Stress response networks in cancer: systematic mapping and therapeutic potential

癌症中的应激反应网络：系统绘图和治疗潜力

基本信息

批准号：
9315782
负责人：
Martin Kampmann
金额：
$ 24.9万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2018-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9315782
关键词：
Allografting Antineoplastic Agents Biological Markers Bortezomib CREBBP gene Cancer Biology Cancer Patient Cancer cell line Cause of Death Cell Line Cell model Cells Cellular Stress Response Chromosome Mapping Clinical Clinical Trials Collaborations Combined Modality Therapy Complex Development Drug Combinations Drug Interactions Drug Targeting Drug resistance Endoplasmic Reticulum Foundations Genes Genetic Genetic Predisposition to Disease Genomic Instability Goals Growth Health Hematopoietic Neoplasms Human Hypersensitivity Individual Lethal Genes MCL1 gene Malignant Neoplasms Mammalian Cell Maps Measures Messenger RNA Multiple Myeloma Mutation Nutrient Oxygen Pathway interactions Patients Pharmaceutical Preparations Pharmacology Pharmacotherapy Phenotype Postdoctoral Fellow Proteasome Inhibitor Proteins Proteome Publishing RNA Interference Relapse Research Research Activity Resistance Resistance development Role Salvelinus Sampling Scientist Stress Technology Testing Therapeutic Tissue Banks Training Training Activity Translations United States Xenograft Model anticancer research base biological adaptation to stress cancer cell career clinically relevant combinatorial drug sensitivity gene function gene interaction improved in vivo inhibitor/antagonist innovation insight knock-down mouse model new combination therapies new technology novel novel therapeutic intervention patient stratification pre-clinical preempt prognostic of survival programs protein folding public health relevance relapse patients response skills targeted treatment tumor

项目摘要

DESCRIPTION (provided by applicant): Cancer cells survive and thrive under extremely stressful conditions, such as inadequate supply of oxygen and nutrients, genomic instability and proteome imbalances. My central hypothesis is that many cancer cells adapt stress response pathways and become uniquely dependent on them. Individual stress response factors have been characterized and some are currently being investigated as drug targets in cancer. However, we lack a systematic description of the complex, redundant organization of individual factors in a stress response net- work. Understanding this network would enable us to characterize cancer-specific adaptations and vulnerabilities that can be exploited for targeted therapies. To gain this depth of insight, new systematic approaches are called for. As a postdoc in the Weissman lab, I have co-developed a technology platform for systematic map- ping of genetic interactions in mammalian cells. Genetic interactions measure how the phenotype of one mutation is modified by a second mutation. A strong synergistic effect of inactivating two genes simultaneously is referred to as "synthetic lethality". Synthetic lethal genes are ideal targets of effective combination therapies that pre-empt drug resistance. Systematic genetic interaction maps reveal gene functions and cellular path- ways. My long-term goal is to use innovative systematic approaches to understand the complexity of stress network adaptations in cancer and exploit their therapeutic potential. This application focuses on multiple myeloma (MM) as a paradigm. MM cells are characterized by constitutive activation of a stress response, the un- folded protein response. Despite recent advances in MM drug therapy, nearly all patients develop resistance and relapse. The development of better combination therapies for MM is thus an unmet clinical need. In preliminary studies with our platform in MM cell lines, we identified novel genetic vulnerabilities in the stress response network. Expression levels of several of these genes are prognostic of survival in MM and other cancer patients. The overall objective of this application is to determine interactions between these and other vulnerabilities in MM, and to validate the therapeutic potential of targeting these synergistic vulnerabilities in MM and other cancers. I propose the following specific aims: (1) Characterize stress-related and intrinsic vulnerabilities in multiple myeloma and other blood cancer cells (2) Test the role of stress-related genes in determining drug sensitivity and resistance in patient cells (3) Validate the therapeutic potential of targeting the stress response network in multiple myeloma mouse models. This project will provide the basis to test new combination therapies and biomarkers for MM in clinical trials, and pave the way for broad application of our genetic interaction mapping approach to a wide variety of cancers. Through the proposed research and training activities, I will acquire the necessary skills to launch my career as an independent scientist, and lay the foundation of my research program, in which I plan to use innovative approaches to elucidate cancer biology and identify new therapeutic strategies to improve human health.

