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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的尽管关于肿瘤浸润的白细胞（TIL）的研究吸引了很大的关注以理解肿瘤微环境和相关的免疫反应，仍有可观的方法论差异用于评估多区域OMICS数据中TIL的肿瘤内异质性。拟议的研究直接由我们的动机与肺癌医学肿瘤学家的合作调查肿瘤内异质性和肺癌症患者的治疗结果。该提案的主要目的是制定准确的统计通过组合多区域的OMIC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，以估计TIL的肿瘤内异质性多区域转录组学数据。该模型克服了现有作品，特别是解决同一肿瘤内的相关性以及患者水平的变异性。我们将进一步概括说明不同数据分布的方法，以解决多摩变的估计设置（AIM 2）。我们将向MD Anderson Cancer Center肿瘤内应用提出的方法肺癌患者和TCGA数据集的异质性（MDACC-ITH）项目。从应用程序透视图，我们提出的最大化现有多区域OMICS数据并合并的方法复杂的数据结构具有成本效益，可以直接提高我们对TIL及其关系的理解与患者的结果。尽管是受肺癌研究的动机，但统计方法将对估计其他癌症类型的TIL的肿瘤内异质性。所有用于开发的统计工具的软件该项目一旦得到验证，将提供给更广泛的研究社区。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
作者：
通讯作者：

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)
影响因子：
0
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.