SLFN5: A Novel Therapeutic Target for Glioblastoma
SLFN5: A Novel Therapeutic Target for Glioblastoma
批准号:
10684893
负责人:
LEONIDAS C. PLATANIAS
金额:
$34.56万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-01 至 2024-08-31
关键词:
AccountingAddressAnimalsAntitumor ResponseBiologicalBiologyBrainCell physiologyCellsComplexDataDevelopmentElementsEngraftmentFamilyFamily memberFeedbackGene ExpressionGene FamilyGene ProteinsGenesGenetic TranscriptionGlioblastomaGoalsGrowthHumanImmune responseImmune systemInterferon ActivationInterferon SuppressionInterferon Type IInterferonsInvestigationMalignant NeoplasmsMediatingModelingMorbidity - disease rateMusNatureNude MicePathway interactionsPatientsPerformance at workPropertyProtein FamilyProteinsRepressionResearchResponse ElementsRoleSTAT1 geneSamplingSignal TransductionTranscription CoactivatorTumor BiologyTumor PromotionWorkXenograft Modelbrain tissuecancer stem cellcellular targetingcheckpoint therapyeffective therapygene repressionin vivoin vivo Modelknock-downmembermortalitymouse modelnew therapeutic targetnovelnovel strategiesnovel therapeutic interventionoverexpressionpatient derived xenograft modelprotein expressionprotein functionpublic databaseresponsestem cellstumortumor immunology
中文摘要
项目摘要/摘要
胶质母细胞瘤(Gbm)是一种高度侵袭性的恶性肿瘤,具有很高的发病率和死亡率。
有效的治疗方法。这项提案的总体目标是在GBM细胞中识别新的细胞靶点,这些靶点可以
导致新的治疗方法。我们发现Schlafen(SLFN)基因家族的一个成员,
与正常脑组织相比,SLFN5在GBM中显著过度表达,且高水平SLFN5
在GBM患者中,表达与低存活率相关。我们的数据表明SLFN5促进GBM
通过与干扰素相互作用抑制干扰素信号和干扰素刺激基因(ISG)的表达而生长
转录激活因子STAT1。这一极具新颖性的发现构成了当前提案的基础。目标1将
确定SLFN5-STAT1复合体的元件,定义形成SLFN5-STAT1复合体所需的上游调控信号
这样的络合物,并确定这些络合物的元素与SLFN5相关的关系
转录抑制。不同SLFN5结构基序的功能及其在植物生长发育中的重要性
将检查干扰素反应的抑制情况。使用GBM患者原始样本的研究将是
也受雇于这样的研究。目标2将使用三种不同的方法定义SLFN5在体内的表达效果
SLFN5表达和不表达SLFN5的原位移植模型和GBM细胞对:i)常规
采用裸鼠建立异种移植模型,检测SLFN5对肿瘤建立和生长的影响;
二)人源化的原位PDX模型,以检查SLFN5对人对人的免疫反应的影响
抗肿瘤,以及检查肿瘤对免疫检查点治疗的反应;以及iii)相同的a ii但
使用宿主动物具有完全功能的免疫系统的小鼠GBM同源基因模型。
总之,这项工作的结果将为SLFN5通过何种机制提供重要信息
表达抑制干扰素反应,促进基底膜生长。这部作品的成功演出
应促进开发以SLFN5为靶点的治疗GBM的高度新颖的方法。
英文摘要
PROJECT SUMMARY/ABSTRACT
Glioblastoma (GBM) is a highly aggressive malignancy with very high morbidity and mortality, due to lack of
effective therapies. The overall goal of this proposal is to identify novel cellular targets in GBM cells that could
lead to new therapeutic approaches. We have found that a member of the Schlafen (SLFN) gene family,
SLFN5, is significantly overexpressed in GBM as compared to normal brain, and that high levels of SLFN5
expression correlate with poor survival among GBM patients. Our data indicates that SLFN5 promotes GBM
growth by repressing IFN signaling and IFN stimulated gene (ISG) expression via an interaction with the
transcriptional activator STAT1. This highly novel finding forms the basis of the current proposal. Aim 1 will
identify elements of SLFN5-STAT1 complexes, define upstream regulatory signals required for the formation of
such complexes, and determine relationships between elements of these complexes and SLFN5-associated
transcriptional repression. The functions of different SLFN5 structural motifs and their importance to the
suppression of IFN-responses will be examined. Studies using primary samples from GBM patients will be
also employed for such studies. Aim 2 will define the effects of SLFN5 expression in vivo using three distinct
orthotopic engraftment models and GBM cell pairs with and without SLFN5 expression: i) conventional
xenograft models using athymic mice for examining the effects of SLFN5 on tumor establishment and growth;
ii) humanized orthotopic PDX models to examine SLFN5 effects for human-on-human immune response
against tumor, as well as to examine tumor response to immune checkpoint therapy; and iii) same a ii but
using mouse GBM syngeneic models in which the host animals have a fully functional immune system.
Altogether, the results of this work will provide important information on the mechanisms by which SLFN5
expression suppresses IFN-responses and promotes GBM growth. The successful performance of this work
should facilitate development of highly novel approaches for the treatment of GBM using SLFN5 as a target.
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