Santamarina-García Martín, Brea-Iglesias Jenifer, Bramsen Jesper Bertram, Fuentes-Losada Mar, Caneiro-Gómez Francisco Javier, Vázquez-Bueno José Ángel, Lázare-Iglesias Héctor, Fernández-Díaz Natalia, Sánchez-Rivadulla Laura, Betancor Yoel Z, Ferreiro-Pantín Miriam, Conesa-Zamora Pablo, Antúnez-López José Ramón, Kawazu Masahito, Esteller Manel, Andersen Claus Lindbjerg, Tubio Jose M C, López-López Rafael, Ruiz-Bañobre Juan
Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.
Translational Oncology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Álvaro Cunqueiro Hospital, 36213 Vigo, Spain.
iScience. 2023 Feb 3;26(3):106127. doi: 10.1016/j.isci.2023.106127. eCollection 2023 Mar 17.
Deficiency in DNA MMR activity results in tumors with a hypermutator phenotype, termed microsatellite instability (MSI). Beyond its utility in Lynch syndrome screening algorithms, today MSI has gained importance as predictive biomarker for various anti-PD-1 therapies across many different tumor types. Over the past years, many computational methods have emerged to infer MSI using either DNA- or RNA-based approaches. Considering this together with the fact that MSI-high tumors frequently exhibit a hypermethylated phenotype, herein we developed and validated MSIMEP, a computational tool for predicting MSI status from microarray DNA methylation tumor profiles of colorectal cancer samples. We demonstrated that MSIMEP optimized and reduced models have high performance in predicting MSI in different colorectal cancer cohorts. Moreover, we tested its consistency in other tumor types with high prevalence of MSI such as gastric and endometrial cancers. Finally, we demonstrated better performance of both MSIMEP models vis-à-vis a MLH1 promoter methylation-based one in colorectal cancer.
DNA错配修复(MMR)活性缺陷会导致具有高突变表型的肿瘤,即微卫星不稳定性(MSI)。除了在林奇综合征筛查算法中的应用外,如今MSI作为多种不同肿瘤类型中各种抗PD-1疗法的预测生物标志物变得越来越重要。在过去几年中,出现了许多计算方法,可使用基于DNA或RNA的方法来推断MSI。考虑到这一点以及MSI高的肿瘤经常表现出高甲基化表型这一事实,我们在此开发并验证了MSIMEP,这是一种用于从结直肠癌样本的微阵列DNA甲基化肿瘤谱预测MSI状态的计算工具。我们证明,优化和简化后的MSIMEP模型在预测不同结直肠癌队列中的MSI方面具有很高的性能。此外,我们在MSI高发性的其他肿瘤类型(如胃癌和子宫内膜癌)中测试了其一致性。最后,我们证明了MSIMEP的两个模型在结直肠癌中相对于基于MLH1启动子甲基化的模型具有更好的性能。