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Journal of Clinical Oncology, Vol 23, No 3 (January 20), 2005: pp. 422-431 © 2005 American Society of Clinical Oncology. DOI: 10.1200/JCO.2005.09.078 Prediction of Docetaxel Response in Human Breast Cancer by Gene Expression ProfilingFrom the Taisho Laboratory of Functional Genomics, Nara Institute of Science and Technology, Nara; Department of Surgical Oncology, Osaka University Medical School, Osaka; and NTT Communication Science Laboratories, Kyoto, Japan Address reprint requests to Kikuya Kato, MD, PhD, Osaka Medical Center for Cancer and Cardiovascular Diseases, 1-3-2 Nakamichi, Higashinari-ku, Osaka, 537-8511, Japan; e-mail: katou-ki{at}mc.pref.osaka.jp
PURPOSE: Docetaxel is one of the most effective anticancer drugs available in the treatment of breast cancer. Nearly half of the treated patients, however, do not respond to chemotherapy and suffer from side effects. The ability to reliably predict a patient's response based on tumor gene expression will improve therapeutic decision making and save patients from unnecessary side effects. PATIENTS AND METHODS: A total of 44 breast tumor tissues were sampled by biopsy before treatment with docetaxel, and the response to therapy was clinically evaluated by the degree of reduction in tumor size. Gene expression profiling of the biopsy samples was performed with 2,453 genes using a high-throughput reverse transcriptase polymerase chain reaction technique. Using genes differentially expressed between responders and nonresponders, a diagnostic system based on the weighted-voting algorithm was constructed. RESULTS: This system predicted the clinical response of 26 previously unanalyzed samples with over 80% accuracy, a level promising for clinical applications. Diagnostic profiles in nonresponders were characterized by elevated expression of genes controlling the cellular redox environment (ie, redox genes, such as thioredoxin, glutathione-S-transferase, and peroxiredoxin). Overexpression of these genes protected cultured mammary tumor cells from docetaxel-induced cell death, suggesting that enhancement of the redox system plays a major role in docetaxel resistance. CONCLUSION: These results suggest that the clinical response to docetaxel can be predicted by gene expression patterns in biopsy samples. The results also suggest that one of the molecular mechanisms of the resistance is activation of a group of redox genes.
Docetaxel is one of the most active anticancer agents in the treatment of breast cancer. It binds to beta-tubulin, one of the major components of microtubules. It exerts its growth-inhibitory effects by stabilizing microtubules, which arrests the growth of tumor cells at the G2-M phase.1 Docetaxel has been used frequently in the treatment of metastatic breast cancer patients, and recent success in the introduction of docetaxel in the adjuvant and the neoadjuvant settings has illustrated its potential for use in primary breast cancer patients in the future.2 Because a considerable proportion of breast cancer patients does not respond positively to docetaxel but, instead, suffers only from its side effects, which include neutropenia, edema, and alopecia, it is of pivotal importance to identify factors of response to docetaxel to avoid unnecessary treatment. A number of parameters, including mutations in p53 and class I ß-tubulin, and expression of P-glycoprotein, various isotypes of ß-tubulin, and BRCA2,3-5 have been evaluated as predictive factors of docetaxel response. None of them, however, provide sufficient predictive power for use in clinical practice. Gene expression profiling, mainly using DNA microarrays, is expected to provide breakthroughs in this area. This approach has already been used to identify genes that could serve as prognostic markers for breast cancer,6,7 and recently, Chang et al8 have made a preliminary report on the application of DNA microarray analysis in the identification of predictive factors of response to docetaxel in breast cancer patients. In this report, we performed gene expression profiling of breast cancer samples to develop a method for prediction of a patients' response to docetaxel.9,10 Here, we measured gene expression using a high-throughput reverse transcriptase polymerase chain reaction (PCR) technique (adapter-tagged competitive PCR [ATAC-PCR]) instead of DNA microarrays. Tumor samples obtained before chemotherapy and available for gene expression analysis are usually limited in volume. Because ATAC-PCR requires a smaller sample volume than DNA microarray analysis, we believe it is a more appropriate method for this application. In this study, we show that a diagnostic system based on the supervised learning theory predicts the response of new patients at over 80% accuracy, providing promise for its future clinical application. In addition, we have found that genes controlling the cellular redox environment are associated with docetaxel resistance.
