Construction of a novel multi-gene assay (42-gene classifier) for prediction of late recurrence in ER-positive breast cancer patients

2018 ◽  
Vol 171 (1) ◽  
pp. 33-41 ◽  
Author(s):  
Ryo Tsunashima ◽  
Yasuto Naoi ◽  
Kenzo Shimazu ◽  
Naofumi Kagara ◽  
Masashi Shimoda ◽  
...  
2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12107-e12107
Author(s):  
Ken-ichi Ito ◽  
Takaaki Oba ◽  
Kenjiro Aogi ◽  
Shozo Ohsumi ◽  
Mina Takahashi ◽  
...  

e12107 Background: Curebest™ 95GC Breast (95GC) is one of the multi-gene assays to predict prognosis of node negative and estrogen receptor (ER) - positive breast cancer patients, developed using 95 gene-set without overlap with that used in Oncotype DXⓇ(ref 1). It has been shown to have the capability to classify the “intermediate” patients determined using Recurrence Online (microarray-based simulation model for Oncotype DXⓇ) but was validated only using the data from single institute and public database. Here we report the result of the first multi-center validation study for this multi-gene assay. Methods: ER-positive and T1-2/N0/M0 breast cancer patients who received adjuvant hormonal therapy were enrolled retrospectively. Fresh frozen tissues were applied to the assay, resulting classification into “L” and “H”, which was used for the validation on 5 year recurrence free survival (5Y-RFS) data of each patient. Results: 73 cases out of 150 enrolled cases were eligible and analyzed. 46 patients were classified as “L” whose 5Y-RFS was 96.5% (95%CI:89.5-98.9) while 27 patients were classified as “H” whose 5Y-RFS was 79.0% (95%CI:63.6-88.5). There was a statistically significant difference between RFS of “L” and “H” group by Log-Rank test (p = 0.0016). Significant association with 95GC were seen in histological grade (p = 0.0012), Recurrence Online (p < 0.001) and PAM50 (p < 0.001). The assay could classify the patients of histological grade 2, intermediate group by Recurrence Online (RS > 17, RS < 31) and Luminal B patients into “L” and “H”. Conclusions: Curebest™ 95GC Breast was well validated by this first multi-centered retrospective study on 5Y-RFS of the ER positive, node-negative patients who received only hormonal therapy in adjuvant setting. This result indicates the usefulness of 95GC as a novel multi-gene assay, as it can classify target patients into 2 groups, “H” and “L” according to predicted prognosis of 5Y-RFS. Reference: 1. Naoi et al. Breast Cancer Res Treat (2011) 128:632-641


Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 771
Author(s):  
Tessa A. M. Mulder ◽  
Mirjam de With ◽  
Marzia del Re ◽  
Romano Danesi ◽  
Ron H. J. Mathijssen ◽  
...  

Tamoxifen is a major option for adjuvant endocrine treatment in estrogen receptor (ER) positive breast cancer patients. The conversion of the prodrug tamoxifen into the most active metabolite endoxifen is mainly catalyzed by the enzyme cytochrome P450 2D6 (CYP2D6). Genetic variation in the CYP2D6 gene leads to altered enzyme activity, which influences endoxifen formation and thereby potentially therapy outcome. The association between genetically compromised CYP2D6 activity and low endoxifen plasma concentrations is generally accepted, and it was shown that tamoxifen dose increments in compromised patients resulted in higher endoxifen concentrations. However, the correlation between CYP2D6 genotype and clinical outcome is still under debate. This has led to genotype-based tamoxifen dosing recommendations by the Clinical Pharmacogenetic Implementation Consortium (CPIC) in 2018, whereas in 2019, the European Society of Medical Oncology (ESMO) discouraged the use of CYP2D6 genotyping in clinical practice for tamoxifen therapy. This paper describes the latest developments on CYP2D6 genotyping in relation to endoxifen plasma concentrations and tamoxifen-related clinical outcome. Therefore, we focused on Pharmacogenetic publications from 2018 (CPIC publication) to 2021 in order to shed a light on the current status of this debate.


Oncotarget ◽  
2017 ◽  
Vol 8 (32) ◽  
pp. 52142-52155 ◽  
Author(s):  
Takashi Takeshita ◽  
Yutaka Yamamoto ◽  
Mutsuko Yamamoto-Ibusuki ◽  
Mai Tomiguchi ◽  
Aiko Sueta ◽  
...  

2004 ◽  
Vol 2 (3) ◽  
pp. 71 ◽  
Author(s):  
V.F Semiglazov ◽  
V.V Semiglazov ◽  
V.G Ivanov ◽  
E.K Ziltsova ◽  
G.A Dashian ◽  
...  

2019 ◽  
Vol 25 (1) ◽  
pp. 9-15
Author(s):  
Takeshi Murata ◽  
Hiromitsu Jinno ◽  
Maiko Takahashi ◽  
Masayuki Shimoda ◽  
Tetsu Hayashida ◽  
...  

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