Table_3_DGM-CM6: A New Model to Predict Distant Recurrence Risk in Operable Endocrine-Responsive Breast Cancer.docx
To investigate the prognostic value of DGM-CM6 (Distant Genetic Model-Clinical variable Model 6) for endocrine-responsive breast cancer (ERBC) patients, we analyzed 752 operable breast cancer patients treated in a Taiwan cancer center from 2005 to 2014. Among them, 490 ERBC patients (identified by the PAM50 or immunohistochemistry method) were classified by DGM-CM6 into low- and high-risk groups (cutoff <33 and ≥33, respectively). Significant differences were observed between the DGM-CM6 low- and high-risk groups for 10-year distant recurrence-free survival (DRFS) in both lymph node (LN)- (P < 0.05) and LN+ patients (P < 0.05). Multivariate analysis confirmed the independent strength of DGM-CM6 for the prediction of high- vs. low- risk groups for DRFS (P < 0.0001, HR: 6.76, 95% CI, 1.8–25.42) and overall survival (P = 0.01, HR: 6.06, 95% CI:1.55–23.47), respectively. In summary, DGM-CM6 may be used to classify low- and high-risk groups for 10-year distant recurrence in both LN- and LN+ ERBC patients in the Asian population. A large scale clinical trial is warranted.
History
References
- https://doi.org//10.1159/000353099
- https://doi.org//10.1159/000475698
- https://doi.org//10.1200/JCO.2007.15.1068
- https://doi.org//10.1158/1078-0432.CCR-07-4723
- https://doi.org//10.1038/bjc.2013.671
- https://doi.org//10.1093/annonc/mdt494
- https://doi.org//10.1001/jamaoncol.2017.5524
- https://doi.org//10.1158/1078-0432.CCR-05-0735
- https://doi.org//10.1200/JCO.2005.04.7985
- https://doi.org//10.1093/annonc/mdv215
- https://doi.org//10.1056/NEJMoa1602253
- https://doi.org//10.1200/JCO.2007.14.3222
- https://doi.org//10.1200/JCO.2017.74.6586
- https://doi.org//10.4048/jbc.2015.18.2.149
- https://doi.org//10.1200/JCO.2013.54.1870
- https://doi.org//10.4143/crt.2018.342
- https://doi.org//10.1186/1477-7819-10-4
- https://doi.org//10.1200/JCO.2006.09.2775
- https://doi.org//10.1200/JCO.2011.36.1105
- https://doi.org//10.1093/annonc/mdr304
- https://doi.org//10.1186/1471-2407-11-143
- https://doi.org//10.1093/bioinformatics/btt124
- https://doi.org//10.1093/bioinformatics/btv693
- https://doi.org//10.1016/j.ijrobp.2019.06.2546
- https://doi.org//10.1245/s10434-018-7059-4
- https://doi.org//10.1007/s10549-011-1709-6
- https://doi.org//10.1038/35021093
- https://doi.org//10.1007/s10549-005-9070-2
- https://doi.org//10.18632/oncotarget.13943
- https://doi.org//10.1016/S1470-2045(18)30812-X
- https://doi.org//10.1158/1055-9965.EPI-13-1023
- https://doi.org//10.1158/1055-9965.EPI-13-1017
- https://doi.org//10.1200/JCO.2015.62.1268
- https://doi.org//10.1200/JCO.2008.18.1370
- https://doi.org//10.1093/jnci/djt244
- https://doi.org//10.1186/s12957-016-0988-0
- https://doi.org//10.1186/s13058-017-0911-9
- https://doi.org//10.5114/pjp.2014.42672
- https://doi.org//10.1016/j.cyto.2019.02.001