Authors:

Nikita Nangia

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About Paper:

HDAC inhibitors (HDACi) are a class of drugs that target histone deacetylases, enzymes involved in gene regulation. By inhibiting HDACs, these drugs increase the acetylation of histones, leadin g to changes in gene expression. HDACi have shown promise in treating various cancers as they c an reactivate tumor-suppressor genes and downregulate oncogenes. Bioinformatics and mach ine learning can play a crucial role in understanding the effects of HDACi on gene expression . We developed a computational model to predict the effects of HDACi on gene expression, lever aging machine learning techniques and a comprehensive dataset of cancer cell line data. Our model incorporated data from the Cancer Cell Line Encyclopedia (CCLE), including whol e exome sequencing (WES), RNAi dependency data, methylation profiles, RNA sequencing da ta (CCLE TPM RNAseq and DepMap Expression Public), copy number variations, quantitative p roteomics data, and gene-level features. By integrating these diverse data sources, our model w as able to effectively capture the complex regulatory mechanisms underlying HDACi-i nduced gene expression changes. We found that genes with specific characteristics, such as higher GC content, transcript counts, and specific GO annotations, were more likely to be differentially expressed in response to HDACi treatment. Moreover, our model demonstrated good accuracy in predicting the effects of HDACi on a different cell line, suggesting its potential for broad appli cability across diverse cancer types. This information can be used to develop more targeted HD ACi therapies by focusing on genes that are most likely to be affected by the drug in a particular cancer type. Additionally, understanding the gene expression changes induced by HDACi can help researchers identify potential biomarkers for drug response and toxicity, leading to more personalized cancer treatments.

Source:

University of Florida / Nikita Nangia, Daiqing Liao / 2024

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Co-authors:

Nikita Nangia