Identifying Malignant Cell Signaling from Protein Interactions an Polyomic Data

描述（由申请人提供）：拟议的奖励计划将教学培训和实践研究相结合，以补充Fertig博士在约翰霍普金斯大学肿瘤生物统计学的博士后经验和生物学熟练程度，补充她的数学背景。此外，这一培训将使Fertig博士能够在她未来作为独立计算肿瘤学家的职业生涯中追求相关的研究问题和富有成效的多学科合作。该提案的主要重点是发展肿瘤发展和维持背后的生物过程的定量模型。约翰霍普金斯大学肿瘤学生物统计学博士Michael Ochs和头颈研究医学博士Joseph Califano将促进Fertig博士提出的培训和实践研究。包括为计算生物学中的统计方法以及头颈癌的生物学和临床治疗提供见解。Fertig博士将运用她的经验，将动态模型与数值天气预报的间接测量相结合，从患者的测量中推断出相关的生物过程。在导师的支持下，她将开发算法，以推断个体患者肿瘤中潜在恶性肿瘤的驱动过程。在提议的技术中，Fertig博士将通过整合基因表达测量、表观遗传测量、EGFR蛋白-蛋白相互作用网络结构和治疗策略来推断头颈癌恶性过程下游的转录因子活性。通过合并这些不同的数据源，假设该工具具有统计能力，可以准确地表示头颈癌中建模过程导致的特定转录因子激活的概率，这将通过靶向细胞系实验进一步验证。因此，这项研究将开发工具，当迁移到临床时，将帮助临床医生确定适当的靶向治疗选择来治疗个体的癌症。

DESCRIPTION (provided by applicant): The proposed award plan combines didactic training and hands-on research to supplement Dr. Fertig's postdoctoral experience in Johns Hopkins Oncology Biostatistics with biological proficiency, complementing her mathematical background. Moreover, this training will enable Dr. Fertig to pursue pertinent research questions and fruitful, multi- disciplinary collaborations in her future career as an independent computational oncologist. The primary focus of this proposal is the development of quantitative models of the biological processes underlying the development and maintenance of tumors Mentors Michael Ochs, PhD of Oncology Biostatistics, and Joseph Califano, MD of Head and Neck Research, at Johns Hopkins will foster Dr. Fertig's proposed training and hands-on research, including providing insight to statistical methods in computational biology and to the biology and clinical treatment of head and neck cancer respectively. Dr. Fertig will apply her experience in merging dynamic models with indirect measurements from Numerical Weather Prediction to inferring relevant biological processes from patient measurements. With the support of her mentors, she will develop algorithms that infer driver processes underlying malignancies in an individual patient's tumor. In the proposed techniques, Dr. Fertig will infer transcription factor activity in head and neck cancer downstream of the malignant processes by integrating gene expression measurements with epigenetic measurements, EGFR protein-protein interaction network structure, and therapeutic strategy. By merging these diverse data sources, this tool is hypothesized to have the statistical power to accurately represent the probability of activation of specific transcription factors resulting from the modeled processes in head and neck cancer, which will be further validated through targeted cell line experiments. Thus, this research will develop tools that, when migrated to the clinic, will assist clinicians in identifying the appropriate choice of targeted therapeutics to treat an individual's cancer. PUBLIC HEALTH RELEVANCE: Project Narrative Advanced head and neck tumors have a 50% cure rate in spite of combined treatment modalities. This project will merge biological structure with head and neck tumor measurements to pinpoint specific pathways that drive individual tumor development, and thus assess personalized treatment plans for that patient. Although applied to head and neck cancers, the developed tools will have broad clinical implications.