Evaluating Etiological Impact of Metatranscriptomic and Immunological Features for Lung Cancer
Evaluating Etiological Impact of Metatranscriptomic and Immunological Features for Lung Cancer
批准号:
10437878
负责人:
Yanhong Liu
金额:
$8.0万
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:
已结题
起止时间:
2021-07-01 至 2023-06-30
关键词:
AlgorithmsB-LymphocytesBiological ProcessBiologyCancer PatientCellsClassificationClinicalClinical DataComplexDataData SetDendritic CellsDevelopmentDiagnosisDiscriminant AnalysisDiseaseEarly identificationEnrollmentEquilibriumEtiologyEvolutionFutureGene Expression ProfileGenesGoalsHistologicHistologyHumanImmuneImmune systemImmunologicsImmunophenotypingImmunotherapyInfiltrationInflammatoryLinear RegressionsLinkLungLung AdenocarcinomaLung NeoplasmsMachine LearningMalignant NeoplasmsMalignant neoplasm of lungMapsMedicineMetabolicMetabolic PathwayMicrobeMicrobiologyNatureOperative Surgical ProceduresOutcomePatientsPopulationPublic Health SchoolsRNARecurrenceReproducibilityResolutionRoleSmokingStructure of parenchyma of lungT memory cellTaxonomyTestingTissuesTumor TissueTumor-associated macrophagesTumor-infiltrating immune cellsValidationbasecollegecomputed tomography screeningcostdesigndifferential expressiondysbiosisepidemiologic datahigh riskimmunoregulationlow dose computed tomographylung microbiomelung microbiotalung tumorigenesismetatranscriptomemetatranscriptomicsmicrobialmicrobiomemicrobiotaneutrophilnovelnovel therapeutic interventionpathogenpathogenic microbepatient stratificationpatient subsetspersonalized cancer therapypublic health relevancetranscriptometranscriptome sequencingtumortumor microenvironmenttumor progressiontumorigenesis
中文摘要
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英文摘要
ABSTRACT
There is increasing evidence that the evolution of cancer is strongly dependent on the complex tumor
microenvironment (TME) in which it develops. Despite the important roles of both microbiota and immune cells
within TME, there are huge gaps in linking specific lung-residing microbiota changes with immune cell
subpopulations. To date, no human lung cancer studies have been performed to characterize the host-pathogen
dynamic changes and dissecting the microbiome-immune interaction in an integrated manner.
We hypothesize that the dysbiosis of lung-residing microbe (microbiota expression changes) triggers
dysregulated balance in the lung immune system (immune cells infiltration levels), which results in an
inflammatory TME, and further promotes lung tumorigenesis and tumor progression. We propose to capitalize
on existing RNA-Seq (tumor and adjacent tissue) from 200 early-stage (I–IIIA) lung adenocarcinoma patients,
from a Baylor College of Medicine (BCM) study (discovery, n = 100) and a Harvard School of Public Health study
(external validation, n = 100), with no cost to this application. Our goal is to reveal the impact of the lung
microbiota on host immune cell profiles and how their interaction contributes to tumorigenesis and tumor
progression. To accomplish our goals, we will utilize a Dual RNA-Seq analytical approach: 1) to identity
intratumoral metatranscriptomic signatures, and 2) to characterize immune infiltration profiles and microbiome-
immune interaction. Specifically, the unmapped quality-filtered RNA reads (non-human, putative microbial reads)
aligned to microbial reference transcriptomes will be used for metatranscriptome analysis (Aim 1); whereas reads
that map to human reference will be used for computational immune profiling analysis (Aim 2).
This is the first study to simultaneously profile lung tissue-specific microbiota expression and immune infiltrates
in lung adenocarcinoma. This project could contribute significantly to our understanding of the biological
processes before/during (adjacent/tumor tissue) lung adenocarcinoma development, in particular the complex
microbiome-immune interaction. Elucidating the nature of interactions between lung microbiome and immune
cells comprising the TME could guide the development of novel therapeutic interventions.
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Evaluating Etiological Impact of Metatranscriptomic and Immunological Features for Lung Cancer
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批准号:10290463
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项目类别:
-
资助金额:$8.0万
-
财政年份:2021
-
负责人:Yanhong Liu
-
依托单位:
Lung Cancer Prediction Models Incorporating Exome Data from Extreme Phenotypes
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批准号:9329288
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项目类别:
-
资助金额:$12.79万
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财政年份:2014
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负责人:Yanhong Liu
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依托单位:
Lung Cancer Prediction Models Incorporating Exome Data from Extreme Phenotypes
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批准号:8766329
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项目类别:
-
资助金额:$12.77万
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财政年份:2014
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负责人:Yanhong Liu
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依托单位:
海外基金