Between-tumor and within-tumor heterogeneity in invasive potential

For women with access to healthcare and early detection, breast cancer deaths are caused primarily by metastasis rather than growth of the primary tumor. Metastasis has been difficult to study because it happens deep in the body, occurs over years, and involves a small fraction of cells from the primary tumor. Furthermore, within-tumor heterogeneity relevant to metastasis can also lead to therapy failures and is obscured by studies of bulk tissue. Here we exploit heterogeneity to identify molecular mechanisms of metastasis. We use “organoids”, groups of hundreds of tumor cells taken from a patient and grown in the lab, to probe tumor heterogeneity, with potentially thousands of organoids generated from a single tumor. We show that organoids have the character of biological replicates: within-tumor and between-tumor variation are of similar magnitude. We develop new methods based on population genetics and variance components models to build between-tumor and within-tumor statistical tests, using organoids analogously to large sibships and vastly amplifying the test power. We show great efficiency for tests based on the organoids with the most extreme phenotypes and potential cost savings from pooled tests of the extreme tails, with organoids generated from hundreds of tumors having power predicted to be similar to bulk tests of hundreds of thousands of tumors. We apply these methods to an association test for molecular correlates of invasion, using a novel quantitative invasion phenotype calculated as the spectral power of the organoid boundary. These new approaches combine to show a strong association between invasion and protein expression of Keratin 14, a known biomarker for poor prognosis, with p = 2 × 10−45 for within-tumor tests of individual organoids and p −6 for pooled tests of extreme tails. Future studies using these methods could lead to discoveries of new classes of cancer targets and development of corresponding therapeutics. All data and methods are available under an open source license at https://github.com/baderzone/invasion_2019.对于能够获得医疗保健与早期筛查的女性而言，乳腺癌患者的死亡主要源于肿瘤转移（metastasis），而非原发肿瘤的生长。肿瘤转移的研究难度颇高：其发生于机体深部，进程绵延数年，且仅涉及原发肿瘤中极小比例的细胞群。此外，与转移进程相关的肿瘤内异质性还会引发治疗失败，而大块组织（bulk tissue）研究往往会掩盖这类异质性。本研究借助异质性特征，旨在解析肿瘤转移的分子机制。我们采用类器官（organoids）——从患者体内提取、于实验室中培养的数百个肿瘤细胞集合——来探究肿瘤异质性，单个肿瘤即可培养出数千个类器官。研究证实，类器官具备生物学重复的特性：肿瘤内变异与肿瘤间变异的幅度相当。我们基于群体遗传学（population genetics）与方差组分模型（variance components models）开发了全新方法，用于构建肿瘤间与肿瘤内的统计检验模型——将类器官类比为大型同胞家系，从而大幅提升检验效力。研究表明，基于表型最极端的类器官开展检验具备极高效率，而对极端尾部样本进行混合检验则可节省成本；从数百个肿瘤中培养得到的类器官，其检验效力预计可与数十万肿瘤的大块组织检验相当。我们将上述方法应用于侵袭（invasion）相关分子关联特征的检验，采用一种基于类器官边界光谱功率计算得到的新型定量侵袭表型。这些全新方法联合应用后，证实侵袭与角蛋白14（Keratin 14）的蛋白表达存在显著关联——角蛋白14是公认的不良预后生物标志物；单个类器官的肿瘤内检验得到p值为2×10⁻⁴⁵，极端尾部样本的混合检验则得到p值为1×10⁻⁶。未来采用此类方法开展的研究，有望发现全新类别癌症靶点，并推动对应治疗药物的研发。所有数据与方法均以开源许可协议发布，可访问https://github.com/baderzone/invasion_2019获取。