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Erschienen in: Breast Cancer Research and Treatment 2/2013

01.07.2013 | Preclinical study

Comparison of efficacy of 95-gene and 21-gene classifier (Oncotype DX) for prediction of recurrence in ER-positive and node-negative breast cancer patients

verfasst von: Yasuto Naoi, Kazuki Kishi, Ryo Tsunashima, Kenzo Shimazu, Atsushi Shimomura, Naomi Maruyama, Masafumi Shimoda, Naofumi Kagara, Yosuke Baba, Seung Jin Kim, Shinzaburo Noguchi

Erschienen in: Breast Cancer Research and Treatment | Ausgabe 2/2013

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Abstract

We recently developed a 95-gene classifier (95GC) for the prognostic prediction for ER-positive and node-negative breast cancer patients treated with only adjuvant hormonal therapy. The aim of this study was to validate the efficacy of 95GC and compare it with that of 21GC (Oncotype DX) as well as to evaluate the combination of 95GC and 21GC. DNA microarray data (gene expression) of ER-positive and node-negative breast cancer patients (n = 459) treated with adjuvant hormone therapy alone as well as those of ER-positive breast cancer patients treated with neoadjuvant chemotherapy (n = 359) were classified with 95GC and 21GC (Recurrence Online at http://​www.​recurrenceonline​.​com/​). 95GC classified the 459 patients into low-risk (n = 285; 10 year relapse-free survival: 88.8 %) and high-risk groups (n = 174; 70.6 %) (P = 5.5e−10), and 21GC into low-risk group (n = 286; 89.3 %), intermediate-risk (n = 81; 75.7 %), and high-risk (n = 92; 64.7 %) groups (P = 2.9e−10). The combination of 95GC and 21GC classified them into low-risk (n = 324; 88.9 %) and high-risk (n = 135; 65.0 %) groups (P = 5.9e−14), and also showed that pathological complete response rates were significantly (P = 2.5e−6) higher for the high-risk (17.9 %) than the low-risk group (3.6 %). In addition, we demonstrated that 95GC was calculated on a single-sample basis if the reference robust multi-array average workflow was used for normalization. The prognostic prediction capability of 95GC appears to be comparable to that of 21GC. Moreover, their combination seems to result in the identification of more low-risk patients who do not need chemotherapy than either classification alone. The patients in the high-risk group were found to be more chemo-sensitive so that they can benefit more from adjuvant chemotherapy.
Literatur
1.
Zurück zum Zitat Sparano JA, Paik S (2008) Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol 26:721–728PubMedCrossRef Sparano JA, Paik S (2008) Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol 26:721–728PubMedCrossRef
2.
Zurück zum Zitat Prat A, Ellis MJ, Perou CM (2011) Practical implications of gene-expression-based assays for breast oncologists. Nat Rev Clin Oncol 9:48–57PubMedCrossRef Prat A, Ellis MJ, Perou CM (2011) Practical implications of gene-expression-based assays for breast oncologists. Nat Rev Clin Oncol 9:48–57PubMedCrossRef
3.
Zurück zum Zitat Prat A, Perou CM (2011) Deconstructing the molecular portraits of breast cancer. Mol Oncol 5:5–23PubMedCrossRef Prat A, Perou CM (2011) Deconstructing the molecular portraits of breast cancer. Mol Oncol 5:5–23PubMedCrossRef
4.
6.
Zurück zum Zitat Kim C, Paik S (2010) Gene-expression-based prognostic assays for breast cancer. Nat Rev Clin Oncol 7:340–347PubMedCrossRef Kim C, Paik S (2010) Gene-expression-based prognostic assays for breast cancer. Nat Rev Clin Oncol 7:340–347PubMedCrossRef
7.
Zurück zum Zitat Ross JS, Hatzis C, Symmans WF et al (2008) Commercialized multigene predictors of clinical outcome for breast cancer. Oncolog 13:477–493CrossRef Ross JS, Hatzis C, Symmans WF et al (2008) Commercialized multigene predictors of clinical outcome for breast cancer. Oncolog 13:477–493CrossRef
