Proteogenomic translator for cancer biomarker discovery towards precision medicine

PROJECT SUMMARY The goal of this Supplement Application is to enhance the proteogenomic integrative analysis for CPTAC/CCDI pediatric brain tumor studies. Towards this goal, our First Aim is to apply multiomics and network-based system learning to reveal causative molecular regulatory relationships contributing to the varieties of phenotypes in cancer using the CPTAC/CCDI proteogenomic data. We will construct protein/PTM causal networks based on global-, phospho-, glyco-, and other PTM-proteomics data. When constructing these networks, we will use and extend advanced computational tools to effectively borrow information from the published literature, publicly available open databases, and transcriptomics profiles. Furthermore, we will leverage iProMix, a cell-type-aware association test tool, to isolate regulatory interactions active within tumor cells, filtering out confounding signals from the microenvironment and refining clinically translatable targets. Our Second Aim is to through proteogenomic normal literature heterogeneity kinase Search) preclinical analysis, nominate novel protein-based cancer biomarkers, drug targets, and therapeutic compounds a multi-faceted approach. Under Aim 2.1, the Multiomics2Target platform will be expanded to analyze data f rom CPTAC-CCDI-KidsFirst, comparing protein and mRNA expression in tumors versus brain tissues. Targets will be prioritized using brain-derived cell line data (DepMap, CCLE), validated through review and AI/ML-driven functional predictions. Aim 2.2 focuses on identifying drivers of tumor by leveraging the ChEA-KG and KEA-KG tools to map transcription-factor regulatory modules and subnetworks, elucidating their roles in tumor subtypes. Moreover, Aim 2.3 mploys (LINCS L1000 Signature L2S2 and DRUG-seqr (utilizing Novartis's DRUG-seq published datasets) to prioritize approved drugs and small molecules capable of halting tumor proliferation. Together, these strategies integrate multiomics regulatory network modeling, and drug discovery platforms to advance targeted therapeutic interventions. e Finally, for all analysis tasks in Aim 1 and 2, we will derive an integrated view of commonalities and differences across multiple tumor types via Pan-Cancer analyses as outlined in the Third Aim.