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Bayesian methods for complex, high-dimensional functional data in cancer research

癌症研究中复杂、高维功能数据的贝叶斯方法

基本信息

批准号：
10023563
负责人：
JEFFREY S MORRIS
金额：
$ 35.64万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-10 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10023563
关键词：
Anatomy Bayesian Method Behavioral Biological Markers Brain Brain region Cancer Etiology Cancer Model Cancer Patient Characteristics Code Complex Computer software Data Discriminant Analysis Discrimination Disease Distant Early Diagnosis Electrodes Emotional Epigenetic Process Event Event-Related Potentials Functional Magnetic Resonance Imaging Genome Genomic Segment Genomics Goals Grant Hybrids Image Individual Interdisciplinary Study Malignant Neoplasms Malignant neoplasm of urinary bladder Methods Modeling Molecular Natural History Neurologic Output Population Prediction of Response to Therapy Process Psychometrics Randomized Regression Analysis Research Running Scientist Structure Testing Work analytical method anticancer research brain volume cancer biomarkers disease natural history emotional stimulus flexibility genetic predictors genomic data genomic platform high dimensionality individual patient insight multidimensional data neuroimaging marker novel outcome forecast parallel processing public health relevance response smoking cessation success targeted treatment tool tumor heterogeneity visual stimulus

项目摘要

DESCRIPTION (provided by applicant): Complex, high-dimensional data like multi-platform genomics and imaging data can be used to discover biomarkers providing insight into cancer etiology, natural history, prognosis, and prediction of response to therapy. Existing analytical methods are not adequate, however, as most either ignore important structure in the data or limit analysis to simple summaries that do not use all of the information in the data. This research will develop a general suite of flexible, automated, novel Bayesian methods for performing regression analyses on complex, high dimensional functional data to discover biomarkers using models that account for their intricate structure, yield inference that adjusts fo multiple testing, and are scalable to high-dimensional settings. While generally applicable, these methods will be developed in the context of two studies conducted by our collaborators to discover early genomic and epigenetic events in the natural history of bladder cancer and neuroimaging biomarkers associated with and predicting smoking cessation success. Specific Aim 1: Modeling multi-platform genomic data as functions, we will develop methods for functional response regression for spatially correlated genomics data on a lattice generated by a novel bladder cancer model developed by our co-I Czerniak. We will apply these methods to identify genomic and epigenetic changes in bladder cancer and determine when first observed in the disease's natural history, revealing early aberrations that are potential disease drivers. We will develop inferential strategies to perform genome-level tests and then ag genomic regions while adjusting for multiplicity. Specific Aim 2: We will develop functional regression approaches for event-related potentials (ERPs) from a randomized smoking cessation trial conducted by our co-Is Cinciripini and Versace to test whether different emotional stimuli evoke differential neurological response, determine whether these effects vary between individuals successful or unsuccessful in their smoking cessation attempt, and assess whether ERPs are independent predictors of success. Our methods will flexibly capture inter-electrode correlation via spatial functional processes or tensor basis functions, and capture intra-electrode correlation using basis function modeling, with strategies to determine which basis is best for ERPs. Specific Aim 3: We will develop functional regression approaches for fMRI data from our smoking cessation trial, first at the subject level to identify brain regions differentially activaed by different visual stimuli, and then introducing a strategy to scale our approach up to group-level analyses to characterize population-level neurological differences, relate them to cessation success, and assess their predictive ability relative to ERP and standard demographic, psychometric, and genetic predictors. Our models for longitudinally correlated volumetric data will capture intra-volume correlation through basis functional modeling, introducing a novel hybrid basis function modeling strategy that captures within-brain correlation in a manner that accounts for known anatomy, spatial proximity, and distant correlations induced by functional connectivity. Specific Aim 4: We will integrate these new methods into a general suite of Bayesian methods for spatially and longitudinally correlated functional response regression, discrimination, and inference for complex, high-dimensional functions along with freely available, automated, scalable software that can be broadly applied.

项目成果

期刊论文数量（26）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The consensus molecular subtypes of colorectal cancer.

DOI：
10.1038/nm.3967
发表时间：
2015-11
期刊：
Nature medicine
影响因子：
82.9
作者：
Guinney J;Dienstmann R;Wang X;de Reyniès A;Schlicker A;Soneson C;Marisa L;Roepman P;Nyamundanda G;Angelino P;Bot BM;Morris JS;Simon IM;Gerster S;Fessler E;De Sousa E Melo F;Missiaglia E;Ramay H;Barras D;Homicsko K;Maru D;Manyam GC;Broom B;Boige V;Perez-Villamil B;Laderas T;Salazar R;Gray JW;Hanahan D;Tabernero J;Bernards R;Friend SH;Laurent-Puig P;Medema JP;Sadanandam A;Wessels L;Delorenzi M;Kopetz S;Vermeulen L;Tejpar S
通讯作者：
Tejpar S

A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models.

DOI：
10.1080/00401706.2016.1274681
发表时间：
2018
期刊：
Technometrics : a journal of statistics for the physical, chemical, and engineering sciences
影响因子：
0
作者：
Zhu H;Caspers P;Morris JS;Wu X;Müller R
通讯作者：
Müller R

Detection and Quantification of Protein Spots by Pinnacle.

Pinnacle 对蛋白质斑点的检测和定量。