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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.

项目总结