Deep-learning Integration of Histopathology and Proteogenomics at a Pan-cancer Level - Resubmission
泛癌水平上组织病理学和蛋白质基因组学的深度学习整合 - 重新提交
基本信息
- 批准号:10606760
- 负责人:
- 金额:$ 4万
- 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:2023-03-01 至 2025-06-30
- 项目状态:未结题
- 来源:
- 关键词:AddressAntibodiesAntigen PresentationArchitectureAutomobile DrivingBiologicalBiomedical ResearchBiopsyCancer PrognosisCellular MorphologyClinicClinicalCollaborationsCommunitiesComplementComplete Blood CountComputing MethodologiesDNA Sequence AlterationDataData SetDecision MakingDiagnosisDiseaseDisease-Free SurvivalDisparateDreamsEndometrial CarcinomaGenesGenetic TranscriptionGlioblastomaHematologyHistologicHistologyHistopathologyImageImmunohistochemistryImmunophenotypingIndividualIntuitionKRAS2 geneKnowledgeKnowledge DiscoveryLearningMalignant NeoplasmsMalignant neoplasm of lungMeasurementMedicalMethodsMitoticModelingMolecularMolecular ProfilingMorphologyMultiomic DataMutationNational Cancer InstituteNeural Network SimulationNuclearOutcomePIK3CA genePathologicPathologistPathologyPathway interactionsPatientsPatternPerformanceProcessPrognosisProteinsProteomicsRegulatory PathwayResearchResolutionRoleStainsTP53 geneTissuesTrainingTrustTumor BiologyTumor PathologyTumor stageUrineValidationVisualVisualizationWorkZFHX3 genecancer therapycancer typeclinical practiceclinical predictorsconvolutional neural networkdata integrationdeep learningdeep learning modeldensitydiagnostic biomarkerdiagnostic tooldiagnostic valuedifferential expressionillness lengthimaging platformimmune cell infiltrateimprovedinterestmachine learning modelmachine learning predictionmultiple omicsnovelpersonalized diagnosticspredict clinical outcomepredictive signatureproteogenomicsreconstitutiontooltranscriptomicstumortumor heterogeneitytumorigenesis
项目摘要
PROJECT SUMMARY
Discovering and understanding novel pathological features that correlate with proteogenomics is crucial in
improving and streamlining cancer prognosis and treatment. Currently, biomedical research efforts to predict
cancer outcomes rely on sequencing approaches not readily accessible in a clinical setting. Instead, clinicians
frequently rely on histopathology images to visually assess for aberrant changes to tissue morphology.
Subsequently, a tool to infer clinical and molecular signatures directly from histopathology images would harness
the power of omics research with the feasibility of image-based diagnosis.
Histopathology imaging data is still vastly underutilized in the quest to better understand tumor biology, largely
because of inadequate tools for analysis and data integration. To identify pan-cancer hallmarks and conserved
pathways of tumorigenesis, I therefore propose to develop multi-resolution deep convolutional neural network
(CNN) models across 10 different cancer types and predict clinical annotations, histology outcomes, and critical
mutations based on tumor histopathology images (Aim 1). Through our lab’s collaboration with the National
Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), we have access to multi-omics,
clinical, and histopathologic data obtained from 1,602 patients. In addition, to understand the biological
mechanisms driving morphology changes, I propose to develop computational methods that integrate
transcriptomic and proteomic expression datasets with imaging to facilitate pathway-level knowledge discovery
(Aim 2). Our proposal is the first to correlate expression perturbations with morphology patterns and identify
enriched canonical pathways directly from histopathology images. Importantly, this proposal aims to connect
scientific efforts of biomedical research with the diagnostic tools of clinicians to expand diagnostic power and
improve clinical practice.
项目摘要
发现和理解与蛋白质基因组学相关的新的病理学特征是至关重要的,
改善和简化癌症预后和治疗。目前,生物医学研究努力预测
癌症结果依赖于在临床环境中不容易获得的测序方法。相反,临床医生
通常依赖于组织病理学图像来视觉评估组织形态的异常变化。
随后,直接从组织病理学图像推断临床和分子特征的工具将利用
组学研究的力量与基于图像的诊断的可行性。
在寻求更好地了解肿瘤生物学方面,组织学成像数据仍然大大未得到充分利用,
因为分析和数据整合工具不足。为了识别泛癌症的特征,
因此,我建议开发多分辨率深度卷积神经网络
(CNN)10种不同癌症类型的模型,并预测临床注释、组织学结局和关键
基于肿瘤组织病理学图像的突变(Aim 1)。通过我们实验室与国家
癌症研究所的临床蛋白质组肿瘤分析联盟(CPTAC),我们可以获得多组学,
从1,602名患者中获得的临床和组织病理学数据。此外,要了解生物
驱动形态变化的机制,我建议开发计算方法,
转录组学和蛋白质组学表达数据集与成像,以促进路径水平的知识发现
(Aim 2)。我们的建议是第一个将表达扰动与形态学模式相关联,
直接从组织病理学图像中丰富了经典途径。重要的是,该提案旨在连接
生物医学研究的科学努力与临床医生的诊断工具,以扩大诊断能力,
改善临床实践。
项目成果
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