MacFeeters Hamilton Neuro-Oncology Program, Princess Margaret Cancer Centre, University Health Network and University of Toronto, Toronto, Ontario, Canada.
Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
Neuro Oncol. 2023 Aug 3;25(8):1452-1460. doi: 10.1093/neuonc/noac264.
Resolving the differential diagnosis between brain metastases (BM), glioblastomas (GBM), and central nervous system lymphomas (CNSL) is an important dilemma for the clinical management of the main three intra-axial brain tumor types. Currently, treatment decisions require invasive diagnostic surgical biopsies that carry risks and morbidity. This study aimed to utilize methylomes from cerebrospinal fluid (CSF), a biofluid proximal to brain tumors, for reliable non-invasive classification that addresses limitations associated with low target abundance in existing approaches.
Binomial GLMnet classifiers of tumor type were built, in fifty iterations of 80% discovery sets, using CSF methylomes obtained from 57 BM, GBM, CNSL, and non-neoplastic control patients. Publicly-available tissue methylation profiles (N = 197) on these entities and normal brain parenchyma were used for validation and model optimization.
Models reliably distinguished between BM (area under receiver operating characteristic curve [AUROC] = 0.93, 95% confidence interval [CI]: 0.71-1.0), GBM (AUROC = 0.83, 95% CI: 0.63-1.0), and CNSL (AUROC = 0.91, 95% CI: 0.66-1.0) in independent 20% validation sets. For validation, CSF-based methylome signatures reliably distinguished between tumor types within external tissue samples and tumors from non-neoplastic controls in CSF and tissue. CSF methylome signals were observed to align closely with tissue signatures for each entity. An additional set of optimized CSF-based models, built using tumor-specific features present in tissue data, showed enhanced classification accuracy.
CSF methylomes are reliable for liquid biopsy-based classification of the major three malignant brain tumor types. We discuss how liquid biopsies may impact brain cancer management in the future by avoiding surgical risks, classifying unbiopsiable tumors, and guiding surgical planning when resection is indicated.
脑转移瘤（BM）、胶质母细胞瘤（GBM）和中枢神经系统淋巴瘤（CNSL）之间的鉴别诊断是对三种主要颅内脑肿瘤类型进行临床管理的一个重要难题。目前，治疗决策需要进行有创的诊断性外科活检，这会带来风险和发病率。本研究旨在利用脑脊液（CSF）中的甲基组学（一种与脑肿瘤接近的生物液体）进行可靠的非侵入性分类，以解决现有方法中靶标丰度低带来的局限性。
使用来自 57 例 BM、GBM、CNSL 和非肿瘤性对照患者的 CSF 甲基组学数据，构建肿瘤类型的二项式 GLMnet 分类器。在 80%的发现集的五十次迭代中进行。使用这些实体和正常脑组织的公开可用的组织甲基化谱（N=197）进行验证和模型优化。
模型可靠地区分了 BM（接受者操作特征曲线下面积 [AUROC]为 0.93，95%置信区间 [CI]：0.71-1.0）、GBM（AUROC 为 0.83，95%CI：0.63-1.0）和 CNSL（AUROC 为 0.91，95%CI：0.66-1.0）在独立的 20%验证集中。对于验证，CSF 甲基组-signature 可在外部组织样本中可靠地区分肿瘤类型，并且在 CSF 和组织中区分肿瘤与非肿瘤性对照。CSF 甲基组信号被观察到与每个实体的组织特征非常吻合。使用组织数据中存在的肿瘤特异性特征构建的一组优化 CSF 模型显示出更高的分类准确性。
CSF 甲基组学可用于基于液体活检的主要三种恶性脑肿瘤类型的分类。我们讨论了液体活检如何通过避免手术风险、分类无法进行活检的肿瘤以及在需要切除时指导手术计划，从而对未来的脑癌管理产生影响。
