Journal ArticleDOI
A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
Soonmyung Paik,Steven Shak,Gong Tang,Chungyeul Kim,Joffre B. Baker,Maureen T. Cronin,Frederick L. Baehner,Michael G. Walker,Drew Watson,Taesung Park,William Hiller,Edwin R. Fisher,D. Lawrence Wickerham,John Bryant,Norman Wolmark +14 more
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TLDR
The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor-positive breast cancer and could be used as a continuous function to predict distant recurrent in individual patients.Abstract:
background The likelihood of distant recurrence in patients with breast cancer who have no involved lymph nodes and estrogen-receptor–positive tumors is poorly defined by clinical and histopathological measures. methods We tested whether the results of a reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assay of 21 prospectively selected genes in paraffin-embedded tumor tissue would correlate with the likelihood of distant recurrence in patients with node-negative, tamoxifen-treated breast cancer who were enrolled in the National Surgical Adjuvant Breast and Bowel Project clinical trial B-14. The levels of expression of 16 cancerrelated genes and 5 reference genes were used in a prospectively defined algorithm to calculate a recurrence score and to determine a risk group (low, intermediate, or high) for each patient. results Adequate RT-PCR profiles were obtained in 668 of 675 tumor blocks. The proportions of patients categorized as having a low, intermediate, or high risk by the RT-PCR assay were 51, 22, and 27 percent, respectively. The Kaplan–Meier estimates of the rates of distant recurrence at 10 years in the low-risk, intermediate-risk, and high-risk groups were 6.8 percent (95 percent confidence interval, 4.0 to 9.6), 14.3 percent (95 percent confidence interval, 8.3 to 20.3), and 30.5 percent (95 percent confidence interval, 23.6 to 37.4). The rate in the low-risk group was significantly lower than that in the high-risk group (P<0.001). In a multivariate Cox model, the recurrence score provided significant predictive power that was independent of age and tumor size (P<0.001). The recurrence score was also predictive of overall survival (P<0.001) and could be used as a continuous function to predict distant recurrence in individual patients. conclusions The recurrence score has been validated as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-receptor–positive breast cancer.read more
Citations
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PIWI-interacting RNA-36712 restrains breast cancer progression and chemoresistance by interaction with SEPW1 pseudogene SEPW1P RNA.
Liping Tan,Dongmei Mai,Bailin Zhang,Xiaobing Jiang,Jialiang Zhang,Ruihong Bai,Ying Ye,Mei Li,Ling Pan,Jiachun Su,Yanfen Zheng,Zexian Liu,Zhixiang Zuo,Qi Zhao,Xiaoxing Li,Xudong Huang,Jie Yang,Wen Tan,Jian Zheng,Dongxin Lin,Dongxin Lin +20 more
TL;DR: Findings suggest that piRNA-36,712 is a novel tumor suppressor and may serve as a potential predictor for the prognosis of breast cancer patients.
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A blood-based prognostic biomarker in IBD.
Daniele Biasci,James Lee,Nurulamin M Noor,Diana R Pombal,Monica Hou,Nina Lewis,Tariq Ahmad,Ailsa Hart,Miles Parkes,Eoin F. McKinney,Paul A. Lyons,Kenneth G. C. Smith +11 more
TL;DR: This is the first validated prognostic biomarker that can predict prognosis in newly diagnosed patients with IBD and represents a step towards personalised therapy.
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Is molecular profiling ready for use in clinical decision making
TL;DR: With the exception of breast cancer, there is little evidence about the incremental discrimination that molecular profiles can provide versus classic risk factors alone and cost-effectiveness is difficult to appreciate until these other challenges are addressed.
Journal ArticleDOI
Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay
Elizabeth J. Sutton,Jung Hun Oh,Brittany Z. Dashevsky,Brittany Z. Dashevsky,Harini Veeraraghavan,Aditya Apte,Sunitha B. Thakur,Joseph O. Deasy,Elizabeth A. Morris +8 more
TL;DR: To investigate the association between a validated, gene‐expression‐based, aggressiveness assay, Oncotype Dx RS, and morphological and texture‐based image features extracted from magnetic resonance imaging (MRI).
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Challenges translating breast cancer gene signatures into the clinic
TL;DR: The hurdles in the development and validation of molecular classification systems, and prognostic and predictive signatures based on microarray gene-expression profiling are discussed and it is suggested that similar challenges are likely to be encountered in translating next-generation sequencing data into clinically useful information.
References
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Gene expression profiling predicts clinical outcome of breast cancer
Laura J. van't Veer,Hongyue Dai,Marc J. van de Vijver,Yudong D. He,Augustinus A. M. Hart,Mao Mao,Hans Peterse,Karin van der Kooy,Matthew J. Marton,Anke T. Witteveen,George J. Schreiber,Ron M. Kerkhoven,Christopher J. Roberts,Peter S. Linsley,René Bernards,Stephen H. Friend +15 more
TL;DR: DNA microarray analysis on primary breast tumours of 117 young patients is used and supervised classification is applied to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis, providing a strategy to select patients who would benefit from adjuvant therapy.
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