Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma.
Machine learning modeling of genome-wide copy number alteration signatures reliably predicts IDH mutational status in adult diffuse glioma.
复制标题
全基因组拷贝数改变特征的机器学习建模可靠地预测成人弥漫性胶质瘤中的IDH突变状态
DOI：
10.1186/s40478-021-01295-3
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发表时间：
2021-12-04
影响因子：
7.1
通讯作者：
Cimino PJ
中科院分区：
文献类型：
--
作者：
Nuechterlein N;Shapiro LG;Holland EC;Cimino PJ
了解 1p/19q 编码缺失和 IDH1/2 突变状态对于解释现代弥漫性胶质瘤的任何研究都是必要的。虽然 DNA 测序是确定 IDH 突变状态的金标准，但全基因组甲基化芯片和基因表达谱已用于替代突变确定。我们小组之前的研究表明，仅通过全基因组体细胞拷贝数改变（SCNA）数据即可预测 1p/19q 编码缺失和 IDH 突变状态，但尚未建立完成此任务的严格模型。在这项研究中，我们使用癌症基因组图谱 (TCGA) 中 786 例成人弥漫性胶质瘤的 SCNA 数据开发了一个两阶段分类系统，该系统可识别 1p/19q 编码缺失的少突胶质细胞瘤，并使用机器学习模型预测星形细胞肿瘤的 IDH 突变状态。 TCGA SCNA 数据的交叉验证结果显示近乎完美的分类结果。此外，我们的星形胶质细胞IDH突变模型在另外四个数据集（AUC = 0.97、AUC = 0.99、AUC = 0.95、AUC = 0.96）上得到了很好的验证，我们的1p/19q-codeleted少突胶质细胞瘤筛选在包含少突胶质细胞瘤的两个数据集上也得到了很好的验证。 （MCC = 0.97，MCC = 0.97）。然后，我们使用这些验证集中的数据重新训练我们的系统，并将我们的系统应用于一组 REMBRANDT 研究对象，这些对象可以获得 SCNA 数据，但不能获得 IDH 突变状态。总体而言，利用全基因组 SCNA，我们成功开发了一个系统，可以稳健地预测弥漫性胶质瘤中的 1p/19q 编码缺失和 IDH 突变状态。该系统可以为缺乏 1p/19q-codeletion 和 IDH 突变状态的回顾性弥漫性神经胶质瘤队列的肿瘤样本分配分子亚型标签，例如 REMBRANDT 研究，将这些数据集重新构建为弥漫性神经胶质瘤研究的验证队列。在线版本包含可在 10.1186/s40478-021-01295-3 获取的补充材料。
Knowledge of 1p/19q-codeletion and IDH1/2 mutational status is necessary to interpret any investigational study of diffuse gliomas in the modern era. While DNA sequencing is the gold standard for determining IDH mutational status, genome-wide methylation arrays and gene expression profiling have been used for surrogate mutational determination. Previous studies by our group suggest that 1p/19q-codeletion and IDH mutational status can be predicted by genome-wide somatic copy number alteration (SCNA) data alone, however a rigorous model to accomplish this task has yet to be established. In this study, we used SCNA data from 786 adult diffuse gliomas in The Cancer Genome Atlas (TCGA) to develop a two-stage classification system that identifies 1p/19q-codeleted oligodendrogliomas and predicts the IDH mutational status of astrocytic tumors using a machine-learning model. Cross-validated results on TCGA SCNA data showed near perfect classification results. Furthermore, our astrocytic IDH mutation model validated well on four additional datasets (AUC = 0.97, AUC = 0.99, AUC = 0.95, AUC = 0.96) as did our 1p/19q-codeleted oligodendroglioma screen on the two datasets that contained oligodendrogliomas (MCC = 0.97, MCC = 0.97). We then retrained our system using data from these validation sets and applied our system to a cohort of REMBRANDT study subjects for whom SCNA data, but not IDH mutational status, is available. Overall, using genome-wide SCNAs, we successfully developed a system to robustly predict 1p/19q-codeletion and IDH mutational status in diffuse gliomas. This system can assign molecular subtype labels to tumor samples of retrospective diffuse glioma cohorts that lack 1p/19q-codeletion and IDH mutational status, such as the REMBRANDT study, recasting these datasets as validation cohorts for diffuse glioma research. The online version contains supplementary material available at 10.1186/s40478-021-01295-3.
