PO.CL01.16 · 临床研究

一种新型组织指导型非定制全基因组 MRD 检测的分析性能

Analytical performance of a novel tissue-informed non-bespoke whole genome MRD detection assay

海报缩略图:一种新型组织指导型非定制全基因组 MRD 检测的分析性能
编号 3927 展板 2 时间 4/20 02:00–05:00 区域 Section 48 主讲 David Delfosse
分会场 Prognostic Biomarkers 2
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作者与单位 Authors & Affiliations

David Delfosse, Alexander Fine, Daokun Sun, Akshay Kakumanu, Tristen Ross, Devika Singh, Ravin Poudel, Maryam Zand, Brian Reilly, Farzana Ahmed, Liv Parsons, Tuan Nguyen, Ena Shinnishi, Noel Vega, Hanna Tukachinsky, Chang Xu, Alex Robertson, Brett Wallden

Foundation Medicine, Inc., Boston, MA

摘要 Abstract

中文摘要
背景:在早期癌症中检测循环肿瘤 DNA(ctDNA)具有重要的预后价值,并有可能指导临床诊疗决策。然而,在此阶段实现可靠检测所需的足够灵敏度具有挑战性。个性化检测组合可能会增加周转时间和操作复杂性。为克服这些局限,我们开发了一种不使用定制组合的组织指导型分子残留病灶(TI-MRD)实验室自建检测。 方法:TI-MRD 检测通过组织 FFPE 的全基因组测序(WGS)定义个性化肿瘤特征,并在同样使用 WGS 测序的血浆游离 DNA(cfDNA)中扫描该特征。TI-MRD 采用一种新颖算法,利用非整倍体和体细胞 SNV 来定义肿瘤特征,而无需配对的正常样本。来自早期肺癌和结肠癌患者的样本用于测定该检测的最低检测限(LLoD)的研究。精密度研究还包括来自乳腺癌、膀胱癌、黑色素瘤、卵巢癌和胰腺癌患者的样本。使用来自无已知癌症病史供者的 cfDNA 以及源自一组癌症 FFPE 样本的肿瘤特征,评估了该检测的分析特异性。 结果:TI-MRD 检测的 LLoD 使用范围从 >44,000 PPM(>4.4%)到 5 PPM 的样本滴度系列建立,并基于检测肿瘤分数(TF)的 ≥95% 概率。TI-MRD 检测的 LLoD 还作为肿瘤突变负荷(TMB)的函数进行了评估,其中所测试的最低 TMB 值为 <1 mut/Mb,最高 TMB 值为 >9 mut/Mb。用于估计 TI-MRD 检测分析灵敏度的滴度数据表明,LLoD 可低至 <10 PPM。通过测试来自 10 名患者(TF 值范围从 380,000 PPM [38%] 到 32 PPM)的 6 个重复样本确定了可重复性。精密度还作为 cfDNA 输入量(3.5-20 ng)和 FFPE 输入量(10-220 ng)的函数进行了评估。ctDNA 的检测在整个 cfDNA 和 FFPE 输入量范围内高度可重复,并在 4 个数量级的 TF 值范围内保持一致。TI-MRD 检测在无已知癌症病史的供者中还实现了 100% 的经验特异性。 结论:这种 TI-MRD 检测展现出稳健的分析性能,对检测 ctDNA 具有高灵敏度、可重复性和特异性,凸显了该检测在多种不同癌症类型的早期阶段指导治疗决策的潜力。
查看英文原文 English abstract
Background: Detection of circulating tumor DNA (ctDNA) in early-stage cancer offers significant prognostic value and has potential for guiding clinical care decisions. However, achieving sufficient sensitivity for reliable detection at this stage is challenging. Personalized panels can increase turnaround time and operational complexity.To overcome these limitations, we developed a tissue-informed molecular residual disease (TI-MRD) laboratory developed test that does not use custom panels. Methods: The TI-MRD assay defines a personalized tumor signature from whole genome sequencing (WGS) of tissue FFPE, and scans for this signature in cell-free DNA (cfDNA) from plasma, also sequenced using WGS. TI-MRD employs a novel algorithm using aneuploidy and somatic SNVs to define a tumor signature without requiring a matched normal sample. Samples from patients with early stage lung and colon cancer were used in a study for determining the lower limit of detection (LLoD) of the assay. The precision study also included samples from patients with breast, bladder, melanoma, ovarian, and pancreatic cancers. Analytical specificity of the assay was assessed using cfDNA from donors with no known history of cancer and a tumor signature derived from a panel of cancer FFPE samples. Results: The LLoD for the TI-MRD assay was established using sample titrations ranging from > 44,000 PPM (> 4.4%) to 5 PPM and was based on a ≥ 95% probability of detecting tumor fraction (TF). LLoD for the TI-MRD assay was also evaluated as a function of tumor mutational burden (TMB), where the lowest TMB value tested was < 1 mut/Mb and the highest TMB value tested was > 9 mut/Mb. The titration data for estimating the analytical sensitivity for the TI-MRD assay demonstrated that the LLoD can be < 10 PPM. Reproducibility was determined by testing 6 replicates from 10 patients with TF values ranging from 380,000 PPM (38%) to 32 PPM. Precision was also evaluated as a function of cfDNA input mass (3.5 - 20ng) and FFPE input mass (10 - 220ng). Detection of ctDNA was highly reproducible across the range of cfDNA and FFPE input masses and was maintained across a 4-log range of TF values. The TI-MRD assay also achieved an empirical specificity of 100% from donors with no known history of cancer. Conclusion: This TI-MRD assay demonstrated robust analytical performance with high sensitivity, reproducibility, and specificity for detecting ctDNA, highlighting the potential of this assay to guide therapeutic decisions in the early setting across many different cancer types.
利益披露 Disclosure
D. Delfosse, Roche Employment, Stock. Guardant Stock. Exact Sciences Stock. A. Fine, Roche Employment, Stock. D. Sun, Roche Employment, Stock. A. Kakumanu, Roche Employment, Stock. T. Ross, Roche Employment, Stock. D. Singh, Roche Employment, Stock. R. Poudel, Roche Employment, Stock. M. Zand, Roche Employment, Stock. B. Reilly, Roche Employment, Stock. F. Ahmed, Roche Employment, Stock. Exact Sciences Stock. L. Parsons, Roche Employment, Stock. Exact Sciences Stock. T. Nguyen, Roche Employment, Stock. E. Shinnishi, Roche Employment, Stock. N. Vega, Roche Employment, Stock. H. Tukachinsky, Roche Employment, Stock. C. Xu, Roche Employment, Stock. A. Robertson, Roche Employment, Stock. B. Wallden, Roche Employment, Stock.

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