Chemotherapy Protocol A total of 70 patients with primary breast cancer or locally recurrent breast cancer (tumor size  3 cm) were recruited for this study. The clinical backgrounds of these patients are listed in Table 1. Sample tissues were obtained either by incisional biopsy or vacuum-assisted core-needle biopsy before chemotherapy with docetaxel (60 mg/m2 every 3 weeks) was initiated. Half of each tumor sample was subjected to histologic examination (hematoxylin and eosin-stained sections) to confirm the correct sampling of tumor tissues in each patient. The other half was snap frozen in liquid nitrogen and kept at –80°C until use. Docetaxel was given for four cycles before surgery in cases of primary breast cancers or until disease progression in cases of locally recurrent breast cancers. Patients with locally recurrent breast cancers received docetaxel as their first-line chemotherapy after recurrence, and no patients were treated with taxanes as adjuvant therapy. The study protocol was approved by the Institutional Review Board of Osaka University Medical School (Osaka, Japan), and written informed consent was obtained from each patient.
Evaluation of Clinical Response The chemotherapeutic response of primary breast tumors and locally recurrent tumors was clinically evaluated according to the WHO criteria11 as follows: (1) complete response (CR), disappearance of all known disease; (2) partial response (PR), 50% or more decrease in the entire tumor burden; (3) no change, less than 50% decrease or less than 25% increase in the entire tumor burden; and (4) progressive disease, 25% increase in the entire tumor burden or appearance of new lesions. Entire tumor burden was measured by magnetic resonance imaging before and after neoadjuvant docetaxel treatment in primary breast cancer patients. In cases of locally recurrent breast cancer, whole tumor burden was measured by computed tomography scan and/or caliper measurement before and every 4 to 8 weeks after docetaxel treatment. In this study, patients with CR and PR were defined as responders, and patients with no change and progressive disease were defined as nonresponders. Among a total of 70 patients, the clinical response rate (CR+PR) was 48.6% (34 of 70 patients), and the complete pathologic response rate was 4.5% (two of 44 patients). Pathologic evaluation of a response was performed only in locally advanced breast cancer patients who underwent surgery, and complete pathologic response was defined as a complete absence of tumor cells.
ATAC-PCR Assay and Data Processing Cancer samples from 26 patients recruited from January 2001 to June 2002 were used for validation of the diagnostic system. Because of the small number of genes in the validation set, their medians may not represent accurately the overall mRNA level. Instead, we used genes whose expression patterns are similar to the median values in the data set of gene expression obtained from the 44 specimens. The similarity measure was the Euclidean distance. Normalization using the expression level of such genes should have the same effect as using the median of a large data set. The following six genes were selected because their expressions patterns best matched the median patterns obtained with the data set described in this section: ribosomal protein L30, ATP synthase alpha subunit, proteasome subunit alpha type 2, RNA polymerase II transcription factor SIII p18 subunit, and nucleophosmin. The relative expression levels of these six genes were measured, in addition to those of the 85 diagnostic genes. The raw data were divided by the average of the six genes. The subsequent logarithmic conversion and setting of the cutoff value were performed as for the large-scale analysis.
Construction of Diagnostic Systems
 ra, na, sra, and sna, are the mean expression level of gene a in responders in the learning set, the mean expression level in nonresponders, the standard deviation in responders, and the standard deviation in nonresponders, respectively. The SNR used for ranking the genes is the absolute value of wa; diagnostic genes are selected in descending order of SNR.
The total vote for responders, Vr, is the sum of positive votes of all diagnostic genes. The total vote for nonresponders, Vn, is the sum of negative votes. In the case of missing values, the vote of the gene is assigned as 0. Prediction strength (PS) is defined by the following equation in which positive and negative PS indicates prediction of responders and nonresponders, respectively:
Support vector machine (SVM) and recursive feature elimination (RFE) were performed as previously described.15 SVM is a classification algorithm that defines a discrimination surface in the utilized-feature (gene) space that attempts to maximally separate classes of training data. A test sample's position relative to the discrimination surface determines its class. RFE is an iterative procedure that eliminates features (genes) with the smallest ranking criterion determined by a cost function and was used here to rank genes for SVM. Except for SVM, the performance of the algorithms was evaluated by leave-one-out cross validation. SVM performance was evaluated using 100 random trials of leave-two-out cross validation; leave-one-out was not successful because of selection bias caused by the small number of samples. The leave-one-out cross validation was considered complete; in each turn of validation, leaving out one sample, a defined number of genes were selected by one of the ranking methods. Subsequently, a prediction algorithm was constructed, and prediction was judged using the left-out sample. Forty- four iterations of this validation process were used to calculate the accuracy of the algorithm. The data used for gene selection were the normalized data of the 1,125 genes described earlier. Validation of the diagnostic system was performed using the WV algorithm constructed with the data of the top 85 genes ranked by SNR using the normalized data of 1,125 genes in 44 patients.
