基于多模态化学基因组学建模的药物再利用用于PDAC的靶向治疗

Pancreatic Ductal Adenocarcinoma (PDAC) is a disease with a high mortality rate primarily due to its aggressive nature, late-stage prognosis, and limited effective treatment regimens. Current standard-of-care drugs for treatment include Gemcitabine and the combination drug FOLFIRINOX. However, precision and personalized treatment for PDAC is still missing. Mutations of KRAS , TP53 , and SMAD4 are the most common and highly effective genetic mutations in PDAC, resulting in the initiation, proliferation, and therapy resistance of tumor cells. Though drug candidates and inhibitors of KRAS G12D , the most prevalent KRAS mutation, are being tested in clinical trials but mutations of TP53 R172H and SMAD4 , seen in 70% and 50% of PDAC cases respectively, remain to be effectively targeted because precision therapy is restricted. To address this challenge, we propose a precision medicine-based drug repurposing strategy to identify and prioritize mutation-specific drug candidates and their combinations for PDAC treatment. Using an isogenic murine organoid platform developed by our lab, we integrate multimodality data, including in house Whole Genome Sequencing (WGS), single cell RNA-seq (scRNAseq) and a preliminary drug screening library. We first compute Differentially Expressed Genes (DEGs) from scRNAseq data and a list of acquired gene mutations from WES data for each of the isogenic organoids lines and then query these gene signatures to computationally screen for potential drug compounds against the Connectivity Map touchstone dataset by computing a similarity based enrichment score. Next, using the chemical screening library of ~5000 compounds, we perform molecular docking simulations against the target proteins associated with the three driver mutations and run a Quantitative Structure Activity Relationship (QSAR) analysis to pinpoint chemical scaffolds and functional groups associated with stronger activity, thereby refining candidate hits from the Connectivity Map analysis. We also use the docking scores to perform an in-silico perturbation screen to capture downstream shifts in order to synergize target identification and engagement. We finally present high confidence drug candidates to target KRAS G12D , TP53 R172H and SMAD4 identified through an integrated chemo-omics analysis. Ongoing work includes computational validation to top hits using Cancer Drug Response models and in vitro validation of selected drugs and combinations.