PO.CL01.01 · 临床研究
检测MTAP纯合缺失的多组学方法可实现从血液样本高灵敏度检测
Multi-omic method to detect MTAP homozygous deletions enables high sensitivity detection from blood samples
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言:甲硫腺苷磷酸化酶(MTAP)基因编码一种肿瘤抑制因子,是甲硫氨酸补救途径的组成部分。MTAP纯合缺失(HomDel)在抑制PRMT5、MAT2A或两者同时抑制时可诱导合成致死。因此,成功检测MTAP HomDel可为靶向治疗提供新的选择。遗憾的是,肿瘤组织常常无法用于基因组分析和MTAP HomDel的检测。虽然cfDNA检测可用于CGP,但在血浆cfDNA中检测MTAP HomDel具有挑战性,因为传统基于覆盖度的方法所进行的检测会被非肿瘤DNA的覆盖度所掩盖,尤其是在低肿瘤分数样本中,从而限制了这些情形下的灵敏度。
方法:我们开发了一种多组学方法,整合了基因组特征和肿瘤特异性甲基化特征,以提高对循环肿瘤DNA(ctDNA)中MTAP HomDel的检测灵敏度。通过分析涵盖多种实体瘤类型的100,089份血浆样本的甲基化谱来识别肿瘤特异性甲基化特征,并用于训练一个经过优化的分类器,使用Guardant360 Liquid检测来检测MTAP HomDel。随后将所得的肿瘤特异性甲基化分类器与基因组拷贝数判定相结合,以最大化血浆中MTAP HomDel检测的灵敏度和特异度。在来自晚期癌症患者的87对配对cfDNA和组织样本(NSCLC=40%、胰腺癌=9%、黑色素瘤=9%、乳腺癌=7%、其他=35%)中评估了分析准确性。MTAP HomDel和单拷贝数缺失被视为不同的类别。使用常规临床诊疗中连续检测的样本来确定检出率。使用120份无癌供者cfDNA样本(30 ng输入量)建立空白限(LoB)特异度,并通过对50份高置信度MTAP HomDel样本进行计算机模拟稀释来确定95%检测限(LoD)。
结果:与基于组织的参考相比,我们的多组学方法在LoD以上实现了100%的阳性预测值(PPV)和100%的阴性一致百分比(NPA)(总体为90.9%和96.6%)。在120名无癌供者中未观察到假阳性。对晚期癌症患者cfDNA样本的评估表明,多组学MTAP HomDel检测的灵敏度是仅基于基因组方法的2.4倍以上。
结论:用肿瘤特异性甲基化增强基因组信息可显著提高血浆cfDNA中MTAP HomDel的检测,尤其是在低肿瘤分数样本中。未来的工作可能会在Guardant360 Tissue检测上利用类似的甲基化覆盖度,以增强对低输入量和低肿瘤纯度组织样本中MTAP HomDel的检测。该方法不仅有潜力改善患者获得PRMT5和MAT2A靶向治疗的机会,还可改善其他以基因缺失为指导的治疗的可及性。
查看英文原文 English abstract
Introduction: The methylthioadenosine phosphorylase ( MTAP ) gene encodes a tumor suppressor and component of the methionine salvage pathway. MTAP homozygous deletion (HomDel) can induce synthetic lethality when either PRMT5, MAT2A, or both are inhibited. Successful detection of MTAP HomDel can, therefore, offer novel options for targeted therapy. Unfortunately, tumor tissue is often unavailable for genomic profiling and detection of MTAP HomDel. While cfDNA testing is available for CGP, detection of MTAP HomDel in plasma cfDNA is challenging, as detection by conventional coverage-based methods is masked by non-tumor DNA coverage, especially in low-tumor fraction samples, thus limiting sensitivity in these settings.
Methods: We developed a multi-omic approach that integrates genomic and tumor-specific methylation features to enhance detection sensitivity for MTAP HomDel in circulating tumor DNA (ctDNA). Tumor-specific methylation features were identified by analyzing methylation profiles from 100,089 plasma samples spanning diverse solid tumor types and used to train a classifier optimized to detect MTAP HomDel using the Guardant360 Liquid test. The resulting tumor-specific methylation classifier was then combined with genomic copy-number calling to maximize sensitivity and specificity for MTAP HomDel detection in plasma. Analytical accuracy was assessed in 87 matched cfDNA and tissue samples (NSCLC = 40%, pancreatic = 9%, melanoma = 9%, breast = 7%, other = 35%) from patients with advanced cancer. MTAP HomDel and single copy number loss were treated as distinct categories. Consecutive samples tested in routine clinical care were used to determine detection rates. Limit of blank (LoB) specificity was established using 120 cancer-free donor cfDNA samples (30 ng input), and the 95% Limit of Detection (LoD) was determined via in silico dilutions of 50 high confidence MTAP HomDel samples.
Results: Our multi-omic methodology achieved a positive predictive value (PPV) of 100% and negative percent agreement (NPA) of 100% above LoD (90.9% and 96.6% overall) against a tissue-based reference. No false positives were observed among 120 cancer-free donors. Assessment of cfDNA samples from patients with advanced cancer demonstrated that multi-omic MTAP HomDel detection was over 2.4 fold as sensitive as genomic-only methods.
Conclusion: Augmenting genomic information with tumor-specific methylation significantly enhances the detection of MTAP HomDel in plasma cfDNA, especially in low-tumor-fraction samples. Future work may leverage similar methylation coverage on the Guardant360 Tissue test to enhance MTAP HomDel detection in low input and low tumor purity tissue samples. This approach has potential to not only improve patient access to PRMT5- and MAT2A-targeted therapies but also to other therapies that are guided by gene deletions.
利益披露 Disclosure
G. Yalamanchili,
Guardant Health Employment, Stock.
S. Gordon,
Guardant Health Employment, Stock.
E. Warner,
Guardant Health Employment, Stock.
S. Zhang,
Guardant Health Employment, Stock.
K. Clemens,
Guardant Health Employment, Stock.
M. Ellis,
Guardant Health Employment, Stock.
V. Ramani,
Guardant Health Employment, Stock.
M. Juntilla,
Guardant Health Employment, Stock.
M. Lefterova,
Guardant Health Employment, Stock.
J. Odegaard,
Guardant Health Employment, Stock.
D. Chudova,
Guardant Health Employment, Stock.