Other Statistical Analyses
Evaluation by false discovery rate (FDR)16 was performed as follows. At first, we set a desired limit q on the FDR. Then, P values were arranged in ascending order, P(1)
The chance of association to a functional group was calculated using the hypergeometric distribution, described as follows.
Cell Culture and Transfection
Terminal Deoxynucleotidyl Transferase-Mediated Deoxyuridine Triphosphate-Biotin Nick-End Labeling Assay
Construction of a Diagnostic System to Predict the Docetaxel Response Gene expression profiling by ATAC-PCR yielded a data matrix consisting of 44 patients x 2,453 genes. Initial cluster analysis of the data, both hierarchical and parametric,17 did not reveal any structures correlated with the observed response to docetaxel. Therefore, we applied supervised learning methods to construct a diagnostic system for response prediction using the data of 44 patients as a learning set. There are a number of factors to be considered for construction of such diagnostic systems. At first, we evaluated prediction algorithms, gene ranking methods, and the number of predictor genes by leave-one-out cross validation. Then, we constructed a diagnostic system based on the top condition obtained by the cross validation, with genes selected using all of the training data. We compared three popular class-prediction algorithms, WV,13 K-NN,14 and SVM.15 Because a small number of genes, fewer than 100, were desired for diagnostic application, we selected genes using popular ranking methods. We used SNR to rank genes for WV and RFE to rank genes for SVM because these ranking methods are closely associated with each algorithm. PPV is a more general ranking method but was used only for K-NN in this study and not for the other algorithms. Gene selection was performed from a pool of 1,125 genes exhibiting stable PCR amplification (see Patients and Methods). The performance of each algorithm was evaluated with up to 100 genes by leave-one-out cross validation or a comparable method. The accuracy curves of these prediction methods with a variable number of up to 100 genes are shown in Figure 1. Our criterion for selection of the diagnostic system was the greatest accuracy with the least number of diagnostic genes. The results indicated that the WV algorithm generally performed best, and, with 85 or 95 genes, its accuracy was highest (72.7%; Fig 1). Therefore, we chose to use the WV algorithm with 85 diagnostic genes.
The 85 diagnostic genes were selected using data from all 44 patients in the descending order determined by SNR. Classification of the 44 patients by the final gene set is summarized in Figure 2. Sixty-one genes were elevated in nonresponders, and 24 genes were elevated in responders (Table 2). Patients were sorted vertically in order of the strength of their respective predictions. Setting the boundary at 0 clearly separated responders from nonresponders, with only four misclassifications (Fig 2). It should be noted that complete responders and patients with progressive disease were not located near the boundary, suggesting that their expression patterns clearly allowed them to be classified as responders or nonresponders.
The WV algorithm constructed with the 85 genes was tested with 26 new patients. We measured the expression levels of the 85 genes by ATAC-PCR and predicted the degree of clinical response to docetaxel. The prediction accuracy was 80.7% (95% CI, 63.5% to 92.5%; Table 3). The sensitivity, specificity, positive predictive value, and negative predictive value were 91.7% (95% CI, 68.1% to 99.5%), 71.4% (95% CI, 46.7% to 89.5%), 73.3% (95% CI, 49.5% to 90.3%), and 90.9% (95% CI, 65.9% to 99.4%), respectively.
In a small validation set, random chance could produce a significant number of correct predictions. However, the probability of observing 21 or more correct guesses is 0.12%, as calculated by the binomial distribution.
