课题基金 / 基金详情

A systems biology approach to elucidate the biology of immune-associated outcomes in breast cancer

A systems biology approach to elucidate the biology of immune-associated outcomes in breast cancer
阐明乳腺癌免疫相关结果生物学的系统生物学方法
批准号:
10644415
负责人:
Rosalyn Wong Sayaman
金额:
$17.32万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-03 至 2028-07-31
关键词:
AddressAffectAgeAutoimmuneBRCA1 geneBedsBiologicalBiological MarkersBiologyBody mass indexBreastBreast Cancer PatientBreast OncologyCaliforniaCancer BurdenCancer CenterCaringCellsClinicalClinical DataClinical TrialsComplexComprehensive Cancer CenterComputational BiologyCopy Number PolymorphismCountryDNA RepairDNA Repair DisorderDataData SetDepartment chairDevelopmentDoctor of PhilosophyEnvironmentEthnic OriginExtracellular MatrixFacultyFamilyFluorescenceGene ExpressionGenesGeneticGenomicsGerm-Line MutationGoalsGuidelinesHeritabilityHypoxiaImmuneImmune responseImmunofluorescence ImmunologicImmunology procedureImmunotherapyIn complete remissionInstitutionInterferonsInternationalInterventionLaboratoriesLearningMachine LearningMalignant NeoplasmsMediatingMedicineMentorsMetabolicMethodologyMethodsModelingMultiomic DataMutationNeoadjuvant TherapyOutcomePathologicPathway interactionsPatientsPopulationPositioning AttributePredictive ValuePredispositionPrincipal InvestigatorPrognosisPublicationsPublishingQualifyingRaceReportingResearchResearch ProposalsResidual CancersRoleSNP genotypingSTING1 geneSamplingSan FranciscoSignal PathwaySignal TransductionSingle Nucleotide Polymorphism MapSolidSomatic MutationStromal CellsSystems BiologyT-Lymphocyte SubsetsTechnologyTestingThe Cancer Genome AtlasTherapeuticTimeTrainingTumor-infiltrating immune cellsUniversitiesWomanWorkalternative treatmentcancer clinical trialcancer genomicscareer developmentcell typechemokinecytokinedensityexome sequencingexperiencefluorescence imaginggene interactionhigh riskimmune cell infiltrateimmunological statusimmunoregulationimprovedimproved outcomein silicomachine learning algorithmmachine learning classificationmachine learning methodmachine learning modelmalignant breast neoplasmmultidisciplinarymultiple omicsneoantigensnoveloncology programoptimal treatmentspatient responsepatient stratificationpersonalized medicinepredict clinical outcomepredicting responsepredictive markerpredictive modelingprogramsreceptorresearch and developmentresponserisk varianttargeted treatmenttenure tracktherapy resistanttraittranscriptomicstranslational medicinetreatment armtreatment responsetreatment strategytrial designtumortumor immunologytumor microenvironment

项目摘要

项目成果

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
PROJECT SUMMARY This K01 application seeks protected time for mentored research and career development training for Dr. Rosalyn Sayaman, PhD to successfully transition to tenure-track faculty with an independent research program in computational and systems biology, supported by the Chair of Department of Laboratory Medicine. Leveraging the advances in computational and Machine Learning methods and spearheading multi-omic technologies, Dr. Sayaman seeks to develop a highly integrative research program that can bridge the gap between in-silico research and translational medicine, with specific focus on advancing personalized medicine in breast cancer. As a computational biologist with broad training and methodological experience, and a solid experimental background, Dr. Sayaman is uniquely positioned to carry out this comprehensive study incorporating the parallel multi-omic dataset for ~2000 women from the I-SPY 2 Trial. The I-SPY 2 neoadjuvant breast cancer clinical trial is a personalized, adaptive trial designed to improve outcomes in high-risk breast cancer patients. Dr. Sayaman’s research proposal employs computational and Machine Learning approaches to dissect the complex interactions between intrinsic host germline and tumor somatic mutations, and extrinsic tumor microenvironment (TME) features that mediate the tumor immune response. In Aim 1, Dr. Sayaman elucidates the role of genomic and TME features in determining the topography of immune populations in the tumor bed. In Aim 2, she assesses the relative predictive value of these genomic and TME features in predicting subtype- specific response to neoadjuvant therapy, and 5-year survival in patients who do not respond to therapy. This work has the potential to generate response-predictive biomarkers that could inform optimal treatment decisions. To address the multi-disciplinary aspect of this study, Dr. Sayaman has assembled an exemplary team of mentors who have complementary domains of expertise. Dr. Sayaman’s primary mentor is Dr. Laura van ‘t Veer, the Co-Leader of the NCI-designated Breast Oncology Program (BOP) and Director of Applied Genomics at the University of California, San Francisco (UCSF), and Chair of the I-SPY 2 Biomarker Committee. Dr. van ‘t Veer is the inventor of the FDA-cleared MammaPrint® test included in many national and international breast cancer guidelines. Dr. Sayaman’s co-mentors include Dr. Laura Esserman, the Director of the UCSF Breast Care Center, the Clinical Co-Leader of the BOP, and the national Principal Investigator of the I-SPY 2 trial; Dr. Elad Ziv, a leading cancer geneticist with expertise in statistical genetics and computational approaches in cancer genomics; and Dr. Michael Campbell, an expert in cancer immunology, who leads the development of multiplex Immune-Fluorescence assays for immune profiling in breast cancer. Dr. Sayaman’s proposed work benefits from the world-class research and clinical expertise of the I-SPY 2 Trial Consortium and the rich institutional environment of UCSF and the Helen Diller Family Comprehensive Cancer Center, one of the premier cancer centers in the country.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
海外基金