Analytical tools for studying the tumor microenvironment leveraging spatial transcriptomics
利用空间转录组学研究肿瘤微环境的分析工具
基本信息
- 批准号:10524921
- 负责人:
- 金额:$ 41.57万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-01 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:ArchitectureAutomobile DrivingBasic ScienceBioconductorBioinformaticsBiologicalBiological AssayCancer CenterCancer PatientCancer PrognosisCancer ScienceCellsClinicalClinical SciencesCollaborationsColoradoCommunicationComputer softwareComputing MethodologiesCytometryDNA Sequence AlterationDataData ScientistDevelopmentEnsureEpigenetic ProcessFutureGene ExpressionGenomicsGoalsHeterogeneityImageImmunofluorescence ImmunologicImmunotherapyIndividualInfiltrationLocationMalignant NeoplasmsManuscriptsMeasurementMeasuresMethodologyMethodsModalityNeoplasm MetastasisOnline SystemsOutcomePatientsPharmaceutical PreparationsPhenotypeProcessRNAResearchResearch PersonnelResearch Project GrantsResolutionSamplingSliceSoftware ToolsSpottingsStatistical MethodsTechnologyTissue FixationTissue SampleTissuesTranscriptTranslatingTranslational ResearchTumor-infiltrating immune cellsUniversitiesVisualizationanalytical methodanalytical toolbasecell typecommercializationcomputerized toolsdata integrationdata structuredesignexperimental studyfallshigh dimensionalityimmune functioninnovationinsightinterestmultidimensional datanovelpatient responsepersonalized medicinephenotypic dataprognosticationresponsesingle-cell RNA sequencingsoftware developmentspatial integrationstatisticstooltranscriptometranscriptome sequencingtranscriptomicstreatment responsetumortumor heterogeneitytumor microenvironmenttumorigenesisusabilityuser friendly softwareweb app
项目摘要
PROJECT SUMMARY/ABSTRACT
Understanding the tumor microenvironment (TME) heterogeneity and architecture are key to stratify cancer
patients responsive to immunotherapy and clinical outcome. Single-cell RNA sequencing (scRNAseq) is a
powerful tool for studying the TME at the single-cell level, however, spatial information between single cells
was not preserved in this technology, which is vital in studying the TME. In contrast, use of spatially resolved
transcriptomics holds the promise in the understanding of the spatial contexture of the TME because of its
power to capture the location of individual cells within the larger tissue architecture. Recently,
commercialization of spatial transcriptomic (ST) technologies have allowed researchers to study at an
unprecedented level the spatial architecture of the TME. Similar to other “omics” technologies, novel
computational tools are urgently needed to decipher and infer biological meaning for these high-dimensional
ST data. In addition to the need for methods for analyzing and visualizing ST data in its current form, there is
the need to develop methods for the future state of ST, whereby the level of cellular resolution is vastly
decreasing to the single cell level. Moreover, the application of multiple assays to one tissue sample is also
producing multiple modalities measured on the same sample (e.g., scRNAseq, image-based cytometry
methods such as multiplex immunofluorescence) which requires effective methods for data integration. Lastly,
many ST studies are completed on multiple samples simultaneous with the goal of correlating TME features
with clinical outcome, thus requiring computational tools to unravel the impact of the spatial architecture of the
TME on clinical response. In the proposed research, we will tackle these challenges by implementing state-of-
the-art statistical and computational methods that account for and leverage the spatial information present in
ST data to understanding the TME. We will develop innovative methods for assessing the TME’s composition
(Aim 1) and studying co-localization and spatial heterogeneity (Aim 2), along with hardening of the analytical
software, spatialGE, for the analysis and visualization of ST data (Aim 3). The statistical and bioinformatics
analytical approaches implemented in spatialGE will allow cancer researchers to easily leverage these
methods in their studies of the TME. These analytical methods, along with the developed software tools
(spatialGE R/Bioconductor package along with web-based software), will be established in collaboration with
clinical, translational, and basic science cancer investigators to ensure usability and interpretability of the
developed approaches.
项目总结/文摘
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)
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Brooke L Fridley其他文献
Polymorphisms in NF-κB Inhibitors and Risk of Epithelial Ovarian Cancer
- DOI:
10.1186/1471-2407-9-170 - 发表时间:
2009-06-06 - 期刊:
- 影响因子:3.400
- 作者:
Kristin L White;Robert A Vierkant;Catherine M Phelan;Brooke L Fridley;Stephanie Anderson;Keith L Knutson;Joellen M Schildkraut;Julie M Cunningham;Linda E Kelemen;V Shane Pankratz;David N Rider;Mark Liebow;Lynn C Hartmann;Thomas A Sellers;Ellen L Goode - 通讯作者:
Ellen L Goode
Gene set analysis of SNP data: benefits, challenges, and future directions
单核苷酸多态性数据的基因集分析:益处、挑战和未来方向
- DOI:
10.1038/ejhg.2011.57 - 发表时间:
2011-04-13 - 期刊:
- 影响因子:4.600
- 作者:
Brooke L Fridley;Joanna M Biernacka - 通讯作者:
Joanna M Biernacka
Brooke L Fridley的其他文献
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{{ truncateString('Brooke L Fridley', 18)}}的其他基金
Bayesian Integrative Clustering for Determining Molecular Based Cancer Subty
用于确定基于分子的癌症亚型的贝叶斯整合聚类
- 批准号:
8625856 - 财政年份:2013
- 资助金额:
$ 41.57万 - 项目类别:
Bayesian hierarchical nonlinear models for pharmacogenomic cytotoxicity studies
用于药物基因组细胞毒性研究的贝叶斯分层非线性模型
- 批准号:
8286143 - 财政年份:2011
- 资助金额:
$ 41.57万 - 项目类别:
Bayesian hierarchical nonlinear models for pharmacogenomic cytotoxicity studies
用于药物基因组细胞毒性研究的贝叶斯分层非线性模型
- 批准号:
7984103 - 财政年份:2011
- 资助金额:
$ 41.57万 - 项目类别:
Integrative genomic models for analysis of pharmacogenomic studies
用于分析药物基因组研究的综合基因组模型
- 批准号:
7698677 - 财政年份:2009
- 资助金额:
$ 41.57万 - 项目类别:
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