[论文解读] Bivariate deconvolution for cancer detection after surgery

Nuria Senar, Stavros Makrodimitris+4 more|arXiv (Cornell University)|Mar 18, 2026

Cancer Genomics and Diagnostics被引用 0

一句话总结

引入双变量去卷积模型，利用 cfDNA 甲基化从术后估算肿瘤比例，将术前与术后测量联系起来，以改进最小残留疾病的检测。

ABSTRACT

Detection of minimal residual disease (MRD) in cancer patients after surgery can provide an early marker for disease recurrence and guide subsequent treatment decisions. Accurate and sensitive estimation of tumour burden after cancer surgery may be obtained through liq- uid biopsies, measuring circulating tumour DNA (ctDNA) using, for example, mutation-based Variant Allele Frequency (VAF) values. However, to be applicable to all patients this ei- ther requires tumour-informed, patient-specific mutation panels or sensitive, tumour-agnostic genome-wide measurements. We propose a solution that accounts for patient-specific charac- teristics in genome-wide screens. For that, we introduce a bivariate deconvolution model to estimate tumour proportion from circulating cell-free DNA (cfDNA) methylation profiles of patients before and after surgery. The observations are modelled as a convolution of two bivariate latent variables, corresponding to tumour and background signals, mixed by the tumour proportion at each measurement. This bivariate approach links pre- and post-surgery measurements improving estimation of the tumour proportion after surgery, when the tumour signal is potentially very weak, or absent. We approximate likelihood of the convolution through a discretisation of the bivariate density for each latent variable into a two-dimensional grid for each pair of observations which allows for fast maximum likelihood estimation. We evaluate the predictive performance of the estimated post-surgery tumour proportions based on cfDNA methylation against available mutation-based VAF values in one-year recurrence-free survival.

研究动机与目标

• 需要敏感的 MRD 检测以在治愈性癌症手术后进行监测。
• 开发一个双变量去卷积框架，联合建模术前和术后 cfDNA 甲基化信号。
• 使用最大似然方法结合快速离散卷积来估计患者特异的肿瘤比例。

提出的方法

• 将观测值建模为两个潜在分量（肿瘤和背景）按患者特异肿瘤比例加权的卷积。
• 假设肿瘤分量和背景分量服从双变量对数正态分布，并具有特征级相关参数。
• 将双变量密度离散化成二维网格以实现快速似然评估，并执行三步块坐标上升优化。
• 利用术前和术后测量来为术后肿瘤比例估计提供信息并与结局相关联。
• 使用肿瘤组织和健康 cfDNA 甲基化谱进行参数初始化，并强制可辨识性约束 pi^0_i > pi^1_i。

Figure 1 : Example of discretisation for $y^{1}=40$ and $y^{0}=30$ with $m=8$ bins. The midpoints of $C_{r}^{0}(C_{s}^{1})$ are represented by centred dots in each cell. For the left grid, the first row ( $r=1$ ) and column ( $s=1$ ) represent the intervals for which values of $\tilde{{T}}^{0}$ and

实验结果

研究问题

• RQ1 双变量去卷积模型是否可以改进从 cfDNA 甲基化信号估计术后肿瘤比例？
• RQ2 纳入术前信息和按特征的相关性如何影响残留病变检测及其与无复发生存的关联？
• RQ3 与单变量去卷积和基于变异的 VAF 相比，双变量方法在预测一年结果方面有何差异？
• RQ4 该方法对初始化和测量之间潜在无相关性的鲁棒性如何？

主要发现

• 仿真研究表明，在信号较弱时，双变量模型比单变量版本更准确地估计术后肿瘤比例。
• 纳入特征级相关参数可改善术后肿瘤比例估计及其与真实肿瘤比例的关联。
• 在真实的 CRLM MeD-seq 数据（112 例患者）中，术后甲基化基的肿瘤比例显著区分复发组与非复发组，并在预测一年无复发生存方面优于基于 VAF 的术后比例。
• 逻辑回归和 ROC 分析显示双变量肿瘤比例是比基于 VAF 的度量更强的复发预测因子。
• 当测量值无关时，该方法仍然稳健，在该情形下并未丧失统计功效。

Figure 2 : $\hat{\boldsymbol{\pi}}^{1}_{\text{VAF}}$ and $\hat{\boldsymbol{\pi}}^{1}$ .