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Nano and biomolecular engineered technologies for neoantigen-specific T cell capture and characterization

Nano and biomolecular engineered technologies for neoantigen-specific T cell capture and characterization
用于新抗原特异性 T 细胞捕获和表征的纳米和生物分子工程技术
批准号:
10673935
负责人:
James R. Heath
金额:
$52.8万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-09-16 至 2026-06-30

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Project summary Much of cancer immunotherapy is focused on engineering or activating tumor-antigen specific CD8+ T cells and, to a lesser extent, CD4+ T cells. In particular, neoantigen-specific T cells are attractive because they can kill cancer cells with high specificity. 1 A general approach starts with identifying T cells that recognize neoantigens broadly expressed within the tumor, isolating the T cells and determining their T cell receptor (TCR) sequences. These TCRs can then be transfected into patient T cells, perhaps with additional genetic engineering2 to promote more durable anti-tumor effects, and expanded into an infusion product for patient treatment. 3 In fact, this approach has recently entered the clinic, with one trial (NCT03970382) drawing from inventions from an NCI- funded CCNE U54 grant led by the PI of this proposal. 4,5 However, there are still a number of fundamental and technological challenges associated with advancing neoantigen-specific TCR-engineered therapies. First, the discovery of neoantigen-specific TCRs relies on guidance from algorithms, such as NET MHCpan 4.1, to predict antigen/MHC presentation (based upon binding and other considerations), and many neoantigens arising from truncal mutations, such as mutant KRAS or mutant TP53, are predicted as unlikely, yet have been reported as clinically effective targets. 6,7,8 Second, neoantigen-specific CD4+ T cells and their class II restricted neoantigens, while identified as important for immunotherapy-induced anti-tumor responses, 9,10 remain a largely untapped therapeutic resource, with prediction algorithms 11,12 for Class II antigen/MHC binding less developed. A third challenge is that analysis of a patient blood for neoantigen-specific T cells typically requires upwards of 20M peripheral blood mononuclear cells (PBMCs), and so isn't particularly efficient. Here we propose 3 specific Aims designed to address these outstanding issues. At the heart of the technology solutions are combinations of engineered nanoparticles (NPs) and biomolecular engineered constructs designed for efficient and selective capture, analysis, and validation of truncal neoantigen-specific CD4+ and CD8+ T cell populations. Significant preliminary data is presented for all 3 Aims, some of which uses COVID-19 patient data generated by our work during the current pandemic. 13,14 The result of this work will be a powerful toolset designed for a minimally-biased search for CD4+ and CD8+ T cell populations against truncal neoantigens (independent of patient HLA haplotype), a toolset designed for the rapid validation and characterization of those neoantigen-specific T cell clonotypes, and a public data base of Class I and Class II truncal neoantigens and T cell receptor genes specific to those neoantigens.
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DOI: 10.1146/annurev-cancerbio-061521-082114
发表时间: 2023
期刊: ANNUAL REVIEW OF CANCER BIOLOGY
影响因子: 7.7
作者: [Martinov, Tijana, Greenberg, Philip D.]
通讯作者: Greenberg, Philip D.
Administrative Core
  • 批准号:
    10526102
  • 项目类别:
  • 资助金额:
    $24.66万
  • 财政年份:
    2022
  • 负责人:
    James R. Heath
  • 依托单位:
Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor: Immunotherapy Combinations (ST-Analytics)
  • 批准号:
    10708901
  • 项目类别:
  • 资助金额:
    $254.9万
  • 财政年份:
    2022
  • 负责人:
    James R. Heath
  • 依托单位:
PROJECT 1: TIME-Based Spatiotemporal Cancer Immunograms Predictive for Immunotherapy-Targeted Therapy Sequential Combinations
  • 批准号:
    10907268
  • 项目类别:
  • 资助金额:
    $14.72万
  • 财政年份:
    2022
  • 负责人:
    James R. Heath
  • 依托单位:
Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor: Immunotherapy Combinations (ST-Analytics)
  • 批准号:
    10526101
  • 项目类别:
  • 资助金额:
    $270.26万
  • 财政年份:
    2022
  • 负责人:
    James R. Heath
  • 依托单位:
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