Background: Cancer researchers use cell lines, patient derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derives from the fidelity with which it represents the tumor type under investigation, however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors. Methods: We developed a machine learning based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence. Results: We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient derived xenografts and cell lines in four out of five tumor types. However, several patient derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity. Conclusions: CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as freely downloadable software and as a web application that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

中文翻译：

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评估癌症模型的转录保真度

背景：癌症研究人员使用细胞系，源自患者的异种移植物，工程小鼠和类瘤作为模型来研究肿瘤生物学并确定治疗方法。模型的普遍性和功效来自于所代表的肿瘤类型的保真度，但是，其真实性的程度通常尚不清楚。模型的优势以及易于生成新模型的能力对工具的需求不断增加，这些工具可以测量癌症模型与天然肿瘤相似或不同的程度和方式。方法：我们开发了基于机器学习的计算工具CancerCellNet，该工具以平台和物种不可知的方式测量癌症模型与22种自然发生的肿瘤类型和36种亚型的相似性。我们将此工具应用于657个癌细胞系，415位患者衍生的异种移植物，26种不同的基因工程小鼠模型和131种类瘤。我们通过应用于独立数据验证了CancerCellNet，并使用免疫荧光测试了一些预测。结果：我们已经记录了对自然肿瘤转录保真度最高的癌症模型，我们已经确定了适当的模型不能提供足够服务的癌症，并且发现了带有不符合其分类标准的注释的模型。通过比较各种模式的模型，我们报告说，平均而言，在五种肿瘤类型中的四种中，基因工程小鼠和类瘤比患者衍生的异种移植物和细胞系具有更高的转录保真度。但是，一些源自患者的异种移植物和类瘤具有与天然肿瘤同等的分类评分，强调它们作为忠实模型类的潜力和异质性。结论：CancerCellNet可以快速评估肿瘤模型的转录保真度。我们已将CancerCellNet作为可免费下载的软件和Web应用程序提供，可将其应用于新的癌症模型，从而可以直接与此处评估的癌症模型进行比较。