Personalization and Failure Testing of Dual Switch Gene Drives in Lung Cancer
肺癌双开关基因驱动的个性化和故障测试
基本信息
- 批准号:10330219
- 负责人:
- 金额:$ 46.24万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-10 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:3-DimensionalBiologicalBiophysicsBystander EffectCancer BiologyCancer PatientCancer cell lineCell TherapyCellsChemicalsClinicalCollaborationsCombined Modality TherapyCoupledCuesDataDiffuseDiffusionDimerizationDirected Molecular EvolutionDrug resistanceEndothelial CellsEngineeringEnvironmentEpidermal Growth Factor ReceptorEvolutionExhibitsExtracellular MatrixFailureFibroblastsGene LibraryGenesGoalsGrantHeterogeneityHumanImmuneIn SituInvestigationLeadLearningLeftLungMalignant NeoplasmsMalignant neoplasm of lungMammalian CellMathematicsMeasurementMeasuresModelingMole the mammalMolecularMutationNon-Small-Cell Lung CarcinomaOncogenesOncologistOrganoidsPatientsPerformancePharmaceutical PreparationsPharmacologyProcessProdrugsProtein Tyrosine KinaseROS1 geneRaceResistanceResistance developmentResolutionRiskSWI1SafetySuicideSuicide Gene TherapySwitch GenesTacrolimus Binding Protein 1ATechniquesTechnologyTestingTherapeuticToxicity TestsToxinTyrosine Kinase InhibitorValidationVariantWorkarmbasecancer cellcell killingcombatcostdesigndimerdrug developmentdrug discoverydual switch selection gene driveeffective therapyengineering designexperimental studyfightingfitnessgene drive systemimprovedinhibitor/antagonistinsightkinase inhibitormimeticsmutantneoplastic cellnovel therapeuticspreventprogramsprototyperesistance mechanismresistance mutationresponsesimulationsmall moleculestandard of caresuicide genesynthetic biologysystemic toxicitytreatment responsetumortumor microenvironment
项目摘要
Project Summary
Different patients with Non-small-cell lung cancers (NSCLC) can harbor mutations that result in constitutively
activated versions of tyrosine kinases (e.g. EGFR, RET, ALK, ROS1, TRK) that can be precisely targeted with
inhibitors. However, tyrosine kinase inhibitors are vulnerable to existing, known and unknown, drug resistance
mechanisms found in tumors. This results in a game of molecular “whack-a-mole” whereby, resistance evolution
appears, the mechanism is isolated, drugs are administered to combat that drug resistance, and then resistance
re-emerges until no effective therapies remain. This process of reverse engineering drug resistance has been a
losing battle with a high cost for patients. A promising approach to combat the challenge of resistance evolution
is to design and test cell therapies that can sense the therapeutic environment and respond through synthetic
biology circuits to reproducibly control evolutionary trajectories. We propose a synthetic biological technology
with proof-of-concept function in mammalian cells that we term “dual-switch selection drives”. These drives use
inducible drug resistance to create a cell therapy that can engineer a tumor’s evolution in situ. The first switch
senses the presence of a dimerizer molecule to create reversible drug resistance. Using the mathematical rules
of biophysics and evolution, our cell therapy calculates a response to small molecules and produces a tunable
amount of cellular fitness that competes with pre-existing drug resistance variants in a tumor. A second switch
with a suicide gene payload hitchhikes on this evolution guided cell therapy until the selection drive cells comprise
the majority of the tumor. Then, at the flip of a second switch, a locally diffusible toxin is produced that kills all
cells--gene drive or pre-existing resistance mutants of any molecular origin--through a bystander effect. This
technology works with the existing standard of care drugs in NSCLC to produce localized combination therapy
that can eradicate pre-existing resistance regardless of the molecular mechanism. Therefore, instead of
responding to and combatting evolution, we use forward engineering of cell therapies to direct evolution. In Aim
1 we will use nonintuitive insights from stochastic models of the evolutionary stability of our designs to propose
further optimized selection drives. Aim 2 expands our forward engineering approach by pushing our model driven
design of safety and efficacy towards the spatial, cellular, and microenvironmental heterogeneity present in
NSCLC. Aim 3 proposes to move evolutionary proof-of-concept experiments into primary human organoids from
NSCLC patients with activating mutations in EGFR. Beyond practical testing of a technology, we will also “build
to understand” the basic cancer biology of resistance evolution.
