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

项目摘要/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 令人衰弱的长期后果。精准医学尚未对儿童癌症产生重大影响 因为，虽然数以千计的儿科肿瘤基因组已经被测序，但大多数儿童只有很少的 与成人癌症相比，体细胞突变。这意味着靶向抗癌药物不是大多数人的选择。 儿童和较少的肿瘤特异性新抗原意味着大多数免疫疗法是无效的。 然而，在过去的5年里，大规模的CRISPR和癌症细胞系药物筛选研究，例如 依赖图(DepMap)显示，在许多癌症中，未突变的基因也可以作为强效药物 目标。这些基因被称为非癌基因依赖关系。这个项目的总体目标是 通过确定儿科患者的可药物非癌基因依赖关系，克服低突变负担 并进行必要的体外和体内实验工作，将这些治疗方法推向临床。 我们将通过应用机器学习的工具来识别这些非癌基因依赖关系 大型临床前筛查数据集(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儿科肿瘤的特定非癌基因依赖关系。 亚型，例如基于全基因组基因表达或甲基化数据定义的。我们会 使用体外实验分析对最高命中率进行机械验证。 此外，几乎所有根治癌症的疗法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未知数据进行测试时表现不佳。因此， 我们的第二个目标是应用我们开发的基于定向CRISPR基因敲除的方法 筛选以确定协同作用的药物组合。我们将在体内用小鼠验证这些组合 使用患者来源的异种移植的模型，利用圣裘德已经建立的共享资源。 最后，肿瘤的异质性最终是所有已知癌症治疗的失败；然而，在儿科 突变负担低的肿瘤，这种异质性很大程度上是由细胞状态驱动的，而不是特定的 体细胞突变。我们将使用单细胞和空间分析细胞状态对耐药性的影响 转录组学技术应用于药物治疗的自发性神经母细胞瘤小鼠模型。这将是 最终允许我们提名明确针对耐药细胞状态的新药物组合。 总体而言，这项研究计划的目标是在圣犹大修建一条管道，以克服一些主要挑战 由儿童肿瘤中低数量的体细胞突变所构成，并确定新的治疗策略 这些病人。我们已经组建了一支多样化的世界级研究团队，配备了所有必要的组件 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.