CGDnet: Cancer Gene Drug Network: Using patient-specific drug-gene networks for recommending targeted cancer therapies.
CGDnet: Cancer Gene Drug Network: Using patient-specific drug-gene networks for recommending targeted cancer therapies.
批准号:
9923991
负责人:
Simina Maria Boca
金额:
$3.67万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-06-06 至 2020-05-31
关键词:
AffectBiological MarkersBiometryCancer PatientCancer cell lineClinicalClinical ResearchCommunitiesComputer softwareDataData AnalysesData SetFAIR principlesFailureGene MutationGene ProteinsGenesGoalsHospitalsImageryIndividualInformaticsInformation NetworksKRAS2 geneKnowledgeLeadLettersMEKsMalignant NeoplasmsMethodsMolecularMolecular BiologyMolecular ProfilingMovementMutationNetwork-basedOncogenesPathway interactionsPatientsPharmaceutical PreparationsPhysiciansPopulationPrevalenceProcessProteinsPublishingRecommendationReportingReproducibilityResearch DesignResearch PersonnelResearch SupportResistanceResourcesSignal PathwaySystemTestingVariantWorkactionable mutationanalytical toolanticancer researchbasecancer typeclinical decision supportclinical decision-makingcomputer frameworkcomputer sciencedata managementdata visualizationdesignevidence baseexperiencegenetic variantgenomic profilesimprovedimproved outcomeindividual patientinhibitor/antagonistinteractive toolknowledge basemembermethod developmentmolecular diagnosticsmolecular markermolecular oncologymultiple omicsoncologyopen sourcepersonalized cancer therapyprecision medicineprecision oncologyresponsesoftware developmentsuccesstargeted cancer therapytargeted treatmenttooltreatment risktumor
中文摘要
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英文摘要
PROJECT SUMMARY/ABSTRACT:
Molecular profiling – the practice of molecularly testing tumors in order to find specific gene or protein
alterations which can be used to recommend targeted therapies – is increasingly used in oncology and is fast
becoming a major part of precision medicine. In practice, each patient or physician generally receives a list of
molecular anomalies and a list of therapies which are predicted to be beneficial or not beneficial based on the
tumor molecular profile. This may lead to a difficult process of prioritizing therapies for individual patients. In
the current proposal, we will develop computational network-based approaches to therapy recommendation by
using existing resources to inform the connections between drugs and gene or protein variants. We will
consider approaches both for creating “average” networks based on population-level data and for creating
“patient-specific” networks based on an individual’s specific tumor profile. We will also design and implement
an interactive data visualization approach for these networks which will be usable by both clinical researchers
and clinicians. The methods and tools developed as part of this project will be entirely reproducible and shared
with the community via open-source software packages and interactive tools. We believe our project could
eventually lead the way to improving the way therapies are targeted to cancer patients.
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