Prediction of pathologic complete response to sequential paclitaxel and 5‐fluorouracil/epirubicin/cyclophosphamide therapy using a 70‐gene classifier for breast …

Y Naoi, K Kishi, T Tanei, R Tsunashima, N Tominaga… - Cancer, 2011 - Wiley Online Library
Y Naoi, K Kishi, T Tanei, R Tsunashima, N Tominaga, Y Baba, SJ Kim, T Taguchi, Y Tamaki…
Cancer, 2011Wiley Online Library
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 …
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. Cancer 2011. © 2011 American Cancer Society.
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