Do Transcriptomic Markers Provide Significant and Stable Information in Addition to Standard Markers, for the Prediction of Pathological Complete Response in Breast Cancer?
Authors: Valet F. , de Cremoux P. , Spyratos F. , Gentien D. , Servant N. , Scott V. , Lehmann-Che J. , Lebigot I. , Sigal-Zafrani B. , Mathieu M. , Bertheau P. , Guinebretière J. , Rolland E. , Pierga J. , Delaloge S. , Brain E. , Tembo O. , Barillot E. , Roman-Roman S. , Asselain B. , Marty M. 
Published: December 2010

Background: The aim of this study was to investigate and quantify the contribution of transcriptomic markers, in addition to strong predictors such as oestrogen receptor status, to the prediction of pathological complete response (pCR) in locally advanced breast cancer.Patients: The RNA profiles were analyzed using U133 plus 2.0 Affymetrix. We included 189 patients out of 340 patients entered in a neoadjuvant chemotherapy trial for large operable and locally advanced breast cancer. After four cycles of epirubicin–cyclophosphamide, patients were randomly allocated to four cycles of docetaxel with or without celecoxib for patients with HER2-negative tumors, and docetaxel with or without trastuzumab for patients with HER2-positive tumors, respectively. Proportions of pCR in each group were equal to 0.12, 0.16, 0.15 and 0.24 respectively. Patients who received trastuzumab (N=36) were discard from our example, in order to deal with similar proportions of pCR.Methods: The whole sample was divided into a training set (N=81) and a validation set (N=72). Using the training set, two predictive models were built using multivariate logistic regression models. In the first model (M1), usual clinical and biological significant markers were included. In the second model (M2), in addition to the significant parameters of M1, significant transcriptomic variables were included. Diagnostics of both predictive models were assessed on the validation set through sensitivity and specificity estimates. Simulations were performed to investigate stability of model M2.Results: In M1, oestrogen receptor status and tumor size were found to have a strong predictive role in the prediction of pCR. In addition to these classical markers, genes belonging to biological pathways involved in proliferation and microtubule stabilization appeared to have a strong role in the prediction of pCR (model M2). Validation of M1 on the validation set provided 70% of sensitivity and 86% of specificity. Validation of M2 on the validation set yielded to a better sensitivity of 80% and a specificity of 81%. Using simulations, we showed that several different predictive models M2 yielded to similar performances on the validation set. Conclusion: Our study showed that transcriptomic markers provided significant information in addition to usual biological markers for the prediction of pCR. In addition, predictive model with both usual and transcriptomic markers may lead in an improvement of the classification performances. However, as illustrated by simulations, predictive models with both classical and transcriptomic markers are not exclusive. The contribution of transcriptomic data for the prediction of pCR is straightforward, but finding a stable predictive model remains a great challenge.Supported by PHRC AOM/2OO2/02117, Pfizer inc., Roche, sanofi-aventis.ISRCTN10059974

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 2035.