Development of 95-gene classifier as a powerful predictor of recurrences in node-negative and ER-positive breast cancer patients

Naoi, Yasuto; Kishi, Kazuki; Tanei, Tomonori; Tsunashima, Ryo; Tominaga, Nobuhiko; Baba, Yosuke; Kim, Seung Jin; Taguchi, Tetsuya; Tamaki, Yasuhiro; Noguchi, Shinzaburo

doi:10.1007/s10549-010-1145-z

Cited by 47 publications

(42 citation statements)

References 24 publications

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“…We compared the prognostic value of the integrated miRNA/mRNA signature to that of different gene sets used for risk stratification of BC: the genes used in the Oncotype DX (23), those used for the Genomic Grade Index (GGI) (24), for MammaPrint (17, 25), the 76-gene (22), the Invasiveness gene (IGS) (26), the 95-gene Japanese (27), and the Table S5). The HRs for expression with significant univariate Cox regression (P < 0.05) are displayed on a log 2 scale.…”

Section: Resultsmentioning

confidence: 99%

Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer

Volinia

Croce

2013

Proc. Natl. Acad. Sci. U.S.A.

138

107

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The optimal management of breast cancer (BC) presents challenges due to the heterogeneous molecular classification of the disease. We performed survival analysis on a cohort of 466 patients with primary invasive ductal carcinoma (IDC), the most frequent type of BC, by integrating mRNA, microRNA (miRNA), and DNA methylation next-generation sequencing data from The Cancer Genome Atlas (TCGA). Expression data from eight other BC cohorts were used for validation. The prognostic value of the resulting miRNA/mRNA signature was compared with that of other prognostic BC signatures. Thirty mRNAs and seven miRNAs were associated with overall survival across different clinical and molecular subclasses of a 466-patient IDC cohort from TCGA. The prognostic RNAs included PIK3CA, one of the two most frequently mutated genes in IDC, and miRNAs such as hsa-miR-328, hsa-miR-484, and hsa-miR-874. The area under the curve of the receiver-operator characteristic for the IDC risk predictor in the TCGA cohort was 0.74 at 60 mo of overall survival (P < 0.001). Most relevant for clinical application, the integrated signature had the highest prognostic value in early stage I and II tumors (receiver-operator characteristic area under the curve = 0.77, P value < 0.001). The genes in the RNA risk predictor had an independent prognostic value compared with the clinical covariates, as shown by multivariate analysis. The integrated RNA signature was successfully validated on eight BC cohorts, comprising a total of 2,399 patients, and it had superior performance for risk stratification with respect to other RNA predictors, including the mRNAs used in MammaPrint and Oncotype DX assays.genomics | prognosis | ncRNA | mutation

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Section: Resultsmentioning

confidence: 99%

Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer

Volinia

Croce

2013

Proc. Natl. Acad. Sci. U.S.A.

138

107

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“…When a tumor had þ3 immunostaining, it was considered HER2-positive. 14 The histologic grade was determined according to the Scarff-Bloom-Richardson grading system. 15 …”

Section: Histologic Evaluation Of Response To Chemotherapymentioning

confidence: 99%

Prediction of pathologic complete response to sequential paclitaxel and 5-fluorouracil/epirubicin/cyclophosphamide therapy using a 70-gene classifier for breast cancers

Naoi

Kishi

Tanei

et al. 2011

Cancer

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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 ¼ 50) and subsequently was validated successfully in the validation set (n ¼ 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 ¼ .015). In an additional study, genetically assumed complete responders were associated significantly (P ¼ .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. Cancer 2011;117:3682-

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“…Recently, other gene classifiers have been developed [21,22]. PAM50 is expected to predict late recurrence in patients with ER-positive breast cancer [21].…”

Section: Discussionmentioning

confidence: 99%

Application of a 70-Gene Expression Profile to Japanese Breast Cancer Patients

et al. 2015

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Background: As data on using MammaPrint®, a 70-gene expression profile for molecular subtyping of breast cancer, are limited in Japanese patients, we aimed to determine the gene profiles of Japanese patients using MammaPrint and to investigate its possible clinical application for selecting adjuvant treatments. Patients and Methods: 50 women treated surgically at our institution were examined. The MammaPrint results were compared with the St Gallen 2007 and intrinsic subtype risk categorizations. Results: Of 38 cases judged to be at intermediate risk based on the St Gallen 2007 Consensus, 11 (29%) were in the high-risk group based on MammaPrint. 1 of the 30 luminal A-like tumors (3%) was judged as high risk based on MammaPrint results, whereas 7 of the 20 tumors (35%) categorized as luminal B-like or triple negative were in the low-risk group. There have been no recurrences to date in the MammaPrint group, and this is possibly attributable to most of the high-risk patients receiving chemotherapy that had been recommended on the basis of their MammaPrint results. Conclusions: Our results indicate that MammaPrint is applicable to Japanese patients and that it is of potential value in current clinical practice for devising individualized treatments.

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Development of 95-gene classifier as a powerful predictor of recurrences in node-negative and ER-positive breast cancer patients

Cited by 47 publications

References 24 publications

Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer

Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer

Prediction of pathologic complete response to sequential paclitaxel and 5-fluorouracil/epirubicin/cyclophosphamide therapy using a 70-gene classifier for breast cancers

Application of a 70-Gene Expression Profile to Japanese Breast Cancer Patients

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