PO.PS01.01 · 人群科学
ASH-MARCC：通过甲基化和基于参考的细胞组成评估人体组织样本中的吸烟史
ASH-MARCC: Assessment of smoking history via methylation and reference-based cell composition in human tissue samples
该海报暂无可下载的资料
AACR 官方页面
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言：吸烟（CS）与不良健康影响相关，包括癌症发生风险增加。全表观基因组关联研究（EWAS）显示，CS 显著改变 DNA 甲基化（DNAm）。虽然若干基于 DNAm 的吸烟史预测因子已使用血样训练，但它们在组织类型中的准确性仍然有限。我们旨在开发一个整合 DNAm 数据与基于参考的细胞组成估计的吸烟史预测模型，应用于人体组织样本。
方法：我们利用来自基因型-组织表达（GTEx）项目的 Infinium MethylationEPIC 芯片数据作为训练和测试，并使用临床蛋白质组学肿瘤分析联盟肺腺癌（LUAD）队列作为外部验证。使用 ENmix 进行质量控制和 beta 值提取。使用 HiTIMED 估计细胞类型比例（CP）。从 GTEx 中导出替代变量（SV）以校正批次效应。进行 EWAS 以识别差异甲基化 CpG（DMC），校正组织类型、性别、年龄、CP 和 SV。将 GTEx 分为训练集和测试集（70:30）。使用 DMC、CP、性别、年龄和组织类型训练弹性网络（Elastic Net）模型以预测吸烟史（吸烟者对非吸烟者）。通过比较报告的吸烟状态与预测概率的受试者工作特征曲线下面积（AUC）评估模型准确性。根据预测将 LUAD 正常样本分为 3 组，使用 Coxph 进行生存分析。
结果：经质控后，保留了 654 例 GTEx 正常样本、183 例正常邻近样本和 199 例 LUAD 原发肿瘤样本。198 个 DMC 位点富集于对异生物质刺激的细胞反应通路。模型在最小 lambda 处选择了 83 个特征，包括 79 个 CpG 位点、年龄、NK 和 CD8 CP，以及乳腺组织类型。cg21566642（Lnc-ECEL1-1）与 3 个已发表的血液预测因子重叠；cg07339236 和 cg11554391 与其中一个重叠。ASH-MARCC 模型表现出近乎完美的内部性能（训练 AUC 0.98，95% CI 0.97-0.99；测试 AUC 0.91，95% CI 0.88-0.95）。在外部验证中，模型在正常样本上保持高准确性（AUC 0.81，95% CI 0.75-0.87），而在肿瘤样本上性能下降但仍具信息价值（AUC 0.66，95% CI 0.58-0.74）。最高风险组相对于最低风险组显示出无统计学意义的风险（adj-HR 2.1，95% CI 0.98-4.56）。
结论：我们开发了 ASH-MARCC，一种基于 DNA 甲基化的模型，用于评估人体组织中的吸烟史。客观而准确的吸烟分类通过治疗个体化和预后精准化，增强了跨癌症类型的临床决策。通过实现对癌症组织中吸烟暴露的直接评估，尤其是在无法获得自我报告信息时，ASH-MARCC 提供了一个强大的工具，在研究和临床环境中均具有关键应用。
查看英文原文 English abstract
Introduction: Cigarette smoking (CS) is associated with adverse health effects, including an increased risk of cancer development. Epigenome-wide association studies (EWAS) show that CS significantly alters DNA methylation (DNAm). While several DNAm-based predictors of smoking history have been trained using blood samples, their accuracy in tissue types remains limited. We aimed to develop a predictive model for smoking history that integrates DNAm data with reference based cell composition estimates in human tissue samples.
Methods: We utilized Infinium MethylationEPIC array data from the Genotype-Tissue Expression (GTEx) project as training and testing, and the Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma (LUAD) cohort as external validation. Quality control and beta value extraction were performed with ENmix. Cell type proportions (CPs) were estimated by HiTIMED. Surrogate variables (SVs) were derived from GTEx to account for batch effects. EWAS was conducted to identify differentially methylated CpGs (DMC), adjusting for tissue type, sex, age, CPs, and SVs. GTEx was split into training and testing (70:30) sets. DMC, CPs, sex, age, and tissue type were used to train an Elastic Net model predicting smoking history (smoker vs. non-smoker). Model accuracy was assessed by the area under the receiver operating characteristic curve (AUC) comparing the reported smoking status to the predicted probabilities. LUAD normal samples were classified into 3 groups based on predictions for survival analysis using Coxph.
Results: After QC, 654 GTEx normal, 183 normal adjacent and 199 primary tumor LUAD samples were retained. 198 DMC sites were enriched for cellular response to xenobiotic stimulus pathways. The model selected 83 features at min lambda, including 79 CpG sites, age, NK and CD8 CPs, and breast tissue type. cg21566642 ( Lnc-ECEL1-1 ) overlapped with 3 published blood predictors; cg07339236 and cg11554391 with one. The ASH-MARCC model demonstrated near-perfect internal performance (training AUC 0.98, 95% CI 0.97-0.99; testing AUC 0.91, 95% CI 0.88-0.95). In external validation, the model maintained high accuracy on normal samples (AUC 0.81, 95% CI 0.75-0.87), while performance decreased but remained informative on tumor samples (AUC 0.66, 95% CI 0.58-0.74). The highest risk group showed a non-significant risk (adj-HR 2.1, 95% CI 0.98-4.56) versus the lowest.
Conclusions: We developed ASH-MARCC, a DNA methylation based model to assess smoking history in human tissue. An objective and accurate smoking classification enhances clinical decision-making across cancer types by treatment personalization and prognostic precision. By enabling direct evaluation of smoking exposure in cancer tissues, especially when self-reported information is unavailable, ASH-MARCC offers a powerful tool with crucial applications in both research and clinical settings.
利益披露 Disclosure
M. Zhang, None.