An informatics framework for single-cell multi-omics from clinical specimens
临床标本单细胞多组学的信息学框架
基本信息
- 批准号:10522449
- 负责人:
- 金额:$ 34.32万
- 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:2022-07-01 至 2026-03-31
- 项目状态:未结题
- 来源:
- 关键词:Adult GliomaAlgorithmsAnatomyBasic ScienceBiological ModelsBiopsyBrain NeoplasmsCaliforniaCell LineCellsCellular AssayChildhood Brain NeoplasmChromatinClinicClinicalCodeCombined Modality TherapyComplexDNA sequencingDataData SetDatabasesDiagnosisDiseaseDisease ProgressionEvolutionGene ExpressionGeneticGenomic approachGenotypeGlioblastomaGliomaGoalsHeterogeneityHistologyImageIn SituInformaticsInstitutionInternal-External ControlInternetIonizing radiationLearningLeukocytesLinkMalignant - descriptorMalignant NeoplasmsMapsMeasurementMeasuresMethodologyMethodsModelingModernizationMonitorMorphologyMutationNon-MalignantOrganoidsOutcomePatientsPharmaceutical PreparationsPhenotypePhylogenetic AnalysisPhylogenyPrecision therapeuticsPrediction of Response to TherapyPredictive Cancer ModelPrimary NeoplasmProcessProspective cohortRNARecommendationRecurrenceReportingResistanceSan FranciscoSelection for TreatmentsSiteSourceSpecimenSystemT cell therapyTissuesTrainingTransposaseTumor-infiltrating immune cellsUniversitiesValidationVisualizationWorkXCL1 genebasecancer recurrencecellular imagingchemotherapyclinical careclinical decision supportclinical decision-makingclinical sequencingclinically actionableclinically relevantcohortcombinatorialdashboardexome sequencinggenomic predictorsgenomic profilesgenotoxicityhuman DNA sequencingimprovedin vivomalignant breast neoplasmmedulloblastomamodels and simulationmultiple omicsneoantigensnoveloutcome predictionprecision medicineprecision oncologypredictive modelingprognostic signatureresearch clinical testingresponserisk stratificationsingle cell sequencingsingle-cell RNA sequencingstatisticstranscriptomicstumortumor heterogeneity
项目摘要
PROJECT SUMMARY
Intra-tumor heterogeneity is a significant barrier to precision oncology. Emerging single-cell and spatial profiling
approaches have enabled basic research into tumor heterogeneity. However, the application of these
emerging approaches to the clinical decision process is limited. There is a critical need for predictive models
that integrate these novel data with existing genomics approaches and histology, to generate actionable
clinical recommendations. This proposal builds on my lab’s recent work, using single-cell RNA sequencing
(scRNA-seq) to map the cellular hierarchies of complex tumors. Our preliminary data extend these studies to
single-cell multi-omics, integrating single-cell assay for transposase-accessible chromatin (scATAC-seq) and
spatial transcriptomics (ST). Our long-term goal is to develop models of malignant progression based on
sequencing data from patient biopsies and deploy them to support clinical decisions. The overall objective of
this project is to develop algorithms to integrate heterogeneous single-cell and imaging data to support therapy
selection, trained on data from multiple cancers and broadly applicable pan-cancer. The rationale for this work
is that these algorithms will be applied to pre-treatment biopsies to predict progression and to recommend
appropriate therapy combinations. In Aim 1 we will develop and validate algorithms to model clonal
composition, phylogeny, and evolutionary trajectory. This will be used to rigorously identify combinatorial
chemotherapy targets and monitor emerging treatment-resistant clones. In Aim 2, we integrate scRNA-seq with
ST as training data to develop a predictive model of gene expression and cellular composition, based on
imaging data alone. We validate these algorithms internally, on prospective cohorts, and in situ in adjacent
tissue. In Aim 3, we develop predictive models of two clinical problems that are challenging in many cancers:
1) the response to ionizing radiation, 2) the emergence of hypermutation at recurrence. Here, we exploit
modern deep-and-wide learning approaches to identify genomic predictors of outcome that are tailored to a
patient’s clinical context. We will validate this approach using both internal and external controls. Algorithms
will be implemented in clinician dashboards in an existing system and the evaluation of clinical support will take
place at two sites: the University of California, San Francisco and the University of Pittsburgh. We anticipate
that this project will identify novel prognostic signatures, enable risk stratification, disease monitoring, and the
selection of precision therapies. These studies will significantly advance our ability to apply single-cell and
spatial profiling in the clinical setting.
项目总结
项目成果
期刊论文数量(0)
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Aaron Antonio Diaz其他文献
Aaron Antonio Diaz的其他文献
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{{ truncateString('Aaron Antonio Diaz', 18)}}的其他基金
An informatics framework for single-cell multi-omics from clinical specimens
临床标本单细胞多组学的信息学框架
- 批准号:
10657695 - 财政年份:2022
- 资助金额:
$ 34.32万 - 项目类别:
An informatics framework for single-cell multi-omics from clinical specimens
临床标本单细胞多组学的信息学框架
- 批准号:
10916710 - 财政年份:2022
- 资助金额:
$ 34.32万 - 项目类别:
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