Organoid-based Discovery of Oncogenic Drivers and Treatment Resistance Mechanisms

项目总结/摘要 癌症基因组图谱计划（TCGA）的多尺度“组学”数据泛滥 和其他癌症分析项目揭示了显着的遗传和表观遗传 复杂性和巨大的患者内差异。因此，特别迫切需要 存在准确的，可扩展的人类癌症模型，可以在功能上询问 这些广泛的数据集，从良性乘客中识别驾驶员致癌事件， 描述其与治疗反应的相关性。 在过去的四年里，斯坦福大学癌症靶点发现和开发 （CTD 2）中心一直致力于人类“类器官”培养方法用于癌症建模， 驱动癌基因发现。原代3D类器官培养提供了不寻常的机会 在培养的原代人表皮/遗传“白板”内重新引发癌症 野生型组织，与相应的，往往是定义不清的复杂性长， 传代的2D癌细胞系。这为癌症创造了一个高度定义的基线 建模和功能驱动癌基因验证，这是整个杠杆。 我们的整体方法应用了最先进的系统生物学和强大的 大规模癌症分析数据集的计算资源，从而提名 在人类类器官培养物中进行直接功能评价的候选驱动因子。 该实验范围利用了卡尔文郭（报告PI， 类器官），Hanlee Ji（多PI，癌症ITH，基因组学），Christina Curtis（多PI，肿瘤 进化，癌症系统生物学），Olivier Gevaert（癌症系统生物学， 表观遗传学）和Michael Bassik（高通量功能基因组学）。因此，委员会认为， 目的1和2将TCGA拷贝数改变（CNA）的生物信息学优先化配对 以及通过基于类器官的条形码慢病毒筛选发现驱动程序的甲基化数据 以及正交cDNA、shRNA和CRISPR方法。Aim 3利用了以下能力 纵向观察癌基因的从头基因组和表观基因组进化， 工程化野生型类器官，以提名合作致癌基因的网络， 进行迭代类器官功能验证。最后，目标4探讨了 类器官，以模拟从头治疗耐药性，使用原型靶向和 化疗干扰作为原理证明，并采用单细胞RNA- seq/肿瘤内异质性和外显子组测序终点。

PROJECT SUMMARY/ABSTRACT The deluge of multi-scale “omics” data from The Cancer Genome Atlas Project (TCGA) and other cancer profiling projects has revealed remarkable genetic and epigenetic complexity and tremendous intrapatient variation. Accordingly, a particularly acute need exists for accurate, scalable human cancer models that can functionally interrogate these extensive datasets, identify driver oncogenic events from benign passengers and characterize their relevance to treatment response. For the last four years the Stanford Cancer Target Discovery and Development (CTD2) Center has pursued human “organoid” culture methods for cancer modeling and driver oncogene discovery. Primary 3D organoid cultures afford the unusual opportunity to initiate cancer de novo within the epi/genetic “tabula rasa” of cultured primary human wild-type tissue, versus the corresponding and often poorly-defined complexity of long- passaged 2D cancer cell lines. This creates a highly defined baseline for cancer modeling and functional driver oncogene validation that is leveraged throughout. Our overall approach applies state-of-the-art systems biology and robust computational resources to large-scale cancer profiling datasets, thus nominating candidate drivers that undergo direct functional evaluation in human organoid culture. This experimental scope leverages a highly synergistic team of Calvin Kuo (reporting PI, organoids), Hanlee Ji (multi-PI, cancer ITH, genomics), Christina Curtis (multi-PI, tumor evolution, cancer systems biology), Olivier Gevaert (cancer systems biology, epigenetics) and Michael Bassik (high-throughput functional genomics). Accordingly, Aims 1 and 2 couple bioinformatic prioritization of TCGA copy number alteration (CNA) and methylation data for driver discovery via organoid-based barcoded lentiviral screens and orthogonal cDNA, shRNA and CRISPR approaches. Aim 3 exploits the ability to longitudinally observe de novo genomic and epigenomic evolution in oncogene- engineered wild-type organoids to nominate networks of cooperating oncogenes that undergo iterative organoid functional validation. Lastly, Aim 4 explores the utility of organoids to model de novo treatment resistance, using archetypal targeted and chemotherapy perturbagens as proof-of-principle and employing single cell RNA- seq/intratumoral heterogeneity and exome sequencing endpoints.