Wong Rachel L Y, Wong Megan R E, Kuick Chik Hong, Saffari Seyed Ehsan, Wong Meng Kang, Tan Sheng Hui, Merchant Khurshid, Chang Kenneth T E, Thangavelu Matan, Periyasamy Giridharan, Chen Zhi Xiong, Iyer Prasad, Tan Enrica E K, Soh Shui Yen, Iyer N Gopalakrishna, Fan Qiao, Loh Amos H P
Duke NUS Medical School, Singapore, Singapore.
VIVA-KKH Paediatric Brain and Solid Tumour Programme, Children's Blood and Cancer Centre, KK Women's and Children's Hospital, Singapore, Singapore.
Front Oncol. 2021 Oct 14;11:709525. doi: 10.3389/fonc.2021.709525. eCollection 2021.
Neuroblastoma is the commonest extracranial pediatric malignancy. With few recurrent single nucleotide variations (SNVs), mutation-based precision oncology approaches have limited utility, but its frequent and heterogenous copy number variations (CNVs) could represent genomic dependencies that may be exploited for personalized therapy. Patient-derived cell culture (PDC) models can facilitate rapid testing of multiple agents to determine such individualized drug-responses. Thus, to study the relationship between individual genomic aberrations and therapeutic susceptibilities, we integrated comprehensive genomic profiling of neuroblastoma tumors with drug screening of corresponding PDCs against 418 targeted inhibitors. We quantified the strength of association between copy number and cytotoxicity, and validated significantly correlated gene-drug pairs in public data and using machine learning models. Somatic mutations were infrequent (3.1 per case), but copy number losses in 1p (31%) and 11q (38%), and gains in 17q (69%) were prevalent. Critically, cytotoxicity significantly correlated only with CNVs, but not SNVs. Among 1278 significantly correlated gene-drug pairs, copy number of GNA13 and DNA damage response genes CBL, DNMT3A, and PPM1D were most significantly correlated with cytotoxicity; the drugs most commonly associated with these genes were PI3K/mTOR inhibitor PIK-75, and CDK inhibitors P276-00, SNS-032, AT7519, flavopiridol and dinaciclib. Predictive Markov random field models constructed from CNVs alone recapitulated the true z-score-weighted associations, with the strongest gene-drug functional interactions in subnetworks involving PI3K and JAK-STAT pathways. Together, our data defined individualized dose-dependent relationships between copy number gains of PI3K and STAT family genes particularly on 17q and susceptibility to PI3K and cell cycle agents in neuroblastoma. Integration of genomic profiling and drug screening of patient-derived models of neuroblastoma can quantitatively define copy number-dependent sensitivities to targeted inhibitors, which can guide personalized therapy for such mutationally quiet cancers.
神经母细胞瘤是最常见的儿童颅外恶性肿瘤。由于复发性单核苷酸变异（SNV）较少，基于突变的精准肿瘤学方法效用有限，但其频繁且异质性的拷贝数变异（CNV）可能代表了可用于个性化治疗的基因组依赖性。患者来源的细胞培养（PDC）模型有助于快速测试多种药物，以确定此类个体化药物反应。因此，为了研究个体基因组畸变与治疗敏感性之间的关系，我们将神经母细胞瘤肿瘤的综合基因组分析与相应PDC针对418种靶向抑制剂的药物筛选相结合。我们量化了拷贝数与细胞毒性之间的关联强度，并在公共数据中以及使用机器学习模型验证了显著相关的基因-药物对。体细胞突变很少见（每例3.1个），但1p（31%）和11q（38%）的拷贝数缺失以及17q（69%）的拷贝数增加很普遍。至关重要的是，细胞毒性仅与CNV显著相关，而与SNV无关。在1278个显著相关的基因-药物对中，GNA13以及DNA损伤反应基因CBL、DNMT3A和PPM1D的拷贝数与细胞毒性最显著相关；与这些基因最常相关的药物是PI3K/mTOR抑制剂PIK-75以及CDK抑制剂P276-00、SNS-032、AT7519、黄酮哌啶醇和地西他滨。仅由CNV构建的预测性马尔可夫随机场模型概括了真实的z评分加权关联，在涉及PI3K和JAK-STAT途径的子网络中具有最强的基因-药物功能相互作用。总之，我们的数据定义了PI3K和STAT家族基因特别是17q上的拷贝数增加与神经母细胞瘤中对PI3K和细胞周期药物的敏感性之间的个体化剂量依赖性关系。神经母细胞瘤患者来源模型的基因组分析与药物筛选相结合可以定量定义对靶向抑制剂的拷贝数依赖性敏感性，这可以指导此类突变较少的癌症的个性化治疗。
