摘要P5-12-04:一种新的数据分析方法来获得DNA甲基化特征,从而对三阴性乳腺癌的复发风险进行分层

B. Downs, L. Cope, M. Fackler, Soonweng Cho, A. Wolff, M. Regan, S. Sukumar, C. Umbricht
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引用次数: 0

摘要

背景:三阴性乳腺癌(TNBC)占所有乳腺癌的10-17%,更可能是较高的组织学分级,低分化,复发率高,总生存率低。TNBC患者的临床病程仍然难以预测,因为具有同质形态特征的肿瘤可能对治疗的反应不同,结果也不同。因此,需要更多的分析方法来更好地对TNBC进行分类。我们的目标是完善甲基组数据集的分析,以获得可靠的分子特征,可以区分预后良好的TNBC患者,这些患者可能受益于较少的积极治疗,而那些预后较差的患者可能需要更积极的治疗。方法:本实验室在本次会议上报告了53例局部治疗的TNBC高危病例和62例局部治疗的低风险对照,以及5例正常乳腺组织样本的450k甲基化阵列数据分析结果。高风险病例定义为自诊断时起0.5 ~ 6.5年内复发的患者,而低风险对照组为无复发且>4年无复发间隔(RFI)。在这项工作中,我们设计并应用了一种名为Hypermethylated Outlier Detector (HOD)的新型甲基化生物标志物发现程序,该程序强调与对照组相比,在病例中选择高度甲基化的标记,以在TNBC发现集中发现高风险特征。在未接受化疗的50例tnbc(16例复发)和接受化疗的131例tnbc(33例复发)的第二组测试中,对HOD鉴定的甲基化特征进行了询问。结果:在单独局部治疗的tnbc发现组(n=115)中,HOD鉴定出39个高甲基化标记物(β >0.20),可以准确区分高风险病例和低风险对照组。在未接受化疗的TNBC (n=50)的测试集中,39个标志物区分出高危和低危个体(似然比检验P=0.049)。在第二组接受化疗的TNBC (n=131)中,39个高甲基化标记物再次区分出高风险个体和低风险个体(似然比检验P=0.0043)。结论:我们已经提供了证据,表明甲基化特征可以用于识别TNBC患者复发的高风险,无论是否接受化疗。这种甲基化特征可能潜在地用于通知医生决定TNBC患者的治疗策略。这可能最终导致对具有较好预后的甲基化谱的患者进行较少的积极治疗。相反,39种标记物高甲基化的患者可能会从更积极的治疗过程中受益。引用格式:Downs BM, Cope LM, Fackler MJ, Cho S, Wolff AC, Regan MM, Sukumar S, Umbricht CB。一种新的数据分析方法,以获得DNA甲基化特征,分层三阴性乳腺癌复发风险[摘要]。2018年圣安东尼奥乳腺癌研讨会论文集;2018年12月4-8日;费城(PA): AACR;中国癌症杂志2019;79(增刊):P5-12-04。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Abstract P5-12-04: A new method of data analysis to derive DNA methylation signatures that stratify risk of recurrence in triple negative breast cancer
Background: Triple negative breast cancer (TNBC) accounts for 10-17% of all breast cancer and is more likely to be of higher histological grade, poorly differentiated, associated with a higher recurrence rate and with decreased overall survival. The clinical course of a TNBC patient remains difficult to predict, as tumors with homogenous morphological characteristics may vary in response to therapy and have divergent outcomes. Therefore, additional analytical methods are needed to better classify TNBC. Our goal is to refine the analysis of methylome datasets to derive reliable molecular signatures that can distinguish TNBC patients with good outcomes who may benefit from less aggressive treatment, from those with poor outcomes who would be candidates for more aggressive treatments. Methods: Our laboratory has conducted and reported, in this meeting, results from analysis of 450k methylation array data on a discovery set of 53 high-risk TNBC cases and 62 low-risk controls treated by locoregional therapy alone, as well as 5 normal breast tissue samples. High-risk cases were defined as patients that relapsed within 0.5 to 6.5 years from the time of diagnosis, while low-risk controls had no relapse and >4 year recurrence-free intervals (RFI). In this work, we devised and applied a novel methylation biomarker discovery program named Hypermethylated Outlier Detector (HOD) that emphasizes the selection of highly methylated markers in cases compared to controls, to find a high-risk signature in the TNBC discovery set. The methylation signature identified by HOD was interrogated in a test set of 50 TNBCs (with 16 recurrences) that did not receive chemotherapy, and in a second test set of 131 TNBCs (with 33 recurrences) that did receive chemotherapy. Results: HOD identified 39 hypermethylated markers (beta >0.20) that could accurately distinguish between the high-risk cases and the low-risk controls in the discovery set of TNBCs (n=115) treated with locoregional therapy alone. In the test set of TNBC (n=50) with no chemotherapy the 39 markers distinguished high from low risk individuals (likelihood ratio test P=0.049). In a second test set of TNBC (n=131) that received chemotherapy the 39 hypermethylated markers again distinguished high from low risk individuals (likelihood ratio test P=0.0043). Conclusions: We have presented evidence that a methylation signature identified by HOD can be used to identify TNBC patients that have a high-risk of relapse regardless of receiving chemotherapy. This methylation signature could potentially be used to inform physician decisions on therapeutic strategies for TNBC patients. This could ultimately lead to less aggressive treatment given to patients possessing a methylation profile consistent with a better prognosis. Conversely, patients with hypermethylation in the 39 markers will likely benefit from a more aggressive course of treatment. Citation Format: Downs BM, Cope LM, Fackler MJ, Cho S, Wolff AC, Regan MM, Sukumar S, Umbricht CB. A new method of data analysis to derive DNA methylation signatures that stratify risk of recurrence in triple negative breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-12-04.
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