Gene-expression signatures for breast cancer prognosis, site of metastasis, and therapy resistance

John Foekens

Josephine Nefkens Institute

Dept. Medical Oncology

Mediterranean School of Oncology: Highlights in the Management of Breast Bancer

Rome, November 16, 2006

Breast cancer incidence

Worldwide ~1,000,000 new cases / year

1 out of 9 women will get breast cancer during life

~40% of the patients will die of breast cancer

Reason: Development of resistance to therapyin metastatic disease

What do we need?

Prognostic factors that accurately can predict which patient will develop a metastasis and who does not.

High-risk patients should receive adjuvanttherapy, while the low-risk patients couldbe spared the burden of the often toxictherapy or could be offered a lessaggressive treatment.

Me

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Time (months)0 30 60 90 120

MFS as a function of the number of involved lymph nodes

100

80

60

40

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0

0

1

2-4

5-9

10

~35%

Me

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MFS as a function of the number of involved lymph nodes

100

80

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Adjuvant hormonal or chemotherapy

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Absolute survival benefit: 5 - 15%

Time (months)

Me

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%)

0 30 60 90 120

100

80

60

40

20

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MFS in lymph-node negative patients

~35%

~65% cured by local treatment:

surgery ± radiotherapy

Adjuvant therapy necessary ??

Time (months)

Consensus criteria for node-negative breast cancer

Age and menopausal status

Histological tumor grade

Tumor size

Steroid hormone-receptor and HER2 status

85 – 90% of node-negative patients should receive adjuvant therapy

Over-treatment since only 5 – 10% of the node-negative patients will benefit by cure

Predictive factors that accurately can predict which patient will respond favorably to a certain type of treatment and who does not.

Final goal: Individualized targeted treatment which is based on prognostic and predictive factors, and new targets for treatment.

What do we need more?

Steps in tumor progression

??

High-throughput methodologies

mRNA

Proteomics

Epigenomics

SNP arrays

CGH of BAC arrays

DNA-methylation profiling

TK profiling

Multiplex ELISA

Mass-spectrometry

Genomics

Genetics

Gene-expression profiling

Multiplex RT-PCR

High-throughput methodologies

mRNA

Proteomics

Gene-expression profiling

Epigenomics

SNP arrays

CGH of BAC arrays

DNA-methylation profiling

TK profiling

Multiplex ELISA

Mass-spectrometry

Genomics

Genetics

Multiplex RT-PCR

Gene expression analysis

<1995: Northern Blotting, RNAse protection etc

1 Week: Analyse several genes on 10s of samples

>1995: DNA Microarrays

1 Week: Analyse whole genome on 10s of samples

Chip design

Microarray

Add Sample

Silicon waferGlass microscope slideNitrocellulose

DNA Probes: 20 – 70 bases

Fluorescently labeled sample

Hybridization between sample and probe

Chip workflow

Sample prep

Perou & Sorlie et al. Nature 2000; PNAS 2001

78 breast carcinomas

3 fibroadenoma’s

4 normal breast tissues

Patients from Norway:

Very heterogeneous with respect to nodal status, adjuvant and neo-adjuvant therapy

Subtypes of breast cancer

“Molecular portraits of human breast tumors”

496 “intrinsic” genes described by Perou et al. (Nature 2000); array with 8102 human genes

65 breast samples / 42 patients

Rotterdam data set:

Affymetrix U133A chip

344 untreated lymph node-negative patients

Subtypes of breast cancer

The Amsterdam prognostic profile

van ‘t Veer et al, Nature 2002

gyui

Training set: 78 patients

Study design

78 breast tumorsPatients < 55 yearsTumor size <5 cmLymph node negative (LN0)No adjuvant therapy

Prognosis reporter genes

Distant metastasis< 5 years (n=34)

NO distant metastasisin 5 years (n=44)

70-gene signature Validation

MFS in 151 LNN patients

van de Vijver et al, NEJM 2002

Testing set: 295 patients, including 151 lymph-node negative patients

The Rotterdam – Veridex study

Aim:

To develop a prognostic profile that can be used for all lymph-node negative breast cancer patients, irrespective of age, tumor size, and steroid hormone-receptor status.

