PURPOSE: Predictive biomarkers offer the potential to improve the benefit:risk ratio of a therapeutic agent. Ixabepilone achieves comparable pathologic complete response (pCR) rates to other active drugs in the neoadjuvant setting. This phase II trial was designed to investigate potential biomarkers that differentiate response to this agent. EXPERIMENTAL DESIGN:Women with untreated, histologically confirmed primary invasive breast adenocarcinoma receivedneoadjuvant doxorubicin/cyclophosphamide, followed by 1:1 randomization to ixabepilone (n = 148) or paclitaxel (n = 147). Rates of pCR were compared between treatment arms based on predefined biomarker sets: TUBB3, TACC3, and CAPG gene expression, a 20- and 26-gene expression model, MDR1 protein expression, and other potential markers of sensitivity. βIII-tubulin protein expression is reported separately but is referred to here for completeness. All patients underwent a core needle biopsy of the primary cancer for molecular marker analysis before chemotherapy. Gene expression profiling data were used for molecular subtyping. RESULTS: There was no significant difference in the rate of pCR in both treatment arms in βIII-tubulin-positive patients. Higher pCR rates were observed among βIII-tubulin-positive patients than in βIII-tubulin-negative patients. Furthermore, no correlation was evident between TUBB3, TACC3, and CAPG gene expression, MDR1 protein expression, multi-gene expression models, and the efficacy of ixabepilone or paclitaxel, even within the estrogen receptor-negative subset. CONCLUSION: These results indicate that βIII-tubulin protein and mRNA expression, MDR1 protein expression, TACC3 and CAPG gene expression, and multigene expression models (20- and 26-gene) are not predictive markers for differentiating treatment benefit between ixabepilone and paclitaxel in early-stage breast cancer.
RCT Entities:
PURPOSE: Predictive biomarkers offer the potential to improve the benefit:risk ratio of a therapeutic agent. Ixabepilone achieves comparable pathologic complete response (pCR) rates to other active drugs in the neoadjuvant setting. This phase II trial was designed to investigate potential biomarkers that differentiate response to this agent. EXPERIMENTAL DESIGN:Women with untreated, histologically confirmed primary invasive breast adenocarcinoma received neoadjuvant doxorubicin/cyclophosphamide, followed by 1:1 randomization to ixabepilone (n = 148) or paclitaxel (n = 147). Rates of pCR were compared between treatment arms based on predefined biomarker sets: TUBB3, TACC3, and CAPG gene expression, a 20- and 26-gene expression model, MDR1 protein expression, and other potential markers of sensitivity. βIII-tubulin protein expression is reported separately but is referred to here for completeness. All patients underwent a core needle biopsy of the primary cancer for molecular marker analysis before chemotherapy. Gene expression profiling data were used for molecular subtyping. RESULTS: There was no significant difference in the rate of pCR in both treatment arms in βIII-tubulin-positive patients. Higher pCR rates were observed among βIII-tubulin-positive patients than in βIII-tubulin-negative patients. Furthermore, no correlation was evident between TUBB3, TACC3, and CAPG gene expression, MDR1 protein expression, multi-gene expression models, and the efficacy of ixabepilone or paclitaxel, even within the estrogen receptor-negative subset. CONCLUSION: These results indicate that βIII-tubulin protein and mRNA expression, MDR1 protein expression, TACC3 and CAPG gene expression, and multigene expression models (20- and 26-gene) are not predictive markers for differentiating treatment benefit between ixabepilone and paclitaxel in early-stage breast cancer.
Authors: Aleix Prat; Ana Lluch; Arran K Turnbull; Anita K Dunbier; Lourdes Calvo; Joan Albanell; Juan de la Haba-Rodríguez; Angels Arcusa; José Ignacio Chacón; Pedro Sánchez-Rovira; Arrate Plazaola; Montserrat Muñoz; Laia Paré; Joel S Parker; Nuria Ribelles; Begoña Jimenez; Abdul Aziz Bin Aiderus; Rosalía Caballero; Barbara Adamo; Mitch Dowsett; Eva Carrasco; Miguel Martín; J Michael Dixon; Charles M Perou; Emilio Alba Journal: Clin Cancer Res Date: 2016-11-30 Impact factor: 12.531
Authors: Alexander Quaas; Amir-Hossein Rahvar; Christoph Burdelski; Christina Koop; Christian Eichelberg; Michael Rink; Roland Dahlem; Thorsten Schlomm; Maria Christina Tsourlakis; Ronald Simon; Sarah Minner; Guido Sauter; Stefan Steurer Journal: World J Urol Date: 2014-12-21 Impact factor: 4.226
Authors: Lee-Chuan C Yeh; Asok Banerjee; Veena Prasad; Jack A Tuszynski; Alexander L Weis; Tamas Bakos; I-Tien Yeh; Richard F Ludueña; John C Lee Journal: Invest New Drugs Date: 2015-12-21 Impact factor: 3.850
Authors: Cristina Saura; Ling-Ming Tseng; Stephen Chan; Raju T Chacko; Mario Campone; Alexy Manikhas; Shona M Nag; Cynthia G Leichman; Lokanatha Dasappa; Peter A Fasching; Fernando Hurtado de Mendoza; W Fraser Symmans; David Liu; Pralay Mukhopadhyay; Christine Horak; Guan Xing; Lajos Pusztai Journal: Oncologist Date: 2013-07-12
Authors: Brian David Lehmann; Yan Ding; Daniel Joseph Viox; Ming Jiang; Yi Zheng; Wang Liao; Xi Chen; Wei Xiang; Yajun Yi Journal: BMC Cancer Date: 2015-03-26 Impact factor: 4.430
Authors: Elena García-Martínez; Ginés Luengo Gil; Asunción Chaves Benito; Enrique González-Billalabeitia; María Angeles Vicente Conesa; Teresa García García; Elisa García-Garre; Vicente Vicente; Francisco Ayala de la Peña Journal: Breast Cancer Res Date: 2014-11-29 Impact factor: 6.466