PO.MD01.02 · 分子诊断与数据
泛儿科基因调控网络分析揭示儿科实体瘤中可成药的依赖性
A pan-pediatric gene-regulatory network analysis reveals druggable dependencies across pediatric solid tumors
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：儿科肿瘤常常挪用正常的发育基因调控程序，在谱系限制性祖细胞中发生错误，从而停止或逆转分化。由于这些癌症在受限的发育窗口内发生、展现胎儿样程序，并且与成人肿瘤相比携带相对较少的驱动突变，我们假设一项泛儿科的、通过转录组推断的基因调控网络（GRN）分析将发现将每种肿瘤锚定于发育停滞状态的谱系特异性调控子（regulon），从而鉴定可操作的生物标志物和治疗靶点。
方法：经批次校正后，我们分析了来自35种儿科颅内和颅外实体瘤样本的2541份bulk RNA-seq。我们从基因表达数据推断了泛儿科GRN，将ARACNe-AP和GENIE3推断的网络整合为一个跨所有肿瘤类型的共识GRN。我们采用一对多（one-vs-rest）策略来鉴定调控子内肿瘤特异性的差异表达基因（DEG）。使用超几何检验，我们通过评估肿瘤特异性DEG在每个调控子内的富集度，量化了转录因子（TF）活性及其调控子在肿瘤间的表现。为将基因映射到药物，我们查询了药物库，包括Mechanistic Interrogation PlatE、Profiling Relative Inhibition Simultaneously in Mixtures、ChEMBL、DrugBank和DrugCentral。我们使用对数倍数变化和校正后p值筛选TF、其调控子成员及其相互作用因子中的可成药基因，并利用效应量对19种具有DepMap数据的肿瘤中的候选者进行排序。
结果：我们在肿瘤间鉴定出281个富集的TF。功能富集分析显示，TF程序通常局限于特定的肿瘤类别，反映其发育起源细胞并突显候选的肿瘤特异性生物标志物。例子包括神经母细胞瘤（NB）中的神经发育和神经嵴相关TF（如PHOX2B、ASCL1和SOX10），以及融合阳性横纹肌肉瘤（FP-RMS）中的肌肉谱系TF（如MYOG、MYOD1和PAX3/7）。我们的分析提示TF作为稳健的、肿瘤类型特异性的表达特征，能够区分组织学上可能相似但源自不同发育谱系的肿瘤。此外，我们利用以TF为中心的方法鉴定了已知和新的药物靶点，例如FP-RMS中的SIX1、RRM2、AURKA和BIRC5，以及NB中的ACVR2B和BMPR1B。
结论与未来方向：泛儿科实体瘤的统一GRN框架分析解析出与肿瘤发生相关的谱系特异性调控子，并产生了一组排序的可成药遗传依赖性。体外和体内验证研究目前正在进行中。
查看英文原文 English abstract
Background: Pediatric tumors often co-opt normal developmental gene-regulatory programs, with errors in lineage-restricted progenitors that halt or reverse differentiation. Because these cancers arise within restricted developmental windows, display fetal-like programs, and carry relatively few driver mutations compared to adult tumors, we hypothesized that a pan-pediatric, transcriptome-inferred gene-regulatory network (GRN) analysis will discover lineage-specific regulons that anchor each tumor to a developmentally arrested state, which would identify actionable biomarkers and therapeutic targets.
Methods: We analyzed 2541 bulk RNA-seq from 35 pediatric cranial and extracranial solid-tumor samples, after batch correction. We inferred a pan-pediatric GRN from gene expression data, integrating networks inferred by ARACNe-AP and GENIE3 into a consensus GRN across all tumor types. We used a one-vs-rest strategy to identify tumor-specific differentially expressed genes (DEGs) within the regulons. Using hypergeometric tests, we quantified transcription factor (TF) activity and their regulons across tumors by assessing the enrichment of tumor-specific DEGs within each regulon. To map genes to drugs, we queried drug libraries, including Mechanistic Interrogation PlatE, Profiling Relative Inhibition Simultaneously in Mixtures, ChEMBL, DrugBank, and DrugCentral. We filtered druggable genes among TFs, their regulon members, and their interactors using log fold change and adjusted p-values, and ranked candidates in 19 tumors with DepMap data by using effect size.
Results: We identified 281 enriched TFs across tumors. The functional enrichment analyses showed that TF programs are usually restricted to specific tumor classes, mirroring their developmental cell-of-origin and highlighting candidate tumor-specific biomarkers. Examples include neurodevelopmental and neural-crest-related TFs (e.g., PHOX2B, ASCL1, and SOX10) in neuroblastoma (NB) and muscle-lineage TFs (e.g., MYOG, MYOD1, and PAX3/7) in fusion-positive rhabdomyosarcomas (FP-RMS). Our analysis suggests that TFs behave as robust, tumor-type-specific expression signatures and can distinguish tumors that may be histologically similar but arise from different developmental lineages. Furthermore, we used our TF-centric approach to identify known and new drug targets, such as SIX1, RRM2, AURKA, and BIRC5 in FP-RMS, and ACVR2B & BMPR1B in NB.
Conclusions and Future Directions: A unified GRN framework analysis of pan pediatric solid tumors resolves lineage-specific regulons associated with tumorigenesis and yields a ranked set of druggable genetic dependencies. In vitro and in vivo validation studies are currently underway.
利益披露 Disclosure
D. Lee, None..
A. A. Reza, None..
S. A. Bukhari, None..
J. S. Wei, None.