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Lee J, Hussain S, Warnick R, Vannucci M, Menchaca I, Seitz AR, Hu X, Peters MAK, Guindani M. A predictor-informed multi-subject bayesian approach for dynamic functional connectivity. PLoS One 2024; 19:e0298651. [PMID: 38753655 PMCID: PMC11098372 DOI: 10.1371/journal.pone.0298651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/30/2024] [Indexed: 05/18/2024] Open
Abstract
Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.
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Affiliation(s)
- Jaylen Lee
- Department of Statistics, University of California, Irvine, Irvine, California, United States of America
| | - Sana Hussain
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Ryan Warnick
- Microsoft Security Research, Microsoft, Redmond, Washington, United States of America
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - Isaac Menchaca
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Aaron R. Seitz
- Department of Psychology, University of California, Riverside, Riverside, California, United States of America
| | - Xiaoping Hu
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
| | - Megan A. K. Peters
- Department of Bioengineering, University of California, Riverside, Riverside, California, United States of America
- Department of Cognitive Sciences, University of California, Irvine, Irvine, California, United States of America
| | - Michele Guindani
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, California, United States of America
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Primavera R, Regmi S, Yarani R, Levitte S, Wang J, Ganguly A, Chetty S, Guindani M, Ricordi C, Meyer E, Thakor AS. Precision Delivery of Human Bone Marrow-Derived Mesenchymal Stem Cells Into the Pancreas Via Intra-arterial Injection Prevents the Onset of Diabetes. Stem Cells Transl Med 2024:szae020. [PMID: 38530131 DOI: 10.1093/stcltm/szae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/26/2024] [Indexed: 03/27/2024] Open
Abstract
Mesenchymal stem cells (MSCs) are a promising therapy to potentially treat diabetes given their potent anti-inflammatory and immune-modulatory properties. While these regenerative cells have shown considerable promise in cell culture, their clinical translation has been challenging. In part, this can be attributed to these cells not reaching the pancreas to exert their regenerative effects following conventional intravenous (IV) injection, with the majority of cells being trapped in the lungs in the pulmonary first-pass effect. In the present study, we will therefore examine whether direct delivery of MSCs to the pancreas via an intra-arterial (IA) injection can improve their therapeutic efficacy. Using a mouse model, in which repetitive low doses of STZ induced a gentle, but progressive, hyperglycemia, we tested bone marrow-derived MSCs (BM-MSCs) which we have shown are enriched with pro-angiogenic and immunomodulatory factors. In cell culture studies, BM-MSCs were shown to preserve islet viability and function following exposure to proinflammatory cytokines (IFN-γ, IL-1β, and TNF-α) through an increase in pAkt. When tested in our animal model, mice receiving IV BM-MSCs were not able to mitigate the effects of STZ, however those which received the same dose and batch of cells via IA injection were able to maintain basal and dynamic glycemic control, to similar levels as seen in healthy control animals, over 10 days. This study shows the importance of considering precision delivery approaches to ensure cell-based therapies reach their intended targets to enable them to exert their therapeutic effects.
