Data underlying individual panels of all Figures.

The study of ubiquitous circadian rhythms in human physiology requires regular measurements across time. Repeated sampling of the different internal tissues that house circadian clocks is both practically and ethically infeasible. Here, we present a novel unsupervised machine learning approach (COFE) that can use single high-throughput omics samples (without time labels) from individuals to reconstruct circadian rhythms across cohorts. COFE can simultaneously assign time labels to samples and identify rhythmic data features used for temporal reconstruction, while also detecting invalid orderings. With COFE, we discovered widespread de novo circadian gene expression rhythms in 11 different human adenocarcinomas using data from The Cancer Genome Atlas (TCGA) database. The arrangement of peak times of core clock gene expression was conserved across cancers and resembled a healthy functional clock except for the mistiming of a few key genes. Moreover, rhythms in the transcriptome were strongly associated with the cancer-relevant proteome. The rhythmic genes and proteins common to all cancers were involved in metabolism and the cell cycle. Although these rhythms were synchronized with the cell cycle in many cancers, they were uncoupled with clocks in healthy matched tissue. The targets of most of FDA-approved and potential anti-cancer drugs were rhythmic in tumor tissue with different amplitudes and peak times. These findings emphasize the utility of considering “time" in cancer therapy, and suggest a focus on clocks in healthy tissue rather than free-running clocks in cancer tissue. Our approach thus creates new opportunities to repurpose data without time labels to study circadian rhythms.对人体生理学中普遍存在的昼夜节律的研究，需要开展跨时间的规律性测量。对携带生物钟的不同人体内部组织进行重复采样，无论从实践还是伦理层面均不可行。在此，我们提出一种全新的无监督机器学习方法——COFE，该方法可利用来自个体的单次高通量组学（high-throughput omics）样本（无需时间标签），在队列中重建昼夜节律。COFE可同时为样本分配时间标签，并识别用于时序重建的节律性数据特征，同时还能检测无效的时序排列。借助COFE，我们利用癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库的数据，在11种不同的人体腺癌中发现了广泛存在的从头（de novo）昼夜节律性基因表达节律。核心生物钟基因的表达峰值时间排布在各类癌症中均保守，且与健康个体的功能性生物钟模式高度相似，仅少数关键基因的时序存在偏差。此外，转录组的节律性与癌症相关的蛋白质组存在强关联。所有癌症共有的节律性基因与蛋白质，均参与代谢与细胞周期调控过程。尽管在多数癌症中，这些节律与细胞周期同步，但它们与匹配的健康组织中的生物钟却发生了解偶联。大多数经美国食品药品监督管理局（Food and Drug Administration, FDA）批准及潜在的抗癌药物靶点，在肿瘤组织中均呈现出节律性表达特征，且具有不同的振幅与峰值时间。这些发现凸显了在癌症治疗中考量‘时间’因素的实用价值，并提示我们应聚焦于健康组织中的生物钟，而非肿瘤组织中的自由运行生物钟。我们的方法由此为复用无时间标签的数据以研究昼夜节律开辟了全新机遇。