Penalized mixture cure models for identifying genomic features associated with outcome in acute myeloid leukemia

与至事件发生时间结局相关的分子特征，如总生存期或无病生存期，可能是 临床相关或潜在的治疗靶点。因此，分析来自高通量基因组的数据， 具有临床随访数据的分析越来越受到关注。癌症基因组图谱（TCGA）计划 收集了11,125名患者的基线人口统计学、临床特征和随访数据， 处理癌症类型和相应的组织样品以检查SNP、拷贝数、甲基化， miRNA表达和mRNA表达。因为变量的数量（P）超过样本量（N）， 当将分子特征与存活数据相关联时，经常采用的一种策略是拟合单变量 考克斯比例风险（PH）模型，随后使用错误发现调整多个假设检验 率方法。然而，大多数慢性病和疾病，包括癌症，可能是由多种原因引起的。 失调的基因或突变。因此，在存在高风险的情况下拟合多变量模型至关重要， 维协变量空间传统的统计方法在特征数量超过 样本大小（例如，P > N），尽管惩罚方法执行自动变量选择并适应 P > N的情况。惩罚方法包括LASSO、平滑剪切绝对偏差（SCAD）， 自适应LASSO和贝叶斯LASSO都被扩展到考克斯的PH模型来处理高维问题 协变量空间然而，当对生存或其他至事件发生时间结局建模时，考克斯PH模型假设 所有受试者将经历感兴趣的事件，当受试者的子集被治愈时，这是违反的。 相反，当数据中的受试者子集被治愈时，应该拟合混合治愈模型。虽然混合物 已经描述了用于传统设置的固化模型 协变量多，变量选择方法有限，目前尚无高维模型拟合方法 存在混合固化模型。因此，该项目将克服这一领域取得进展的一个关键障碍 通过开发惩罚参数和半参数混合固化模型适用于高维 数据集。具体目标是：（1）建立惩罚参数混合物硫化模型 高维数据集;（2）开发惩罚半参数比例风险混合治愈 用于高维数据集的模型。对于这两个目标，我们将使用以下方法来表征方法的性能： 广泛的模拟研究，开发软件，并将R软件包分发给CRAN。在目标（3）中，我们将确定 与治愈和生存相关的分子特征，使用我们的大型独特AML数据集， 肿瘤临床试验，并使用Gene Expression Omnibus的AML数据集评估结果的稳健性 和癌症基因组图谱项目。这项研究将填补一个关键的空白，因为目前还没有混合物治愈 高维数据的模型。我们预计将我们的方法应用于我们的AML数据将增强现有的 用于日常临床实践的风险分层系统，用于确定治疗强度和方式。

Molecular features associated with time-to-event outcomes, such as overall or disease-free survival, may be prognostically relevant or potential therapeutic targets. Therefore, analyzing data from high-throughput genomic assays with clinical follow-up data has been of growing interest. The Cancer Genome Atlas (TCGA) Project has collected baseline demographic, clinical characteristics, and follow-up data for 11,125 patients for 32 different cancer types and corresponding tissue samples were processed for examining SNPs, copy number, methylation, miRNA expression, and mRNA expression. Because the number of variables (P ) exceeds the sample size (N), one strategy frequently employed when associating molecular features to survivorship data is to fit univariable Cox proportional hazards (PH) models followed by adjustment for multiple hypothesis tests using a false discovery rate approach. However, most chronic conditions and diseases, including cancer, are likely caused by multiple dysregulated genes or mutations. It is therefore critical to fit multivariable models in the presence of a high- dimensional covariate space. Traditional statistical methods cannot be used when the number of features exceeds the sample size (e.g., P > N), though penalized methods perform automatic variable selection and accommodate the P > N scenario. Penalized approaches including LASSO, smoothly clipped absolute deviation (SCAD), adaptive LASSO, and Bayesian LASSO have all been extended to Cox's PH model for handling high-dimensional covariate spaces. However, when modeling survival or other time-to-event outcomes, the Cox PH model assumes that all subjects will experience the event of interest, which is violated when a subset of subjects are cured. Instead, when a subset of subjects in the data are cured, mixture cure models should be fit. Although mixture cure models have been described for traditional settings where the number of samples exceeds the number of covariates, limited variable selection methods and no methods for high-dimensional model fitting currently exist for mixture cure models. Therefore, this project will overcome a critical barrier to progress in this field by developing penalized parametric and semi-parametric mixture cure models applicable for high-dimensional datasets. The specific aims of this application are to: (1) Develop penalized parametric mixture cure models for high-dimensional datasets; and (2) Develop a penalized semi-parametric proportional hazards mixture cure model for high-dimensional datasets. For both aims we will characterize the performance of the methods using extensive simulation studies, develop software, and distribute R packages to CRAN. In aim (3) we will identify molecular features associated with cure and survival using our large unique AML dataset from the Alliance for Clinical Trials in Oncology and assess robustness of findings using AML datasets from Gene Expression Omnibus and The Cancer Genome Atlas project. This research will fill a critical gap as there are currently no mixture cure models for high-dimensional data. We anticipate application of our methods to our AML data will enhance existing risk stratification systems used in daily clinical practice that determine treatment intensity and modality.