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ID 115038
Title Alternative
Prediction Models of Breast Cancer Outcome
Author
Shigemizu, Daichi Tokyo Medical and Dental University|RIKEN Center for Integrative Medical Sciences|Japan Science and Technology Agency|National Center for Geriatrics and Gerontology
Iwase, Takuji Japanese Foundation for Cancer Research
Yoshimoto, Masataka Yoshimoto Breast Clinic
Suzuki, Yasuyo Sapporo Medical University
Miya, Fuyuki Tokyo Medical and Dental University|RIKEN Center for Integrative Medical Sciences
Boroevich, Keith A RIKEN Center for Integrative Medical Sciences
Zembutsu, Hitoshi National Cancer Center Research Institute
Tsunoda, Tatsuhiko Tokyo Medical and Dental University|RIKEN Center for Integrative Medical Sciences|Japan Science and Technology Agency
Keywords
Breast cancer
disease-free survival
MammaPrint genes
overall survival
prediction model
Content Type
Journal Article
Description
The goal of this study is to establish a method for predicting overall survival (OS ) and disease‐free survival (DFS ) in breast cancer patients after surgical operation. The gene expression profiles of cancer tissues from the patients, who underwent complete surgical resection of breast cancer and were subsequently monitored for postoperative survival, were analyzed using cDNA microarrays. We detected seven and three probes/genes associated with the postoperative OS and DFS , respectively, from our discovery cohort data. By incorporating these genes associated with the postoperative survival into MammaPrint genes, often used to predict prognosis of patients with early‐stage breast cancer, we constructed postoperative OS and DFS prediction models from the discovery cohort data using a Cox proportional hazard model. The predictive ability of the models was evaluated in another independent cohort using Kaplan–Meier (KM ) curves and the area under the receiver operating characteristic curve (AUC ). The KM curves showed a statistically significant difference between the predicted high‐ and low‐risk groups in both OS (log‐rank trend test P = 0.0033) and DFS (log‐rank trend test P = 0.00030). The models also achieved high AUC scores of 0.71 in OS and of 0.60 in DFS . Furthermore, our models had improved KM curves when compared to the models using MammaPrint genes (OS : P = 0.0058, DFS : P = 0.00054). Similar results were observed when our model was tested in publicly available datasets. These observations indicate that there is still room for improvement in the current methods of predicting postoperative OS and DFS in breast cancer.
Journal Title
Cancer Medicine
ISSN
20457634
Publisher
John Wiley & Sons
Volume
6
Issue
7
Start Page
1627
End Page
1638
Published Date
2017-05-24
Rights
© 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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DOI (Published Version)
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language
eng
TextVersion
Publisher
departments
Institute of Advanced Medical Sciences