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影响因子：
4.6
作者：
Arita H;Kinoshita M;Kawaguchi A;Takahashi M;Narita Y;Terakawa Y;Tsuyuguchi N;Okita Y;Nonaka M;Moriuchi S;Takagaki M;Fujimoto Y;Fukai J;Izumoto S;Ishibashi K;Nakajima Y;Shofuda T;Kanematsu D;Yoshioka E;Kodama Y;Mano M;Mori K;Ichimura K;Kanemura Y
通讯作者：
Kanemura Y
影响因子：
7.3
作者：
Gao J;Aksoy BA;Dogrusoz U;Dresdner G;Gross B;Sumer SO;Sun Y;Jacobsen A;Sinha R;Larsson E;Cerami E;Sander C;Schultz N
通讯作者：
Schultz N
影响因子：
64.8
作者：
Capper D;Jones DTW;Sill M;Hovestadt V;Schrimpf D;Sturm D;Koelsche C;Sahm F;Chavez L;Reuss DE;Kratz A;Wefers AK;Huang K;Pajtler KW;Schweizer L;Stichel D;Olar A;Engel NW;Lindenberg K;Harter PN;Braczynski AK;Plate KH;Dohmen H;Garvalov BK;Coras R;Hölsken A;Hewer E;Bewerunge-Hudler M;Schick M;Fischer R;Beschorner R;Schittenhelm J;Staszewski O;Wani K;Varlet P;Pages M;Temming P;Lohmann D;Selt F;Witt H;Milde T;Witt O;Aronica E;Giangaspero F;Rushing E;Scheurlen W;Geisenberger C;Rodriguez FJ;Becker A;Preusser M;Haberler C;Bjerkvig R;Cryan J;Farrell M;Deckert M;Hench J;Frank S;Serrano J;Kannan K;Tsirigos A;Brück W;Hofer S;Brehmer S;Seiz-Rosenhagen M;Hänggi D;Hans V;Rozsnoki S;Hansford JR;Kohlhof P;Kristensen BW;Lechner M;Lopes B;Mawrin C;Ketter R;Kulozik A;Khatib Z;Heppner F;Koch A;Jouvet A;Keohane C;Mühleisen H;Mueller W;Pohl U;Prinz M;Benner A;Zapatka M;Gottardo NG;Driever PH;Kramm CM;Müller HL;Rutkowski S;von Hoff K;Frühwald MC;Gnekow A;Fleischhack G;Tippelt S;Calaminus G;Monoranu CM;Perry A;Jones C;Jacques TS;Radlwimmer B;Gessi M;Pietsch T;Schramm J;Schackert G;Westphal M;Reifenberger G;Wesseling P;Weller M;Collins VP;Blümcke I;Bendszus M;Debus J;Huang A;Jabado N;Northcott PA;Paulus W;Gajjar A;Robinson GW;Taylor MD;Jaunmuktane Z;Ryzhova M;Platten M;Unterberg A;Wick W;Karajannis MA;Mittelbronn M;Acker T;Hartmann C;Aldape K;Schüller U;Buslei R;Lichter P;Kool M;Herold-Mende C;Ellison DW;Hasselblatt M;Snuderl M;Brandner S;Korshunov A;von Deimling A;Pfister SM
通讯作者：
Pfister SM
影响因子：
4.1
作者：
Carter, Jamal H.;McNulty, Samantha N.;Duncavage, Eric J.
通讯作者：
Duncavage, Eric J.
影响因子：
15.9
作者：
Cimino, Patrick J.;McFerrin, Lisa;Holland, Eric C.
通讯作者：
Holland, Eric C.