2

BAG1GSTM1

REFERENCEBeta-actinGAPDHRPLPO

GUSTFRC

CD68

16 Cancer and 5 Reference Genes From 3 Studies

Category RS (0-100)Low risk RS <18

Int risk RS ≥18 and <31

High risk RS ≥31

Paik et al. N Engl J Med. 2004;351:2817-2826.

RS = + 0.47 x HER2 Group Score - 0.34 x ER Group Score + 1.04 x Proliferation Group Score+ 0.10 x Invasion Group Score + 0.05 x CD68- 0.08 x GSTM1- 0.07 x BAG1

Prediction of recurrence in N0 ER positive patients (TAM treated)

Paik et al. N Engl J Med. 2004;351:2817-2826

Prediction of chemotherapy benefit inNode-negative, ER-positive breast cancer: NSABP B-20

(Paik S et al. JCO 2006)

4.4 absolute benefit from TAM+CT

TAM vs TAM + CT - 651 evaluable patients Patient reclassification

RS

Low risk

Intermediate risk

High risk

RS predicts chemotherapy benefit

Schema: TAILORx

Node-Neg, ER-Pos Breast CancerNode-Neg, ER-Pos Breast Cancer

RS <10HormoneTherapy

RS <10HormoneTherapy

RS 11-25RandomizeHormone

vsChemotherapy +

Hormone

RS 11-25RandomizeHormone

vsChemotherapy +

Hormone

RS >25Chemotherapy

+Hormone

RS >25Chemotherapy

+Hormone

Oncotype DX® AssayOncotype DX® Assay

Primary study group

To determine whether adjuvant hormonal therapy is not inferior to adjuvant chemohormonal for patients in the “primary study group”

Oncologist treatment recommendations after RS: N- ER+ patients

Others Genetic Element of Interest

Chin L, Gray JW. Nature 2008

Why proteomic?

Cellular signaling events are driven by protein-protein interactions, post-translational protein modifications and enzymatic activities that cannot be predicted accurately or described by transcriptional profiling methods alone.

Proteomic analysis of human breast cancerJ Proteome Res, 2008

EGFR family signaling, AKT/mTOR pathway activation, c-kit/abl growth factor signaling and ERK pathway

Tumour as a system = organoid

Tumour stem cells

Tumour cell subpopulations

Microenvironment

Immune cells