The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.

真核细胞的基因组由于不断暴露于无数异质化合物中，往往容易受到内在和外在威胁。尽管存在先天 DNA 损伤反应，但一些基因组损伤会引发细胞的恶性转化。由于关于真正（非）致癌物的信息有限，准确预测致癌物是一项极具挑战性的任务。我们开发了 Metabokiller，这是一种集成分类器，可通过定量评估致癌物的亲电性、诱导增殖的潜力、氧化应激、基因组不稳定性、表观基因组改变和抗细胞凋亡反应来准确识别致癌物。伴随致癌性预测，Metabokiller 是完全可解释的，并且优于现有的致癌性预测最佳实践方法。Metabokiller 揭示了潜在的致癌人类代谢物。为了交叉验证 Metabokiller 预测，我们使用酿酒酵母和具有两种 Metabokiller 标记的人类代谢物（即 4-硝基儿茶酚和 3,4-二羟基苯乙酸）的人类细胞，并观察到 ​​Metabokiller 预测和实验验证之间的高度协同作用。