Integrating targeted and immunotherapy to treat genetically heterogeneous cancers

整合靶向治疗和免疫治疗来治疗遗传异质性癌症

• 批准号: 10199951
• 负责人: ALLAN BALMAIN
• 金额: $ 101.04万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-17 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10199951
• 关键词: Address; Advanced Malignant Neoplasm; Antibodies; Antigen Presentation; Antigens; Antineoplastic Agents; CRISPR screen; Cancer Model; Candidate Disease Gene; Categories; Cells; Clinical; Clinical Trials; Collection; Competence; DNA Damage; Databases; Diagnostic; Disease; Drug Combinations; Drug Targeting; Effector Cell; Gene Expression; Genes; Genetic; Goals; Grant; Human; Immune; Immune response; Immune system; Immunologics; Immunooncology; Immunophenotyping; Immunotherapy; Infiltration; Laboratories; Lead; Malignant Neoplasms; Measures; Metabolic stress; Modeling; Mus; Mutation; Myeloid Cells; Network-based; Neuroblastoma; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Phase; Point Mutation; Predisposition; Proteomics; Reagent; Squamous cell carcinoma; T cell response; T-Cell Activation; T-Lymphocyte; Testing; Tumor Cell Line; Tumor-infiltrating immune cells; anti-CTLA4; anti-PD-1; cancer immunotherapy; cancer therapy; data mining; druggable target; high throughput screening; immune checkpoint blockade; immunoregulation; improved; inhibitor/antagonist; innovation; large scale data; monocyte; mouse model; neoplastic cell; novel; pre-clinical; response; screening; single-cell RNA sequencing; small molecule; treatment response; tumor; tumor microenvironment

Identification of cancer drug targets using high throughput screens of tumor cell lines has led to a number of agents presently in clinical trials. In addition, recent advances in drugs that attack immune cells within tumors, such as αCTLA4 and αPD-1, have highlighted the importance of immune modulation as a strategy for cancer therapy. The next phase of cancer drug target discovery will seek to integrate these strategies to identify combinations of drugs that most efficiently target both tumor cells and the immune components in advanced cancers. The goal of this proposal is to identify and validate these combinations using large-scale data mining and mouse pre-clinical cancer models that mimic the major genetic features of human cancer. This proposal addresses both mechanisms of immune escape by a) finding genetic targets that may enhance tumor mutation load, and b) carrying out high throughout screens in T cells or myeloid cells for targets that promote immune cell infiltration. We will exploit unique mouse models that mirror major genetic categories of human cancer – high vs low mutation load, and strong vs weak immune infiltrate. Applying single-cell RNAseq and mass cytometric proteomic analyses, cutting edge immune composition databases and novel computational network approaches to cancer target discovery using existing large databases, we propose to identify vulnerabilities addressed by combining small molecule drugs with immunotherapy. We will make immunologically “cold” tumors, that do not engage the immune system, into “hot” tumors that present more or stronger antigens, or that encourage infiltration by immune effector cells. To achieve this goal, we propose three highly innovative aims centered on perturbation of specific targets: first by a CRISP/Cas9 screen in immune cells of the tumor microenvironment, second through increasing antigen load in tumors to optimize immune recognition and finally through a network-based identification of tumor-expressed targets that may confer susceptibility to existing immune-oncology therapies. This represents a true `network' of our collective expertise as well as a measured collection of candidate and screening approaches. AIM 1 –We will perform CRISPR screens in monocytes and T-cells to identify genes associated with tumor entry and function in two distinct tumor types. AIM 2– We will use genetic or pharmacological perturbation of newly generated candidate genes involved in metabolic stress and ROS-induced DNA damage to increase mutation load and antigen abundance in a tumor- specific manner, leading to improved responses to immunotherapy. AIM 3 – We will exploit gene expression networks to identify druggable targets and pathways that augment immune responses. This proposal identifies pathways and perturbants for accelerating immunotherapies.

