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Prediction of the therapeutic response to paclitaxel by gene expression profiling in neoadjuvant chemotherapy for breast cancer
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2004
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Paclitaxel SensitivityBreast OncologyEngineeringPathologyTumor BiologyOncologyBreast Cancer PatientsCancer Cell BiologyMolecular DiagnosticsRadiation OncologyCancer ResearchMolecular OncologyCancer TreatmentCancer GeneticsGene ExpressionCell BiologyTumor MicroenvironmentNeoadjuvant ChemotherapyCancer RiskCancer GenomicsBreast CancerSystems BiologyMedicineCancer Growth
500 Background: Paclitaxel is one of the most active agents for breast cancer, but the sensitivity is heterogeneous. In order to avoid unnecessary treatment, identification of a reliable predictive marker is desired to distinguish between patients who are likely to respond and those who are not. We report the discovery of a gene expression profile that predicts response to paclitaxel in breast cancer patients. Methods: We took core needle samples from 75 patients with primary breast cancer (size > 3cm) before treatment and then assessed tumor response to neoadjuvant weekly paclitaxel (80 mg/m2 ×12 cycles) under IC. Clinical response rate was 75%, and pCR rate was 3%. Patents were divided into five groups according to clinical and pathological responses (Group-1, extremely resistant; Grp-2, resistant; Grp-3, moderate responder; Grp-4, responder; Grp-5, high responder). RNA extracted from biopsy samples using microdissection method were profiled on cDNA microarrays of 23,000 human transcripts. Differentially expressed 197 genes between high responder (n=7) and extremely resistant (n=7) groups were selected by Mann-Whittney U-test (p<0.05). Secondly, machine-learning method (AdaBoost) was performed to determine the greatest estimated accuracy between 24 responders (Grp-3,4,5) and 16 non-responders (Grp-1,2), and high-scored predictive set of 23 genes were selected. At the time of this submission, tests were performed on 40 pts. Results: In leave-one-out cross validation analysis using the 23 genes, all responders and non-responders were correctly classified with an accuracy of 100%. Correlation between RNA expression measured by the arrays and semiquantitative RT-PCR was also ascertained. Conclusions: If validated, this gene expression profile could allow development of a clinical test for paclitaxel sensitivity, and may help physicians to select individual patients who are likely to benefit from paclitaxel. No significant financial relationships to disclose.