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Finding the optimal balance of immunotherapy efficacy and toxicity.

寻找免疫治疗功效和毒性的最佳平衡。

基本信息

批准号：
10630125
负责人：
David Eric Gerber
金额：
$ 56.82万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-12 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10630125
关键词：
Address Autoantibodies Autoimmune Diseases Autoimmunity Bioinformatics Biological Assay Biological Markers Biology Blood specimen Cancer Patient Cellular Immunity Classification Clinical Clinical Data Clinical Trials Collaborations Combination immunotherapy Complex Correlative Study Cytometry Cytotoxic T-Lymphocytes DNA Resequencing Data Data Analyses Data Set Databases Development Disease Equilibrium Event Exclusion Funding Mechanisms Future Genetic Goals Immune Immune Tolerance Immune checkpoint inhibitor Immune response Immune system Immunology Immunotherapy Incidence Inflammatory Institute of Medicine (U.S.)Interdisciplinary Study Laboratories Molecular Monitor Musculoskeletal Organ Outcome Pathway interactions Patient Selection Patients Pattern Peripheral Blood Mononuclear Cell Phenotype Population Protocols documentation Public Health Publishing Recommendation Recording of previous events Regimen Research Research Personnel Resources SNP array Sampling Severities T cell receptor repertoire sequencing T-Lymphocyte Subsets Testing Time Toxic effect Tumor Biology Validation Variant adjudication assay development biomarker panel biomarker validation cancer immunotherapy cancer therapy checkpoint therapy chemokine chemotherapy clinical application clinical efficacy clinically significant cohort cytokine data integration experience immune-related adverse events immunological diversity immunoregulation insight molecular targeted therapies multidisciplinary predictive marker single-cell RNA sequencing tumor tumor immunology

项目摘要

U01 Abstract Despite extensive research into cancer immunotherapy, immune-related adverse events (irAE) remain a critical and poorly understood issue. To address this critical need, we have assembled a multidisciplinary research team with broad and relevant expertise. The co-PIs of this proposal have expertise in cancer immunotherapy, immunology, assay development, and bioinformatics. Together, we have assembled a cohort of ~400 cancer patients treated with ICI, collecting longitudinal treatment, efficacy, and toxicity data, as well as blood samples at pre-treatment baseline, throughout therapy, and at time of toxicity. In our real-world data set, over 10 percent of cases have a history of autoimmune disease, providing insight into use of ICI in a population widely excluded from clinical trials yet routinely treated with these therapies off protocol. Our high-quality clinical data annotation—without which correlative studies have little meaning—addresses the reality that irAE may occur months after ICI initiation and are far more complex to detect and characterize than toxicities of conventional chemotherapy or molecularly targeted therapies. Through existing funding mechanisms, we have already completed autoantibody, cytokine, genetic, and functional assays in these cases. However, we do not currently have resources for comprehensive, integrated analysis of these diverse laboratory and clinical data. The overarching goal of this U01 proposal is to determine the optimal balance between ICI efficacy and toxicity, ultimately identifying a set of biomarkers useful for selection of patients, treatment type and duration, and clinical monitoring. We will achieve this through determination of cellular immunity, comprehensive data analysis, and clinical validation. We have three Aims: (1) Determine cellular immunity in patients experiencing irAE and/or achieving beneficial responses from ICI. We will perform mass cytometry (CyTOF) and T-cell receptor sequencing at multiple time-points. (2) Determine genetic, humoral, and cellular factors associated with irAE and/or beneficial responses from ICI. We will develop a database to integrate and analyze the CyTOF and T-cell receptor sequencing data with clinical efficacy and toxicity data, as well as data from the assays already completed through other mechanisms. (3) Perform analytical and clinical validation of emerging biomarkers. We will apply the best classifying phenotypes emerging from our comprehensive and integrated data analysis to a test set of patients from our existing cohort, eventually identifying a subset of biomarkers with potential for clinical application. Together, these Aims directly address the FOA purpose of reducing the incidence and/or severity of irAE while retaining anti- tumor efficacy.

U01文摘