8.
Zurück zum Zitat Colombo PE, Milanezi F, Weigelt B, Reis-Filho JS (2011) Microarrays in the 2010s: the contribution of microarray-based gene expression profiling to breast cancer classification, prognostication and prediction. Breast Cancer Res 13:212PubMedCrossRef Colombo PE, Milanezi F, Weigelt B, Reis-Filho JS (2011) Microarrays in the 2010s: the contribution of microarray-based gene expression profiling to breast cancer classification, prognostication and prediction. Breast Cancer Res 13:212PubMedCrossRef
9.
Zurück zum Zitat Gokmen-Polar Y, Badve S (2012) Molecular profiling assays in breast cancer: are we ready for prime time? Oncolog (Williston Park) 26:350–357, 361 Gokmen-Polar Y, Badve S (2012) Molecular profiling assays in breast cancer: are we ready for prime time? Oncolog (Williston Park) 26:350–357, 361
10.
Zurück zum Zitat Naoi Y, Kishi K, Tanei T et al (2011) Development of 95-gene classifier as a powerful predictor of recurrences in node-negative and ER-positive breast cancer patients. Breast Cancer Res Treat 128:633–641PubMedCrossRef Naoi Y, Kishi K, Tanei T et al (2011) Development of 95-gene classifier as a powerful predictor of recurrences in node-negative and ER-positive breast cancer patients. Breast Cancer Res Treat 128:633–641PubMedCrossRef
11.
Zurück zum Zitat Paik S (2007) Development and clinical utility of a 21-gene recurrence score prognostic assay in patients with early breast cancer treated with tamoxifen. Oncologist 12:631–635PubMedCrossRef Paik S (2007) Development and clinical utility of a 21-gene recurrence score prognostic assay in patients with early breast cancer treated with tamoxifen. Oncologist 12:631–635PubMedCrossRef
12.
Zurück zum Zitat Harris L, Fritsche H, Mennel R et al (2007) American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 25:5287–5312PubMedCrossRef Harris L, Fritsche H, Mennel R et al (2007) American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 25:5287–5312PubMedCrossRef
14.
Zurück zum Zitat Sparano JA (2006) TAILORx: trial assigning individualized options for treatment (Rx). Clin Breast Cancer 7:347–350PubMedCrossRef Sparano JA (2006) TAILORx: trial assigning individualized options for treatment (Rx). Clin Breast Cancer 7:347–350PubMedCrossRef
15.
Zurück zum Zitat Paik S, Shak S, Tang G et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826PubMedCrossRef Paik S, Shak S, Tang G et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826PubMedCrossRef
16.
Zurück zum Zitat Gyorffy B, Benke Z, Lanczky A et al (2012) RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat 132:1025–1034PubMedCrossRef Gyorffy B, Benke Z, Lanczky A et al (2012) RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat 132:1025–1034PubMedCrossRef
17.
Zurück zum Zitat Tsunashima R, Naoi Y, Kishi K et al (2012) Estrogen receptor positive breast cancer identified by 95-gene classifier as at high risk for relapse shows better response to neoadjuvant chemotherapy. Cancer Lett 324:42–47PubMedCrossRef Tsunashima R, Naoi Y, Kishi K et al (2012) Estrogen receptor positive breast cancer identified by 95-gene classifier as at high risk for relapse shows better response to neoadjuvant chemotherapy. Cancer Lett 324:42–47PubMedCrossRef
18.
Zurück zum Zitat Naoi Y, Kishi K, Tanei T et al (2011) Prediction of pathologic complete response to sequential paclitaxel and 5-fluorouracil/epirubicin/cyclophosphamide therapy using a 70-gene classifier for breast cancers. Cancer 117:3682–3690PubMedCrossRef Naoi Y, Kishi K, Tanei T et al (2011) Prediction of pathologic complete response to sequential paclitaxel and 5-fluorouracil/epirubicin/cyclophosphamide therapy using a 70-gene classifier for breast cancers. Cancer 117:3682–3690PubMedCrossRef
