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

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.

Automated summary

The progression and resistance of cancer can be attributed to the heterogeneity of tumor cells caused by their plasticity. The development programs related to the epithelial-to-mesenchymal transition and acquisition of stem cell properties play essential roles in this process. Through computational analysis, we identified time-specific molecular states in colorectal cancer and lung adenocarcinoma, which are associated with different patterns of gene expression. This temporal classification allows for a better understanding of cancer phenotype switching and its impact on patient prognosis.