Durmaz Arda, Gurnari Carmelo, Hershberger Courtney E, Pagliuca Simona, Daniels Noah, Awada Hassan, Awada Hussein, Adema Vera, Mori Minako, Ponvilawan Ben, Kubota Yasuo, Kewan Tariq, Bahaj Waled S, Barnard John, Scott Jacob, Padgett Richard A, Haferlach Torsten, Maciejewski Jaroslaw P, Visconte Valeria
Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA.
Systems Biology and Bioinformatics Department, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
iScience. 2023 Feb 18;26(3):106238. doi: 10.1016/j.isci.2023.106238. eCollection 2023 Mar 17.
RNA splicing dysfunctions are more widespread than what is believed by only estimating the effects resulting by splicing factor mutations (SF) in myeloid neoplasia (MN). The genetic complexity of MN is amenable to machine learning (ML) strategies. We applied an integrative ML approach to identify co-varying features by combining genomic lesions (mutations, deletions, and copy number), exon-inclusion ratio as measure of RNA splicing (percent spliced in, PSI), and gene expression (GE) of 1,258 MN and 63 normal controls. We identified 15 clusters based on mutations, GE, and PSI. Different PSI levels were present at various extents regardless of SF suggesting that changes in RNA splicing were not strictly related to SF. Combination of PSI and GE further distinguished the features and identified PSI similarities and differences, common pathways, and expression signatures across clusters. Thus, multimodal features can resolve the complex architecture of MN and help identifying convergent molecular and transcriptomic pathways amenable to therapies.
RNA剪接功能障碍比仅通过估计髓系肿瘤（MN）中剪接因子突变（SF）所产生的影响所认为的更为普遍。MN的遗传复杂性适合采用机器学习（ML）策略。我们应用了一种综合ML方法，通过结合1258例MN和63例正常对照的基因组损伤（突变、缺失和拷贝数）、作为RNA剪接指标的外显子包含率（剪接百分率，PSI）以及基因表达（GE）来识别共同变化的特征。我们基于突变、GE和PSI识别出了15个簇。无论SF如何，不同程度上都存在不同的PSI水平，这表明RNA剪接的变化与SF并不严格相关。PSI和GE的组合进一步区分了特征，并识别出了各簇之间的PSI异同、共同途径和表达特征。因此，多模态特征可以解析MN的复杂结构，并有助于识别适合治疗的趋同分子和转录组途径。