使用肿瘤细胞系的高通量筛选来识别癌症药物靶点已经导致了许多 目前正在进行临床试验的药物。此外，攻击肿瘤内免疫细胞的药物的最新进展， 如αCTLA4和αPD-1，已经强调了免疫调节作为癌症治疗策略的重要性 心理治疗。抗癌药物靶点发现的下一阶段将寻求整合这些策略以识别 最有效地针对晚期肿瘤细胞和免疫成分的药物组合 癌症。该提案的目标是使用大规模数据挖掘来识别和验证这些组合 以及模仿人类癌症主要遗传特征的小鼠临床前癌症模型。这项建议 通过a)找到可能增强肿瘤突变的基因靶点来解决免疫逃逸的两种机制 负载，以及b)在T细胞或髓系细胞中进行高通量筛选，以寻找促进免疫的靶点 细胞渗入。我们将开发独特的小鼠模型，反映人类癌症的主要遗传类别- 高VS低突变负荷，强VS弱免疫渗透。应用单细胞RNAseq和MASS 细胞蛋白质组学分析、前沿免疫成分数据库和新型计算网络 利用现有的大型数据库发现癌症目标的方法，我们建议识别漏洞 通过将小分子药物与免疫疗法相结合来解决。我们会让免疫系统变得“冷” 不参与免疫系统的肿瘤，变成呈现更多或更强抗原的“热”肿瘤，或者 促进免疫效应细胞的渗透。为了实现这一目标，我们提出了三个高度创新的建议 以特定靶点的扰动为中心的目标：首先通过在肿瘤的免疫细胞中进行CRISP/Cas9筛选 微环境，第二，通过增加肿瘤中的抗原负荷来优化免疫识别和 最后，通过基于网络的肿瘤表达靶点的识别，这些靶点可能会使人对 现有的免疫肿瘤学疗法。这代表了我们集体专业知识的一个真正的“网络”，以及 衡量候选人和筛选方法的收集。 目标1-我们将在单核细胞和T细胞中进行CRISPR筛查，以确定与肿瘤相关的基因 在两种不同的肿瘤类型中的进入和功能。 目标2-我们将使用新生成的候选基因的遗传或药物扰动 代谢应激和ROS诱导的DNA损伤以增加肿瘤中的突变负荷和抗原丰度- 特定的方式，导致对免疫治疗的更好反应。 目标3-我们将利用基因表达网络来确定可用药的靶点和增强 免疫反应。 这项建议确定了加速免疫疗法的途径和干扰因素。

项目成果

ALK upregulates POSTN and WNT signaling to drive neuroblastoma.

ALK 上调 POSTN 和 WNT 信号传导以驱动神经母细胞瘤。

• DOI: 10.1016/j.celrep.2024.113927
• 发表时间: 2024
• 期刊: Cell reports
• 影响因子: 8.8
• 作者: Huang,Miller;Fang,Wanqi;Farrel,Alvin;Li,Linwei;Chronopoulos,Antonios;Nasholm,Nicole;Cheng,Bo;Zheng,Tina;Yoda,Hiroyuki;Barata,MegumiJ;Porras,Tania;Miller,MatthewL;Zhen,Qiqi;Ghiglieri,Lisa;McHenry,Lauren;Wang,Linyu;Asgharza
• 通讯作者: Asgharza

Antisecretory Factor-Mediated Inhibition of Cell Volume Dynamics Produces Antitumor Activity in Glioblastoma.

• DOI: 10.1158/1541-7786.mcr-17-0413
• 发表时间: 2018-05
• 期刊: Molecular cancer research : MCR
• 作者: Ilkhanizadeh S;Sabelström H;Miroshnikova YA;Frantz A;Zhu W;Idilli A;Lakins JN;Schmidt C;Quigley DA;Fenster T;Yuan E;Trzeciak JR;Saxena S;Lindberg OR;Mouw JK;Burdick JA;Magnitsky S;Berger MS;Phillips JJ;Arosio D;Sun D;Weaver VM;Weiss WA;Persson AI
• 通讯作者: Persson AI