Development of a prediction model for lymph node metastasis in luminal A subtype breast cancer
Project/Area Number |
25461981
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
General surgery
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Research Institution | Osaka University |
Principal Investigator |
Naoi Yasuto 大阪大学, 医学(系)研究科(研究院), 助教 (30646211)
|
Project Period (FY) |
2013-04-01 – 2016-03-31
|
Project Status |
Completed (Fiscal Year 2015)
|
Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
Keywords | 乳癌 / マイクロアレイ / 多重遺伝子診断 / 腋窩リンパ節転移 / 腋窩リンパ節 / 転移予測 |
Outline of Final Research Achievements |
The present study aimed to construct a prediction model for axillary lymph node metastasis (ALNM) using a DNA microarray assay for gene expression in breast tumor tissues. Luminal A breast cancers, diagnosed by PAM50 testing, were analyzed, and a prediction model (genomic nodal index (GNI)) consisting of 292 probe sets for ALNM was constructed in a training set of patients (n = 388), and was validated in the first (n = 59) and the second (n = 103) validation sets. AUCs of ROC were 0.820, 0.717, and 0.749 in the training, first, and second validation sets, respectively. GNI was most significantly associated with ALNM, independently of the other conventional clinicopathological parameters in all cohorts. It is suggested that GNI can be used to identify the patients with a low risk for ALNM so that sentinel lymph node biopsy can be spared safely.
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Report
(4 results)
Research Products
(8 results)
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[Journal Article] Development of a prediction model for lymph node metastasis in luminal A subtype breast cancer: the possibility to omit sentinel lymph node biopsy.2014
Author(s)
Nakauchi C, Naoi Y, Shimazu K, Tsunashima R, Nishio M, Maruyama N, Shimomura A, Kagara N, Shimoda M, Kim SJ, Noguchi S.
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Journal Title
Cancer Lett.
Volume: Oct 10;353(1)
Issue: 1
Pages: 52-8
DOI
Related Report
Peer Reviewed / Open Access / Acknowledgement Compliant
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[Journal Article] 72-gene classifier for predicting prognosis of estrogen receptor-positive and node-negative breast cancer patients using formalin-fixed, paraffin-embedded tumor tissues.2014
Author(s)
Nishio M, Naoi Y, Tsunashima R, Nakauchi C, Kagara N, Shimoda M, Shimomura A, Maruyama N, Shimazu K, Kim SJ, Noguchi S.
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Journal Title
Clin Breast Cancer
Volume: Jun;14(3)
Issue: 3
Pages: e73-e80
DOI
Related Report
Peer Reviewed / Open Access / Acknowledgement Compliant
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[Journal Article] Construction of novel immune-related signature for prediction of pathological complete response to neoadjuvant chemotherapy in human breast cancer.2014
Author(s)
Sota Y, Naoi Y, Tsunashima R, Kagara N, Shimazu K, Maruyama N, Shimomura A, Shimoda M, Kishi K, Baba Y, Kim SJ, Noguchi S.
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Journal Title
Ann Oncol.
Volume: Jan;25(1)
Issue: 1
Pages: 100-6
DOI
Related Report
Peer Reviewed / Open Access / Acknowledgement Compliant
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