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Affiliation(s)
- Rosita Primavera
- Interventional Radiology Innovation at Stanford (IRIS), Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Shobha Regmi
- Interventional Radiology Innovation at Stanford (IRIS), Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Reza Yarani
- Interventional Radiology Innovation at Stanford (IRIS), Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Translational Type 1 Diabetes Research, Department of Clinical Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
| | - Steven Levitte
- Interventional Radiology Innovation at Stanford (IRIS), Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jing Wang
- Interventional Radiology Innovation at Stanford (IRIS), Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Abantika Ganguly
- Interventional Radiology Innovation at Stanford (IRIS), Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Shashank Chetty
- Interventional Radiology Innovation at Stanford (IRIS), Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Michele Guindani
- Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, UCLA, Los Angeles, CA, USA
| | - Camillo Ricordi
- Diabetes Research Institute (DRI) and Clinical Cell Transplant Program, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Everett Meyer
- Division of Blood and Marrow Transplantation, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Avnesh S Thakor
- Interventional Radiology Innovation at Stanford (IRIS), Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
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3
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Garcia NS, Du M, Guindani M, McIlvin MR, Moran DM, Saito MA, Martiny AC. Proteome trait regulation of marine Synechococcus elemental stoichiometry under global change. ISME J 2024; 18:wrae046. [PMID: 38513256 PMCID: PMC11020310 DOI: 10.1093/ismejo/wrae046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/27/2024] [Accepted: 03/19/2024] [Indexed: 03/23/2024]
Abstract
Recent studies have demonstrated regional differences in marine ecosystem C:N:P with implications for carbon and nutrient cycles. Due to strong co-variance, temperature and nutrient stress explain variability in C:N:P equally well. A reductionistic approach can link changes in individual environmental drivers with changes in biochemical traits and cell C:N:P. Thus, we quantified effects of temperature and nutrient stress on Synechococcus chemistry using laboratory chemostats, chemical analyses, and data-independent acquisition mass spectrometry proteomics. Nutrient supply accounted for most C:N:Pcell variability and induced tradeoffs between nutrient acquisition and ribosomal proteins. High temperature prompted heat-shock, whereas thermal effects via the "translation-compensation hypothesis" were only seen under P-stress. A Nonparametric Bayesian Local Clustering algorithm suggested that changes in lipopolysaccharides, peptidoglycans, and C-rich compatible solutes may also contribute to C:N:P regulation. Physiological responses match field-based trends in ecosystem stoichiometry and suggest a hierarchical environmental regulation of current and future ocean C:N:P.
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Affiliation(s)
- Nathan S Garcia
- Department of Earth System Science, University of California, Irvine, Irvine, CA 92697, United States
| | - Mingyu Du
- Department of Statistics, University of California, Irvine, Irvine, CA 92697, United States
| | - Michele Guindani
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - Matthew R McIlvin
- Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States
| | - Dawn M Moran
- Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States
| | - Mak A Saito
- Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, United States
| | - Adam C Martiny
- Department of Earth System Science, University of California, Irvine, Irvine, CA 92697, United States
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, United States
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4
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Liu CC, Abdelhafez YG, Yap SP, Acquafredda F, Schirò S, Wong AL, Sarohia D, Bateni C, Darrow MA, Guindani M, Lee S, Zhang M, Moawad AW, Ng QKT, Shere L, Elsayes KM, Maroldi R, Link TM, Nardo L, Qi J. AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data. J Digit Imaging 2023; 36:1049-1059. [PMID: 36854923 PMCID: PMC10287587 DOI: 10.1007/s10278-023-00785-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 03/02/2023] Open
Abstract
Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.
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Affiliation(s)
- Chih-Chieh Liu
- Department of Biomedical Engineering, University of California, Davis, CA, USA
| | - Yasser G Abdelhafez
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
- Radiotherapy and Nuclear Medicine Department, South Egypt Cancer Institute, Assiut University, Assiut, Egypt
| | - S Paran Yap
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | | | - Silvia Schirò
- Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Andrew L Wong
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Dani Sarohia
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Cyrus Bateni
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Morgan A Darrow
- Pathology and Laboratory Medicine, University of California Davis, Sacramento, CA, USA
| | - Michele Guindani
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Sonia Lee
- Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Michelle Zhang
- Department of Diagnostic Radiology, McGill University Health Center, Montreal, Canada
| | - Ahmed W Moawad
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Diagnostic Radiology, Mercy Catholic Medical Center, Darby, PA, USA
| | | | - Layla Shere
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khaled M Elsayes
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Lorenzo Nardo
- Department of Radiology, UC Davis Health, Sacramento, CA, USA
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA, USA.
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Denti F, Peluso S, Guindani M, Mira A. Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies. Stat Med 2023; 42:1931-1945. [PMID: 36914221 DOI: 10.1002/sim.9705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 10/02/2022] [Accepted: 02/24/2023] [Indexed: 03/15/2023]
Abstract
The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non-local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian false discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly-available datasets from genomic studies of heterogeneous nature.
