The Sylvester Cancer Center Support Grant

西尔维斯特癌症中心支持补助金

• 批准号: 10293879
• 负责人: Stephen D. Nimer
• 金额: $ 12.48万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-10 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10293879
• 关键词: Achievement; Address; Advanced Malignant Neoplasm; Affect; Argentina; Award; Basic Science; Behavioral Research; Bioinformatics; Biometry; Cancer Biology; Cancer Burden; Cancer Center; Cancer Center Support Grant; Cancer Control; Cancer Control Research Program; Cancer Etiology; Cancer Patient; Caribbean region; Catchment Area; Clinical; Clinical Research; Clinical Sciences; Communities; Community Outreach; Competence; Comprehensive Cancer Center; County; Cultural Backgrounds; Cultural Diversity; Development; Diagnosis; Discipline; Education; Ensure; Environment; Environmental Risk Factor; Epigenetic Process; Extramural Activities; Florida; Flow Cytometry; Fostering; Funding; Future; Grant; Growth; Health Professional; Heterogeneity; Home; Human; Individual; Intervention; Investments; Knowledge; Latin America; Leadership; Learning; Malignant Neoplasms; Mission; National Cancer Institute; Oncology; Outcome; Outreach Research; Patient Care; Patients; Peer Review; Play; Population Research; Population Sciences; Positioning Attribute; Prevention; Research; Research Infrastructure; Research Personnel; Resource Sharing; Rewards; Role; SECTM1 gene; Schools; Scientific Advances and Accomplishments; Scientist; Services; Source; Strategic Planning; Training; Training and Education; Translational Research; Translations; Tumor Biology; United States National Institutes of Health; Universities; anticancer research; base; bench to bedside; cancer genomics; cancer health disparity; cancer prevention; cancer risk; college; community based research; community center; community engagement; ethnic diversity; ethnic identity; ethnic minority population; health equity; innovation; insight; interest; multidisciplinary; novel; novel strategies; outreach; post-doctoral training; pre-doctoral; programs; racial and ethnic; racial diversity; recruit; research facility; socioeconomic diversity; socioeconomics

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.

卡波西肉瘤 (KS) 由 KS 疱疹病毒 (KSHV) 引起，是一种与艾滋病相关的恶性肿瘤 (AIDSKS)。 KS 对抗逆转录病毒治疗和化疗有反应。然而，预计超过一半的KS患者将无法治愈。除了迫切需要新的疗法来治疗晚期和抗 ART 的 KS 之外，我们对导致 AIDSKS 的各种病症及其不同表现的生物学理解也很缺乏。我们建议对属于三个临床类别的患者的 KS 活检进行深入的表达谱分析：HIV-（也称为经典 KS）和 HIV/AIDS KS 的两种临床变体：局部惰性（T0）、晚期/播散性（T1）。我们将从可用存储库中获取具有临床特征的患者的匹配肿瘤和 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概况。经过临床验证的基因组数据（致癌网络、分层 KS 疾病特征）将用作大数据驱动的计算系统生物学工具的输入，以识别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：第 1 层：AIDS-KS 的基因组和转录组学，以确定可药物致癌网络。我们认为 KS 疾病的严重程度是由病毒致癌基因和宿主突变驱动的致癌宿主信号级联的结果，这些突变导致 PDGFRA 驱动的 KSHV 肉瘤发生。我们将确定临床上可行的致癌网络，以确定 AIDS-KS 的目标和治疗方法。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 肿瘤突变负荷。我们将进行生物信息学分析，以识别 KS 肿瘤中的 DEG、富集途径、分层疾病特征和宿主突变负担，以表征致癌网络并确定 AIDS-KS 中的可药物靶点。第 2 层：药物的计算识别。我们将采用 KS 病毒、治疗、分层 KS 疾病特征和优先目标作为我们的大数据和人工智能驱动的计算工具和管道的输入。使用我们的 SynergySeq 平台将优先考虑精准药物治疗组合；例如，在 AIDS-KS ART 治疗中，阿霉素和/或抗 PDGFRA 药物的新型有效药物组合。我们还将使用基于人工智能的大规模计算机筛选来识别新型易处理的多靶点化学型，包括 PDGFRA 和 HDAC 双抑制剂。第三层：候选药物和组合的筛选和测试。候选药物和药物组合将在一系列经过验证的 KSHV 肉瘤发生的临床前细胞和动物模型中进行测试，其中包括使用受感染的小鼠和人类 KS 细胞及其受 KSHV 感染的小鼠肿瘤。