Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Research Institute Children's Cancer Center Hamburg, Hamburg, Germany.
Oncoimmunology. 2021 Jun 17;10(1):1932365. doi: 10.1080/2162402X.2021.1932365. eCollection 2021.
The interaction of CNS tumors with infiltrating lymphocytes plays an important role in their initiation and progression and might be related to therapeutic responses. Gene expression-based methods have been successfully used to characterize the tumor microenvironment. However, methylation data are now increasingly used for molecular diagnostics and there are currently only few methods to infer information about the microenvironment from this data type. Using an approach based on differential methylation and principal component analysis, we developed DIMEimmune (Differential Methylation Analysis for Immune Cell Estimation) to estimate CD4 and CD8 T cell abundance as well as tumor-infiltrating lymphocytes (TILs) scores from bulk methylation data. Well-established approaches based on gene expression data and immunohistochemistry-based lymphocyte counts were used as benchmarks. The comparison of DIMEimmune to the previously published MethylCIBERSORT and MeTIL algorithms showed an improved correlation with both gene expression-based and immunohistological results across different brain tumor types. Further, we applied our method to large datasets of glioma, medulloblastoma, atypical teratoid/rhabdoid tumors (ATRTs) and ependymoma. High-grade gliomas showed higher scores of tumor-infiltrating lymphocytes than lower-grade gliomas. There were overall only few tumor-infiltrating lymphocytes in medulloblastoma subgroups. ATRTs were highly infiltrated by lymphocytes, most prominently in the MYC subgroup. DIMEimmune-based estimates of TILs were a significant prognostic factor in the overall cohort of gliomas and medulloblastomas, but not within methylation-based diagnostic subgroups. To conclude, DIMEimmune allows for robust estimates of TIL abundance and might contribute to establishing them as a prognostic or predictive factor in future studies of CNS tumors.
中枢神经系统肿瘤与浸润淋巴细胞的相互作用在其发生和进展中起着重要作用，并且可能与治疗反应有关。基于基因表达的方法已成功用于描述肿瘤微环境。然而，甲基化数据现在越来越多地用于分子诊断，目前只有少数方法可以从这种数据类型推断出关于微环境的信息。我们使用基于差异甲基化和主成分分析的方法，开发了 DIMEimmune（用于免疫细胞估计的差异甲基化分析），以从批量甲基化数据中估计 CD4 和 CD8 T 细胞丰度以及肿瘤浸润淋巴细胞（TIL）评分。我们使用基于基因表达数据的成熟方法和基于免疫组化的淋巴细胞计数作为基准。DIMEimmune 与先前发表的 MethylCIBERSORT 和 MeTIL 算法的比较表明，与不同脑肿瘤类型的基于基因表达和免疫组织化学的结果相关性更好。此外，我们将我们的方法应用于胶质母细胞瘤、髓母细胞瘤、非典型畸胎瘤/横纹肌样瘤（ATRT）和室管膜瘤的大型数据集。高级别胶质瘤的肿瘤浸润淋巴细胞评分高于低级别胶质瘤。髓母细胞瘤亚组的肿瘤浸润淋巴细胞总体数量较少。ATRT 被淋巴细胞高度浸润，在 MYC 亚组中最为明显。DIMEimmune 基于 TIL 的估计是胶质母细胞瘤和髓母细胞瘤总体队列的显著预后因素，但不是基于甲基化的诊断亚组。总之，DIMEimmune 可以对 TIL 丰度进行稳健估计，并可能有助于在未来的中枢神经系统肿瘤研究中确立其作为预后或预测因素。
