Abstract
Background: Many prognostic and predictive multigene signatures have been established in breast
cancer patients. For treatment decision the assessment of individual prognosis is
essential. The choice of specific therapy is basically driven by empirical data although
several predictive gene signatures already exist. In this context it would be valuable
if specific signatures could be used for estimation of prognosis and prediction of
therapy concurrently. Material and Methods: Microarray data (Affymetrix HG U133A) of a small samples set of n = 48 breast cancer
patients who received an anthracycline-based adjuvant chemotherapy were analyzed.
Tumor samples were classified according to four prognostic and two predictive previously
described gene signatures and compared with standard parameters as histologic subtype,
tumor size, nodal status, pathohistological grading as well as estrogen receptor and
Her-2 status. Results: The gene expression values of ER, PR and Her-2 from microarray revealed a high concordance
with protein expression assessed by means of immunohistochemistry. The determination
of proliferative state of the tumors using gene expression of Ki67 showed a significant
correlation with ER-status (p = 0.040, Mann-Whitney U-test) and pathohistological
grading (p = 0.005, Kruskal-Wallis test). Neither of the six different signatures
was able to predict event status of patients sufficiently. The main discriminatory
power of the signatures was related to the ER status and to some extent to pathohistological
grading. Conclusion: In a small cohort of uniformly treated patients prognostic and predictive gene signatures
are incapable to predict disease outcome unambiguously. The main driving force of
all signatures are ER-status and proliferation. The value of the individual signatures
may be restricted to the specific setting from which they were derived.
Zusammenfasung
Hintergrund: Zahlreiche prognostische und prädiktive Multigensignaturen sind bisher für Mammakarzinompatientinnen
generiert worden. Die Einschätzung der individuellen Prognose ist für eine optimale
Therapieentscheidung wesentlich. Die Auswahl einer spezifischen Therapie ist grundsätzlich
durch die empirische Datenlage bestimmt, obwohl bereits zahlreiche prädiktive Gensignaturen
existent sind. In diesem Zusammenhang wäre es hilfreich, wenn spezifische Signaturen
sowohl zur Abschätzung der Prognose als auch zur Prädiktion des Therapieansprechens
gleichzeitig genutzt werden könnten. Material und Methoden: Genexpressionsdaten (Affymetrix HgU133A) eines kleinen Probenkollektivs von n = 48
Mammakarzinompatientinnen, die eine adjuvante, anthrazyklinhaltige Chemotherapie erhalten
haben, wurde analysiert. Die Tumorproben wurden nach 4 prognostischen und 2 prädiktiven
bereits publizierten Gensignaturen eingeteilt und mit den Standardparametern wie histologischer
Subtyp, Tumorgröße, Nodalstatus, pathohistologisches Grading, sowie dem Östrogenrezeptor-
und Her-2-Status verglichen. Ergebnisse: Die Genexpressionswerte bezüglich ER, PR und Her-2 zeigten im Vergleich zur immunhistochemisch
bestimmten Proteinexpression eine hohe Konkordanz. Die Bestimmung des Proliferationszustands
mittels Genexpression von Ki67 zeigte eine signifikante Korrelation mit ER-Status
(p = 0,040, Mann-Whitney-U-Test) und pathohistologischem Grading (p = 0,005, Kruskal-Wallis-Test).
Keine der 6 verschiedenen Signaturen war in der Lage, den Ereignisstatus der Patienten
ausreichend vorherzusagen. Die hauptsächlich diskriminierenden Eigenschaften der Signaturen
basieren auf dem ER-Status und zu einem gewissen Maße auf dem pathohistologischen
Grading. Schlussfolgerung: In einem kleinen, einheitlich behandelten Patientenkollektiv sind prognostische und
prädiktive Gensignaturen nicht in der Lage, den Krankheitsverlauf unzweifelhaft vorherzusagen.
Die wesentlichen Einflussgrößen für alle Signaturen sind der ER-Status und die Proliferation.
Die Wertigkeit der jeweiligen Signaturen ist offenbar ausschließlich auf die spezifische
Situation beschränkt, für die sie identifiziert wurden.
Key words
prognostic and predictive multigene signatures - breast cancer - anthracycline chemotherapy
- microarray analysis
Schlüsselwörter
Gensignatur - Mikroarrayanalyse - Anthrazyklin - Brustkrebs
References
1
Perou C M, Jeffrey S S, van de Rijn M. et al .
Distinctive gene expression patterns in human mammary epithelial cells and breast
cancers.
Proc Natl Acad Sci USA.
1999;
96
9212-9217
2
van't Veer L J, Dai H, van de Vijver M J. et al .
Gene expression profiling predicts clinical outcome of breast cancer.
Nature.
2002;
415
530-536
3
van de Vijver M J, He Y D, van't Veer L J. et al .
A gene-expression signature as a predictor of survival in breast cancer.
