The circadian clock circuitry deconvolutes colorectal cancer and lung adenocarcinoma heterogeneity in a dynamic time-related framework

• Published in: Cancer gene therapy Vol. 30; no. 10; pp. 1323 - 1329
• Main Authors: Melocchi, Valentina, Cuttano, Roberto, Murgo, Emanuele, Mazzoccoli, Gianluigi, Bianchi, Fabrizio
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.10.2023; Nature Publishing Group
• Subjects: 38/39; 38/61; 38/91; 631/208/199; 692/699/67; Adenocarcinoma; Biomedical and Life Sciences; Biomedicine; Brief Communication; Cell activation; Circadian rhythm; Circadian rhythms; Colorectal cancer; Colorectal carcinoma; Gene Expression; Gene Therapy; Lung cancer; Phenotypes; Stochasticity; Transcriptomes; Transcriptomics
• ISSN: 0929-1903; 1476-5500
• DOI: 10.1038/s41417-023-00646-7

Increasing evidence imputes cancer progression and resistance to therapy to intra-tumor molecular heterogeneity set off by cancer cell plasticity. Re-activation of developmental programs strictly linked to epithelial-to-mesenchymal transition and gaining of stem cells properties are crucial in this setting. Many biological processes involved in cancer onset and progression show rhythmic fluctuations driven by the circadian clock circuitry. Novel cancer patient stratification tools taking into account the temporal dimension of these biological processes are definitely needed. Lung cancer and colorectal cancer (CRC) are the leading causes of cancer death worldwide. Here, by developing an innovative computational approach we named Phase-Finder , we show that the molecular heterogeneity characterizing the two deadliest cancers, CRC and lung adenocarcinoma (LUAD), rather than a merely stochastic event is the readout of specific cancer molecular states which correlate with time-qualified patterns of gene expression. We performed time-course transcriptome analysis of CRC and LUAD cell lines and upon computing circadian genes expression-based correlation matrices we derived pseudo-time points to infer time-qualified patterns in the transcriptomic analysis of real-world data (RWD) from large cohorts of CRC and LUAD patients. Our temporal classification of CRC and LUAD cohorts was able to effectively render time-specific patterns in cancer phenotype switching determining dynamical distribution of molecular subtypes impacting patient prognosis.