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

与事件发生时间结局相关的分子特征，如总体或无病存活，可能是 预测相关的或潜在的治疗靶点。因此，分析来自高通量基因组的数据 临床随访数据的分析越来越引起人们的兴趣。癌症基因组图谱计划已经 收集了32种不同类型的11,125名患者的基线人口学、临床特征和随访数据 对癌症类型和相应的组织样本进行处理以检测SNPs、拷贝数、甲基化 MiRNA的表达，以及mRNA的表达。因为变量的数量(P)超过样本大小(N)， 在将分子特征与生存数据相关联时，经常使用的一种策略是fit单变量 COX比例风险(PH)模型及使用错误发现对多重假设检验进行调整 费率法。然而，大多数慢性病和疾病，包括癌症，很可能是由多发性 失调的基因或突变。因此，fit多变量模型在存在高值时是至关重要的。 维协变量空间。当要素数量超过时，无法使用传统统计方法 样本大小(例如，P&>N)，尽管受处罚的方法执行自动变量选择并适应 宝洁的情况。惩罚方法包括套索、平滑截断绝对偏差(SCAD)、 自适应套索和贝叶斯套索都已扩展到Cox的PH模型，用于处理高维数据 协变量空间。然而，当对存活率或其他事件发生时间结果进行建模时，COX PH模型假定 所有受试者都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，混合治愈模型应该是fit。尽管混合 已针对样本数量超过数量的传统设置描述了固化模型 协变量，有限的变量选择方法，目前还没有高维模型fi设置的方法 存在混合固化模型。因此，这个项目将克服在这个fi领域取得进展的关键障碍。 通过建立适用于高维的惩罚参数和半参数混合固化模型 数据集。本应用的具体目的是：(1)建立惩罚参数混合曲线模型(fi)。 对于高维数据集；以及(2)开发了惩罚半参数比例风险混合曲线 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包分发给CRAN。在AIM(3)中，我们将确定 使用我们来自美国急性髓细胞白血病联盟的大型独特的急性髓细胞白血病数据集，研究与治愈和生存相关的分子特征 使用来自基因表达总表的急性髓细胞白血病数据集进行肿瘤学临床试验和评估fi结合的稳健性 和癌症基因组图谱项目。这项研究将是一个关键的缺口，因为目前还没有混合治愈方法(fill 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 确定治疗强度和方式的日常临床实践中使用的风险策略(Risk Stratifi)系统。

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.