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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

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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.

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Abbreviations

ER:

Estrogen receptor

PR:

Progesterone receptor

HER2:

Human epidermal growth factor receptor 2

References

  1. Sparano JA, Paik S (2008) Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol 26:721–728

    Article  PubMed  Google Scholar 

  2. Prat A, Ellis MJ, Perou CM (2011) Practical implications of gene-expression-based assays for breast oncologists. Nat Rev Clin Oncol 9:48–57

    Article  PubMed  Google Scholar 

  3. Prat A, Perou CM (2011) Deconstructing the molecular portraits of breast cancer. Mol Oncol 5:5–23

    Article  PubMed  CAS  Google Scholar 

  4. Paik S (2011) Is gene array testing to be considered routine now? Breast 20(3):S87–S91

    Article  PubMed  Google Scholar 

  5. Perou CM, Borresen-Dale AL (2011) Systems biology and genomics of breast cancer. Cold Spring Harb Perspect Biol 3(2):a003293. doi:10.1101/cshperspect.a003293

    Article  PubMed  Google Scholar 

  6. Kim C, Paik S (2010) Gene-expression-based prognostic assays for breast cancer. Nat Rev Clin Oncol 7:340–347

    Article  PubMed  CAS  Google Scholar 

  7. Ross JS, Hatzis C, Symmans WF et al (2008) Commercialized multigene predictors of clinical outcome for breast cancer. Oncolog 13:477–493

    Article  Google Scholar 

  8. 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 13:212

    Article  PubMed  Google Scholar 

  9. 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

    Google Scholar 

  10. 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 128:633–641

    Article  PubMed  Google Scholar 

  11. 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 12:631–635

    Article  PubMed  CAS  Google Scholar 

  12. 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 25:5287–5312

    Article  PubMed  CAS  Google Scholar 

  13. NCCN Clinical Practice Guidelines in Oncology, Breast Cancer (Version 1.2011). http://www.nccn.org/professionals/physician_gls/f_guidelines.asp

  14. Sparano JA (2006) TAILORx: trial assigning individualized options for treatment (Rx). Clin Breast Cancer 7:347–350

    Article  PubMed  Google Scholar 

  15. 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 351:2817–2826

    Article  PubMed  CAS  Google Scholar 

  16. 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 132:1025–1034

    Article  PubMed  CAS  Google Scholar 

  17. 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 324:42–47

    Article  PubMed  CAS  Google Scholar 

  18. 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 117:3682–3690

    Article  PubMed  CAS  Google Scholar 

  19. Naoi Y, Tanei T, Kishi K et al (2012) 70-Gene classifier for differentiation between paclitaxel- and docetaxel-sensitive breast cancers. Cancer Lett 314:206–212

    Article  PubMed  CAS  Google Scholar 

  20. Parker JS, Mullins M, Cheang MC et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167

    Article  PubMed  Google Scholar 

  21. 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 100:1062–1068

    Article  PubMed  CAS  Google Scholar 

  22. 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 19:403–410

    Article  PubMed  CAS  Google Scholar 

  23. Goldstein DR (2006) Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays. Bioinformatics 22:2364–2372

    Article  PubMed  CAS  Google Scholar 

  24. 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 7:464

    Article  PubMed  Google Scholar 

  25. Irizarry RA, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264

    Article  PubMed  Google Scholar 

  26. 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 108:233–240

    Article  PubMed  CAS  Google Scholar 

  27. Symmans WF, Hatzis C, Sotiriou C et al (2010) Genomic index of sensitivity to endocrine therapy for breast cancer. J Clin Oncol 28:4111–4119

    Article  PubMed  Google Scholar 

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Acknowledgments

This study was supported, in part, by the Knowledge Cluster Initiative of the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Conflict of interest

SN received honoraria and research funding from Sysmex Corp. KK and YB are employees of Sysmex Corp.

Ethical standards

The study complied with the current laws of Japan.

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Authors and Affiliations

  1. Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, 2-2-E10 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan

    Yasuto Naoi, Ryo Tsunashima, Kenzo Shimazu, Atsushi Shimomura, Naomi Maruyama, Masafumi Shimoda, Naofumi Kagara, Seung Jin Kim & Shinzaburo Noguchi

  2. Central Research Laboratories, Sysmex Corporation, Kobe, Japan

    Kazuki Kishi & Yosuke Baba

Authors
  1. Yasuto Naoi
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  2. Kazuki Kishi
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  3. Ryo Tsunashima
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  4. Kenzo Shimazu
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  5. Atsushi Shimomura
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  6. Naomi Maruyama
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  7. Masafumi Shimoda
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  8. Naofumi Kagara
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  9. Yosuke Baba
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  10. Seung Jin Kim
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  11. Shinzaburo Noguchi
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Correspondence to Shinzaburo Noguchi.

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Naoi, Y., Kishi, K., Tsunashima, R. et al. 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. Breast Cancer Res Treat 140, 299–306 (2013). https://doi.org/10.1007/s10549-013-2640-9

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  • DOI: https://doi.org/10.1007/s10549-013-2640-9

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