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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要此 K01 申请旨在为 Dr. 寻求受指导的研究和职业发展培训的保护时间。罗莎琳·萨亚曼（Rosalyn Sayaman）博士通过独立研究项目成功过渡为终身教授计算和系统生物学博士，由实验医学系主任支持。计算和机器学习方法的进步以及领先的多组学技术，博士。 Sayaman 寻求开发一个高度综合的研究计划，以弥补计算机模拟与计算机科学之间的差距。研究和转化医学，特别关注推进乳腺癌的个性化医学。作为一名计算生物学家，拥有广泛的训练和方法论经验，以及扎实的实验基础背景，Sayaman 博士具有独特的优势来开展这项综合研究，其中包括平行研究 I-SPY 2 试验中约 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、适应性试验，旨在改善高风险乳腺癌患者的治疗结果。 Sayaman 博士的研究提案采用计算和机器学习方法来剖析内在宿主种系和肿瘤体细胞突变与外在肿瘤之间的复杂相互作用 Sayaman 博士在目标 1 中阐明了介导肿瘤免疫反应的微环境 (TME) 特征。基因组和 TME 特征在确定肿瘤床中免疫群体的拓扑方面的作用。目标 2，她评估了这些基因组和 TME 特征在预测亚型方面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。这项工作有可能产生反应预测生物标志物，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 Sayaman 博士的主要导师是 Laura van ‘t 博士。 Veer，NCI 指定乳腺肿瘤学项目 (BOP) 联合负责人兼应用基因组学主任 van ‘t 博士是加州大学旧金山分校 (UCSF) 的教授，I-SPY 2 生物标志物委员会主席。 Veer 是经 FDA 批准的 MammaPrint® 测试的发明者，该测试已被许多国家和国际乳腺疾病所采用 Sayaman 博士的共同导师包括 UCSF 乳腺科主任 Laura Esserman 博士。 Care Center，BOP 临床联合负责人，I-SPY 2 试验的国家首席研究员； Elad Ziv，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 Michael Campbell 博士，他领导了 Sayaman 博士提出的用于乳腺癌免疫分析的多重免疫荧光测定。受益于 I-SPY 2 试验联盟的世界一流研究和临床专业知识以及丰富的加州大学旧金山分校 (UCSF) 和海伦迪勒家庭综合癌症中心（美国首屈一指的癌症中心之一）的制度环境该国的癌症中心。