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BAY AREA & ANDERSON TEAM AGAINST ACQUIRED RESISTANCE - U54 PROGRAM (BAATAAR-UP)

BAY AREA & ANDERSON TEAM AGAINST ACQUIRED RESISTANCE - U54 PROGRAM (BAATAAR-UP)
海湾地区
批准号:
10705103
负责人:
Trever G Bivona
金额:
$106.75万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
未结题
起止时间:
2017-09-30 至 2027-08-31

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Project Summary/Abstract: The goal of this BAATAAR-UP renewal program application within the NCI ARTNet is to characterize the mechanisms of, and therapeutically counteract, acquired resistance to molecular therapies in non-small cell lung cancer (NSCLC) by delineating the tumor-tumor microenvironment (TME) ecosystem and its plasticity during treatment. Acquired resistance is defined as tumor progression that occurs during therapy and after an initial tumor response. The overarching hypothesis is that acquired resistance to molecular therapies can be thwarted by defining and exploiting vulnerabilities in the cellular, signaling, and geographic tumor ecosystem networks that allow tumors to survive and grow during therapy. In lung cancer and other cancer types, the use of targeted therapies that inhibit important and common oncogenic driver alterations such as mutant EGFR and KRAS (G12C) and block immunosuppressive checkpoints such as PD1/PDL1 is improving patient outcomes. A major challenge to transforming cancers into chronic or curable diseases is acquired resistance, which enables lethal cancer progression in patients. Understanding the mechanisms underlying acquired resistance is essential to develop counteracting strategies that improve patient survival. During the prior NCI U54 DRSC funding period, our team uncovered several mechanisms of acquired resistance to targeted therapy in human NSCLC by studying clinical specimens and innovative patient-derived models including humanized murine models bearing patient-derived xenografts (PDXs) and patient-derived organoids (PDOs) with an intact TME. Our expert team proposes to investigate these mechanisms, and identify others, synergistically and iteratively via 3 Research Projects and optimal interactions with 2 Cores. A Data Science Core will analyze, harmonize, centralize, and share data obtained across the basic and translational continuum using innovative methods. An Administrative Core will ensure optimal project integration and internal and external interactions with the ARTNet Consortium, and scientific and lay communities. Project 1 (Clinical tumor-TME acquired resistance) is translational and uses clinical specimens and patient-derived models to test the hypothesis that tumor macrophages and tumor fibroblasts promote acquired resistance via paracrine signaling interactions including cytokine, CD47, and extracellular matrix (ECM) cues sensed by cancer cells and converging on survival pathways such as YAP and NF-kB. Project 2 (PDX tumor-TME acquired resistance) is translational and uses humanized mouse models to test the hypothesis that an immune-suppressive TME and activation of macrophage and fibroblast signaling circuits that support tumor cell survival via PDK1, YAP, and NF-kB signaling promote acquired resistance. Project 3 (PDO tumor-TME acquired resistance) is basic and uses synthetic lethal and proteomic profiling in PDOs with a TME to test the hypothesis that signaling interactions involving the ECM, TROP2, and CD47 promote acquired resistance. Synergistic, iterative interactions to study these mechanisms across projects and systems will yield robust, translatable treatment strategies to counteract acquired resistance.
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国内基金
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