Early detection of neurofibromatosis type 1 (NF1) associated peripheral nerve sheath tumors (PNST) informs clinical decision-making, potentially averting deadly outcomes. Here, we describe a cell-free DNA (cfDNA) fragmentomic approach which distinguishes non-malignant, pre-malignant and malignant forms of NF1 PNST. Using plasma samples from a novel cohort of 121 NF1 patients and 21 healthy controls, we validated that our previous cfDNA copy number alteration (CNA)-based approach identifies malignant peripheral nerve sheath tumor (MPNST) but cannot distinguish among benign and premalignant states. We therefore investigated the ability of fragment-based cfDNA features to differentiate NF1-associated tumors including binned genome-wide fragment length ratios, end motif analysis, and non-negative matrix factorization deconvolution of fragment lengths. Fragmentomic methods were able to differentiate pre-malignant states including atypical neurofibromas (AN). Fragmentomics also adjudicated AN cases suspicious for MPNST, correctly diagnosing samples noninvasively, which could have informed clinical management. Overall, this study pioneers the early detection of malignant and premalignant peripheral nerve sheath tumors in NF1 patients using plasma cfDNA fragmentomics. In addition to screening applications, this novel approach distinguishes atypical neurofibromas from benign plexiform neurofibromas and malignant peripheral nerve sheath tumors, enabling more precise clinical diagnosis and management.

中文翻译：

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利用游离 DNA 片段组学早期检测恶性和癌前周围神经肿瘤

早期检测 1 型神经纤维瘤病 (NF1) 相关的周围神经鞘瘤 (PNST) 可为临床决策提供信息，有可能避免致命的结果。在这里，我们描述了一种无细胞 DNA (cfDNA) 片段组学方法，该方法可区分 NF1 PNST 的非恶性、恶变前和恶性形式。使用来自 121 名 NF1 患者和 21 名健康对照的新队列的血浆样本，我们验证了我们之前基于 cfDNA 拷贝数改变 (CNA) 的方法可以识别恶性周围神经鞘瘤 (MPNST)，但无法区分良性和癌前状态。因此，我们研究了基于片段的 cfDNA 特征区分 NF1 相关肿瘤的能力，包括合并的全基因组片段长度比率、末端基序分析和片段长度的非负矩阵分解反卷积。片段组学方法能够区分包括非典型神经纤维瘤（AN）在内的癌前状态。片段组学还裁定了疑似 MPNST 的 AN 病例，以无创方式正确诊断样本，这可以为临床管理提供信息。总体而言，这项研究开创了利用血浆 cfDNA 片段组学早期检测 NF1 患者恶性和癌前周围神经鞘瘤的先河。除了筛查应用之外，这种新方法还可以区分非典型神经纤维瘤与良性丛状神经纤维瘤和恶性周围神经鞘瘤，从而实现更精确的临床诊断和管理。