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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

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中文摘要
翻译
项目摘要 此K 01应用程序寻求受保护的时间为博士指导的研究和职业发展培训。 罗莎琳Sayaman博士成功过渡到终身教职与独立的研究计划 在计算和系统生物学,由实验室医学系主任的支持。利用 在计算和机器学习方法的进步和领先的多组学技术,博士。 Sayaman寻求开发一个高度综合的研究计划,可以弥合计算机之间的差距, 研究和转化医学,特别侧重于推进乳腺癌的个性化医疗。 作为一个具有广泛训练和方法论经验的计算生物学家, 背景下,Sayaman博士是唯一的定位,以进行这项全面的研究,结合平行 来自I-SPY 2试验的约2000名女性的多组学数据集。I-SPY 2新辅助乳腺癌临床试验 是一项个性化的适应性试验,旨在改善高危乳腺癌患者的结局。 Sayaman博士的研究方案采用计算和机器学习方法来剖析 内源性宿主生殖系和肿瘤体细胞突变之间的复杂相互作用,以及外源性肿瘤 肿瘤微环境(TME)是介导肿瘤免疫应答的重要特征。在目标1中,Sayaman博士阐述了 基因组和TME特征在确定肿瘤床中免疫群体的地形图中的作用。在 目的2,她评估了这些基因组和TME特征在预测亚型中的相对预测价值, 对新辅助治疗的特异性反应,以及对治疗无反应的患者的5年生存率。这 这项工作有可能产生反应预测生物标志物,可以为最佳治疗决策提供信息。 为了解决这项研究的多学科方面,Sayaman博士组建了一个模范团队, 拥有互补专业领域的导师。萨亚曼博士的主要导师是劳拉货车特博士 Veer,NCI-designated乳腺肿瘤学项目(BOP)的联合负责人和应用基因组学主任 他是加州大学旧金山分校弗朗西斯科(UCSF)的教授,也是I-SPY 2生物标志物委员会主席。货车t医生 Veer是FDA批准的MammaPrint®测试的发明者,该测试包括在许多国家和国际乳房检查中。 癌症指南。Sayaman博士的共同导师包括UCSF乳腺癌主任Laura Esserman博士 护理中心,BOP的临床联合负责人,以及I-SPY 2试验的国家主要研究者; Elad Ziv是一位领先的癌症遗传学家,在统计遗传学和计算方法方面具有专业知识, 癌症基因组学;迈克尔坎贝尔博士,癌症免疫学专家,谁领导的发展, 用于乳腺癌免疫谱分析的多重免疫荧光测定。萨亚曼博士的工作计划 受益于I-SPY 2试验联盟的世界级研究和临床专业知识, 加州大学旧金山分校和海伦迪勒家庭综合癌症中心的机构环境,其中一个总理 全国的癌症中心。
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.
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