CAREER: Integrative Pathway Analysis for Cancer Subtyping, Patient Stratification, and Risk Prediction

癌症是一个总括性术语，包括一系列疾病，从快速生长和致命的疾病到进展至死亡的可能性较低的惰性病变。近几十年来，癌症治疗的重要临床进展归因于针对特定基因的分子亚型和靶向治疗。然而，很大一部分患者对靶向治疗没有反应，或者随着时间的推移产生耐药性。这意味着目前用于肿瘤表征和治疗干预的方法不够准确。该项目旨在开发能够更好地区分诊断为相同癌症类型的患者的新技术。这种个性化分析方法的基础是能够解释为什么患有相似癌症的患者在治疗成功方面会有很大差异。该方法还将采用多种类型数据的有效整合方法。这项工作将提高我们区分处于直接危险中并需要最积极治疗的患者和疾病进展缓慢的患者的能力。这将导致降低医疗保健成本和个人痛苦，同时通过为每位患者确定正确的个性化治疗来改善患者护理。这项研究将为未来的项目铺平道路，以确定可用于诊断，风险预测和监测治疗反应和结果的临床适用的生物标志物。该项目还具有广泛的教育和外展组成部分，包括课程开发，本科生研究，儿童博物馆展览，以及内华达州社区学院和K-12学校的外展活动。该项目将解决癌症亚型分型中常见的两个重要挑战：（1）在癌症亚型分型、患者分层和风险预测中并入途径知识，以及（2）多群组和多组学数据的有效整合。为了应对第一个挑战，该项目将开发新型机器学习技术，以识别受影响的通路，并计算个体患者的个性化通路特征。这一想法的创新源于将经典概率成分与现有技术中未捕获的重要生物学因素相结合：i）每个途径所描述的所有基因-基因相互作用，ii）多组学层之间的拓扑结构，以及iii）途径之间的串扰。该方法将把所有分子数据转换到一个共同的途径空间，从而有可能有效地解决第二个挑战：系统地整合多组学和多队列数据。这将通过非负内核、变分自编码器来实现。非负核将有效地积累生物标志物的一致信号，同时缩小非相关成分的随机噪声。该项目的目标将通过三个方面来实现：1）计算可用于亚型分型的个性化途径概况，2）整合多个患者群组以增加样本量和亚型分型方法的统计功效，以及3）使用10种亚型发现方法、6种患者分层技术、6个风险预测模型将在70多个癌症数据集上进行测试。研究者将通过Bioconductor软件包和基于网络的平台公开这些方法，从而增加它们被研究团体广泛采用的潜力。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low potential for progression to death. In recent decades, important clinical advances in cancer treatments have been attributed to molecular subtyping and targeted treatments aiming at specific genes. However, a significant percentage of patients do not respond to targeted therapies or develop resistance over time. This implies that current methods for tumor characterization and therapeutic interventions are not sufficiently accurate. This project aims to develop novel technologies able to better differentiate among patients diagnosed with the same cancer type. Fundamental to this personalized analysis approach is the capability to explain why patients with similar cancer can greatly differ in terms of treatment success. The approach will also feature an effective integration methodology of multiple types of data. This work will enhance our ability to distinguish among patients who are in immediate danger and need the most aggressive treatments and those whose disease will progress slowly. This will lead to reduced health care costs and personal suffering while improving patient care by identifying the correct personalized treatment for each patient. This research will pave the way for future projects in identifying clinically applicable biomarkers that can be used in diagnosis, risk prediction, and monitoring treatment response and outcome. The project also has an extensive education and outreach component, including curriculum development, undergraduate research, museum exhibits for children, and outreach activities to community colleges and K-12 schools in Nevada.This project will address two important challenges commonly faced in cancer subtyping: (1) incorporation of pathway knowledge in cancer subtyping, patient stratification, and risk prediction, and (2) efficient integration of multi-cohort and multi-omics data. To address the first challenge, the project will develop novel machine learning technologies to identify impacted pathways and compute personalized pathway profiles in individual patients. The innovation of this idea stems from combining classical probabilistic components with important biological factors that are not captured in existing techniques: i) all gene-gene interactions as described by each pathway, ii) topology among multi-omics layers, and iii) the crosstalk among pathways. The approach will transform all molecular data to a common pathway space, making it possible to efficiently address the second challenge: systematically integrate multi-omics and multi-cohort data. This will be realized by a non-negative-kernel, variational autoencoders. The non-negative kernel will effectively accumulate consistent signals of biomarkers while shrinking random noise of non-relevant components. The goal of this project will be achieved by three thrusts: 1) compute personalized pathway profiles that can be used for subtyping, 2) integrate multiple patient cohorts to increase sample size and statistical power of subtyping methods, and 3) validate the proposed methodologies using 10 subtype discovery methods, 6 patient stratification techniques, and 6 risk prediction models that will be tested on more than 70 cancer datasets. The investigator will make the methodologies publicly available via a Bioconductor package and a web-based platform, thus increasing their potential for wide adoption by the research communities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.