Lancet 365:671-679 (2005)

Patients & Methods

Total: 286 primary breast cancer patients

No (neo-)adjuvant systemic therapy ( pure prognosis)

Median follow-up 101 months

Clinical endpoint: metastasis-free survival (MFS)

Patients

Quality check of RNA by Agilent BioAnalyzer

Affymetrix oligonucleotide microarray U133A GeneChip(22,000 transcripts)

Methods

RNA isolation

frozen primary breast cancer tissue

>70% tumor area check check

30 sections 30 sections

RNA isolation RNA isolation

combine

RNA quality check

Clear distinct 18S and 28S peaks

No minor peaks present

Area under 18S and 28S peaks >15% of total RNA area

28S/18S ratio should be between 1.2 and 2.0

Agilent BioAnalyzer

Analysis of metastasis-free survival

primary tumor

surgery metastasis

time

Affymetrix oligonucleotide

microarray

metastasis-free survival

NO adjuvant systemic therapy

Gene-expression profiling

Training set to generate profile

Independent testing set for validation of the profile

Multi-center (retrospective) study

Prospective clinical trial

Steps to follow in the clinical development of expression profiles

Gene-expression profiling

Training set to generate profile

Independent testing set for validation of the profile

Multi-center (retrospective) study

Prospective clinical trial

Steps to follow in the clinical development of expression profiles

Unsupervised clustering analysis

ER- ER+

Ge

ne

s

Tumors

Determining the signature for ER+ and ER- patients

286 LNN patients

209 patients 77 patients

supervised classification

gene selection(Cox model, bootstrapping)

76 gene set

ER status

ER-positive ER-negative

validation

80 patients (training)

35 patients (training)

171 patients (testing)

Determining the 76-gene signature

Wang et al, Lancet 2005

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0.80

0.85

0.90

0.95

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ER positive

ER negative 16 genes

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Number of genes

50 100 150 200

60 genes

115 training set patients

scalelog2invalueexpressiontheisx

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10 level ER if

10 level ER if

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Gene-expression profiling

Training set to generate profile

Independent testing set for validation of the profile

Multi-center (retrospective) study

Prospective clinical trial

Steps to follow in the clinical development of expression profiles

Comparison of the 76-gene signature and the current conventional consensus on treatment of LNN breast cancer

Patients guided to receive adjuvant therapy

Metastatic disease at 5 years

Metastatic disease free at 5 years

St. Gallen 2003

NIH 2000

76-gene signature

52/55 (95%)

52/55 (95%)

52/65 (93%)

104/115 (90%)

101/114 (89%)

60/115 (52%)

MFS in patients with T1 tumorsM

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0.0

0.2

0.4

0.6

0.8

1.0

Months

0 80

HR: 14.1 (95% CI: 3.34–59.2), P = 1.6x10-4

good signature (n = 32)

poor signature (n = 47)

Sensitivity 96% (24/25)

Specificity 57% (31/54)

40 6020

Gene-expression profiling

Training set to generate profile

Independent testing set for validation of the profile

Multi-center (retrospective) study

Prospective clinical trial

Steps to follow in the clinical development of expression profiles

Participating institutions:

- University Medical Center Nijmegen, The Netherlands

- Technische Universität München, Germany

- National Cancer Institue, Bari, Italy

- Institute of Oncology, Ljubljana, Slovenia

2nd validation: EORTC - RBG

Total: 180 node-negative primary breast cancer patients

No (neo-)adjuvant systemic therapy

Median follow-up: 100 months

Clinical endpoint: metastasis-free survival (MFS)

Patients

Tissues sent to Rotterdam for RNA isolation

Affymetrix dedicated VDX2 oligonucleotide microarray(76 genes + 221 control genes) analysis at Veridex

Methods

Quality check of RNA by Agilent BioAnalyzer

Methods EORTC – PBG validation study

43% of the tumors have a ‘good’ signature

2nd validation: MFS in 180 patientsM

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0.0

0.2

0.4

0.6

0.8

1.0

Years

0 5 10

HR: 7.41 (95% CI: 2.63–20.9), P = 8.5x10-6

good signature (n = 78)

poor signature (n = 102)

Foekens et al, JCO 2006

Multivariate analysis in multi-center validation

Age (per 10 yr increment) 0.70 (0.44-1.11) 0.13

Menopausal status (post vs. pre) 1.26 (0.43-3.70) 0.67

Tumor size (>20 mm vs. ≤20 mm) 1.71 (0.84-3.49) 0.14

Grade (moderate/good vs. poor) 1.24 (0.61-2.52) 0.56

ER (per 100 increment) 1.00 (0.99-1.01) 0.13

76-gene signature (poor vs. good) 11.36 (2.67-48.4) 0.001

HR (95% CI) P-value

Metastasis-Free Survival

MFS in post-menopausal patientsM

eta

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0.0

0.2

0.4

0.6

0.8

1.0

Years

0 5 10

HR: 9.84 (95% CI: 2.31–42.0), P = 0.0001

good signature (n = 57)

poor signature (n = 69)

MFS in St. Gallen average risk groupM

eta

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0.0

0.2

0.4

0.6

0.8

1.0

Years

0 5 10

HR: 6.08 (95% CI: 2.15–17.2), P = 0.0001

good signature (n = 64)

poor signature (n = 97)

Site of metastasis

AIM: Identify genes associated with a relapse to the bone since biological features (e.g. homing) may be present in the primary breast tumor.

