The Sylvester Cancer Center Support Grant

由KS疱疹病毒（KSHV）引起的卡波西肉瘤（KS）是一种与艾滋病相关的恶性肿瘤（aids）。KS对抗逆转录病毒治疗和化疗有反应。但是，预计有一半以上的KS患者无法治愈。除了迫切需要新的治疗方法来治疗晚期和耐药的KS之外，我们对导致aids的所有条件及其不同表现形式的生物学理解也很缺乏。我们建议对三种临床类型的患者的KS活检进行深入的表达谱分析：HIV-（也称为经典KS），以及两种HIV/AIDS KS的临床变体：局部惰性（T0），晚期/弥散性（T1）。我们将从现有资料库中获取临床特征患者的匹配肿瘤和PMBC。我们将应用高通量NGS（外显子组测序，RNAseq）来了解决定不同KS临床表现的完整肿瘤生物学特征。临床验证的基因组数据（致癌网络、分层KS疾病特征）将被用作大数据驱动的计算系统生物学工具的输入，以确定将在细胞和动物模型中进行测试的新靶点、药物和组合。我们的目标是构建AIDS-KS药物发现的管道：一级：AIDS-KS的基因组和转录组学，以确定可药物的致癌网络。我们提出，KS疾病的严重程度是由病毒癌基因和宿主突变驱动的致癌宿主信号级联反应的结果，这些突变有助于pdgfra驱动的KSHV肉瘤形成。我们将确定临床可操作的致癌网络，以确定AIDS-KS靶点和治疗方法。我们将在KS活检和对照组织（包括PMBC）中使用NGS方法（全外显子组测序和RNAseq）来生成宿主和KSHV转录组并确定KS肿瘤突变负担。我们将开展生物信息学分析，以鉴定KS肿瘤中的deg、富集通路、分层疾病特征和宿主突变负担，以表征致癌网络并确定艾滋病-KS的可药物靶点。第2层：药物的计算识别。我们将利用KS病毒、治疗、分层KS疾病特征和优先目标作为我们大数据和人工智能驱动的计算工具和管道的输入。精准药物治疗组合将优先使用我们的SynergySeq平台；例如，在艾滋病- ks抗逆转录病毒治疗的背景下，阿霉素和/或抗pdgfra药物的新型有效药物组合。我们还将使用大规模的基于人工智能的硅筛选来鉴定新的可处理的多靶点化学型，包括PDGFRA和HDAC双抑制剂。第三层：药物和联合候选药物的筛选和测试。候选药物和药物组合将在一系列经过验证的KSHV肉瘤临床前细胞和动物模型中进行测试，其中包括使用感染的小鼠和人类ks细胞及其KSHV感染的小鼠肿瘤。

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.