Visvanathan Kala, Fackler MaryJo S, Zhang Zhe, Lopez-Bujanda Zoila A, Jeter Stacie C, Sokoll Lori J, Garrett-Mayer Elizabeth, Cope Leslie M, Umbricht Christopher B, Euhus David M, Forero Andres, Storniolo Anna M, Nanda Rita, Lin Nancy U, Carey Lisa A, Ingle James N, Sukumar Saraswati, Wolff Antonio C
Kala Visvanathan, Johns Hopkins University School of Medicine and Bloomberg School of Public Health; MaryJo S. Fackler, Zhe Zhang, Zoila A. Lopez-Bujanda, Stacie C. Jeter, Lori J. Sokoll, Leslie M. Cope, Christopher B. Umbricht, David M. Euhus, Saraswati Sukumar, and Antonio C. Wolff, Johns Hopkins University School of Medicine, Baltimore, MD; Elizabeth Garrett-Mayer, Medical University of South Carolina, Charleston, SC; Andres Forero, University of Alabama at Birmingham, Birmingham, AL; Anna M. Storniolo, Indiana University, Bloomington, IN; Rita Nanda, University of Chicago, Chicago, IL; Nancy U. Lin, Dana-Farber Cancer Institute, Boston, MA; Lisa A. Carey, University of North Carolina, Chapel Hill, NC; and James N. Ingle, Mayo Clinic, Rochester, MN.
J Clin Oncol. 2017 Mar;35(7):751-758. doi: 10.1200/JCO.2015.66.2080. Epub 2016 Nov 21.
Purpose Epigenetic alterations measured in blood may help guide breast cancer treatment. The multisite prospective study TBCRC 005 was conducted to examine the ability of a novel panel of cell-free DNA methylation markers to predict survival outcomes in metastatic breast cancer (MBC) using a new quantitative multiplex assay (cMethDNA). Patients and Methods Ten genes were tested in duplicate serum samples from 141 women at baseline, at week 4, and at first restaging. A cumulative methylation index (CMI) was generated on the basis of six of the 10 genes tested. Methylation cut points were selected to maximize the log-rank statistic, and cross-validation was used to obtain unbiased point estimates. Logistic regression or Cox proportional hazard models were used to test associations between the CMI and progression-free survival (PFS), overall survival (OS), and disease status at first restaging. The added value of the CMI in predicting survival outcomes was evaluated and compared with circulating tumor cells (CellSearch). Results Median PFS and OS were significantly shorter in women with a high CMI (PFS, 2.1 months; OS, 12.3 months) versus a low CMI (PFS, 5.8 months; OS, 21.7 months). In multivariable models, among women with MBC, a high versus low CMI at week 4 was independently associated with worse PFS (hazard ratio, 1.79; 95% CI, 1.23 to 2.60; P = .002) and OS (hazard ratio, 1.75; 95% CI, 1.21 to 2.54; P = .003). An increase in the CMI from baseline to week 4 was associated with worse PFS ( P < .001) and progressive disease at first restaging ( P < .001). Week 4 CMI was a strong predictor of PFS, even in the presence of circulating tumor cells ( P = .004). Conclusion Methylation of this gene panel is a strong predictor of survival outcomes in MBC and may have clinical usefulness in risk stratification and disease monitoring.
检测血液中的表观遗传改变可能有助于指导乳腺癌治疗。开展了多中心前瞻性研究TBCRC 005,以使用一种新的定量多重检测方法(cMethDNA)来检验一组新型游离DNA甲基化标志物预测转移性乳腺癌(MBC)生存结局的能力。
对141名女性的基线、第4周和首次重新分期时的血清样本进行重复检测,检测10个基因。基于所检测的10个基因中的6个生成累积甲基化指数(CMI)。选择甲基化切点以最大化对数秩统计量,并使用交叉验证来获得无偏点估计。使用逻辑回归或Cox比例风险模型来检验CMI与无进展生存期(PFS)、总生存期(OS)以及首次重新分期时的疾病状态之间的关联。评估CMI在预测生存结局方面的附加价值,并与循环肿瘤细胞(CellSearch)进行比较。
CMI高的女性的中位PFS和OS显著短于CMI低的女性(PFS:CMI高为2.1个月,CMI低为5.8个月;OS:CMI高为12.3个月,CMI低为21.7个月)。在多变量模型中,在MBC女性中,第4周时CMI高与低相比,独立地与更差的PFS(风险比,1.79;95%CI,1.23至2.60;P = 0.002)和OS(风险比,1.75;95%CI,1.21至2.54;P = 0.003)相关。从基线到第4周CMI的增加与更差的PFS(P < 0.001)和首次重新分期时的疾病进展(P < 0.001)相关。第4周的CMI是PFS的有力预测指标,即使存在循环肿瘤细胞时也是如此(P = 0.004)。
该基因panel的甲基化是MBC生存结局的有力预测指标,可能在风险分层和疾病监测中具有临床应用价值。