Systems biology modeling of radiation resistance and chemotherapy-radiation combination therapies in head and neck squamous cell carcinoma

肿瘤对放射治疗的抵抗仍然是癌症患者长期生存的重大障碍， 尤其是头颈部鳞状细胞癌(HNSCC)，这是一种长期预后较差的癌症类型 (晚期五年生存率低于50%)。为了克服辐射抵抗的问题，辐射 治疗正与辐射增敏化疗相结合。预测个体对疾病的敏感性 治疗前的放射治疗和特定的化疗-放射联合治疗将改善 为癌症患者制定个性化治疗计划。正在努力创建系统 用于发现生物标记物和预测治疗反应的癌细胞生物学模型；然而，由于 方法学上的缺陷，未能在基因组规模上整合多组数据，以及缺乏对 对于个别患者肿瘤，这些预测模型尚未在临床上实施。为了满足这些需求， 本课题的目标是开发一个面向个性化需求的个性化系统生物学建模平台 预测HNSCC患者对放疗和化疗-放射联合治疗的反应。 这些模型将通过首先整合来自 癌症基因组图谱(TCGA)这种方法将允许比较新陈代谢、信号和 放射敏感和耐辐射患者肿瘤之间的表型特征。然后通过将 模型中放射治疗和放射增敏化疗的作用机制 框架，个体放射治疗中对特定化疗-放射联合治疗的反应- 耐药患者的肿瘤是可以预测的。机器学习分类器将从TCGA患者数据中开发 以及模型预测，以确定哪些生物和临床因素对辐射敏感性最具预测性 化疗和放疗联合治疗取得成功。假设不同的反应对 化疗-放疗联合治疗耐辐射HNSCC肿瘤成功 通过氧化还原代谢和信号，以及氧化还原生物学中的组件进行建模 框架将极大地丰富联合治疗成功的预测生物标记物的清单。 尽管本项目的重点将是HNSCC，但这种系统生物学建模方法将是适用的 对任何癌症类型都是如此。该项目的结果将是一套减少的临床可测量的生物标志物 准确预测HNSCC患者对放射治疗和特异性化疗-放疗的反应 联合疗法，以及用于测试临床相关治疗策略的精准医学平台。 该项目具有创新性，因为它将多组癌症患者数据与最先进的系统相结合 生物模拟技术，以研究辐射抗性的生物学机制，以及 预测个体放射抵抗患者的化疗-放射联合治疗反应。

Tumor resistance to radiation therapy remains a significant obstacle to long-term cancer patient survival, especially for head and neck squamous cell carcinoma (HNSCC), a cancer type with poor long-term outcomes (less than 50% advanced stage five-year survival). To overcome the problem of radiation resistance, radiation therapy is being combined with radiation-sensitizing chemotherapies. Prediction of an individual’s sensitivity to radiation and specific chemotherapy-radiation combination therapies prior to treatment would improve the development of personalized treatment plans for cancer patients. Efforts are being made to create systems biology models of cancer cells for biomarker discovery and prediction of treatment response; however, due to methodological shortcomings, failure to integrate multi-omic data on a genome-scale, and lack of specificity to individual patient tumors, these predictive models have yet to be implemented clinically. To address these needs, the objective of this project is to develop a personalized systems biology modeling platform for individualized prediction of HNSCC patient tumor response to radiation and chemotherapy-radiation combination therapies. These models will be created by first integrating comprehensive biological data on individual patients from The Cancer Genome Atlas (TCGA). This approach will allow for the comparison of metabolic, signaling, and phenotypic signatures between radiation-sensitive and radiation-resistant patient tumors. By then integrating the mechanisms of action of radiation therapy and radiation-sensitizing chemotherapies into the modeling framework, the response to particular chemotherapy-radiation combination therapies in individual radiation- resistant patient tumors can be predicted. Machine learning classifiers will be developed from TCGA patient data and model predictions to determine which biological and clinical factors are most predictive of radiation sensitivity and chemotherapy-radiation combination therapy success. It is hypothesized that differential response to chemotherapy-radiation combination therapies in radiation-resistant HNSCC tumors is accomplished through redox metabolism and signaling, and components of redox biology within the modeling framework will significantly enrich the list of predictive biomarkers for combination therapy success. Although the focus of this project will be on HNSCC, this systems biology modeling approach will be applicable to any cancer type. The outcomes of this project will be a reduced set of clinically-measurable biomarkers for accurate prediction of HNSCC patient response to radiation therapy and specific chemotherapy-radiation combination therapies, as well as a precision medicine platform to test clinically relevant therapeutic strategies. This project is innovative because it combines multi-omic cancer patient data with state-of-the-art systems biology modeling techniques to investigate the biological mechanisms of radiation resistance, as well as to predict chemotherapy-radiation combination therapy response in individual radiation-resistant patients.