[Jackie07]

Here's an abstract about using sequential Taxane (the T [equivalent to Paclitaxel] in TCH ('Texatere, Carboplatin, Herceptin' - a standard combo for Her2 Breast Cancer from about 2005. 'My understanding' is that the anthacycline [such as Epirubicin in FEC] prevents cell division http://en.wikipedia.org/wiki/Anthracycline and Taxanes
http://en.wikipedia.org/wiki/Paclitaxel affects microtubules inside of the cell [thus influence the cell division] while Herceptin http://en.wikipedia.org/wiki/Herceptin binds to the Her2 protein receptors on the surface of the cell http://her2support.org/her2-breast-c...cancer?start=1
) and FEC. This abstract talks about using gene tests (such as the 70 gene tests cited here) to see if a patient is a good candidate for a pariticular chemo combo.


Cancer. 2011 Aug 15;117(16):3682-90. doi: 10.1002/cncr.25953. Epub 2011 Feb 8.
Prediction of pathologic complete response to sequential paclitaxel and 5-fluorouracil/epirubicin/cyclophosphamide therapy using a 70-gene classifier for breast cancers.

Naoi Y, Kishi K, Tanei T, Tsunashima R, Tominaga N, Baba Y, Kim SJ, Taguchi T, Tamaki Y, Noguchi S.
Source

Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.

Abstract

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.