PURPOSE: Several different multivariate prediction models using routine clinical variables or multigene signatures have been proposed to predict pathologic complete response to combination chemotherapy in breast cancer. Our goal was to compare the performance of four conceptually different predictors in an independent cohort of patients. EXPERIMENTAL DESIGN: Gene expression profiling was done on fine-needle aspirations of 100 stage I to III breast cancers before preoperative paclitaxel, 5-fluorouracil, doxorubicin, and cyclophosphamide combination chemotherapy. Pathologic response was correlated with prediction results from a clinical nomogram, a human cancer-derived genomic predictor (DLDA30), a cell line-based genomic predictor [in vitro coexpression extrapolation (COXEN)], and an optimized cell line-derived (in vivo COXEN) predictor. None of the 100 test cases were used in the development of these predictors. RESULTS: The in vitro COXEN using a combination of four individual drug sensitivity predictions derived from cell lines was not predictive [area under the receiver operator characteristic curve (AUC), 0.5; 95% confidence interval, (95% CI), 0.41-0.59]. The clinical nomogram (AUC, 0.73; 95% CI, 0.65-0.80) and the DLDA30 (AUC, 0.73; 95% CI, 0.66-0.80) genomic predictor had similar performances. The in vivo COXEN that used informative genes from cell lines but was trained on a separate human data set also showed significant predictive value (AUC, 0.67; 95% CI, 0.60-0.74). These three different prediction scores correlated with each other and were significant in univariate but not in multivariate analysis. CONCLUSIONS: Three conceptually different predictors performed similarly in this validation study and tended to identify the same patients as responders. A genomic predictor that relied solely on a composite of individual drug sensitivity predictions from cell lines did not show any predictive value.
PURPOSE: Several different multivariate prediction models using routine clinical variables or multigene signatures have been proposed to predict pathologic complete response to combination chemotherapy in breast cancer. Our goal was to compare the performance of four conceptually different predictors in an independent cohort of patients. EXPERIMENTAL DESIGN: Gene expression profiling was done on fine-needle aspirations of 100 stage I to III breast cancers before preoperative paclitaxel, 5-fluorouracil, doxorubicin, and cyclophosphamide combination chemotherapy. Pathologic response was correlated with prediction results from a clinical nomogram, a humancancer-derived genomic predictor (DLDA30), a cell line-based genomic predictor [in vitro coexpression extrapolation (COXEN)], and an optimized cell line-derived (in vivo COXEN) predictor. None of the 100 test cases were used in the development of these predictors. RESULTS: The in vitro COXEN using a combination of four individual drug sensitivity predictions derived from cell lines was not predictive [area under the receiver operator characteristic curve (AUC), 0.5; 95% confidence interval, (95% CI), 0.41-0.59]. The clinical nomogram (AUC, 0.73; 95% CI, 0.65-0.80) and the DLDA30 (AUC, 0.73; 95% CI, 0.66-0.80) genomic predictor had similar performances. The in vivo COXEN that used informative genes from cell lines but was trained on a separate human data set also showed significant predictive value (AUC, 0.67; 95% CI, 0.60-0.74). These three different prediction scores correlated with each other and were significant in univariate but not in multivariate analysis. CONCLUSIONS: Three conceptually different predictors performed similarly in this validation study and tended to identify the same patients as responders. A genomic predictor that relied solely on a composite of individual drug sensitivity predictions from cell lines did not show any predictive value.
Authors: Roman Rouzier; Lajos Pusztai; Suzette Delaloge; Ana M Gonzalez-Angulo; Fabrice Andre; Kenneth R Hess; Aman U Buzdar; Jean-Remi Garbay; Marc Spielmann; Marie-Christine Mathieu; W Fraser Symmans; Peter Wagner; David Atallah; Vicente Valero; Donald A Berry; Gabriel N Hortobagyi Journal: J Clin Oncol Date: 2005-11-20 Impact factor: 44.544
Authors: Kenneth R Hess; Keith Anderson; W Fraser Symmans; Vicente Valero; Nuhad Ibrahim; Jaime A Mejia; Daniel Booser; Richard L Theriault; Aman U Buzdar; Peter J Dempsey; Roman Rouzier; Nour Sneige; Jeffrey S Ross; Tatiana Vidaurre; Henry L Gómez; Gabriel N Hortobagyi; Lajos Pusztai Journal: J Clin Oncol Date: 2006-08-08 Impact factor: 44.544
Authors: Marjorie C Green; Aman U Buzdar; Terry Smith; Nuhad K Ibrahim; Vicente Valero; Marguerite F Rosales; Massimo Cristofanilli; Daniel J Booser; Lajos Pusztai; Edgardo Rivera; Richard L Theriault; Cynthia Carter; Debra Frye; Kelly K Hunt; W Fraser Symmans; Eric A Strom; Aysegul A Sahin; William Sikov; Gabriel N Hortobagyi Journal: J Clin Oncol Date: 2005-08-08 Impact factor: 44.544
Authors: Lance D Miller; Johanna Smeds; Joshy George; Vinsensius B Vega; Liza Vergara; Alexander Ploner; Yudi Pawitan; Per Hall; Sigrid Klaar; Edison T Liu; Jonas Bergh Journal: Proc Natl Acad Sci U S A Date: 2005-09-02 Impact factor: 11.205
Authors: W Fraser Symmans; Mark Ayers; Edwin A Clark; James Stec; Kenneth R Hess; Nour Sneige; Thomas A Buchholz; Savitri Krishnamurthy; Nuhad K Ibrahim; Aman U Buzdar; Richard L Theriault; Marguerite F M Rosales; Eva S Thomas; Karin M Gwyn; Marjorie C Green; Abdul R Syed; Gabriel N Hortobagyi; Lajos Pusztai Journal: Cancer Date: 2003-06-15 Impact factor: 6.860