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: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-03 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644415
• 关键词: Address; Affect; Age; Autoimmune; BRCA1 gene; Beds; Biological; Biological Markers; Biology; Body mass index; Breast; Breast Cancer Patient; Breast Oncology; California; Cancer Burden; Cancer Center; Caring; Cells; Clinical; Clinical Data; Clinical Trials; Complex; Comprehensive Cancer Center; Computational Biology; Copy Number Polymorphism; Country; DNA Repair; DNA Repair Disorder; Data; Data Set; Department chair; Development; Doctor of Philosophy; Environment; Ethnic Origin; Extracellular Matrix; Faculty; Family; Fluorescence; Gene Expression; Genes; Genetic; Genomics; Germ-Line Mutation; Goals; Guidelines; Heritability; Hypoxia; Immune; Immune response; Immunofluorescence Immunologic; Immunology procedure; Immunotherapy; In complete remission; Institution; Interferons; International; Intervention; Laboratories; Learning; Machine Learning; Malignant Neoplasms; Mediating; Medicine; Mentors; Metabolic; Methodology; Methods; Modeling; Multiomic Data; Mutation; Neoadjuvant Therapy; Outcome; Pathologic; Pathway interactions; Patients; Population; Positioning Attribute; Predictive Value; Predisposition; Principal Investigator; Prognosis; Publications; Publishing; Qualifying; Race; Reporting; Research; Research Proposals; Residual Cancers; Role; SNP genotyping; STING1 gene; Sampling; San Francisco; Signal Pathway; Signal Transduction; Single Nucleotide Polymorphism Map; Solid; Somatic Mutation; Stromal Cells; Systems Biology; T-Lymphocyte Subsets; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Time; Training; Tumor-infiltrating immune cells; Universities; Woman; Work; alternative treatment; cancer clinical trial; cancer genomics; career development; cell type; chemokine; cytokine; density; exome sequencing; experience; fluorescence imaging; gene interaction; high risk; immune cell infiltrate; immunological status; immunoregulation; improved; improved outcome; in silico; machine learning algorithm; machine learning classification; machine learning method; machine learning model; malignant breast neoplasm; multidisciplinary; multiple omics; neoantigens; novel; oncology program; optimal treatments; patient response; patient stratification; personalized medicine; predict clinical outcome; predicting response; predictive marker; predictive modeling; programs; receptor; research and development; response; risk variant; targeted treatment; tenure track; therapy resistant; trait; transcriptomics; translational medicine; treatment arm; treatment response; treatment strategy; trial design; tumor; tumor immunology; tumor microenvironment

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.

项目总结 这个K01应用程序寻求有保护的时间来指导研究和职业发展培训博士。 Rosalyn Sayaman博士将通过独立研究项目成功过渡到终身教职教师 在计算和系统生物学方面，由实验室医学系主任支持。利用 在计算和机器学习方法以及引领多组学技术方面的进展。 Sayaman寻求开发一种高度综合的研究计划，可以弥合硅谷和硅谷之间的差距 研究和转化医学，特别侧重于推进乳腺癌的个性化药物。 作为一名具有广泛培训和方法论经验的计算生物学家，以及扎实的实验 背景，Sayaman博士处于独特的地位，可以开展这项综合研究 来自I-SPY 2试验的约2000名女性的多组数据集。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预后。 Sayaman博士的研究提案使用了计算和机器学习方法来剖析 内源性宿主种系和肿瘤体细胞突变与外源性肿瘤的复杂相互作用 微环境(TME)是调节肿瘤免疫反应的重要因素。在目标1中，萨亚曼博士阐明了 基因组和TME特征在确定肿瘤床免疫群体分布中的作用。在……里面 目的2，她评估这些基因组和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，以及对治疗无效的患者的5年生存率。这 这项工作有可能产生反应预测生物标记物，从而为最佳治疗决策提供信息。 为了解决这项研究的多学科问题，萨亚曼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·范特博士 韦尔，NCI指定的乳腺肿瘤学项目(BOP)的联合负责人和应用基因组学主任 加州大学旧金山分校(UCSF)教授，I-SPY 2生物标记物委员会主席。范特医生 Veer是FDA批准的MammaPrint®测试的发明者，该测试包括在许多国内和国际乳房 癌症指南。Sayaman博士的联合导师包括加州大学旧金山分校乳房主任Laura Esserman博士 护理中心，防喷剂临床联合负责人，I-SPY 2试验的国家首席研究员； 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拥有专业知识 癌症基因组学；以及癌症免疫学专家迈克尔·坎贝尔博士，他领导了 多重免疫荧光分析在乳腺癌免疫分析中的应用。Sayaman博士提议的工作 受益于i-spy 2试验联盟和RICH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制度环境 全国的癌症中心。