Project Title: Decoding the Gene Expression Effects of HDACi and PROTACs for Cancer Therapy using Bioinformatics and Machine Learning

 

​​​Overview: Histone deacetylase (HDAC) inhibitors have emerged as promising anti-cancer therapeutics. Proteolysis Targeting chimeras (PROTACs) offer a targeted approach by using the ubiquitin-proteasome system to degrade specific proteins. Bioinformatics tools can be used to comprehensively analyze the impacts of HDACi and PROTACs on gene expression, demonstrating their mechanisms of action and potential alternate effects.

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The Liao Lab is a leader in developing inhibitors of histone deacetylases and proteolysis-targeting chimeras, targeting the enzymes that promote oncogenesis. Previous work identified a potent and specific HDAC inhibitor, UF010. Works have shown that UF010 provokes histone hyperacetylation and profoundly perturbs the transcriptome. It also has potent anticancer efficacy in vivo in mouse cancer models. Current work focuses on understanding the precise mechanism by which UF010 deregulates the cancer epigenome and transcriptome and assesses the translational potential of this class of HDAC inhibitors for treating human cancer patients. 
 

My work includes performing bioinformatic and machine learning analysis of genomic data using Python, R, and other programming and biostatistical modeling tools. Understanding the gene expression changes induced by HDACi and PROTACs can help identify potential biomarkers for drug response and toxicity, leading to more personalized cancer treatments.

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Examples include:

RNA-Seq: Using edgeR to compare DMSO controls with UF010 treatment samples to determine the differentially expressed genes by UF010 in cancer cells.
Pathway Analysis: Analyze upregulated and downregulated genes after differential analysis to identify oncogenic pathways perturbed by UF010.

Machine Learning Modeling: ​Developing a computational model to predict the effects of HDACi and PROTACs on gene expression, leveraging machine learning techniques and a comprehensive dataset of cancer cell line data.

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