Statistical models for intratumor heterogeneity of tumor-infiltrated leukocytes in lung cancer

肺癌肿瘤浸润白细胞瘤内异质性统计模型

• 批准号: 10435087
• 负责人: Ziyi Li
• 金额: $ 8.1万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-18 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10435087
• 关键词: Accounting; Address; Attention; Bayesian Method; Bayesian Modeling; Bioconductor; Biological Markers; Cancer Biology; Cancer Center; Cancer Etiology; Cancer Patient; Cells; Cessation of life; Characteristics; Clinical; Collaborations; Communities; Complex; Computer software; Data; Data Set; Databases; Development; Disease; Disease Progression; Epigenetic Process; Genes; Goals; Heterogeneity; Immune response; Investigation; Knowledge; Leukocytes; Malignant Neoplasms; Malignant neoplasm of lung; Medical Oncologist; Methodology; Methods; Modality; Modeling; Molecular; Multiomic Data; Non-Small-Cell Lung Carcinoma; Patient-Focused Outcomes; Patients; Population Study; Recurrence; Research; Risk; Sampling; Shapes; Statistical Methods; Statistical Models; Technology; The Cancer Genome Atlas; Treatment outcome; Tumor-infiltrating immune cells; United States; Variant; Work; anticancer research; cancer heterogeneity; cancer type; cell type; clinical practice; complex data; computerized tools; cost; cost effective; data modeling; data structure; improved; innovation; molecular phenotype; multiple omics; neoplastic cell; novel; prognostic; public health relevance; single cell technology; tool; transcriptomics; treatment response; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; user-friendly

ABSTRACT Although studies about tumor-infiltrated leukocytes (TILs) have attracted substantial attention to understand tumor microenvironment and related immune response, considerable methodological gaps remain for evaluating intratumor heterogeneity of TILs in multi-region omics data. The proposed study is directly motivated by our collaborations with lung cancer medical oncologists in the investigation of intratumor heterogeneity and lung cancer patients' treatment outcome. The primary objective of this proposal is to develop accurate statistical models to quantify TILs by combining multi-region omics data and the prior knowledge about leukocytes. In this project, (Aim 1) we propose a Bayesian modeling approach to estimate intratumor heterogeneity of TILs from multi-region transcriptomics data. The model overcomes the limitations in existing works and specifically addresses the correlations within the same tumor and the variability at the patients' level. We will further generalize the approaches to account for different data distributions to address the estimations in the multi-omics setting (Aim 2). We will apply the proposed methods to the MD Anderson Cancer Center Intra-Tumor Heterogeneity (MDACC-ITH) project for lung cancer patients and the TCGA datasets. From an application perspective, our proposed methods of maximizing the use of existing multi-region omics data and incorporating complex data structure is cost-effective and may directly improve our understanding of TILs and their relationship with patient outcomes. Although motivated by lung cancer research, the statistical methods will be useful for estimating intratumor heterogeneity of TILs in other cancer types. All software for statistical tools developed in this project, once validated, will be made available to the broader research community.

摘要 尽管关于肿瘤浸润白细胞（TIL）的研究已经吸引了大量的注意力来理解肿瘤浸润白细胞（TIL）的作用。 肿瘤微环境和相关的免疫反应，相当大的方法学差距仍然存在评估 多区域组学数据中TIL的肿瘤内异质性。这项研究的直接动机是我们的 与肺癌医学肿瘤学家合作，研究肿瘤内异质性和肺 癌症患者的治疗结果。这项建议的主要目的是制定准确的统计数字， 通过结合多区域组学数据和关于白细胞的先验知识来量化TILs的模型。在这 项目，（目标1）我们提出了一种贝叶斯建模方法来估计肿瘤内的TIL异质性， 多区域转录组学数据。该模型克服了现有工作中的局限性， 解决了同一肿瘤内的相关性和患者水平的变异性。我们将进一步 概括了不同数据分布的方法，以解决多组学中的估计问题 设定（目标2）。我们将把所提出的方法应用于MD安德森癌症中心的肿瘤内 肺癌患者和TCGA数据集的异质性（MDACC-ITH）项目。从应用 从这个角度来看，我们提出的方法，最大限度地利用现有的多区域组学数据，并结合 一个复杂的数据结构是经济有效的，可以直接提高我们对TILs及其关系的理解 患者的结果。虽然是肺癌研究的动机，但统计方法将有助于 估计其他癌症类型中TIL的肿瘤内异质性。开发的所有统计工具软件 这一项目一旦得到验证，将提供给更广泛的研究界。