Overcoming selection bias in synthetic lethality prediction.
Overcoming selection bias in synthetic lethality prediction.
复制标题
DOI：
10.1093/bioinformatics/btac523
复制
发表时间：
2022-09-15
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
当两个基因同时丧失功能导致细胞死亡时，就会发生合成致死（SL）。这为开发针对合成致命的内源性破坏基因对的抗癌疗法带来了巨大的希望。由于大量的候选对，通过详尽的实验筛选确定新的SL关系是具有挑战性的。计算SL预测因此寻求确定有希望的SL基因对进一步的实验。然而，目前的SL预测方法在SL数据存在选择偏差的情况下缺乏对概括性的考虑。我们发现SL数据显示出相当大的基因选择偏差。我们为评估SL预测的稳健性而设计的实验表明，由已知SL相互作用的拓扑（例如图、矩阵分解）驱动的模型对选择偏差特别敏感。我们引入了选择偏倚弹性合成致死率（SBSL）预测使用正则化逻辑回归或随机森林。每个基因对由27个来自癌细胞系、癌症患者组织和健康供体组织样本的分子特征来描述。SBSL模型的建立和测试使用了大约8000对实验衍生的SL对，涉及乳腺癌、结肠癌、肺癌和卵巢癌。与其他SL预测方法相比，SBSL具有更高的预测性能、更好的泛化能力和对选择偏差的鲁棒性。基因依赖性，量化基因对细胞存活的重要性，对SBSL的预测贡献最大。在缺乏依赖特征的情况下，随机森林优于线性模型，突出了体细胞突变的相互排他性、健康组织中的共表达和肿瘤样本中的差异表达的相关性。https://github.com/joanagoncalveslab/sbsl补充数据可在Bioinformatics在线获取。
Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data. We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples. https://github.com/joanagoncalveslab/sbsl Supplementary data are available at Bioinformatics online.
登录
查看更多内容
影响因子：
14.9
作者：
Gene Ontology Consortium
通讯作者：
Gene Ontology Consortium
DOI：
10.1186/cc2955
发表时间：
2004-10
期刊：
Critical care (London, England)
影响因子：
--
作者：
Bewick V;Cheek L;Ball J
通讯作者：
Ball J
影响因子：
9.3
作者：
De Kegel, Barbara;Quinn, Niall;Ryan, Colm J.
通讯作者：
Ryan, Colm J.
用于预测人类癌症综合致死率的双丢失图卷积网络
影响因子：
5.8
作者：
Cai, Ruichu;Chen, Xuexin;Hao, Yuexing
通讯作者：
Hao, Yuexing
影响因子：
64.8
作者：
Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
通讯作者：
Sellers, William R.