N Engl J Med.
2002;
347
1999-2009
4
Sorlie T, Perou C M, Tibshirani R. et al .
Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical
implications.
Proc Natl Acad Sci USA.
2001;
98
10869-10874
5
Ahr A, Holtrich U, Solbach C. et al .
Molecular classification of breast cancer patients by gene expression profiling.
J Pathol.
2001;
195
312-320
6
Ahr A, Karn T, Solbach C. et al .
Identification of high risk breast-cancer patients by gene expression profiling.
Lancet.
2002;
359
131-132
7
Minckwitz G von, Raab G, Caputo A. et al .
Doxorubicin with cyclophosphamide followed by docetaxel every 21 days compared with
doxorubicin and docetaxel every 14 days as preoperative treatment in operable breast
cancer: the GEPARDUO study of the German Breast Group.
J Clin Oncol.
2005;
23
2676-2685
8
Remvikos Y, Beuzeboc P, Zajdela A. et al .
Correlation of pretreatment proliferative activity of breast cancer with the response
to cytotoxic chemotherapy.
J Natl Cancer Inst.
1989;
81
1383-1387
9
Bottini A, Berruti A, Bersiga A. et al .
Effect of neoadjuvant chemotherapy on Ki67 labelling index, c-erbB‐2 expression and
steroid hormone receptor status in human breast tumours.
Anticancer Res.
1996;
16
3105-3110
10
Collecchi P, Baldini E, Giannessi P. et al .
Primary chemotherapy in locally advanced breast cancer (LABC): effects on tumour proliferative
activity, bcl-2 expression and the relationship between tumour regression and biological
markers.
Eur J Cancer.
1998;
34
1701-1704
11
Elston C W, Ellis I O.
Pathological prognostic factors in breast cancer. I. The value of histological grade
in breast cancer: experience from a large study with long-term follow-up.
Histopathology.
1991;
19
403-410
12
Paik S, Shak S, Tang G. et al .
A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast
cancer.
N Engl J Med.
2004;
351
2817-2826
13
Robbins P, Pinder S, de Klerk N. et al .
Histological grading of breast carcinomas: a study of interobserver agreement.
Hum Pathol.
1995;
26
873-879
14
Hopton D S, Thorogood J, Clayden A D. et al .
Observer variation in histological grading of breast cancer.
Eur J Surg Oncol.
1989;
15
21-23
15
Sotiriou C, Wirapati P, Loi S. et al .
Gene expression profiling in breast cancer: understanding the molecular basis of histologic
grade to improve prognosis.
J Natl Cancer Inst.
2006;
98
262-272
16
Gruvberger S, Ringnér M, Chen Y. et al .
Estrogen receptor status in breast cancer is associated with remarkably distinct gene
expression patterns.
Cancer Res.
2001;
61
5979-5984
17
Ein-Dor L, Kela I, Getz G. et al .
Outcome signature genes in breast cancer: is there a unique set?.
Bioinformatics.
2005;
21
171-178
18
Rody A, Holtrich U, Gaetje R. et al .
Poor outcome in estrogen receptor-positive breast cancers predicted by loss of plexin
B1.
Clin Cancer Res.
2007;
13
1115-1122
19
Rody A, Karn T, Solbach C. et al .
The erbB2+ cluster of the intrinsic gene set predicts tumor response of breast cancer
patients receiving neoadjuvant chemotherapy with docetaxel, doxorubicin and cyclophosphamide
within the GEPARTRIO trial.
Breast.
2007;
16
235-240
20
Eisen M B, Spellman P T, Brown P O. et al .
Cluster analysis and display of genome-wide expression patterns.
Proc Natl Acad Sci USA.
1998;
95
14863-14868
21
Wang Y, Klijn J G, Zhang Y. et al .
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary
breast cancer.
Lancet.
2005;
365
671-679
22
Sotiriou C, Wirapati P, Loi S. et al .
Gene expression profiling in breast cancer: understanding the molecular basis of histologic
grade to improve prognosis.
J Natl Cancer Inst.
2006;
98
262-272
23
Hess K R, Anderson K, Symmans W F. et al .
Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel
and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer.
J Clin Oncol.
2006;
24
4236-4244
24
Rody A, Karn T, Munnes M. et al .
Predictive gene signatures for response to neoadjuvant TACchemotherapy from expression
profiling.
Breast Cancer Res Treatm.
2005;
94, Suppl. 1
abstract 5046
25
Pusztai L, Anderson K, Hess K R.
Pharmacogenomic predictor discovery in phase II clinical trials for breast cancer.
Clin Cancer Res.
2007;
13
6080-6086
1 both authors contributed equally
MD Achim Rody
Department of Obstetrics and Gynecology J. W. Goethe-University
Theodor-Stern-Kai 7
60590 Frankfurt
Germany
eMail: achim.rody@em.uni-frankfurt.de