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科学家完成单样本肿瘤亚克隆重建的众包基准测试
作者:小柯机器人 发布时间:2024/6/14 15:10:07

美国加州大学洛杉矶分校Paul C. Boutros等研究人员合作完成单样本肿瘤亚克隆重建的众包基准测试。这一研究成果于2024年6月11日在线发表在国际学术期刊《自然—生物技术》上。

研究人员发起了ICGC-TCGA(国际癌症基因组联盟-癌症基因组图谱)DREAM体细胞突变调用肿瘤异质性和演化挑战赛,对现有的亚克隆重建算法进行基准测试。这项为期7年的社区工作利用云计算在51个模拟肿瘤上对31种亚克隆重建算法进行了基准测试。算法在七个独立任务中进行评分,总运行次数达12061次。算法选择对性能的影响远远大于肿瘤特征,但纯度调整读取深度、拷贝数状态和读取映射性与大多数算法在大多数任务中的性能有关。
 
没有一种算法在所有七项任务中都表现优异,现有的组合策略也无法超越最好的单个方法,这凸显了一个关键的研究需求。所有容器化方法、评估代码和数据集都可用于进一步评估亚克隆重建准确性的决定因素,以及开发更好的方法来了解肿瘤的演变。

研究人员表示,亚克隆重建算法利用大量DNA测序数据来量化肿瘤演化参数,从而评估癌症是如何开始、发展和应对选择性压力的。

附:英文原文

Title: Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction

Author: Salcedo, Adriana, Tarabichi, Maxime, Buchanan, Alex, Espiritu, Shadrielle M. G., Zhang, Hongjiu, Zhu, Kaiyi, Ou Yang, Tai-Hsien, Leshchiner, Ignaty, Anastassiou, Dimitris, Guan, Yuanfang, Jang, Gun Ho, Mootor, Mohammed F. E., Haase, Kerstin, Deshwar, Amit G., Zou, William, Umar, Imaad, Dentro, Stefan, Wintersinger, Jeff A., Chiotti, Kami, Demeulemeester, Jonas, Jolly, Clemency, Sycza, Lesia, Ko, Minjeong, Wedge, David C., Morris, Quaid D., Ellrott, Kyle, Van Loo, Peter, Boutros, Paul C.

Issue&Volume: 2024-06-11

Abstract: Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC–TCGA (International Cancer Genome Consortium–The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

DOI: 10.1038/s41587-024-02250-y

Source: https://www.nature.com/articles/s41587-024-02250-y

期刊信息

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:68.164
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex