新算法将防止错误识别癌细胞

诸平

Cancer cell lines sample images.

据英国肯特大学（University of Kent）2020年12月10日提供的消息，该大学的研究人员已经开发出一种计算机算法，可以基于显微图像识别癌细胞系中的差异，这是朝着消除实验室中细胞的错误识别的方向发展的一种独特方法。相关研究结果于2020年11月16日（Published: 16 November 2020）已经在《科学报告》（Scientific Reports）杂志网站上发表——Deogratias Mzurikwao, Muhammad Usman Khan, Oluwarotimi Williams Samuel, Jindrich Cinatl, Mark Wass, Martin Michaelis, Gianluca Marcelli, Chee Siang Ang. Towards image-based cancer cell lines authentication using deep neural networks. Scientific Reports, 2020; 10, Article number: 19857. DOI: 10.1038/s41598-020-76670-6

肯特大学的研究人员开发了一种计算机算法，该算法可以基于显微图像识别癌细胞系中的差异，这是朝着消除实验室中细胞的错误识别的方向发展的一种独特方法。

癌细胞系是在实验室中作为细胞培养物分离和生长的细胞，用于研究和开发抗癌药物。但是，许多细胞系在被其他细胞系交换或污染后会被错误识别，这意味着许多研究人员可能会使用不正确的细胞。

自从癌细胞系的研究开始以来，这一直是一个持续存在的问题。短串联重复序列（Short tandem repeat, STR）分析通常用于鉴定癌细胞系，但昂贵且耗时。此外，STR无法区分来自同一个人或动物的细胞。

肯特大学工程与数字艺术学院（Kent's School of Engineering and Digital Arts）和计算机学院（School of Computing）的研究人员基于一组实验细胞系的显微图像并利用能够“深度学习”的计算机模型，对计算机进行了一段时间的质量比较癌细胞数据。据此，他们开发了一种算法，使计算机可以检查细胞系的单独的微观数字图像，并准确地识别和标记它们。

这一突破有可能提供一种易于使用的工具，该工具无需专家的设备和知识即可在实验室中快速识别所有细胞系。

这项研究由计算机学院的Chee（Jim）Ang博士和工程与数字艺术学院的Gianluca Marcelli博士与领先的癌细胞系专家Martin Michaelis教授和生物科学学院（School of Biosciences）的Mark Wass博士共同领导。

多媒体/数字系统高级讲师Chee（Jim）Ang博士说：“我们的合作证明了巨大的成果，有望在实验室和癌症研究中实现潜在的未来应用。利用这种新算法将产生进一步的结果，可以改变科学中细胞鉴定的格式，为研究人员提供更好正确鉴定细胞的机会，从而减少癌症研究中的错误并尽可能挽救更多生命。

“结果还表明，计算机模型可以分配用于正确识别细胞系的确切标准，这意味着将来研究人员接受培训以准确识别细胞的潜力也可能大大增强。”更多信息请注意浏览原文或者相关报道。

 Abstract

Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704rCDDP1000, EFO-21rCDDP2000, EFO-27rCDDP2000) or oxaliplatin (UKF-NB-3rOXALI2000), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.