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Grant Details

Grant Number: 5R01CA178744-04 Interpret this number
Primary Investigator: Morris, Jeffrey
Organization: University Of Tx Md Anderson Can Ctr
Project Title: Bayesian Methods for Complex, High-Dimensional Functional Data in Cancer Research
Fiscal Year: 2018
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? 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.

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Robust and Gaussian spatial functional regression models for analysis of event-related potentials.
Authors: Zhu H. , Versace F. , Cinciripini P.M. , Rausch P. , Morris J.S. .
Source: Neuroimage, 2018-07-06 00:00:00.0; 181, p. 501-512.
EPub date: 2018-07-06 00:00:00.0.
PMID: 30057352
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A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models.
Authors: Zhu H. , Caspers P. , Morris J.S. , Wu X. , Müller R. .
Source: Technometrics : A Journal Of Statistics For The Physical, Chemical, And Engineering Sciences, 2018; 60(1), p. 112-123.
EPub date: 2017-05-25 00:00:00.0.
PMID: 29749977
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Radiomic analysis in prediction of Human Papilloma Virus status.
Authors: Yu K. , Zhang Y. , Yu Y. , Huang C. , Liu R. , Li T. , Yang L. , Morris J.S. , Baladandayuthapani V. , Zhu H. .
Source: Clinical And Translational Radiation Oncology, 2017 Dec; 7, p. 49-54.
EPub date: 2017-11-06 00:00:00.0.
PMID: 29594229
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Classifying colorectal cancer by tumor location rather than sidedness highlights a continuum in mutation profiles and Consensus Molecular Subtypes.
Authors: Loree J.M. , Pereira A.A. , Lam M. , Willauer A.N. , Raghav K. , Dasari A. , Morris V.K. , Advani S.M. , Menter D.G. , Eng C. , et al. .
Source: Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research, 2017-11-27 00:00:00.0; , .
EPub date: 2017-11-27 00:00:00.0.
PMID: 29180604
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Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer.
Authors: Robertson A.G. , Kim J. , Al-Ahmadie H. , Bellmunt J. , Guo G. , Cherniack A.D. , Hinoue T. , Laird P.W. , Hoadley K.A. , Akbani R. , et al. .
Source: Cell, 2017-10-19 00:00:00.0; 171(3), p. 540-556.e25.
EPub date: 2017-10-05 00:00:00.0.
PMID: 28988769
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Rejoinder to statistical contributions to bioinformatics: Design, modelling, structure learning and Integration.
Authors: Morris J.S. , Baladandayuthapani V. .
Source: Statistical Modelling, 2017 Aug; 17(4-5), p. 338-357.
EPub date: 2017-09-12 00:00:00.0.
PMID: 30034293
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Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.
Authors: Zhu H. , Morris J.S. , Wei F. , Cox D.D. .
Source: Computational Statistics & Data Analysis, 2017 Jul; 111, p. 88-101.
EPub date: 2017-02-15 00:00:00.0.
PMID: 29051679
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Comparison and Contrast of Two General Functional Regression Modeling Frameworks.
Authors: Morris J.S. .
Source: Statistical Modelling, 2017 Feb; 17(1-2), p. 59-85.
EPub date: 2017-02-16 00:00:00.0.
PMID: 28736502
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Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.
Authors: Morris J.S. , Baladandayuthapani V. .
Source: Statistical Modelling, 2017; 17(4-5), p. 245-289.
EPub date: 2017-06-15 00:00:00.0.
PMID: 29129969
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Tumor Microenvironment in Gene Signatures: Critical Biology or Confounding Noise?
Authors: Morris J.S. , Kopetz S. .
Source: Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research, 2016-08-15 00:00:00.0; 22(16), p. 3989-91.
EPub date: 2016-08-15 00:00:00.0.
PMID: 27334836
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Association Of Cpg Island Methylator Phenotype And Ereg/areg Methylation And Expression In Colorectal Cancer
Authors: Lee M.S. , McGuffey E.J. , Morris J.S. , Manyam G. , Baladandayuthapani V. , Wei W. , Morris V.K. , Overman M.J. , Maru D.M. , Jiang Z.Q. , et al. .
Source: British Journal Of Cancer, 2016-06-14 00:00:00.0; 114(12), p. 1352-61.
PMID: 27272216
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Identification of differentially methylated loci using wavelet-based functional mixed models.
Authors: Lee W. , Morris J.S. .
Source: Bioinformatics (oxford, England), 2016-03-01 00:00:00.0; 32(5), p. 664-72.
EPub date: 2016-03-01 00:00:00.0.
PMID: 26559505
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Detection and Quantification of Protein Spots by Pinnacle.
Authors: Morris J.S. , Gutstein H.B. .
Source: Methods In Molecular Biology (clifton, N.j.), 2016; 1384, p. 185-201.
PMID: 26611416
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Functional Car Models For Large Spatially Correlated Functional Datasets
Authors: Zhang L. , Baladandayuthapani V. , Zhu H. , Baggerly K.A. , Majewski T. , Czerniak B.A. , Morris J.S. .
Source: Journal Of The American Statistical Association, 2016; 111(514), p. 772-786.
PMID: 28018013
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The consensus molecular subtypes of colorectal cancer.
Authors: Guinney J. , Dienstmann R. , Wang X. , de Reyniès A. , Schlicker A. , Soneson C. , Marisa L. , Roepman P. , Nyamundanda G. , Angelino P. , et al. .
Source: Nature Medicine, 2015 Nov; 21(11), p. 1350-6.
EPub date: 2015-10-12 00:00:00.0.
PMID: 26457759
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Bayesian function-on-function regression for multilevel functional data.
Authors: Meyer M.J. , Coull B.A. , Versace F. , Cinciripini P. , Morris J.S. .
Source: Biometrics, 2015 Sep; 71(3), p. 563-74.
PMID: 25787146
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