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: 10610938
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10610938
• 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; Goals; Heterogeneity; Immune response; Investigation; Knowledge; Leucocytic infiltrate; 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; Phenotype; Population Study; Recurrence; Research; Risk; Sampling; Shapes; Statistical Methods; Statistical Models; Technology; The Cancer Genome Atlas; Treatment outcome; 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; immune cell infiltrate; improved; innovation; 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.

抽象的 尽管有关肿瘤浸润白细胞（TIL）的研究引起了人们的广泛关注，以了解 肿瘤微环境和相关的免疫反应，评估仍存在相当大的方法学差距 多区域组学数据中 TIL 的瘤内异质性。拟议的研究直接受到我们的推动 与肺癌医学肿瘤学家合作研究肿瘤内异质性和肺部 癌症患者的治疗结果。该提案的主要目标是制定准确的统计数据 通过结合多区域组学数据和有关白细胞的先验知识来量化 TIL 的模型。在这个 项目，（目标 1）我们提出了一种贝叶斯建模方法来估计 TIL 的肿瘤内异质性 多区域转录组数据。该模型克服了现有工作的局限性，特别是 解决同一肿瘤内的相关性和患者水平的变异性。我们将进一步 概括解释不同数据分布的方法，以解决多组学中的估计问题 设置（目标 2）。我们将把所提出的方法应用于 MD 安德森癌症中心肿瘤内 针对肺癌患者的异质性 (MDACC-ITH) 项目和 TCGA 数据集。来自应用程序 从角度来看，我们提出的方法可以最大限度地利用现有的多区域组学数据并结合 复杂的数据结构具有成本效益，并且可以直接提高我们对 TIL 及其关系的理解 与患者的结果。尽管是出于肺癌研究的动机，但统计方法将有助于 估计其他癌症类型中 TIL 的瘤内异质性。开发的所有统计工具软件 该项目一经验证，将提供给更广泛的研究界。

项目成果

A novel statistical method for decontaminating T-cell receptor sequencing data.

一种净化 T 细胞受体测序数据的新统计方法。

• DOI: 10.1093/bib/bbad230
• 发表时间: 2023
• 期刊: Briefings in bioinformatics
• 影响因子: 9.5
• 作者: Li,Ruoxing;Altan,Mehmet;Reuben,Alexandre;Lin,Ruitao;Heymach,JohnV;Tran,Hai;Chen,Runzhe;Little,Latasha;Hubert,Shawna;Zhang,Jianjun;Li,Ziyi
• 通讯作者: Li,Ziyi

MDM2 antagonist improves therapeutic activity of azacitidine in myelodysplastic syndromes and chronic myelomonocytic leukemia.

• DOI: 10.1080/10428194.2022.2116932
• 发表时间: 2022-12
• 期刊: Leukemia & lymphoma
• 影响因子: 2.6

EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.

EDClust：一种 EM-MM 混合方法，用于多受试者单细胞 RNA 测序中的细胞聚类。

• DOI: 10.1093/bioinformatics/btac168
• 发表时间: 2022
• 期刊: Bioinformatics (Oxford, England)
• 作者: Wei,Xin;Li,Ziyi;Ji,Hongkai;Wu,Hao
• 通讯作者: Wu,Hao

Altered hydroxymethylome in the substantia nigra of Parkinson's disease.

帕金森病黑质中羟甲基组的改变。

• DOI: 10.1093/hmg/ddac122
• 发表时间: 2022
• 期刊: Human molecular genetics
• 影响因子: 3.5
• 作者: Min,Shishi;Xu,Qian;Qin,Lixia;Li,Yujing;Li,Ziyi;Chen,Chao;Wu,Hao;Han,Junhai;Zhu,Xiongwei;Jin,Peng;Tang,Beisha
• 通讯作者: Tang,Beisha

Validation of the ALFA-1200 model in older patients with AML treated with intensive chemotherapy.

• DOI: 10.1182/bloodadvances.2022007172
• 发表时间: 2023-03-14
• 期刊: BLOOD ADVANCES
• 影响因子: 7.5
• 作者: Abbas, Hussein A.;Sun, Hanxiao;Pierce, Sherry;Kanagal-Shamanna, Rashmi;Li, Ziyi;Yilmaz, Musa;Borthakur, Gautam;DiPippo, Adam J.;Jabbour, Elias;Konopleva, Marina;Short, Nicholas J.;DiNardo, Courtney;Daver, Naval;Ravandi, Farhad;Kadia, Tapan M.
• 通讯作者: Kadia, Tapan M.