Molecular detection of hrHPV-induced high-grade squamous intraepithelial lesions of the cervix through a targeted RNA next generation sequencing assay.
Julia Faillace Thiesen, Elise Jacquemet, Pascal Campagne, Denis Chatelain, Etienne Brochot, Yves-Edouard Herpe, Nolwenn M Dheilly, Fabrice Bouilloux, Bénédicte Rognon, Alexandre Douablin, Guillaume Leboucher, Florent Percher, Marc Eloit, Philippe Pérot
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引用次数: 0
Abstract
Background: Cervical cancer screening programs are increasingly relying on sensitive molecular approaches as primary tests to detect high-risk human papillomaviruses (hrHPV), the causative agents of cervix cancer. Although hrHPV infection is a pre-requisite for the development of most precancerous lesions, the mere detection of viral nucleic acids, also present in transient infections, is not specific of the underlying cellular state, resulting in poor positive predictive values (PPV) regarding lesional states. There is a need to increase the specificity of molecular tests for better stratifying individuals at risk of cancer and to adapt follow-up strategies.
Methods: HPV-RNA-SEQ, a targeted RNA next generation sequencing assay allowing the detection of up to 16 hrHPV splice events and key human transcripts, has previously shown encouraging PPV for the detection of precancerous lesions. Herein, on 302 patients with normal cytology (NILM, n = 118), low-grade (LSIL, n = 104) or high-grade squamous intraepithelial lesions (HSIL, n = 80), machine learning-based model improvement was applied to reach 2-classes (NILM vs HSIL) or 3-classes (NILM, LSIL, HSIL) predictive models.
Results: Linear (elastic net) and nonlinear (random forest) approaches resulted in five 2-class models that detect HSIL vs NILM in a validation set with specificity up to 0.87, well within the range of PPV of other competing RNA-based tests in a screening population.
Conclusions: HPV-RNA-SEQ improves the detection of HSIL lesions and has the potential to complete and eventually replace current molecular approaches as a first-line test. Further performance evaluation remains to be done on larger and prospective cohorts.
背景:宫颈癌筛查计划越来越依赖于敏感的分子方法作为检测高危人乳头瘤病毒(hrHPV)的主要测试,hrHPV是宫颈癌的病原体。虽然hrHPV感染是大多数癌前病变发展的先决条件,但仅仅检测病毒核酸(也存在于短暂感染中)并不针对潜在的细胞状态,导致对病变状态的阳性预测值(PPV)较差。有必要提高分子检测的特异性,以便更好地对有癌症风险的个体进行分层,并调整后续策略。方法:HPV-RNA-SEQ是一种靶向RNA下一代测序方法,可检测多达16个hrHPV剪接事件和关键的人类转录物,先前已显示PPV用于检测癌前病变。本文对302例细胞学正常(NILM, n = 118)、低级别(LSIL, n = 104)或高级别鳞状上皮内病变(HSIL, n = 80)的患者进行了基于机器学习的模型改进,达到2级(NILM vs HSIL)或3级(NILM、LSIL、HSIL)预测模型。结果:线性(弹性网)和非线性(随机森林)方法产生了5个2类模型,在验证集中检测HSIL和NILM,特异性高达0.87,完全在筛选人群中其他竞争性rna测试的PPV范围内。结论:HPV-RNA-SEQ提高了HSIL病变的检测,有可能完成并最终取代目前的分子方法作为一线检测。对更大的前瞻性队列进行进一步的性能评估。
期刊介绍:
Molecular Medicine is an open access journal that focuses on publishing recent findings related to disease pathogenesis at the molecular or physiological level. These insights can potentially contribute to the development of specific tools for disease diagnosis, treatment, or prevention. The journal considers manuscripts that present material pertinent to the genetic, molecular, or cellular underpinnings of critical physiological or disease processes. Submissions to Molecular Medicine are expected to elucidate the broader implications of the research findings for human disease and medicine in a manner that is accessible to a wide audience.