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Biomedical subjects

Teppei Shimamura

Publications and source records attributed to Teppei Shimamura.

2 recordsLinked to original sources

YAP1 induces hepatocellular carcinoma via DNA demethylation rather than by canonical driver gene mutations.

Large-scale genome sequencing analyses have identified driver gene mutations (DGMs) in most cancers as well as their associated tumorigenic mechanisms. However, a small fraction of cancers are not positive for these canonical DGMs, leaving the mechanisms underpinning their formation a mystery. We hypothesized that canonical DGM-negative cancers might be driven by activation of the transcriptional coactivator YAP1 that led to the induction of epigenetic changes. To test this theory, we established a mouse mosaic model of hepatocellular carcinoma (HCC) in which we induced YAP1-TEAD activation in a few hepatocytes. Whole-exome sequencing did not identify canonical DGMs in HCCs, but bisulfite sequencing revealed widespread DNA demethylation leading to the transcriptional activation of multiple oncogenes. Knockdown of the DNA demethylation-promoting gene, Tet1, attenuated HCC formation in these mice. Single-cell spatial transcriptomics identified a Tet1-high subpopulation of HCC cells that interacted with other hepatic cell types. Our mechanistic mouse data align with the observation that YAP1-TEAD-TET1-associated signatures were also elevated in hepatocytes from patients with Fontan-associated liver disease (FALD), a condition associated with the development of HCCs with lower frequencies of canonical DGMs. Our study suggests that the YAP1-TEAD-TET1 axis promotes canonical DGM-negative HCC development, and provides new insights into the molecular processes involved.

Animals

scSurv: a deep generative model for single-cell survival analysis.

MOTIVATION: Single-cell omics analysis has unveiled the heterogeneity of various cell types within tumors. However, no methodology currently reveals how this heterogeneity influences cancer patient survival at single-cell resolution. Here, we introduce scSurv, combining a Cox proportional hazards model with a deep generative model of single-cell transcriptome, to estimate individual cellular contributions to clinical outcomes. RESULTS: The accuracy of scSurv was validated using both simulated and real datasets. This method identifies cells associated with favorable or adverse prognoses and extracts genes correlated with their contribution levels. In melanoma, scSurv reproduces known prognostic macrophage classifications and facilitates hazard mapping through spatial transcriptomics in renal cell carcinoma. We also identified genes consistently associated with prognosis across multiple cancers and demonstrated the applicability of this method to infectious diseases. scSurv is a novel framework for quantifying the heterogeneity of individual cellular effects on clinical outcomes. AVAILABILITY: The implementation of scSurv is available on GitHub (https://github.com/3254c/scSurv) and Zenodo (https://doi.org/10.5281/zenodo.17793054).

Humans