A multimarker model to predict outcome in tamoxifen-treated breast cancer patients.
OBJECTIVE
This study was designed to produce a model to predict outcome in tamoxifen-treated breast cancer patients based on clinicopathologic features and multiple molecular markers.


METHODS
This was a retrospective study of 324 stage I to III female breast cancer patients treated with tamoxifen for whom standard clinicopathologic data and tumor tissue microarrays were available. Nine molecular markers were studied by semiquantitative immunohistochemistry and/or fluorescence in situ hybridization. Cox proportional hazards analysis was used to determine the contributions of each variable to disease-specific and overall survival, and machine learning was used to produce a model to predict patient outcome.


RESULTS
On a univariate basis, the following features were significantly associated with worse survival: high pathologic tumor or nodal class, histologic grade, epidermal growth factor receptor, ERBB2, MYC, or TP53; absent estrogen receptor (ER) or progesterone receptor; and low BCL2. CCND1 and CDKN1B did not reach statistical significance. On a multivariate basis, nodal class, ER, and MYC were statistically significant as independent factors for survival. However, the benefit of ER-positive status was moderated by BCL2, ERBB2, and progesterone receptor. BCL2 and TP53 also interacted as an independent risk factor. A kernel partial least squares polynomial model was developed with an area under the receiver operating characteristic curve of 0.90.


CONCLUSIONS
Our data show the predictive value of BCL2, ERBB2, MYC, and TP53 in addition to the standard hormone receptors and clinicopathologic features, and they show the importance of conditional interpretation of certain molecular markers. Our multimarker predictive model performed significantly better than standard guidelines.
Linke
Steven P
SP
Prediction Sciences, La Jolla, California 92037, USA. slinke@predict.net
Bremer
Troy M
TM
Herold
Christopher D
CD
Sauter
Guido
G
Diamond
Cornelius
C
D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000235:genetics; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D019938:Cyclin D1 / Q000235:genetics; D004965:Estrogen Antagonists / Q000627:therapeutic use; D005260:Female; D006801:Humans; D007150:Immunohistochemistry / Q000706:statistics & numerical data; D017404:In Situ Hybridization, Fluorescence / Q000706:statistics & numerical data; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D019253:Proto-Oncogene Proteins c-bcl-2 / Q000032:analysis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000235:genetics; D011960:Receptors, Estrogen / Q000032:analysis; D012189:Retrospective Studies; D016019:Survival Analysis; D013629:Tamoxifen / Q000627:therapeutic use; D016896:Treatment Outcome; D016159:Tumor Suppressor Protein p53 / Q000032:analysis
D014408:Biomarkers, Tumor; D019938:Cyclin D1; D004965:Estrogen Antagonists; D019253:Proto-Oncogene Proteins c-bcl-2; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D013629:Tamoxifen; D016159:Tumor Suppressor Protein p53