Biology subjects

Trink, Y.

Publications and source records attributed to Trink, Y..

3 recordsLinked to original sources

bioRxiv2023

Characterization of alternative splicing in high-risk Wilms tumors

The significant heterogeneity of Wilms tumors between different patients is thought to arise from genetic and epigenetic distortions that occur during various stages of fetal kidney development in a way that is poorly understood. To address this, we characterized the heterogeneity of alternative mRNA splicing in Wilms tumors using a publicly available RNAseq dataset of high-risk Wilms tumors and normal kidney samples. Through Pareto task inference and cell deconvolution, we found that the tumors and normal kidney samples are organized according to progressive stages of kidney development within a triangle-shaped region in latent space, whose vertices, or "archetypes," resemble the cap mesenchyme, the nephrogenic stroma, and epithelial tubular structures of the fetal kidney. We identified a set of genes that are alternatively spliced between tumors located in different regions of latent space and found that many of these genes are associated with the Epithelial to Mesenchymal Transition (EMT) and muscle development. Using motif enrichment analysis, we identified putative splicing regulators, some of which are associated with kidney development. Our findings provide new insights into the etiology of Wilms tumors and suggest that specific splicing mechanisms in early stages of development may contribute to tumor development in different patients.

cancer biology↗

bioRxiv2022

Uncovering cellular processes in HeLa cells using unsupervised machine learning

Gene expression measurements of tissues, tumors, or cell lines taken over multiple time points are valuable for describing dynamic biological phenomena such as the response to growth factors. However, such phenomena typically involve multiple biological processes occurring in parallel, making it difficult to identify and discern their respective contributions at any time point. Here, we demonstrate the use of unsupervised machine learning to deconvolve a series of time-dependent gene expression measurements into its underlying temporal components. We first downloaded publicly available RNAseq data obtained from synchronized HeLa cells at consecutive time points following release from cell cycle arrest. Then, we used Fourier analysis and Topic modeling to reveal three underlying components and their relative contributions at each time point. We identified two temporal components with oscillatory behavior, corresponding to the G1-S and G2-M phases of the cell cycle, and a third component with a transient expression pattern, associated with the immediate-early response gene program, regulation of cell proliferation, and cervical cancer. This study demonstrates the use of unsupervised machine learning to identify hidden temporal components in biological systems, with potential applications to early detection and monitoring of diseases and recovery processes.

genomics↗

bioRxiv2022

Characterization of the continuous transcriptional heterogeneity in Wilms tumors using unsupervised machine learning

Wilms tumors are pediatric malignancies that are thought to arise from faulty kidney development. To date, the course of treatment depends on manual histopathological classification which is difficult since the tumors differ between patients in a continuous manner. Here, we used three computational approaches to characterize the continuous heterogeneity of Wilms tumors. We first chose a published dataset of microarray gene expression measurements from high-risk blastemal-type Wilms tumors. Then, we used Pareto Task Inference to show that the tumors form a triangle-shaped continuum in latent space that is bounded by three tumor archetypes with "stromal", "epithelial", and "blastemal" characteristics, that resemble the un-induced mesenchyme, the Cap mesenchyme, and early epithelial structures of the fetal kidney. We confirmed this by fitting a generative probabilistic "grade of membership" model whereby each tumor is represented as a unique mixture of three hidden "topics" with blastemal, stromal, and epithelial characteristics. Finally, we used cellular deconvolution to show that each tumor is composed of a unique mixture of cell populations resembling the un-induced mesenchyme, the cap mesenchyme, and the early epithelial structures of the fetal kidney. We anticipate that these methodologies will pave the way for more quantitative strategies for tumor stratification and classification.

cancer biology↗