Broche Julian, Kelemen Olga, Sekar Aishwarya, Schütz Leon, Muyas Francesc, Forschner Andrea, Schroeder Christopher, Ossowski Stephan
Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
German Cancer Consortium (DKTK), partner site Tübingen, a partnership between DKFZ and University Hospital Tübingen, Tübingen, Germany.
J Transl Med. 2025 Aug 27;23(1):964. doi: 10.1186/s12967-025-06993-3.
BACKGROUND: Circulating tumour DNA (ctDNA) in liquid biopsies has emerged as a powerful biomarker in cancer patients. Its relative abundance in cell-free DNA serves as a proxy for the overall tumour burden. Here we present GeneBits, a method for cancer therapy monitoring and relapse detection. GeneBits employs tumour-informed enrichment panels targeting 20-100 somatic single-nucleotide variants (SNVs) in plasma-derived DNA, combined with ultra-deep sequencing and unique molecular barcoding. In conjunction with the newly developed computational method umiVar, GeneBits enables accurate detection of molecular residual disease and early relapse identification. RESULTS: To assess the performance of GeneBits and umiVar, we conducted benchmarking experiments using three different commercial cell-free DNA reference standards. These standards were tested with targeted next-generation sequencing (NGS) workflows from both IDT and Twist, allowing us to evaluate the consistency and accuracy of our approach across different oligo-enrichment strategies. GeneBits achieved comparable depth of coverage across all target sites, demonstrating robust performance independent of the enrichment kit used. For duplex reads with ≥ 4x UMI-family size, umiVar achieved exceptionally low error rates, ranging from 7.4×10 to 7.5×10. Even when including mixed consensus reads (duplex & simplex), error rates remained low, between 6.1×10 and 9×10. Furthermore, umiVar enabled variant detection at a limit of detection as low as 0.0017%, with no false positive calls in mutation-free reference samples. In a reanalysed melanoma cohort, variant allele frequency kinetics closely mirrored imaging results, confirming the clinical relevance of our method. CONCLUSION: GeneBits and umiVar enable highly accurate therapy and relapse monitoring in plasma as well as identification of molecular residual disease within four weeks of tumour surgery or biopsy. By leveraging small, tumour-informed sequencing panels, GeneBits provides a targeted, cost-effective, and scalable approach for ctDNA-based cancer monitoring. The benchmarking experiments using multiple commercial cell-free DNA reference standards confirmed the high sensitivity and specificity of GeneBits and umiVar, making them valuable tools for precision oncology. UmiVar is available at https://github.com/imgag/umiVar .
背景：液体活检中的循环肿瘤DNA（ctDNA）已成为癌症患者中一种强大的生物标志物。其在游离DNA中的相对丰度可作为整体肿瘤负担的替代指标。在此，我们介绍一种用于癌症治疗监测和复发检测的方法——GeneBits。GeneBits采用肿瘤信息富集面板，靶向血浆来源DNA中的20 - 100个体细胞单核苷酸变异（SNV），并结合超深度测序和独特分子条形码技术。结合新开发的计算方法umiVar，GeneBits能够准确检测分子残留疾病并早期识别复发。 结果：为评估GeneBits和umiVar的性能，我们使用三种不同的商业游离DNA参考标准进行了基准实验。这些标准通过IDT和Twist的靶向新一代测序（NGS）工作流程进行测试，使我们能够评估我们的方法在不同寡核苷酸富集策略下的一致性和准确性。GeneBits在所有靶位点实现了相当的覆盖深度，表明其性能稳健，与所使用的富集试剂盒无关。对于UMI家族大小≥4x的双链读数，umiVar实现了极低的错误率，范围从7.4×10到7.5×10。即使包括混合一致读数（双链和单链）时，错误率仍保持较低，在6.1×10到9×10之间。此外，umiVar能够在低至0.0017%的检测限下检测变异，在无突变的参考样本中无假阳性呼叫。在一个重新分析的黑色素瘤队列中，变异等位基因频率动力学与成像结果密切反映，证实了我们方法的临床相关性。 结论：GeneBits和umiVar能够在血浆中实现高度准确的治疗和复发监测，并在肿瘤手术或活检后四周内识别分子残留疾病。通过利用小型的、肿瘤信息测序面板，GeneBits为基于ctDNA的癌症监测提供了一种靶向、经济高效且可扩展的方法。使用多种商业游离DNA参考标准进行的基准实验证实了GeneBits和umiVar的高灵敏度和特异性，使其成为精准肿瘤学的宝贵工具。UmiVar可在https://github.com/imgag/umiVar获取。
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