PO.CH01.07 · 化学
FGFR3-TACC3融合：跨肿瘤类型的多组学与机器学习特征刻画
FGFR3-TACC3 fusion: multi-omics and machine learning characterization across tumor types
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：FGFR3-TACC3（FGFR3-TACC3）融合是在胶质瘤、尿路上皮癌以及头颈部癌中观察到的一种反复出现的致癌性改变。该融合驱动FGFR3激酶的组成型激活，并通过TACC3破坏有丝分裂纺锤体的组织，导致不受控制的增殖和转录重编程。为阐明FGFR3-TACC3阳性肿瘤的分子图谱及治疗易感性，我们整合了基因组学、转录组学和计算分析。
方法：分析来自TCGA队列的转录组数据，以比较FGFR3-TACC3阳性肿瘤与野生型肿瘤。将差异表达基因（倍数变化 > 2 或 < -2）与可成药性数据库取交集，以鉴定可干预的靶点。同时，我们分析了58个FGFR3-TACC3融合样本和9,642个非融合病例，以确定反复突变的基因和富集的基因本体论（GO）条目。基于突变和功能特征训练了一个结合神经网络与随机森林的分类器，用以区分融合与非融合谱型。
结果：FGFR3-TACC3融合肿瘤表现出独特的转录和突变特征，包括1,984个上调基因和2,504个下调基因，并富集于RTK/MAPK信号通路、有丝分裂纺锤体组装和囊泡运输通路。与药理学数据库整合后揭示了48个共表达的可成药基因，涵盖致癌激酶（FGFR3、EGFR、CDK4、PDGFRA、NTRK3）以及神经传递相关受体（OXTR、ADORA1、GRIA3/4）。机器学习分析鉴定出9个反复突变的基因和69个信息量丰富的GO条目，实现了0.85的AUC和0.79的准确率，凸显了FGFR3-TACC3融合肿瘤的稳健功能特征。
结论：这项整合多组学与机器学习的研究提示了FGFR3-TACC3融合驱动型癌症的功能、转录及药理学图谱。该结合突变-GO-转录组的框架揭示了潜在的可成药共依赖关系，并为FGFR3-TACC3阳性肿瘤的合理联合策略和精准治疗提供了值得实验验证的假设。
查看英文原文 English abstract
Background: The FGFR3-TACC3 (FGFR3-TACC3) fusion is a recurrent oncogenic alteration observed in gliomas, urothelial, and head and neck cancers. This fusion drives constitutive FGFR3 kinase activation and disrupts mitotic spindle organization through TACC3, leading to uncontrolled proliferation and transcriptional reprogramming. To elucidate the molecular landscape and therapeutic vulnerabilities of FGFR3-TACC3-positive tumors, we integrated genomic, transcriptomic, and computational analyses.
Methods: Transcriptomic data from TCGA cohorts were analyzed to compare FGFR3-TACC3-positive and wild-type tumors. Differentially expressed genes (fold change > 2 or < -2) were intersected with druggability databases to identify actionable targets. In parallel, we analyzed 58 FGFR3-TACC3 fusion samples and 9,642 non-fusion cases to determine recurrently mutated genes and enriched Gene Ontology (GO) terms. A combined neural network and random forest classifier was trained on mutational and functional features to discriminate fusion from non-fusion profiles.
Results: FGFR3-TACC3 fusion tumors exhibited a distinct transcriptional and mutational signature, including 1,984 upregulated and 2,504 downregulated genes, with enrichment in RTK/MAPK signaling, mitotic spindle assembly, and vesicle transport pathways. Integration with pharmacologic databases revealed 48 co-expressed druggable genes, encompassing oncogenic kinases (FGFR3, EGFR, CDK4, PDGFRA, NTRK3) and neurotransmission-associated receptors (OXTR, ADORA1, GRIA3/4). Machine-learning analysis identified 9 recurrently mutated genes and 69 informative GO terms, achieving an AUC of 0.85 and accuracy of 0.79, highlighting a robust functional signature of FGFR3-TACC3 fusion tumors.
Conclusions: This integrated multi-omics and machine-learning study suggests a functional, transcriptional, and pharmacologic landscape of FGFR3-TACC3 fusion-driven cancers. The combined mutation-GO-transcriptome framework uncovers potential druggable co-dependencies and provides hypotheses for rational combination strategies and precision therapies for FGFR3-TACC3-positive tumors that warrant experimental validation.
利益披露 Disclosure
M. Pedregal, None..
E. Cabañas Morafraile, None..
B. Győrffy, None..
E. Garcia, None..
M. Dorta, None..
B. Doger de Spéville, None..
E. Calvo, None..
A. Ocaña, None..
V. Moreno Garcia, None.