Clinicopathologic and Genetic Profiling through Machine Learning and Natural Language Processing for Precision Lung Cancer Management

项目总结/摘要 肺癌是第二常见的癌症类型，也是男性癌症死亡的主要原因， 妇女在不同类型的肺癌中，非小细胞肺癌（NSCLC）是最常见的类型 占所有肺癌病例的85%到90%。目前的癌症研究表明， 突变影响患者对用于NSCLC治疗的各种药物的敏感性。这些突变 确定每个NSCLC患者最有效的“个性化”治疗的基本因素;然而， 大多数NSCLC患者在治疗的第一年就对这些靶向治疗产生了耐药性。许多 这种抗性的机制仍然未知。设计和处方更好的NSCLC靶向治疗 患者需要进一步了解，特别是关于NSCLC肿瘤之间的关系， 病理学和临床发现、遗传谱和靶向治疗反应/抗性。目前还 没有计算方法将病理报告、医疗记录、躯体疾病 突变和靶向治疗耐药性。本计画提供一个建立一个新颖的计算方法的计画 确定NSCLC肿瘤的病理学结果与 存在临床可行的体细胞突变。此外，这些协会与一个 从病理报告和电子医疗记录中获得的一套创新的特征分析将被利用， 建立并验证机器学习模型，以识别具有临床可操作的体细胞癌的NSCLC患者 突变。最后，NSCLC患者的相关临床、病理学和遗传学结果将用于 一个新的机器学习框架来预测患者对靶向治疗的耐药性。所需的 在本项目中建立和验证拟议模型的数据将通过与 达特茅斯-希区柯克病理学系临床基因组学和先进技术实验室 医学中心除了内部验证外，该提案中的研究人员还与 佛蒙特大学医学中心病理学系应用和验证开发的 外部数据源上的模型。在成功实施这种生物信息学方法后， 所开发的模型将能够揭示临床和病理学发现之间的统计学显著联系， 临床可行的体细胞突变和靶向治疗反应，以更好地了解NSCLC 肿瘤的发展和治疗。所提出的方法将提供一个准确，快速，廉价的预， 筛选具有临床可行突变的NSCLC患者用于转化研究的选择方法， 精准医疗此外，提出的机器学习方法，以确定NSCLC患者的耐药性， 靶向治疗将帮助医疗保健提供者为这些患者选择最佳治疗策略， 他们的健康结果，并为其他类型的癌症建立这种精准医学模式。

PROJECT SUMMARY/ABSTRACT Lung cancer is the second-most common type of cancer and the leading cause of cancer death in men and women. Among the different types of lung cancer, non-small cell lung cancer (NSCLC) is the most common type and it constitutes 85% to 90% of all lung cancer cases. Current cancer research has shown that multiple somatic mutations affect the sensitivity of patients to various drugs used for NSCLC treatment. These mutations are essential factors for determining the most effective, “personalized” treatment for each NSCLC patient; however, most NSCLC patients develop resistance to these targeted therapies in their first year of treatment. Many mechanisms of this resistance are still unknown. Designing and prescribing better targeted therapies for NSCLC patients requires further understanding, particularly with respect to the relationship between NSCLC tumors’ pathological and clinical findings, genetic profiles, and targeted therapy responses/resistance. Currently, there is no computational method to connect observations and findings from pathology reports, medical records, somatic mutations, and the targeted therapy resistance. This project provides a plan to build a novel computational method to identify statistically significant associations between the pathological findings of NSCLC tumors and the presence of clinically-actionable somatic mutations. Furthermore, these associations, in combination with an innovative set of feature analysis from pathology reports and electronic medical records, will be leveraged to build and validate a machine-learning model to identify NSCLC patients with clinically-actionable somatic mutations. Finally, the associated clinical, pathological, and genetic findings for NSCLC patients will be used in a new machine-learning framework to predict patients’ time-to-resistance to targeted therapies. The required data to build and validate the proposed models in this project will be obtained through a collaboration with the Department of Pathology’s Laboratory for Clinical Genomics and Advanced Technologies at Dartmouth-Hitchcock Medical Center. In addition to internal validation, the investigators in this proposal established a collaboration with the Department of Pathology at the University of Vermont Medical Center to apply and validate the developed models on an external data source. Upon successful implementation of this bioinformatics approach, the developed models will be able to reveal statistically significant links between clinical and pathological findings, clinically-actionable somatic mutations, and targeted-therapy responses for a better understanding of NSCLC tumor development and treatment. The proposed approach will provide an accurate, fast, and inexpensive pre- selection method for screening NSCLC patients with clinically-actionable mutations for translational research and precision medicine. Furthermore, the proposed machine-learning method to identify NSCLC patients’ resistance to targeted therapies will help healthcare providers to select the best treatment strategies for these patients, improve their health outcomes, and establish this precision medicine paradigm for other types of cancer.