Developing new therapeutic strategies for pediatric tumors that lack clinically actionable mutations

项目总结/摘要 癌症是导致儿童死亡的主要疾病相关原因。治疗基本保持不变 几十年来，主要依靠积极的细胞毒性化疗和放射治疗，这些治疗方法已经 长期的后果。精准医疗尚未对儿童癌症产生重大影响 因为，虽然已经对数千个儿科肿瘤基因组进行了测序，但大多数儿童的基因组很少。 与成人癌症相比，体细胞突变。这意味着靶向癌症药物不是大多数人的选择 儿童和更少的肿瘤特异性新抗原意味着大多数免疫疗法无效。 然而，在过去的5年里，在癌细胞系中进行的大规模CRISPR和药物筛选研究，如 基因组图谱（DepMap）已经表明，在许多癌症中，未突变的基因也可以作为有效的药物 目标的这些基因被称为非癌基因依赖性。该项目的总体目标是 克服低突变负担，通过确定儿科患者的药物非癌基因依赖性， 肿瘤，并进行必要的体外和体内实验工作，将这些疗法推向临床。 我们将通过应用机器学习工具来识别这些非癌基因依赖性， 大型临床前筛选数据集（如DepMap、CCLE和PRISM）与患者的综合分析 肿瘤组学数据。这将使我们能够提名特定的非癌基因的依赖性，为儿科肿瘤 亚型，基于例如全基因组基因表达或甲基化数据定义。我们将 使用体外实验测定来机械地验证最高命中。 此外，几乎所有的治愈性癌症治疗都涉及多种疗法的合理组合， 然而，现有的预测有效组合的方法在对看不见的数据进行测试时表现不佳。因此，在本发明中， 我们的第二个目标是应用我们开发的基于靶向CRISPR敲除的方法， 筛选以鉴定协同药物组合。我们将使用小鼠在体内验证这些组合。 使用患者来源的异种移植物模型，利用圣犹达已经建立的共享资源。 最后，肿瘤异质性最终是每一种已知癌症治疗的失败;然而，在儿科， 在突变负荷低的肿瘤中，这种异质性大部分是由细胞状态驱动的，而不是特异性的。 体细胞突变我们将使用单细胞和空间分析来剖析细胞状态对耐药性的影响。 转录组学技术应用于神经母细胞瘤的药物治疗的自发小鼠模型。这将 最终允许我们提名明确针对耐药细胞状态的新药物组合。 总的来说，这项研究计划的目标是在圣裘德建立一个管道，以克服一些主要的挑战 儿童肿瘤中的体细胞突变数量少，并确定新的治疗策略， 这些病人。我们已经组建了一支多元化的世界级研究团队， 对病人护理的最终影响。

PROJECT SUMMARY / ABSTRACT Cancer is the leading disease-related cause of death in children. Treatment has remained largely unchanged in decades, relying primarily on aggressive cytotoxic chemotherapy and radiation—these therapies have debilitating long-term consequences. Precision medicine has yet to make a major impact on childhood cancer because, while thousands of pediatric tumor genomes have been sequenced, most children have very few somatic mutations compared to adult cancers. This means targeted cancer drugs are not an option for most children and fewer tumor-specific neoantigens means most immunotherapies are ineffective. However, in the last 5 years, large-scale CRISPR and drug screening studies in cancer cell lines, such as the Dependency Map (DepMap), have shown that in many cancers, unmutated genes can also act as potent drug targets. These genes are known as non-oncogene dependencies. The overall goal of this project is to overcome the low mutation burden, by identifying the druggable non-oncogene dependencies of pediatric tumors and to perform the requisite in vitro and in vivo experimental work to move these therapies to the clinic. We will identify these non-oncogene dependencies by applying tools from machine learning to perform integrative analysis of large pre-clinical screening datasets (such as DepMap, CCLE, and PRISM) with patient tumor -omics data. This will allow us to nominate specific non-oncogene dependencies for pediatric tumor subtypes, defined based on, for example, whole-genome gene expression or methylation data. We will mechanistically validate the top hits using in vitro experimental assays. Additionally, almost all curative cancer treatments involve the rational combination of multiple therapies, however, existing methods to predict effective combinations perform poorly when tested on unseen data. Thus, our second aim is to apply an approach that we have developed based on targeted CRISPR knockout screening to identify synergistic drug combinations. We will validate these combinations in vivo using mouse models with patient-derived xenografts, leveraging shared resources already established at St. Jude. Finally, tumor heterogeneity is ultimately the downfall of every known cancer treatment; however, in pediatric tumors where the mutation burden is low, much of this heterogeneity is driven by cell state, rather than specific somatic mutations. We will dissect the influence of cell state on drug resistance using single-cell and spatial transcriptomics technologies applied to a drug-treated spontaneous mouse model of neuroblastoma. This will ultimately allow us to nominate new drug combinations explicitly targeting drug-resistant cell states. Overall, this research program will aim to build a pipeline at St. Jude to overcome some of the main challenges posed by the low number of somatic mutations in pediatric tumors and identify new therapeutic strategies for these patients. We have assembled a diverse world-class team of researchers with all components necessary for an eventual impact on patient care.