Bone metastasis

The bone is the most abundant site of distant relapse in breast, prostate, thyroid, kidney and lung cancer patients.

Bone micro-environment may facilitate circulating cancer cells to home and proliferate.

Bisphosphonate therapy available.

Profile for bone metastasis

286 patients, 107 relapses (Lancet, 2005)

72 patients:- 46 x bone- 26 x non-bone

Training

SAM and PAM analysis

Validation

31 - gene set

35 patients:- 23 x bone- 12 x non-bone

Performance of the 31-gene predictor

Sensitivity: 100% (23/23)

Specificity: 50% (6/12)

Validation set of 35 patients

Smid et al, JCO 2006

All gene signatures for separating patients into different

risk groups, so far, were derived based on the performance

of individual genes, regardless of its biological processes

or functions.

It might be more appropriate to study biological themes,

rather than individual genes.

Pathway analysis

There is criticism and non-understanding about the minimal overlap of individual genes between various multigene prognostic signatures.

Diagnosis / Surgery Relapse

Systemic therapy

Predictive signatures

Response

No response

? Predictive profile

Analysis of type of response

primary tumor

surgery metastasis

tamoxifen

time

PD

CR / PR

Microarray

metastasis-free survival

Tamoxifen profile in ER+ tumors

112 patients (60 progressive disease, PD, 52 objective response, OR)

46 patients (25 PD, 21 OR)

Training

BRB, duplicate arraysP<0.05, QC spots

66 patients (35 PD, 31 OR)

Validation

QC arrays

44 - gene set81 - gene set

Discriminatory genes Predictive signature

cDNA array analysis

Molecular classification: 1st line tamoxifen

Jansen et al, JCO 2005

112 ER+ primary breast tumors from patients with recurrent disease and treated with first-line tamoxifen

Training set: 21 OR v 25 PD

81 genes differentially expressed44-gene predictive signature

Validation: 31 OR v 35 PD

Response : OR = 3.16 (P=0.03)

PFS: HR = 0.48 (P=0.03)

What do we need more?

Predictive factors that accurately can predict which patient will respond favorably to a certain type of treatment and who does not.

Approach:

Microarray analysis of primary tumor RNA to assess the type of response (objective measure) in the metastatic setting;- 1st line tamoxifen therapy- 1st line chemotherapy

Analysis of type of response

primary tumor

surgery metastasis

chemotherapy

time

PD

CR / PR

Affymetrix U133plus2 array: 54,000 probe IDs

metastasis-free survival

- 76-gene prognostic signature

Summary gene expression signatures

- Bone metastasis signature

- Chemotherapy resistance signature

- Tamoxifen resistance signature

- Liver metastasis signature (in progress)

- Pathway-derived signatures

- Others ……

+ a growing number of published signatures for various clinical questions

Contributors gene-expression profiling

Yixin Wang, Yi Zhang, Dimitri Talantov, Jack Yu, Tim Jatkoe & David Atkins

Veridex LLC (Johnson & Johnson), La Jolla, USA

-Nijmegen: P. Span, V. Tjan-Heijnen, L.V.A.M. Beex, C.G.J. Sweep

-Munich: N. Harbeck, K. Specht, H. Höfler, M. Schmitt

-Bari: A. Paradiso, A. Mangia, A.F. Zito, F. Schittulli

-Ljubljana: R. Golouh, T. Cufer

Third multi-center validation, institutions above +

+Basel S. Eppenberger et al.

+Dresden M. Kotzsch et al.

+Innsbruck G. Daxenbichler et al.

EORTC – RBG members (1st multi-center validation)

Anieta Sieuwerts, Mieke Timmermans, Marion Meijer-van Gelder, Maxime Look, Anita Trapman, Miranda Arnold, Anneke Goedheer, Roberto Rodriguez-Garcia, Els Berns, Marcel Smid, John Martens, Jan Klijn & John Foekens

Erasmus MC

TransBig group: second multicenter validation study