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      Gene Expression and Benefit of Chemotherapy in Women With Node-Negative, Estrogen Receptor–Positive Breast Cancer

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          Abstract

          Purpose

          The 21-gene recurrence score (RS) assay quantifies the likelihood of distant recurrence in women with estrogen receptor–positive, lymph node–negative breast cancer treated with adjuvant tamoxifen. The relationship between the RS and chemotherapy benefit is not known.

          Methods

          The RS was measured in tumors from the tamoxifen-treated and tamoxifen plus chemotherapy–treated patients in the National Surgical Adjuvant Breast and Bowel Project (NSABP) B20 trial. Cox proportional hazards models were utilized to test for interaction between chemotherapy treatment and the RS.

          Results

          A total of 651 patients were assessable (227 randomly assigned to tamoxifen and 424 randomly assigned to tamoxifen plus chemotherapy). The test for interaction between chemotherapy treatment and RS was statistically significant (P = .038). Patients with high-RS (≥ 31) tumors (ie, high risk of recurrence) had a large benefit from chemotherapy (relative risk, 0.26; 95% CI, 0.13 to 0.53; absolute decrease in 10-year distant recurrence rate: mean, 27.6%; SE, 8.0%). Patients with low-RS (< 18) tumors derived minimal, if any, benefit from chemotherapy treatment (relative risk, 1.31; 95% CI, 0.46 to 3.78; absolute decrease in distant recurrence rate at 10 years: mean, −1.1%; SE, 2.2%). Patients with intermediate-RS tumors did not appear to have a large benefit, but the uncertainty in the estimate can not exclude a clinically important benefit.

          Conclusion

          The RS assay not only quantifies the likelihood of breast cancer recurrence in women with node-negative, estrogen receptor–positive breast cancer, but also predicts the magnitude of chemotherapy benefit.

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          Most cited references23

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          Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer.

          Systemic chemotherapy for operable breast cancer substantially decreases the risk of death. Patients often have de novo resistance or incomplete response to docetaxel, one of the most active agents in this disease. We postulated that gene expression profiles of the primary breast cancer can predict the response to docetaxel. We took core biopsy samples from primary breast tumours in 24 patients before treatment and then assessed tumour response to neoadjuvant docetaxel (four cycles, 100 mg/m2 daily for 3 weeks) by cDNA analysis of RNA extracted from biopsy samples using HgU95-Av2 GeneChip. From the core biopsy samples, we extracted sufficient total RNA (3-6 microg) for cDNA array analysis using HgU95-Av2 GeneChip. Differential patterns of expression of 92 genes correlated with docetaxel response (p=0.001). Sensitive tumours had higher expression of genes involved in cell cycle, cytoskeleton, adhesion, protein transport, protein modification, transcription, and stress or apoptosis; whereas resistant tumours showed increased expression of some transcriptional and signal transduction genes. In leave-one-out cross-validation analysis, ten of 11 sensitive tumours (90% specificity) and 11 of 13 resistant tumours (85% sensitivity) were correctly classified, with an accuracy of 88%. This 92-gene predictor had positive and negative predictive values of 92% and 83%, respectively. Correlation between RNA expression measured by the arrays and semiquantitative RT-PCR was also ascertained, and our results were validated in an independent set of six patients. If validated, these molecular profiles could allow development of a clinical test for docetaxel sensitivity, thus reducing unnecessary treatment for women with breast cancer.
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            Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay.

            Throughout the last decade many laboratories have shown that mRNA levels in formalin-fixed and paraffin-embedded (FPE) tissue specimens can be quantified by reverse transcriptase-polymerase chain reaction (RT-PCR) techniques despite the extensive RNA fragmentation that occurs in tissues so preserved. We have developed RT-PCR methods that are sensitive, precise, and that have multianalyte capability for potential wide use in clinical research and diagnostic assays. Here it is shown that the extent of fragmentation of extracted FPE tissue RNA significantly increases with archive storage time. Probe and primer sets for RT-PCR assays based on amplicons that are both short and homogeneous in length enable effective reference gene-based data normalization for cross comparison of specimens that differ substantially in age. A 48-gene assay used to compare gene expression profiles from the same breast cancer tissue that had been either frozen or FPE showed very similar profiles after reference gene-based normalization. A 92-gene assay, using RNA extracted from three 10- micro m FPE sections of archival breast cancer specimens (dating from 1985 to 2001) yielded analyzable data for these genes in all 62 tested specimens. The results were substantially concordant when estrogen receptor, progesterone receptor, and HER2 receptor status determined by RT-PCR was compared with immunohistochemistry assays for these receptors. Furthermore, the results highlight the advantages of RT-PCR over immunohistochemistry with respect to quantitation and dynamic range. These findings support the development of RT-PCR analysis of FPE tissue RNA as a platform for multianalyte clinical diagnostic tests.
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              Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer.

              We sought to identify gene expression markers that predict the likelihood of chemotherapy response. We also tested whether chemotherapy response is correlated with the 21-gene Recurrence Score assay that quantifies recurrence risk. Patients with locally advanced breast cancer received neoadjuvant paclitaxel and doxorubicin. RNA was extracted from the pretreatment formalin-fixed paraffin-embedded core biopsies. The expression of 384 genes was quantified using reverse transcriptase polymerase chain reaction and correlated with pathologic complete response (pCR). The performance of genes predicting for pCR was tested in patients from an independent neoadjuvant study where gene expression was obtained using DNA microarrays. Of 89 assessable patients (mean age, 49.9 years; mean tumor size, 6.4 cm), 11 (12%) had a pCR. Eighty-six genes correlated with pCR (unadjusted P < .05); pCR was more likely with higher expression of proliferation-related genes and immune-related genes, and with lower expression of estrogen receptor (ER) -related genes. In 82 independent patients treated with neoadjuvant paclitaxel and doxorubicin, DNA microarray data were available for 79 of the 86 genes. In univariate analysis, 24 genes correlated with pCR with P < .05 (false discovery, four genes) and 32 genes showed correlation with P < .1 (false discovery, eight genes). The Recurrence Score was positively associated with the likelihood of pCR (P = .005), suggesting that the patients who are at greatest recurrence risk are more likely to have chemotherapy benefit. Quantitative expression of ER-related genes, proliferation genes, and immune-related genes are strong predictors of pCR in women with locally advanced breast cancer receiving neoadjuvant anthracyclines and paclitaxel.
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                Author and article information

                Journal
                Journal of Clinical Oncology
                JCO
                American Society of Clinical Oncology (ASCO)
                0732-183X
                1527-7755
                August 10 2006
                August 10 2006
                : 24
                : 23
                : 3726-3734
                Affiliations
                [1 ]From the Division of Pathology, Operations Center, and Biostatistical Center, National Surgical Adjuvant Breast and Bowel Project; Department of Biostatistics, School of Public Health, University of Pittsburgh; Department of Human Oncology, Allegheny General Hospital, Pittsburgh, PA; Genomic Health Inc, Redwood City, CA; and University of California, San Francisco, San Francisco, CA
                Article
                10.1200/JCO.2005.04.7985
                16720680
                254982f1-fe43-40d7-b9f7-11a87ea67cc4
                © 2006
                History

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