Brown Derek W, Sun Daokun, Fine Alexander D, He Shai, McDevitt Michael, Pontbriand Kerriann, Polisecki Eliana, Kou Angela, Li Mingyue, Zhang Shumeng, Kuang Zheng, Fabrizio David, Madison Russell W, He Jie, Assaf Zoe June, Powles Thomas, Sweeney Christopher, Gandara David, Antonarakis Emmanuel S, Albacker Lee A, Aiyer Aparna, Yilmazel Bahar, Decker Brennan J, Hughes Jason D, Tukachinsky Hanna, Xu Chang
Foundation Medicine, Inc., Boston, MA, USA.
Genentech, Inc., South San Francisco, CA, USA.
J Liq Biopsy. 2025 Jul 11;9:100311. doi: 10.1016/j.jlb.2025.100311. eCollection 2025 Sep.
Genomic profiling of tumors by liquid biopsy (LBx) is a pragmatic alternative to profiling tissue. Despite recent methodologic advances, clonal hematopoiesis (CH) variants arising from hematopoietic stem cells may confound LBx results. Distinguishing the origin of variants detected by LBx will greatly enhance treatment decision-making for patients with cancer.
We sequenced DNA isolated from paired plasma and white blood cells (WBC) at equal depth to train (n = 1977) and validate (n = 658) Variant Origin Prediction (VOP), a machine learning algorithm that leverages fragmentomics to generate probabilities that a short variant (SV) detected by LBx is tumor-somatic, germline, or CH in origin. The algorithm's classifications were validated for accuracy using paired WBC DNA and for reproducibility using LBx replicates.
We show that 68% of LBx detected at least one reportable variant of CH origin. Our fragmentomic-based algorithm differentiated reportable tumor and CH variants with high sensitivity, high positive predictive value (PPA >93%, PPV >91%), and high reproducibility (>94%). Critically, VOP performs well for SVs with VAFs ≤1% (PPV >90%), as well as in genes known to harbor both CH and tumor-somatic SVs, such as (PPV >88%). In a longitudinal cohort of 422 metastatic castration-resistant prostate cancer (mCRPC) cases, VOP accurately predicted baseline variant origins, and allowed separate tracking of tumor-somatic and CH variants, including newly detected variants, at subsequent timepoints.
VOP is a highly accurate and reproducible method to predict the origin of SVs detected in LBx without reliance on WBC sequencing. VOP can reduce inappropriate use of targeted therapies and their toxicities for patients with variants of CH origin and enables accurate tumor profiling and monitoring.
通过液体活检（LBx）对肿瘤进行基因组分析是一种切实可行的组织分析替代方法。尽管最近在方法学上取得了进展，但造血干细胞产生的克隆性造血（CH）变异可能会混淆LBx结果。区分LBx检测到的变异来源将极大地改善癌症患者的治疗决策。
我们对从配对的血浆和白细胞（WBC）中分离出的DNA进行等深度测序，以训练（n = 1977）和验证（n = 658）变异起源预测（VOP），这是一种机器学习算法，利用片段组学生成LBx检测到的短变异（SV）起源于肿瘤体细胞、种系或CH的概率。使用配对的WBC DNA验证算法分类的准确性，并使用LBx重复样本验证其可重复性。
我们发现68%的LBx检测到至少一种可报告的CH起源变异。我们基于片段组学的算法以高灵敏度、高阳性预测值（PPA>93%，PPV>91%）和高可重复性（>94%）区分了可报告的肿瘤和CH变异。至关重要的是，VOP对于VAF≤1%的SV（PPV>90%）以及已知同时存在CH和肿瘤体细胞SV的基因（如PPV>88%）表现良好。在422例转移性去势抵抗性前列腺癌（mCRPC）病例的纵向队列中，VOP准确预测了基线变异起源，并允许在后续时间点分别追踪肿瘤体细胞和CH变异，包括新检测到的变异。
VOP是一种高度准确且可重复的方法，无需依赖WBC测序即可预测LBx中检测到的SV起源。VOP可以减少对CH起源变异患者不适当使用靶向治疗及其毒性，并能够进行准确的肿瘤分析和监测。