19.
Zurück zum Zitat Naoi Y, Tanei T, Kishi K et al (2012) 70-Gene classifier for differentiation between paclitaxel- and docetaxel-sensitive breast cancers. Cancer Lett 314:206–212PubMedCrossRef Naoi Y, Tanei T, Kishi K et al (2012) 70-Gene classifier for differentiation between paclitaxel- and docetaxel-sensitive breast cancers. Cancer Lett 314:206–212PubMedCrossRef
20.
Zurück zum Zitat Parker JS, Mullins M, Cheang MC et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167PubMedCrossRef Parker JS, Mullins M, Cheang MC et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167PubMedCrossRef
21.
Zurück zum Zitat Morimoto K, Kim SJ, Tanei T et al (2009) Stem cell marker aldehyde dehydrogenase 1-positive breast cancers are characterized by negative estrogen receptor, positive human epidermal growth factor receptor type 2, and high Ki67 expression. Cancer Sci 100:1062–1068PubMedCrossRef Morimoto K, Kim SJ, Tanei T et al (2009) Stem cell marker aldehyde dehydrogenase 1-positive breast cancers are characterized by negative estrogen receptor, positive human epidermal growth factor receptor type 2, and high Ki67 expression. Cancer Sci 100:1062–1068PubMedCrossRef
22.
Zurück zum Zitat Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19:403–410PubMedCrossRef Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19:403–410PubMedCrossRef
23.
Zurück zum Zitat Goldstein DR (2006) Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays. Bioinformatics 22:2364–2372PubMedCrossRef Goldstein DR (2006) Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays. Bioinformatics 22:2364–2372PubMedCrossRef
24.
Zurück zum Zitat Katz S, Irizarry RA, Lin X et al (2006) A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database. BMC Bioinformatics 7:464PubMedCrossRef Katz S, Irizarry RA, Lin X et al (2006) A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database. BMC Bioinformatics 7:464PubMedCrossRef
25.
Zurück zum Zitat Irizarry RA, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264PubMedCrossRef Irizarry RA, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264PubMedCrossRef
26.
Zurück zum Zitat Chang JC, Makris A, Gutierrez MC et al (2008) Gene expression patterns in formalin-fixed, paraffin-embedded core biopsies predict docetaxel chemosensitivity in breast cancer patients. Breast Cancer Res Treat 108:233–240PubMedCrossRef Chang JC, Makris A, Gutierrez MC et al (2008) Gene expression patterns in formalin-fixed, paraffin-embedded core biopsies predict docetaxel chemosensitivity in breast cancer patients. Breast Cancer Res Treat 108:233–240PubMedCrossRef
27.
Zurück zum Zitat Symmans WF, Hatzis C, Sotiriou C et al (2010) Genomic index of sensitivity to endocrine therapy for breast cancer. J Clin Oncol 28:4111–4119PubMedCrossRef Symmans WF, Hatzis C, Sotiriou C et al (2010) Genomic index of sensitivity to endocrine therapy for breast cancer. J Clin Oncol 28:4111–4119PubMedCrossRef
Metadaten
Titel
Comparison of efficacy of 95-gene and 21-gene classifier (Oncotype DX) for prediction of recurrence in ER-positive and node-negative breast cancer patients
verfasst von
Yasuto Naoi
Kazuki Kishi
Ryo Tsunashima
Kenzo Shimazu
Atsushi Shimomura
Naomi Maruyama
Masafumi Shimoda
Naofumi Kagara
Yosuke Baba
Seung Jin Kim
Shinzaburo Noguchi
Publikationsdatum
01.07.2013
Verlag
Springer US
Erschienen in
Breast Cancer Research and Treatment / Ausgabe 2/2013
Print ISSN: 0167-6806
Elektronische ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-013-2640-9

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