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.

项目摘要该K01申请为医生的研究和职业发展培训提供了受保护的时间。罗莎琳·赛曼（Rosalyn Sayaman）在计算和系统生物学上，得到了实验室医学系主席的支持。利用计算和机器学习方法的进步以及率领多摩变技术的发展，博士 Sayaman试图制定一项高度综合的研究计划，该计划可以弥合silico之间的差距研究和翻译医学，特别关注乳腺癌的个性化医学。作为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博士是独特的，可以进行这项编码平行的全面研究来自I-SPY 2试验的〜2000名女性的多OMIC数据集。 I-SPY 2 NEOADJUVANT乳腺癌临床试验是一项个性化的自适应试验，旨在改善高危乳腺癌患者的预后。 Sayaman博士的研究建议员工计算和机器学习方法剖析固有宿主种系和肿瘤体细胞突变与外部肿瘤之间的复杂相互作用微环境（TME）具有介导肿瘤免疫反应的介导。在AIM 1中，Sayaman博士阐明了基因组和TME特征在确定肿瘤床中免疫种群地形的作用。在 AIM 2，她评估了这些基因组和TME特征的相对预测值，以预测亚型对不反应治疗的患者的新辅助治疗的特定反应和5年生存。这工作有可能产生响应预测的生物标志物，可以为最佳的治疗决策提供信息。为了解决这项研究的多学科方面，Sayaman博士组建了一个示例团队具有完整专业领域的导师。 Sayaman博士的主要导师是Laura van'T博士 Veer，NCI指定的乳腺癌计划（BOP）的共同领导者和应用基因组学总监在加利福尼亚大学，旧金山大学（UCSF）和I-SPY 2生物标志物委员会主席。 Van't博士 VEER是许多国家和国际乳房中包含的FDA清除Mammaprint®测试的清单癌症指南。 Sayaman博士的联合会人员包括UCSF乳房主任Laura Esserman博士 Care Center，BOP的临床共同领导者和I-SPY 2试验的国家首席研究员；博士 Elad Ziv，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家癌症基因组学；癌症免疫学专家迈克尔·坎贝尔（Michael Campbell）博士领导多重免疫荧光测定乳腺癌的免疫分析。 Sayaman博士的拟议工作来自世界一流的研究和I-SPY 2试验财团和富人的临床专业知识的好处 UCSF的机构环境和Helen Diller家族综合癌症中心，以前是该国的癌症中心。