新辅助化疗（NAC）是肌层侵犯性膀胱癌（MIBC）的标准护理方法，然而治疗强度大，整体效益较小，需要有效的生物标志物来识别最有益的患者。本次旨在对SWOG S1314中接受NAC的患者进行细胞无 DNA（cfDNA）甲基化特征分析，并将甲基化特征与根治性膀胱切除术中的病理学反应相关联。SWOG S1314是一项面向MIBC患者（cT2-T4aN0M0，≥5 mm活细胞瘤体积）的前瞻性合作组试验，其主要目标是评估共表达外推（COXEN）基因表达特征作为NAC反应的预测因子，其反应定义为在根治性膀胱切除术中达成 pT0N0 或≤pT1N0。本次探索性分析对来自S1314的72名患者进行了前瞻性采集血液样本，并使用Infinium MethylationEPIC BeadChip阵列检测血浆cfDNA甲基化。除了血浆采样外，没有进行其他干预。分析了病理学反应者（≤pT1N0）和非反应者之间的差异甲基化，并使用随机森林机器学习算法生成了预测治疗反应的分类器。我们使用化疗前血浆cfDNA开发了基于甲基化的反应评分（mR-score）以预测病理学反应。经过第一周期NAC的血浆样本也可以产生具有类似预测能力的mR-score。此外，我们利用cfDNA甲基化数据计算了循环膀胱DNA分数，这对治疗反应的预测有一定独立能力。在结合mR-分数和循环膀胱DNA分数的模型中，我们可以根据基线和化疗后采集的血浆准确预测79％的患者病理学反应。本研究的局限性包括样本量有限和循环膀胱DNA水平相对较低。我们的研究提供了cfDNA甲基化可用于生成膀胱癌患者NAC反应分类器的概念证明。在本次S1314的探索性分析中，我们证明了可对cfDNA甲基化进行配置，以生成与新辅助化疗反应相关的生物标志物特征。通过对其他队列的验证，这种微创方法可能被用于预测局部晚期膀胱癌和可能也适用于转移性疾病的化疗反应。版权所有©2023年欧洲泌尿器科协会。由Elsevier B.V.出版。保留所有权利。

Neoadjuvant chemotherapy (NAC) is the standard of care in muscle-invasive bladder cancer (MIBC). However, treatment is intense, and the overall benefit is small, necessitating effective biomarkers to identify patients who will benefit most.To characterize cell-free DNA (cfDNA) methylation in patients receiving NAC in SWOG S1314, a prospective cooperative group trial, and to correlate the methylation signatures with pathologic response at radical cystectomy.SWOG S1314 is a prospective cooperative group trial for patients with MIBC (cT2-T4aN0M0, ≥5 mm of viable tumor), with a primary objective of evaluating the coexpression extrapolation (COXEN) gene expression signature as a predictor of NAC response, defined as achieving pT0N0 or ≤pT1N0 at radical cystectomy. For the current exploratory analysis, blood samples were collected prospectively from 72 patients in S1314 before and during NAC, and plasma cfDNA methylation was measured using the Infinium MethylationEPIC BeadChip array.No additional interventions besides plasma collection.Differential methylation between pathologic responders (≤pT1N0) and nonresponders was analyzed, and a classifier predictive of treatment response was generated using the Random Forest machine learning algorithm.Using prechemotherapy plasma cfDNA, we developed a methylation-based response score (mR-score) predictive of pathologic response. Plasma samples collected after the first cycle of NAC yielded mR-scores with similar predictive ability. Furthermore, we used cfDNA methylation data to calculate the circulating bladder DNA fraction, which had a modest but independent predictive ability for treatment response. In a model combining mR-score and circulating bladder DNA fraction, we correctly predicted pathologic response in 79% of patients based on their plasma collected at baseline and after one cycle of chemotherapy. Limitations of this study included a limited sample size and relatively low circulating bladder DNA levels.Our study provides the proof of concept that cfDNA methylation can be used to generate classifiers of NAC response in bladder cancer patients.In this exploratory analysis of S1314, we demonstrated that cell-free DNA methylation can be profiled to generate biomarker signatures associated with neoadjuvant chemotherapy response. With validation in additional cohorts, this minimally invasive approach may be used to predict chemotherapy response in locally advanced bladder cancer and perhaps also in metastatic disease.Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All rights reserved.