Early detection can benefit cancer patients with more effective treatments and better prognosis, but existing early screening tests are limited, especially for multi-cancer detection. This study investigated the most prevalent and lethal cancer types, including primary liver cancer (PLC), colorectal adenocarcinoma (CRC), and lung adenocarcinoma (LUAD). Leveraging the emerging cell-free DNA (cfDNA) fragmentomics, we developed a robust machine learning model for multi-cancer early detection. 1,214 participants, including 381 PLC, 298 CRC, 292 LUAD patients, and 243 healthy volunteers, were enrolled. The majority of patients (N = 971) were at early stages (stage 0, N = 34; stage I, N = 799). The participants were randomly divided into a training cohort and a test cohort in a 1:1 ratio while maintaining the ratio for the major histology subtypes. An ensemble stacked machine learning approach was developed using multiple plasma cfDNA fragmentomic features. The model was trained solely in the training cohort and then evaluated in the test cohort. Our model showed an Area Under the Curve (AUC) of 0.983 for differentiating cancer patients from healthy individuals. At 95.0% specificity, the sensitivity of detecting all cancer reached 95.5%, while 100%, 94.6%, and 90.4% for PLC, CRC, and LUAD, individually. The cancer origin model demonstrated an overall 93.1% accuracy for predicting cancer origin in the test cohort (97.4%, 94.3%, and 85.6% for PLC, CRC, and LUAD, respectively). Our model sensitivity is consistently high for early-stage and small-size tumors. Furthermore, its detection and origin classification power remained superior when reducing sequencing depth to 1× (cancer detection: ≥ 91.5% sensitivity at 95.0% specificity; cancer origin: ≥ 91.6% accuracy). In conclusion, we have incorporated plasma cfDNA fragmentomics into the ensemble stacked model and established an ultrasensitive assay for multi-cancer early detection, shedding light on developing cancer early screening in clinical practice.

中文翻译：

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致编辑的信：使用无细胞 DNA 片段组学进行多癌早期检测的超灵敏检测

早期发现可以使癌症患者获得更有效的治疗和更好的预后，但现有的早期筛查测试是有限的，特别是对于多癌检测。本研究调查了最普遍和最致命的癌症类型，包括原发性肝癌 (PLC)、结直肠腺癌 (CRC) 和肺腺癌 (LUAD)。利用新兴的无细胞 DNA (cfDNA) 片段组学，我们开发了一个强大的机器学习模型，用于多癌早期检测。1,214 名参与者，包括 381 名 PLC、298 名 CRC、292 名 LUAD 患者和 243 名健康志愿者入组。大多数患者（N = 971）处于早期阶段（0 期，N = 34；I 期，N = 799）。参与者以 1:1 的比例随机分为训练队列和测试队列，同时保持主要组织学亚型的比例。使用多个血浆 cfDNA 片段组学特征开发了一种集成堆叠机器学习方法。该模型仅在训练队列中进行训练，然后在测试队列中进行评估。我们的模型显示用于区分癌症患者和健康个体的曲线下面积 (AUC) 为 0.983。在 95.0% 的特异性下，检测所有癌症的灵敏度达到 95.5%，而对于 PLC、CRC 和 LUAD 分别为 100%、94.6% 和 90.4%。癌症起源模型在测试队列中预测癌症起源的总体准确率为 93.1%（PLC、CRC 和 LUAD 分别为 97.4%、94.3% 和 85.6%）。我们的模型对早期和小尺寸肿瘤的敏感性一直很高。此外，当将测序深度降低到 1 倍（癌症检测：≥ 91. 5% 灵敏度，95.0% 特异性；癌症起源：≥ 91.6% 准确度）。总之，我们已将血浆 cfDNA 片段组学纳入整体堆叠模型，并建立了一种用于多癌早期检测的超灵敏检测方法，为在临床实践中开展癌症早期筛查提供了启示。