Zhao Qi, Zhou Tianjun, Li Lin, Hong Guofan, Chen Luonan
Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Comput Struct Biotechnol J. 2024 Dec 16;27:149-159. doi: 10.1016/j.csbj.2024.12.005. eCollection 2025.
Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis. This approach may enable more accurate and rapid identification of multiple hrHPV types within a single sample. Compared to traditional methods, MTIOT has the potential to overcome their core limitations and offer a more efficient and cost-effective solution for cervical cancer screening. When tested on both simulated samples (to mimic real-world complexities) and clinical samples, MTIOT achieved F1 scores (the harmonic mean of sensitivity and specificity) of 98 % and 92 % respectively for identifying subtypes with a sample size ≥ 50, suggesting that it may significantly improve the precision of cervical cancer screening programs. This work with MTIOT represents a significant step forward in the molecular diagnosis of hrHPV and may suggest a promising avenue for enhancing early detection strategies and potentially reducing the incidence of cervical cancer. This study also underscores the importance of methodological innovation in tackling public health challenges and sets the stage for future clinical trials to validate MTIOT's efficacy in practice.
高危型人乳头瘤病毒（hrHPV）的持续感染是宫颈癌的主要病因。目前的HPV-DNA检测对于早期检测至关重要，但其有效性在几个方面受到限制，包括灵敏度低、准确性问题以及无法进行全面的hrHPV分型。为了解决这些限制，我们引入了MTIOT（一次性检测多种亚型），这是一种新颖的检测方法，它利用机器学习和新的多通道整合方案来增强HPV-DNA分析。这种方法可能能够在单个样本中更准确、快速地鉴定多种hrHPV类型。与传统方法相比，MTIOT有潜力克服其核心限制，并为宫颈癌筛查提供更高效、更具成本效益的解决方案。在模拟样本（以模拟现实世界的复杂性）和临床样本上进行测试时，对于样本量≥50的亚型鉴定，MTIOT的F1分数（灵敏度和特异性的调和平均值）分别达到了98%和92%，这表明它可能会显著提高宫颈癌筛查项目的精度。这项关于MTIOT的工作代表了hrHPV分子诊断方面的重大进展，并可能为加强早期检测策略以及潜在降低宫颈癌发病率指明一条有前景的途径。这项研究还强调了方法创新在应对公共卫生挑战中的重要性，并为未来验证MTIOT在实际应用中的疗效的临床试验奠定了基础。
