Drug Mechanism of Action-based targeting of tumor subpopulations

基于作用的肿瘤亚群靶向药物机制

• 批准号: 10729387
• 负责人: ANDREA CALIFANO
• 金额: $ 47.94万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729387
• 关键词: Abate; Address; Algorithms; Behavior; Cancer Center; Cancer Patient; Cell Line; Cell model; Cells; Clinical; Complement 1q; Complex; Data; Dependence; Disease-Free Survival; Drug Targeting; Drug resistance; Duct (organ) structure; Excision; Fibroblasts; Fostering; Funding; Genetically Engineered Mouse; Goals; Health; Heterogeneity; Human; Immune; Immune checkpoint inhibitor; Immune response; Immunotherapy; In Vitro; Individual; Infiltration; Inter-tumoral heterogeneity; Intervention; Isogenic transplantation; KPC model; Logic; MAP Kinase Gene; Macrophage; Malignant - descriptor; Malignant Neoplasms; Mediating; Metaplasia; Methodology; Modeling; Molecular; Network-based; Neurosecretory Systems; Olives - dietary; Organoids; Outcome; Pancreatic Ductal Adenocarcinoma; Paracrine Communication; Pathway interactions; Patients; Pharmaceutical Preparations; Pre-Clinical Model; Prediction of Response to Therapy; Printing; Proteins; Proteome; Publications; Reagent; Regulatory T-Lymphocyte; Relapse; Sampling; System; TREM2 gene; Therapeutic; Validation; Xenograft Model; actionable mutation; cancer cell; castration resistant prostate cancer; cell behavior; clinically relevant; design; drug mechanism; drug sensitivity; drug-sensitive; genome database; genome-wide; human model; immune checkpoint blockade; improved; in vivo Model; neoplastic cell; novel; panacea; patient derived xenograft model; patient responsibilities; pharmacologic; programs; recruit; relapse patients; response; single cell analysis; small molecule inhibitor; targeted treatment; tumor; tumor heterogeneity; tumor microenvironment; tumor-immune system interactions; virtual

Patients with aggressive cancers often present with no pharmacologically actionable mutations and fail to respond to immune checkpoint blockade, thus deriving only modest improvement in disease-free survival from targeted therapy and immunotherapy. Tumor heterogeneity further complicates these challenges by fostering paracrine signal-mediated reprogramming, adaptation, selection, and expansion of drug-resistant cell states, as well as emergence of an immunosuppressive tumor microenvironment (TME), which are ultimately responsible for patient relapse and poor outcome . We propose that addressing these challenges—i.e., identifying more universal, mechanistic targets for pharmacological intervention and assessing their potential value in highly heterogeneous tumors—is critically dependent on the availability of accurate and comprehensive cellular networks, which underlie both the cell-autonomous behavior of cancer cells and their interaction with other TME subpopulations. In Project 3 we propose to match the proteome-wide Mechanism of Action (MoA) of clinically relevant compounds—as dissected from in our PanACEA database of genome-wide molecular perturbations in high-fidelity models of human malignancies—to the non-oncogene dependencies of molecularly distinct, yet coexisting subpopulations, representing either transformed, malignant cells or healthy cells recruited to the TME to create a pro-malignant, immunosuppressive milieu, as dissected by single cell analyses. Targeting individual subpopulations is becoming increasingly critical because the heterogeneity and plasticity of both transformed and non-transformed TME subpopulations have emerged as, perhaps, the most fundamental obstacles to achieving durable responses in cancer patients and distinct subpopulations appear to either have potentially orthogonal drug sensitivities or to represent healthy, immunosuppressive cells that will require an entirely different approach to targeting their recruitment to the TME rather than causing their demise. To accomplish these goals, Project 3 will leverage data, models and reagents generated during the prior CSBC funding cycle for the study of metastatic castration resistant prostate cancer (mCRPC) and pancreatic ductal adenocarcinoma (PDAC), two aggressive, highly heterogeneous malignancies, with ≤ 20% 5-year survival. We will focus on mCRPC and on its aggressive neuroendocrine subtype (NEPC) to explore a novel molecular triangulation methodology (OncoLoop) designed to identify high-fidelity models—i.e., cell lines, organoids, genetically engineered mouse models (GEMMs) and patient derived xenografts (PDXs)—to generate patient-relevant drug perturbation profiles in vitro and to validate drugs predicted from patient-derived sample analysis in preclinical models in vivo. We will then focus on 6 molecularly distinct malignant PDAC subpopulations—comprising Lineage, Morphogenic, and Acinar to Ductal Metaplasia-like cells, each detected in either a MAPK pathway active or inactive state—exploring multiple pro-malignant TME subpopulations (including tumor infiltrating T regulatory cells, macrophages and fibroblasts), to identify small molecule inhibitors that effectively deplete them.

具有侵略性癌症的患者通常出现没有药物可行的突变，并且无法 应对免疫检查点的封锁，因此仅得出适度的无疾病生存的改善 靶向治疗和免疫疗法。肿瘤异质性进一步使这些挑战复杂化 旁分泌信号介导的重新编程，适应，选择和耐药细胞状态的膨胀，AS 以及免疫抑制肿瘤微环境（TME）的出现，最终是负责的 患者缓解和结果不佳。我们建议解决这些挑战 - 即确定更多 普遍的药物干预机械目标，并评估其高度的潜在价值 异质性肿瘤 - 严重取决于准确和全面的细胞的可用性 网络，这是癌细胞的细胞自主行为及其与其他TME的相互作用的基础 亚群。在项目3中，我们建议与临床上全蛋白质组的作用机理（MOA）匹配 相关化合物 - 从我们的全基因组分子扰动的灵丹妙药数据库中解剖 人类恶性肿瘤的高保真模型 - 分子截然不同的非c依依赖性 共存的亚群，代表转化的，恶性细胞或募集到TME的健康细胞 通过单细胞分析所解剖的，创建一个促恶性的免疫抑制环境。针对个人 亚群变得越来越关键，因为两者的异质性和可塑性转化 未转化的TME亚群也可能是最根本的障碍 在癌症患者中达到持久的反应和不同的亚群似乎有可能 正交药物敏感性或代表健康的免疫抑制细胞，这将完全需要一个 针对TME的招聘而不是导致他们的灭亡的不同方法。完成 这些目标3将利用先前CSBC资金周期中生成的数据，模型和试剂 为了研究转移性cast割抗性前列腺癌（MCRPC）和胰腺导管腺癌 （PDAC），两个侵略性，高度异质性恶性肿瘤，≤20％5年生存率。我们将重点关注 MCRPC及其侵略性神经内分泌亚型（NEPC）探索一种新型分子三角测量 方法论（OnColoop）旨在识别高保真模型 - 即细胞系，器官，遗传学 工程鼠标模型（GEMM）和患者衍生的Xenographictic（PDXS） - 生成与患者相关的药物 体外扰动曲线并验证临床前的患者衍生样品分析预测的药物 体内模型。然后，我们将重点关注6个分子截然不同的恶性PDAC亚群 - 谱系，形态学和掺杂到导管化生的细胞，每个细胞都在MAPK途径中检测到每个细胞 主动或无活性状态 - 探索多个促触者TME亚群（包括肿瘤浸润T 调节细胞，巨噬细胞和成纤维细胞），以鉴定有效翻译它们的小分子抑制剂。