Kaposi's sarcoma (KS), caused by the KS herpesvirus (KSHV) is an AIDS-associated malignancy (AIDSKS). KS responds to anti-retroviral therapy, and chemotherapy. However, it is estimated that more than half of KS patients will not be cured. In addition to the dire need of novel therapies to treat advanced and ARTresistant KS, there is paucity on our biological understanding of the full range of conditions leading to AIDSKS and its different presentations. We propose an in-depth expression profiling of KS biopsies from patients belonging to three clinical categories: HIV- (also called classic KS), and two clinical variants of HIV/AIDS KS: localized indolent (T0), advanced/disseminated (T1). We will access matched tumor and PMBC from clinically characterized patients from available repositories. We will apply high throughput NGS (exome sequencing, RNAseq) to understand the full oncobiology profiles that determine different KS clinical manifestations. The clinically validated genomic data (oncogenic networks, stratified KS disease signatures) will be used as input for big data-driven computational systems biology tools to identify novel targets, drugs, and combinations that will be tested in the cell and animal models. We aim to construct a pipeline for AIDS-KS drug discovery: Tier 1: Genomic and transcriptomic of AIDS-KS to identify druggable oncogenic networks. We propose that KS disease severity is the consequence of oncogenic host signaling cascades driven by viral oncogenes and host mutations that contribute to PDGFRA-driven KSHV sarcomagenesis. We will identify clinically actionable oncogenic networks to identify AIDS-KS targets and treatments. We will use NGS approaches (whole-exome sequencing and RNAseq) in KS biopsies and control tissues (including PMBC) to generate host and KSHV transcriptomes and determine KS tumor mutational burden. We will carry out bioinformatics analyses for identifying DEGs, enriched pathways, stratified disease signatures, and host mutational burden in KS tumors to characterize oncogenic networks and identify druggable targets in AIDS-KS. Tier 2: Computational identification of drugs. We will employ KS viral, treatment, stratified KS disease signatures, and prioritized targets as input into our big data and AIdriven computational tools and pipelines. Precision drug-treatment combinations will be prioritized using our SynergySeq platform; for example novel efficacious drug combinations for Doxorubicin and/or antiPDGFRA drugs in the context of ART treatments for AIDS-KS. We will also use large-scale AI-based in silico screening to identify novel tractable multitarget chemotypes including PDGFRA and HDAC dual inhibitors. Tier 3: Screening and testing of drug and combination candidates. Drug and drug combination candidates will be tested in a battery of validated preclinical cell and animal models of KSHV sarcomagenesis, which include using both infected murine and human KS-cells and their KSHV infected mouse tumors.