Ananthasubramaniam Bharath, Venkataramanan Ramji
Institute for Theoretical Biology, Humboldt Universität zu Berlin, Berlin, Germany.
Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
PLoS Biol. 2025 May 27;23(5):e3003196. doi: 10.1371/journal.pbio.3003196. eCollection 2025 May.
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进行研究需要跨时间进行定期测量。对包含生物钟的不同内部组织进行重复采样在实际操作和伦理上都是不可行的。在此，我们提出了一种新颖的无监督机器学习方法（COFE），该方法可以使用个体的单个高通量组学样本（无时间标签）来重建不同队列的昼夜节律。COFE 可以同时为样本分配时间标签，并识别用于时间重建的节律性数据特征，同时还能检测无效排序。利用 COFE，我们使用来自癌症基因组图谱（TCGA）数据库的数据，在 11 种不同的人类腺癌中发现了广泛的从头昼夜节律基因表达。核心生物钟基因表达峰值时间的排列在各种癌症中是保守的，除了少数关键基因的时间错乱外，类似于一个健康的功能性生物钟。此外，转录组中的节律与癌症相关蛋白质组密切相关。所有癌症共有的节律性基因和蛋白质参与了代谢和细胞周期。尽管这些节律在许多癌症中与细胞周期同步，但它们与健康匹配组织中的生物钟解耦。大多数 FDA 批准的和潜在的抗癌药物的靶点在肿瘤组织中具有不同幅度和峰值时间的节律性。这些发现强调了在癌症治疗中考虑“时间”的实用性，并建议关注健康组织中的生物钟，而不是癌症组织中自由运行的生物钟。因此，我们的方法为重新利用无时间标签的数据来研究昼夜节律创造了新机会。
