BACKGROUND: Sequential administration of paclitaxel plus combined fluorouracil, epirubicin, and cyclophosphamide (P-FEC) is 1 of the most common neoadjuvant chemotherapies for patients with primary breast cancer and produces pathologic complete response (pCR) rates of 20% to 30%. However, a predictor of pCR to this chemotherapy has yet to be developed. The authors developed such a predictor by using a proprietary DNA microarray for gene expression analysis of breast tumor tissues. METHODS: Tumor samples were obtained from 84 patients with breast cancer by core-needle biopsy before the patients received P-FEC, and the gene expression profile was analyzed in those samples to construct a classifier for predicting pCR to P-FEC. In addition, the authors analyzed the gene expression profile of tumor tissues that were obtained at surgery from 105 patients with lymph node-negative and estrogen receptor-positive breast cancer who received adjuvant hormone therapy alone to determine the prognostic significance of the classifier. RESULTS: The 70-gene classifier for predicting pCR to P-FEC was constructed by using the training set (n = 50) and subsequently was validated successfully in the validation set (n = 34), revealing high sensitivity (88%; 95% confidence interval [CI], 47%-100%) and high negative predictive value (93%; 95% CI, 68%-100%). Specificity and positive predictive value were 54% (95% CI, 33%-73%) and 37% (95% CI, 16%-62%), respectively. Among the various parameters (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 status, etc), the 70-gene classifier had the strongest association with pCR (P = .015). In an additional study, genetically assumed complete responders were associated significantly (P = .047) with a poor prognosis. CONCLUSIONS: The 70-gene classifier that was constructed for predicting pCR to P-FEC for breast tumors was successful, with high sensitivity and high negative predictive value. The classifier also appeared to be useful for predicting the prognosis of patients with lymph node-negative and estrogen receptor-positive breast cancer who receive adjuvant hormone therapy alone.
BACKGROUND: Sequential administration of paclitaxel plus combined fluorouracil, epirubicin, and cyclophosphamide (P-FEC) is 1 of the most common neoadjuvant chemotherapies for patients with primary breast cancer and produces pathologic complete response (pCR) rates of 20% to 30%. However, a predictor of pCR to this chemotherapy has yet to be developed. The authors developed such a predictor by using a proprietary DNA microarray for gene expression analysis of breast tumor tissues. METHODS:Tumor samples were obtained from 84 patients with breast cancer by core-needle biopsy before the patients received P-FEC, and the gene expression profile was analyzed in those samples to construct a classifier for predicting pCR to P-FEC. In addition, the authors analyzed the gene expression profile of tumor tissues that were obtained at surgery from 105 patients with lymph node-negative and estrogen receptor-positive breast cancer who received adjuvant hormone therapy alone to determine the prognostic significance of the classifier. RESULTS: The 70-gene classifier for predicting pCR to P-FEC was constructed by using the training set (n = 50) and subsequently was validated successfully in the validation set (n = 34), revealing high sensitivity (88%; 95% confidence interval [CI], 47%-100%) and high negative predictive value (93%; 95% CI, 68%-100%). Specificity and positive predictive value were 54% (95% CI, 33%-73%) and 37% (95% CI, 16%-62%), respectively. Among the various parameters (estrogen receptor, progesterone receptor, humanepidermal growth factor receptor 2, and Ki-67 status, etc), the 70-gene classifier had the strongest association with pCR (P = .015). In an additional study, genetically assumed complete responders were associated significantly (P = .047) with a poor prognosis. CONCLUSIONS: The 70-gene classifier that was constructed for predicting pCR to P-FEC for breast tumors was successful, with high sensitivity and high negative predictive value. The classifier also appeared to be useful for predicting the prognosis of patients with lymph node-negative and estrogen receptor-positive breast cancer who receive adjuvant hormone therapy alone.
Authors: Rikke Leth-Larsen; Mikkel G Terp; Anne G Christensen; Daniel Elias; Thorsten Kühlwein; Ole N Jensen; Ole W Petersen; Henrik J Ditzel Journal: Mol Med Date: 2012-09-25 Impact factor: 6.354
Authors: Dmitry Rykunov; Noam D Beckmann; Hui Li; Andrew Uzilov; Eric E Schadt; Boris Reva Journal: Nucleic Acids Res Date: 2016-04-20 Impact factor: 16.971
Authors: Sylvia Timme; Martin Sillem; Peter Bronsert; Lioudmila Bogatyreva; Dieter Hauschke; Axel Zur Hausen; Martin Werner; Elmar Stickeler Journal: Breast Care (Basel) Date: 2017-08-02 Impact factor: 2.860
Authors: Jorma J de Ronde; Marc Jan Bonder; Esther H Lips; Sjoerd Rodenhuis; Lodewyk F A Wessels Journal: PLoS One Date: 2014-02-18 Impact factor: 3.240