Defining therapeutic strategies for boosting T-cell infiltration into cold tumors with spatial proteomics and machine learning
利用空间蛋白质组学和机器学习确定促进 T 细胞浸润冷肿瘤的治疗策略
基本信息
- 批准号:10743501
- 负责人:
- 金额:$ 42.31万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-05 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAdhesionsAdjuvantAlgorithmsArchitectureBiologicalBiological MarkersBreastCCL19 geneCCL8 geneCD8-Positive T-LymphocytesCXCL9 geneCell TherapyCellular immunotherapyClinicalCollaborationsColon CarcinomaComputing MethodologiesCuesCytometryDataData CollectionData SetData SourcesDependenceDiseaseEngineeringEnvironmentExhibitsHumanImageImaging technologyImmune checkpoint inhibitorImmunologicsImmunotherapyInfiltrationInterventionLibrariesLigandsMachine LearningMalignant neoplasm of liverMalignant neoplasm of prostateMammary NeoplasmsMapsMeasuresMedical centerMethodsModelingMolecularMutationPathologistPatientsPatternPhenotypeProstateProstatic NeoplasmsProteinsProteomicsResearchResolutionSamplingSignal TransductionSignaling MoleculeSolid NeoplasmStromal Cell-Derived Factor 1Stromal CellsT cell infiltrationT cell responseT-LymphocyteTechnologyTestingTherapeuticTherapeutic InterventionTissuesTrainingUrologic SurgeonWorkbasecancer cellcancer imagingcancer immunotherapycancer infiltrating T cellscancer typecell behaviorchemokinechimeric antigen receptorcombinatorialconvolutional neural networkdesignexhaustionhuman datahuman tissueimmune checkpointimprovedlearning strategymachine learning frameworkmalignant breast neoplasmmelanomamolecular imagingneural networkneural network architecturenovelpatient subsetsprecision medicinereceptorresponsesuccesstherapeutic targettraffickingtranscriptomicstriple-negative invasive breast carcinomatumortumor microenvironmenttumor progressiontumor-immune system interactions
项目摘要
Project Summary
Immunotherapies such as immune checkpoint inhibitors and chimeric antigen receptor (CAR-T) cell therapy have
been highly successful in reversing cancer progression in a subset of patients. However, immunotherapies fail
in patients with “cold tumors,” where T-cell infiltration and function are suppressed by inhibitory signaling
environments generated by cancer and stromal cells. Poor CD8+ T-cell infiltration due to suppressive signaling
environments is a primary obstacle to effective immunotherapy in many solid tumors including breast, liver,
prostate, and colon cancer. Recent advances in high-resolution molecular imaging technologies, known as
spatial proteomic methods, now allow micron-resolution profiling of signaling environments in cold and hot
human tumors across up to 50 molecular channels providing a new data source for identifying signaling cues
that promote or suppress T-cell infiltration. There is an urgent unmet need for computational strategies that can
analyze large-scale, spatial proteomic data sets collected from human patient data to identify features of the
tumor microenvironment that promote cold vs hot tumor phenotypes. Computational methods must be designed
to extract concrete and specific therapeutic strategies that can be tested clinically for reprogramming the tumor
microenvironment to promote T-cell infiltration and function. In this project, we develop a machine learning
framework that uses cutting-edge spatial proteomic data to identify signaling molecules and guidance cues that
promote the infiltration and function of T-cells into a tumor microenvironment. Our approach first trains a neural
network on spatial proteomic data to predict T-cell infiltration using signaling and guidance cues. We, then, apply
“counterfactual reasoning” to the classifier to predict optimal signaling perturbations for increasing CD8 T-cell
infiltration into tumors. In preliminary data, we applied our strategy to melanoma and identified a therapeutic
strategy that involves manipulation of five chemokine and signaling molecules in melanoma based on spatial
proteomic data from 300 patients. In the work to be performed here, we aim to generalize our approach to a
broader range of cancer types and larger patient data sets. We will systematically test neural network
architectures to identify optimal architectures for different cancer types. Since spatial proteomic training data is
currently limited, we will collect new training data from human patients across a broader set of tumors, for which
we will profile chemokine and signaling molecules through a collaboration between Cedars-Sinai Medical Center
and Caltech. We will generalize our counterfactual reasoning strategy to breast and prostate cancer to identify
optimal therapeutic targets and to compare targets for different base tumor types. Broadly, our work will develop
a novel machine learning approach for converting large-scale spatial proteomic data into specific molecular
hypotheses for increasing T-cell infiltration into cold tumors across a range of solid tumor types.
项目总结
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)
专利数量(0)
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Matthew W. Thomson其他文献
Matthew W. Thomson的其他文献
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{{ item.author }}
{{ truncateString('Matthew W. Thomson', 18)}}的其他基金
Quantitative models for controlling collective cell fate selection in stem cells
控制干细胞集体细胞命运选择的定量模型
- 批准号:
9412062 - 财政年份:2012
- 资助金额:
$ 42.31万 - 项目类别:
Quantitative models for controlling collective cell fate selection in stem cells
控制干细胞集体细胞命运选择的定量模型
- 批准号:
8720580 - 财政年份:2012
- 资助金额:
$ 42.31万 - 项目类别:
Quantitative models for controlling collective cell fate selection in stem cells
控制干细胞集体细胞命运选择的定量模型
- 批准号:
8416032 - 财政年份:2012
- 资助金额:
$ 42.31万 - 项目类别:
Quantitative models for controlling collective cell fate selection in stem cells
控制干细胞集体细胞命运选择的定量模型
- 批准号:
9135548 - 财政年份:2012
- 资助金额:
$ 42.31万 - 项目类别:
Quantitative models for controlling collective cell fate selection in stem cells
控制干细胞集体细胞命运选择的定量模型
- 批准号:
8550848 - 财政年份:2012
- 资助金额:
$ 42.31万 - 项目类别:
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