gene-expression signatures for breast cancer prognosis, site of metastasis, and therapy resistance...
TRANSCRIPT
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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
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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
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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.
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Me
tas
tas
is-F
ree
Su
rviv
al (
%)
Time (months)0 30 60 90 120
MFS as a function of the number of involved lymph nodes
100
80
60
40
20
0
0
1
2-4
5-9
10
~35%
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Me
tas
tas
is-F
ree
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rviv
al (
%)
0 30 60 90 120
MFS as a function of the number of involved lymph nodes
100
80
60
40
20
0
Adjuvant hormonal or chemotherapy
}
}
}
Absolute survival benefit: 5 - 15%
Time (months)
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Me
tas
tas
is-F
ree
Su
rviv
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%)
0 30 60 90 120
100
80
60
40
20
0
MFS in lymph-node negative patients
~35%
~65% cured by local treatment:
surgery ± radiotherapy
Adjuvant therapy necessary ??
Time (months)
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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
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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?
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Steps in tumor progression
??
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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
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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
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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
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Chip design
Microarray
Add Sample
Silicon waferGlass microscope slideNitrocellulose
DNA Probes: 20 – 70 bases
Fluorescently labeled sample
Hybridization between sample and probe
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Chip workflow
Sample prep
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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
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Rotterdam data set:
Affymetrix U133A chip
344 untreated lymph node-negative patients
Subtypes of breast cancer
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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
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MFS in 151 LNN patients
van de Vijver et al, NEJM 2002
Testing set: 295 patients, including 151 lymph-node negative patients
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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)
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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
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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
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Analysis of metastasis-free survival
primary tumor
surgery metastasis
time
Affymetrix oligonucleotide
microarray
metastasis-free survival
NO adjuvant systemic therapy
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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
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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
![Page 25: Gene-expression signatures for breast cancer prognosis, site of metastasis, and therapy resistance John Foekens Josephine Nefkens Institute Dept. Medical](https://reader034.vdocuments.mx/reader034/viewer/2022042822/56649e7e5503460f94b81f4a/html5/thumbnails/25.jpg)
Unsupervised clustering analysis
ER- ER+
Ge
ne
s
Tumors
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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)
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Determining the 76-gene signature
Wang et al, Lancet 2005
AU
Cs
of
RO
C
0
0.80
0.85
0.90
0.95
1.00
ER positive
ER negative 16 genes
~
Number of genes
50 100 150 200
60 genes
115 training set patients
scalelog2invalueexpressiontheisx
tcoefficienregressionCoxedstandardiztheisw
constantsareBandA
10 level ER if
10 level ER if
0
1I
where
xwI1I)(1BxwIIAScoreRelapse
i
i
jj
16
1ji
60
1ii
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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
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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%)
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MFS in patients with T1 tumorsM
eta
sta
sis
-Fre
e S
urv
ival
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
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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
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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
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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
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2nd validation: MFS in 180 patientsM
eta
sta
sis
-Fre
e S
urv
ival
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
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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
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MFS in post-menopausal patientsM
eta
sta
sis
-Fre
e S
urv
ival
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)
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MFS in St. Gallen average risk groupM
eta
sta
sis
-Fre
e S
urv
ival
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)
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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.
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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.
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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
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Performance of the 31-gene predictor
Sensitivity: 100% (23/23)
Specificity: 50% (6/12)
Validation set of 35 patients
Smid et al, JCO 2006
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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.
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Diagnosis / Surgery Relapse
Systemic therapy
Predictive signatures
Response
No response
? Predictive profile
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Analysis of type of response
primary tumor
surgery metastasis
tamoxifen
time
PD
CR / PR
Microarray
metastasis-free survival
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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
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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)
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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
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Analysis of type of response
primary tumor
surgery metastasis
chemotherapy
time
PD
CR / PR
Affymetrix U133plus2 array: 54,000 probe IDs
metastasis-free survival
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- 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
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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
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