Georgakopoulos-Soares Ilias, Yizhar-Barnea Ofer, Mouratidis Ioannis, Chan Candace S Y, Patsakis Michail, Nayak Akshatha, Bradley Rachael, Mahajan Mayank, Sims Jasmine, Cintron Dianne Laboy, Easterlin Ryder, Kim Julia S, Chen Emmalyn, Pineda Geovanni, Parada Guillermo E, Witte John S, Maher Christopher A, Feng Felix, Vathiotis Ioannis, Syrigos Nikolaos, Panagiotou Emmanouil, Charpidou Andriani, Syrigos Konstantinos, Chapman Jocelyn, Kvale Mark, Hemberg Martin, Ahituv Nadav
Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
Commun Med (Lond). 2025 Aug 21;5(1):363. doi: 10.1038/s43856-025-01067-3.
Cancer diagnosis using cell-free DNA (cfDNA) has the potential to improve treatment and survival but has several technical limitations.
In this study, we developed a prediction model based on neomers, DNA sequences 13-17 nucleotides in length that are predominantly absent from the genomes of healthy individuals and are created by tumor-associated mutations.
We show that neomer-based classifiers can accurately detect cancer, including early stages, and distinguish subtypes and features. Analysis of 2577 cancer genomes from 21 cancer types shows that neomers can distinguish tumor types with higher accuracy than state-of-the-art methods. Generation and analysis of 465 cfDNA whole-genome sequences demonstrates that neomers can precisely detect lung and ovarian cancer, including early stages, with an area under the curve ranging from 0.89 to 0.94. By testing various promoters or over 9000 candidate enhancer sequences with massively parallel reporter assays, we show that neomers can identify cancer-associated mutations that alter regulatory activity.
Combined, our results identify a sensitive, specific, and simple cancer diagnostic tool that can also identify cancer-associated mutations in gene regulatory elements.
使用游离DNA（cfDNA）进行癌症诊断有改善治疗和提高生存率的潜力，但存在一些技术限制。
在本研究中，我们基于新异构体开发了一种预测模型，新异构体是长度为13 - 17个核苷酸的DNA序列，在健康个体的基因组中基本不存在，由肿瘤相关突变产生。
我们表明基于新异构体的分类器能够准确检测癌症，包括早期癌症，并区分亚型和特征。对来自21种癌症类型的2577个癌症基因组的分析表明，新异构体比现有技术方法能更准确地区分肿瘤类型。对465个cfDNA全基因组序列的生成和分析表明，新异构体能够精确检测肺癌和卵巢癌，包括早期癌症，曲线下面积范围为0.89至0.94。通过使用大规模平行报告基因检测法测试各种启动子或9000多个候选增强子序列，我们表明新异构体能够识别改变调控活性的癌症相关突变。
综合来看，我们的结果确定了一种灵敏、特异且简单的癌症诊断工具，并能识别基因调控元件中的癌症相关突变。
