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

癌症是一个总括的术语，包括一系列疾病，从快速增长和致命的疾病到低进展到死亡的惰性病变。近几十年来，肿瘤治疗的重要临床进展归功于分子亚型和针对特定基因的靶向治疗。然而，随着时间的推移，相当大比例的患者对靶向治疗没有反应或产生耐药性。这意味着目前的肿瘤特征和治疗干预方法还不够准确。该项目旨在开发能够更好地区分被诊断为同一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类似癌症的患者在治疗成功方面可能会有很大差异。该方法还将以多种类型数据的有效整合方法为特色。这项工作将增强我们区分哪些患者面临直接危险，需要最积极的治疗，哪些患者病情进展缓慢的能力。这将降低医疗保健成本和个人痛苦，同时通过为每个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在确定临床适用的生物标志物方面铺平道路，这些标志物可用于诊断、风险预测和监测治疗反应和结果。该项目还包括广泛的教育和推广部分，包括课程开发、本科研究、儿童博物馆展览，以及与内华达州社区大学和K-12学校的推广活动。该项目将解决癌症亚型中常见的两个重要挑战：(1)将通路知识纳入癌症亚型、患者分层和风险预测，以及(2)多队列和多组学数据的有效整合。为了解决第一个挑战，该项目将开发新的机器学习技术，以识别受影响的路径并计算单个患者的个性化路径配置文件。这一思想的创新源于将经典的概率成分与现有技术中没有捕捉到的重要生物因素相结合：i)每条途径所描述的所有基因-基因相互作用；ii)多组学层次之间的拓扑；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路径空间，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。这将通过非负核、变分自动编码器来实现。非负核将有效地积累生物标志物的一致信号，同时缩小无关成分的随机噪声。该项目的目标将通过三项努力来实现：1)计算可用于亚型的个性化路径轮廓；2)整合多个患者队列以增加样本大小和亚型方法的统计能力；3)使用10种亚型发现方法、6种患者分层技术和6种风险预测模型来验证所提出的方法，这些方法将在70多个癌症数据集上进行测试。研究人员将通过BioConductor包和基于网络的平台公开提供这些方法，从而增加它们被研究界广泛采用的潜力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.