项目摘要
不同的非小细胞肺癌(NSCLC)患者可能携带导致组成性
酪氨酸激酶的活化形式(例如EGFR、RET、ALK、ROS 1、TRK),其可被
抑制剂的然而,酪氨酸激酶抑制剂容易受到现有的,已知的和未知的耐药性的影响
在肿瘤中发现的机制。这导致了一场分子“打地鼠”的游戏,
出现时,机制是孤立的,药物被用来对抗耐药性,然后耐药性
直到没有有效的治疗方法为止。这种逆向工程耐药性的过程一直是一个
在与病人的高成本的斗争中失败。一种有前途的方法来应对抗性进化的挑战
是设计和测试细胞疗法,可以感知治疗环境,并通过合成
生物电路来可重复地控制进化轨迹。我们提出一种合成生物技术
在哺乳动物细胞中具有概念验证功能,我们称之为“双开关选择驱动器”。这些驱动器使用
诱导耐药性,以创造一种细胞疗法,可以工程肿瘤的演变在原位。第一开关
检测到二聚分子的存在,从而产生可逆的耐药性。利用数学规则
生物物理学和进化,我们的细胞疗法计算小分子的反应,并产生可调的
与肿瘤中预先存在的耐药性变体竞争的细胞适应性的量。第二开关
自杀基因有效载荷在这种进化引导的细胞疗法上搭便车,直到选择驱动细胞包含
肿瘤的大部分。然后,在第二个开关的触发下,产生了一种局部扩散的毒素,
细胞-基因驱动或任何分子来源的预先存在的抗性突变体-通过旁观者效应。这
该技术与NSCLC的现有标准治疗药物配合使用,以产生局部联合治疗
不管分子机制如何,都能根除已经存在的抗药性所以与其
为了应对和对抗进化,我们使用细胞疗法的前瞻性工程来指导进化。在Aim中
1我们将使用我们设计的进化稳定性随机模型的非直观见解,
进一步优化选择驱动器。Aim 2通过推动我们的模型驱动来扩展我们的向前工程方法
安全性和有效性设计,以应对存在的空间、细胞和微环境异质性
NSCLC。目标3提出将进化概念验证实验从
EGFR激活突变的NSCLC患者。除了对技术进行实际测试外,我们还将“建立
了解”抵抗进化的基本癌症生物学。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Justin Pritchard其他文献
Justin Pritchard的其他文献
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{{ truncateString('Justin Pritchard', 18)}}的其他基金
Personalization and Failure Testing of Dual Switch Gene Drives in Lung Cancer
肺癌双开关基因驱动的个性化和故障测试
- 批准号:
10818035 - 财政年份:2021
- 资助金额:
$ 46.24万 - 项目类别:
Personalization and Failure Testing of Dual Switch Gene Drives in Lung Cancer
肺癌双开关基因驱动的个性化和故障测试
- 批准号:
10818053 - 财政年份:2021
- 资助金额:
$ 46.24万 - 项目类别:
Personalization and Failure Testing of Dual Switch Gene Drives in Lung Cancer
肺癌双开关基因驱动的个性化和故障测试
- 批准号:
10487531 - 财政年份:2021
- 资助金额:
$ 46.24万 - 项目类别:
Model Driven Construction of Dual-switch Selection Gene Drives to Combat Drug Resistance
对抗耐药性的双开关选择基因驱动的模型驱动构建
- 批准号:
9973217 - 财政年份:2019
- 资助金额:
$ 46.24万 - 项目类别:
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