Relationship Between Response to Docetaxel and Clinicopathologic Parameters
Molecular Features Characterizing the Docetaxel Response
Protection of MCF-7 From Docetaxel-Induced Cell Death by Redox Genes
In the present study, we have shown that the diagnostic system described here, based on the expression of 85 genes, can predict a patient's response with more than 80% accuracy, making it promising for future clinical applications and warranting a study including a larger number of patients. In a clinical setting, ATAC-PCR has several advantages over other gene expression profiling techniques. Because this technique only uses the 3' end of cDNA or mRNA, it can tolerate RNA degradation to some extent. This protocol is also advantageous in that it can be converted easily to a more popular technique, real-time PCR, because both techniques are based on PCR, not on hybridization. As demonstrated by another clinically oriented study,21 PCR-based diagnostic systems are realistic solutions to problems in diagnosis, and our technique is clearly suited for practical clinical application. Some may argue that none of the genes selected for diagnosis in the previous study8 overlap with those in the present study except for calreticulin, glutathione peroxidase 4, and ATP synthase. However, this overlapping is unlikely to be obtained by chance; its probability is 0.02 assuming that the 2,453 genes of our study were included among the genes on the Affymetrix GeneChip (Santa Clara, CA). In addition, several factors make such a direct comparison difficult. The Affymetrix GeneChip has a wider coverage of genes, but recent analysis revealed that the quantitative data were obtained only with abundant genes, even in budding yeast, in which the mRNA population is much less complex than that of human tissues.22 Therefore, genes with low expression were excluded from the analysis.8 ATAC-PCR can be used to measure expression of rare genes, but its gene coverage is not as large as that of the GeneChip. Consequently, the gene populations that the two studies examined are not likely to be the same. In addition, the criteria for discriminating responders and nonresponders are different. Therefore, it is not surprising that only a few diagnostic genes overlap in the two studies. Both the clinical and pathologic response rates to docetaxel observed in the present study (48.6%, 34 of 70 patients; and 2.5%, two of 44 patients, respectively) are lower than those reported for other neoadjuvant chemotherapies consisting of both an anthracycline and a taxane, such as doxorubicin and cyclophosphamide, followed by docetaxel (90.7% and 26.1%, respectively), suggesting the superiority of sequential chemotherapy with a taxane and an anthracycline over taxane alone.23 In addition, the fact that the approved dose of docetaxel in Japan (60 mg/m2) is lower than that (100 mg/m2) commonly used in the Western countries seems to explain, at least in part, the lower response rates in the present study. Our results, both the gene expression patterns observed in patients and the in vitro transfection findings, strongly suggest that redox genes play a major role in docetaxel resistance. The glutathione and thioredoxin systems represent two major mechanisms for maintaining the intracellular redox environment through the reduction and oxidation of thiol groups.24-26 These genes have been found to be involved in resistance to other anticancer agents; inhibition of glutathione S-transferase pi activity by antisense cDNA increased sensitivity to anticancer agents in a colorectal cancer cell line,27 and elevated expression of glutathione S-transferase pi was correlated with resistance to cisplatin in lung cancer and head and neck cancer.28,29 Furthermore, increased thioredoxin levels have been reported to reduce the sensitivity of anticancer drugs in various cases.30-32 Our findings with docetaxel are consistent with these observations and are likely to apply generally to malignancies other than breast cancer. Thus far, the glutathione and thioredoxin systems have been studied individually, but both systems seem to work cooperatively in docetaxel resistance. Because expression of redox genes is low in responders to docetaxel, administration of chemicals inhibiting redox enzymes to nonresponders could improve their response profile. This is a new concept in anticancer drug design, and the combination of these agents with the diagnostic system of this study may substantially advance current docetaxel-based chemotherapy regimens. Several inhibitors of thioredoxin have already been identified, and one of them, PX-12,33 has entered phase I clinical trials as an anticancer agent. These inhibitors are promising candidates for use in combination therapy. This speculation indicates that gene expression profiling of a well-designed clinical trial could be an important resource for pharmaceutical developments.
The following authors or their immediate family members have indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. Research Funding: Kikuya Kato, Taisho Pharmaceutical Co Ltd. For a detailed description of this category, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration form and the "Disclosures of Potential Conflicts of Interest" section of Information for Contributors found in the front of every issue.
We thank Y. Kurokawa, MD, S. Oba, PhD, and S. Ishii, PhD, for their helpful suggestions in the analysis of the data. We also thank J. Yodoi, MD, PhD, for his critique of the manuscript, H. Kita-Matsuo, PhD, for technical advice, and S. Maki-Kinjo, K. Miyaoka-Ikenaga, and Y. Ishida for their valuable technical assistance.
Supported by a Grant-in-Aid for the Development of Innovative Technology from the Ministry of Education, Culture, Sports, Science and Technology of Japan, and by a grant from Taisho Pharmaceuticals Co, Ltd. Authors' disclosures of potential conflicts of interest are found at the end of this article.
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Copyright © 2005 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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