Saucier David, Jiang Xuexia, Rajendran Divya, Ravishankar Roshan, Butler Erin, Marchetto Aruna, Kurmasheva Raushan T, Grünewald Thomas G P, Amatruda James F, Danuser Gaudenz
Green Center for Systems Biology and Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA.
Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX, USA.
bioRxiv. 2025 Jun 1:2025.05.28.656734. doi: 10.1101/2025.05.28.656734.
Organotropism results from the functional versatility of metastatic cancer cells to survive and proliferate in diverse microenvironments. This adaptivity can originate in clonal variation of the spreading tumor and is often empowered by epigenetic and molecular reprogramming of cell regulatory circuits. Related to organotropic colonization of metastatic sites are environmentally-sensitive, differential responses of cancer cells to therapeutic attack. Accordingly, understanding the organotropic profile of a cancer and probing the underlying driver mechanisms are of high clinical importance. However, determining systematically the organotropism of one cancer versus the organotropism of another cancer, potentially with the granularity of comparing the same cancer type between patients or tracking the evolution of a cancer in a single patient for the purpose of personalized treatment, has remained very challenging. It requires a host organism that allows observation of the spreading pattern over relatively short experimental times. Moreover, organotropic patterns often tend to be statistically weak and superimposed by experimental variation. Thus, an assay for organotropism must give access to statistical powers that can separate 'meaningful heterogeneity', i.e., heterogeneity that determines organotropism, from 'meaningless heterogeneity', i.e., heterogeneity that causes experimental noise. Here we describe an experimental workflow that leverages the physiological properties of zebrafish larvae for an imaging-based assessment of organotropic patterns over a time-frame of 3 days. The workflow incorporates computer vision pipelines to automatically integrate the stochastic spreading behavior of a particular cancer xenograft in tens to hundreds of larvae allowing subtle trends in the colonization of particular organs to emerge above random cell depositions throughout the host organism. We validate our approach with positive control experiments comparing the spreading patterns of a metastatic sarcoma against non-transformed fibroblasts and the spreading patterns of two melanoma cell lines with previously established differences in metastatic propensity. We then show that integration of the spreading pattern of xenografts in 40 - 50 larvae is necessary and sufficient to generate a page that is representative of the organotropism of a particular oncogenotype and experimental condition. Finally, we apply the power of this assay to determine the function of the fusion oncogene and its transcriptional target SOX6 as plasticity factors that enhance the adaptive capacity of metastatic Ewing sarcoma.
器官嗜性源于转移性癌细胞在不同微环境中存活和增殖的功能多样性。这种适应性可能源于扩散肿瘤的克隆变异,并且通常由细胞调节回路的表观遗传和分子重编程所增强。与转移部位的器官嗜性定植相关的是癌细胞对治疗攻击的环境敏感、差异反应。因此,了解癌症的器官嗜性特征并探究其潜在驱动机制具有很高的临床重要性。然而,系统地确定一种癌症与另一种癌症的器官嗜性,可能细化到比较患者之间相同癌症类型或追踪单个患者癌症的演变以进行个性化治疗,仍然极具挑战性。这需要一个宿主生物体,以便在相对较短的实验时间内观察扩散模式。此外,器官嗜性模式往往在统计学上较弱,并且会被实验变异所叠加。因此,一种用于器官嗜性的检测方法必须具备统计能力,能够将“有意义的异质性”,即决定器官嗜性的异质性,与“无意义的异质性”,即导致实验噪声的异质性区分开来。在这里,我们描述了一种实验工作流程,该流程利用斑马鱼幼体的生理特性,在3天的时间范围内对器官嗜性模式进行基于成像的评估。该工作流程结合了计算机视觉管道,以自动整合特定癌症异种移植在数十至数百只幼体中的随机扩散行为,使特定器官定植的细微趋势在整个宿主生物体的随机细胞沉积之上显现出来。我们通过阳性对照实验验证了我们的方法,该实验比较了转移性肉瘤与未转化成纤维细胞的扩散模式,以及两种具有先前确定的转移倾向差异的黑色素瘤细胞系的扩散模式。然后我们表明,将异种移植在40 - 50只幼体中的扩散模式进行整合,对于生成代表特定致癌基因型和实验条件的器官嗜性的图谱是必要且充分的。最后,我们应用这种检测方法的能力来确定融合致癌基因及其转录靶点SOX6作为增强转移性尤因肉瘤适应能力的可塑性因子的功能。