描述（由申请人提供）：癌细胞在极度紧张的条件下生存和繁衍，例如氧气和营养供应不足、基因组不稳定和蛋白质组失衡。我的中心假设是许多癌细胞适应应激反应途径并变得独特地依赖它们。个体应激反应因子已被表征，其中一些目前正在作为癌症药物靶点进行研究。然而，我们缺乏对压力反应网络中个体因素的复杂、冗余组织的系统描述。了解这个网络将使我们能够描述癌症特异性的适应和脆弱性，这些适应和脆弱性可用于靶向治疗。为了获得这种深度的洞察力，需要新的系统方法。作为韦斯曼实验室的博士后，我与他人共同开发了一个技术平台，用于系统地绘制哺乳动物细胞中的遗传相互作用。遗传相互作用衡量一种突变的表型如何被第二种突变改变。同时灭活两个基因的强烈协同效应被称为“综合致死率”。合成致死基因是预防耐药性的有效联合疗法的理想靶点。系统的遗传相互作用图谱揭示了基因功能和细胞通路。我的长期目标是使用创新的系统方法来了解癌症中压力网络适应的复杂性并开发其治疗潜力。该应用重点关注多发性骨髓瘤 (MM) 作为范例。 MM细胞的特征是应激反应的组成型激活，即展开的蛋白质反应。尽管多发性骨髓瘤药物治疗最近取得了进展，但几乎所有患者都会出现耐药性和复发。因此，开发更好的 MM 联合疗法是一个未满足的临床需求。在我们的平台对 MM 细胞系的初步研究中，我们发现了应激反应网络中新的遗传漏洞。其中一些基因的表达水平可预测多发性骨髓瘤和其他癌症患者的生存情况。该应用的总体目标是确定 MM 中的这些弱点和其他弱点之间的相互作用，并验证针对 MM 和其他癌症中的这些协同弱点的治疗潜力。我提出以下具体目标：（1）表征多发性骨髓瘤和其他血癌细胞中与压力相关的和内在的脆弱性（2）测试压力相关基因在确定患者细胞的药物敏感性和耐药性中的作用（3）验证在多发性骨髓瘤小鼠模型中靶向应激反应网络的治疗潜力。该项目将为在临床试验中测试新的多发性骨髓瘤联合疗法和生物标志物奠定基础，并为我们的遗传相互作用图谱方法在多种癌症中的广泛应用铺平道路。通过拟议的研究和培训活动，我将获得必要的技能来开展我作为独立科学家的职业生涯，并为我的研究计划奠定基础，我计划使用创新方法来阐明癌症生物学并确定新的治疗策略改善人类健康。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

CRISPR-based genetic interaction maps inform therapeutic strategies in cancer.

基于 CRISPR 的遗传相互作用图谱为癌症治疗策略提供了信息。

DOI：
10.21037/tcr.2018.01.02
发表时间：
2018
期刊：
Translational cancer research
影响因子：
0.9
作者：
Ramkumar,Poornima;Kampmann,Martin
通讯作者：
Kampmann,Martin

Targeting the AAA ATPase p97 as an Approach to Treat Cancer through Disruption of Protein Homeostasis.

DOI：
10.1016/j.ccell.2015.10.002
发表时间：
2015-11-09
期刊：
Cancer cell
影响因子：
50.3
作者：
Anderson DJ;Le Moigne R;Djakovic S;Kumar B;Rice J;Wong S;Wang J;Yao B;Valle E;Kiss von Soly S;Madriaga A;Soriano F;Menon MK;Wu ZY;Kampmann M;Chen Y;Weissman JS;Aftab BT;Yakes FM;Shawver L;Zhou HJ;Wustrow D;Rolfe M
通讯作者：
Rolfe M