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

用于识别与急性髓系白血病结果相关的基因组特征的惩罚混合治疗模型

• 批准号: 10544523
• 负责人: Kellie J. Archer
• 金额: $ 25.93万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10544523
• 关键词: Acute Myelocytic Leukemia; American Cancer Society; Archives; Biological; Biological Assay; Cessation of life; Characteristics; Chronic; Clinical; Clinical Trials; Clip; Computer software; Cox Proportional Hazards Models; Data; Data Science; Data Set; Decision Making; Development; Diagnosis; Disease; Disease-Free Survival; Effectiveness; Event; Gene Expression; Genes; Genomics; Malignant Neoplasms; Methodology; Methods; Methylation; MicroRNAs; Modality; Modeling; Molecular; Mutation; Oncology; Outcome; Patients; Performance; Population Study; Predisposition; Probability; Process; Prognosis; Property; RUNX1 gene; Research; Research Personnel; Research Project Grants; Risk; Sample Size; Sampling; Sampling Studies; Statistical Methods; Stem cell transplant; Subgroup; System; Techniques; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Therapeutic Agents; Time; Tissue Sample; United States National Library of Medicine; aged; biomedical informatics; cancer type; chemotherapy; clinical practice; experience; follow-up; hazard; high dimensionality; improved; interest; mRNA Expression; multidimensional data; new therapeutic target; novel; prognostic; risk stratification; semiparametric; simulation; software development; survival outcome; survivorship; targeted treatment; therapeutic target

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.

与事件时间结局相关的分子特征，例如总体或无病生存期，可能是 预后相关或潜在的治疗靶标。因此，从高通量基因组中分析了数据 带有临床随访数据的测定越来越引起人们的关注。癌症基因组图集（TCGA）项目 收集了11,125例患者的基线人口统计学，临床特征和随访数据，32例不同 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（p）的数量超过样本大小（n），所以 将分子特征与生存数据关联时经常采用的一种策略是可拟合单变量 COX比例危害（pH）模型，然后使用虚假发现进行多个假设检验调整 费率方法。但是，大多数慢性病和疾病（包括癌症）可能是由多种引起的 基因或突变失调。因此，在存在高 - 尺寸协变空间。当特征数量超过时，传统的统计方法无法使用 样本量（例如，p> n），尽管受惩罚的方法执行自动变量选择并接受 p> n场景。惩罚方法，包括拉索，平稳剪切的绝对偏差（SCAD）， 自适应拉索和贝叶斯套索都扩展到Cox的pH模型，用于处理高维 协变空间。但是，当对生存或其他事件结果进行建模时，Cox pH模型假设 所有受试者都会遇到感兴趣的事件，这是在治愈一部分受试者时违反的。 取而代之的是，当数据中的受试者的子集得到固化时，应拟合混合物治疗模型。虽然混合物 已经描述了用于样本数量数量数量的传统设置的治疗模型 协变量，有限的可变选择方法和当前高维模型拟合的方法 存在用于混合固化模型。因此，该项目将克服这个领域的关键障碍 通过开发适用于高维的惩罚参数和半参数混合物固化模型 数据集。该应用程序的特定目的是：（1）开发惩罚参数混合物治疗模型 用于高维数据集； （2）开发惩罚的半参数比例危害混合物治疗 高维数据集的模型。对于这两个目标，我们将使用 广泛的仿真研究，开发软件和分布式的R包给Cran。在目标（3）我们将确定 使用我们的大型独特AML数据集与Alliance相关的分子特征和生存相关 肿瘤学的临床试验并使用基因表达综合的AML数据集评估发现的鲁棒性 和癌症基因组图集项目。这项研究将填补关键的差距，因为目前尚无混合疗法 高维数据的模型。我们希望将我们的方法应用于AML数据将增强现有 日常临床实践中使用的风险地层系统决定了治疗强度和方式。