Cerebrospinal fluid derived circulating tumor DNA as a clinically relevant biomarker in medulloblastoma

项目总结 髓母细胞瘤是儿童最常见的恶性脑肿瘤之一。尽管取得了进展，但 多模式的患者护理和对肿瘤生物学的了解，三分之一的受影响儿童仍然屈从于他们的 疾病。由于缺乏敏感的生物标志物来分层，改善患者预后的努力受到了阻碍 治疗反应和预测复发。在复发患者中，随着治疗的进行，潜在的肿瘤演变意味着 可能需要捕获复发时的基因组图谱来确定可操作的治疗方法 漏洞。因此，迫切需要新的、灵敏的生物标记物驱动的分析来补充 传统的基于成像的疾病评估和(Ii)在复发时通知分子靶点。液体活组织检查 最近显示出检测和跟踪肿瘤特异性基因组变化的希望，包括靶向 脑肿瘤患者脑脊液中肿瘤来源的游离DNA序列分析 病人。然而，cfDNA分析对患有MB的儿童的实用性仍未得到研究。我们最近设计了一种 以及用于推断脑脊液来源的cfDNA中的体细胞拷贝数变体(CNV)的实验管道 关于低覆盖率WGS(LcWGS)。在初步研究中，我们论证了推断全基因组CNV的可行性。 基于亚纳克cfDNA输入产生的lcWGS。70%的肿瘤相关CNV被检测到 在基线获得的cfDNA样本中，MB转移性疾病患者的可检测性高于 那些没有的。在治疗和随访期间对脑脊液连续cfDNA样本的分析表明 CNV检出能力与病程的关系观察到体细胞CNV在 在复发前3个月进展、可辨认的患者经放射学诊断。此外, 在选定的患者匹配的初发和复发配对中观察到不同的CNV。基于这些发现， 我们假设，在脑脊液来源的cfDNA中检测到肿瘤特异性的体细胞变化将与 扩大的MB儿童队列中的患者预后，以及对这种情况的纵向描述将增强 了解肿瘤演变和复发的潜在机制。为了检验这些假设，我们建议 目的：(I)建立脑脊液来源的cfDNA图谱与疾病负担的相关性和预测 预期治疗的MB儿童衍生队列的进展；(Ii)验证cfDNA的算法 在一项独立的MB试验队列中进行分析；以及(Iii)通过比较 纵向cfDNA样本中的体细胞变化。这些研究将在前所未有的队列中进行 从参加两项前瞻性、多机构MB试验(n=140)的儿童采集的连续脑脊液样本 患者；N&GT；600份脑脊液样本)。这项拟议的研究有望将脑脊液的使用作为一种最低限度的 对具有革命性潜力的MB患者进行侵入性、敏感性和强健的常规液体活检 对受影响儿童进行风险分层、疾病监测和干预。

PROJECT SUMMARY Medulloblastoma (MB) is among the most common malignant brain tumors in children. Despite advances in multi-modal patient care and understanding of tumor biology, one-third of affected children still succumb to their disease. Efforts to improve patient outcome have been hindered by the lack of sensitive biomarkers to stratify treatment response and predict relapse. In relapsing patients, potential tumor evolution with treatment implies that capturing genomic profiles at recurrence might be required for identifying actionable therapeutic vulnerabilities. There is thus a dire need for novel, sensitive biomarker-driven assays to (i) complement conventional imaging-based disease evaluation and (ii) inform molecular targets at relapse. Liquid biopsies have recently shown promise for detecting and tracking tumor-specific genomic alterations, including targeted sequencing of tumor-derived cell-free DNA (cfDNA) collected from the cerebrospinal fluid (CSF) of brain tumor patients. However, the utility of cfDNA analysis for children with MB remains understudied. We recently devised and an experimental pipeline for inferring somatic copy number variants (CNVs) in CSF-derived cfDNA based on low-coverage WGS (lcWGS). In pilot studies, we demonstrated feasibility of inferring genome-wide CNVs based on lcWGS generated from sub-nanogram cfDNA inputs. Tumor-associated CNVs were detected in 70% of cfDNA samples obtained at baseline, with higher detectability in MB patients with metastatic disease than in those without. Analysis of serial cfDNA samples from CSF during treatment and follow-up indicated an association between CNV detectability and disease course. Re-emergence of somatic CNVs was observed in patients who progressed, identifiable >3 months before relapse was diagnosed radiographically. In addition, divergent CNVs were observed in selected patient-matched primary and relapse pairs. Based on these findings, we hypothesize that the detection of tumor-specific somatic alterations in CSF-derived cfDNA will correlate with patient outcomes in an expanded cohort of children with MB, and longitudinal profiling of such will enhance understanding of mechanisms underlying tumor evolution and recurrence. To test these hypotheses, we propose to (i) establish the utility of CSF-derived cfDNA profiling for correlation with disease burden and prediction of progression in a derivation cohort of prospectively treated children with MB; (ii) validate the algorithm of cfDNA analysis in an independent MB trial cohort; and (iii) investigate tumor evolution in MB through comparison of somatic alterations in longitudinal cfDNA samples. These studies will be conducted in an unprecedented cohort of serial CSF samples collected from children enrolled on two prospective, multi-institutional MB trials (n=140 patients; n>600 CSF samples). The proposed research is anticipated to establish the use of CSF as a minimally invasive, sensitive, and robust form of routine liquid biopsy for MB patients with the potential of revolutionizing risk stratification, disease monitoring, and intervention for affected children.