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Affiliation(s)
- Francesco Denti
- Department of Statistics, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Stefano Peluso
- Department of Statistics and Quantitative Methods, University of Milan - Bicocca, Milan, Italy
| | - Michele Guindani
- Department of Biostatistics, University of California Los Angeles, California, Los Angeles, USA
| | - Antonietta Mira
- Faculty of Economics, Università della Svizzera italiana, Lugano, Switzerland.,Department of Science and High Technology, University of Insubria, Como, Italy
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6
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D'Angelo L, Canale A, Yu Z, Guindani M. Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data. Biometrics 2022. [PMID: 35191539 DOI: 10.1111/biom.13626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 01/13/2022] [Indexed: 11/30/2022]
Abstract
Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intra-cellular calcium signals. An on-going challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time-series. In this manuscript, we propose a nested Bayesian finite mixture specification that allows the estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. Furthermore, the spikes' intensity values are also clustered within and between experimental conditions to determine the existence of common (recurring) response amplitudes. Simulation studies and the analysis of a data set from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Laura D'Angelo
- Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy
| | - Antonio Canale
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | - Zhaoxia Yu
- Department of Statistics, University of California, Irvine Irvine, U.S.A
| | - Michele Guindani
- Department of Statistics, University of California, Irvine Irvine, U.S.A
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7
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Yu Z, Guindani M, Grieco SF, Chen L, Holmes TC, Xu X. Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron 2022; 110:21-35. [PMID: 34784504 PMCID: PMC8763600 DOI: 10.1016/j.neuron.2021.10.030] [Citation(s) in RCA: 132] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/06/2021] [Accepted: 10/19/2021] [Indexed: 01/07/2023]
Abstract
In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings.
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Affiliation(s)
- Zhaoxia Yu
- Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA 92697-3425, USA; The Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA 92697, USA.
| | - Michele Guindani
- Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA 92697-3425, USA
| | - Steven F Grieco
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA 92697-1275, USA
| | - Lujia Chen
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA 92697-1275, USA
| | - Todd C Holmes
- Department of Physiology and Biophysics, School of Medicine, University of California, Irvine, Irvine, CA 92697- 4560, USA; The Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA 92697, USA
| | - Xiangmin Xu
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA 92697-1275, USA; Department of Biomedical Engineering, University of California, Irvine, Irvine, CA 92697-2715, USA; Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA 92697-4025, USA; Department of Computer Science, University of California, Irvine, Irvine, CA 92697-3435, USA; The Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA 92697, USA.
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8
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Denti F, Camerlenghi F, Guindani M, Mira A. A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data. J Am Stat Assoc 2021; 118:405-416. [PMID: 37089274 PMCID: PMC10120855 DOI: 10.1080/01621459.2021.1933499] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 05/12/2021] [Accepted: 05/19/2021] [Indexed: 09/30/2022]
Abstract
The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested common atoms model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to scalable posterior inference through the use of a computationally efficient nested slice sampler algorithm. We further discuss how to extend the proposed modeling framework to handle discrete measurements, and we conduct posterior inference on a real microbiome dataset from a diet swap study to investigate how the alterations in intestinal microbiota composition are associated with different eating habits. We further investigate the performance of our model in capturing true distributional structures in the population by means of a simulation study.
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Affiliation(s)
- Francesco Denti
- Department of Statistics, University of California, Irvine, CA
| | - Federico Camerlenghi
- Department of Economics, Management and Statistics, University of Milano - Bicocca, Milan, Italy
| | | | - Antonietta Mira
- Università della Svizzera italiana, Lugano, Switzerland
- University of Insubria, Como, Italy
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9
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Bianchetti A, Chinello C, Guindani M, Braga S, Neva A, Verardi R, Piovani G, Pagani L, Lisignoli G, Magni F, Russo D, Almici C. A Blood Bank Standardized Production of Human Platelet Lysate for Mesenchymal Stromal Cell Expansion: Proteomic Characterization and Biological Effects. Front Cell Dev Biol 2021; 9:650490. [PMID: 34055779 PMCID: PMC8160451 DOI: 10.3389/fcell.2021.650490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/06/2021] [Indexed: 01/14/2023] Open
Abstract
Human platelet lysate (hPL) is considered a valid substitute to fetal bovine serum (FBS) in the expansion of mesenchymal stromal cells (MSC), and it is commonly produced starting from intermediate side products of whole blood donations. Through freeze-thaw cycles, hPL is highly enriched in chemokines, growth factors, and adhesion and immunologic molecules. Cell therapy protocols, using hPL instead of FBS for the expansion of cells, are approved by regulatory authorities without concerns, and its administration in patients is considered safe. However, published data are fairly difficult to compare, since the production of hPL is highly variable. This study proposes to optimize and standardize the hPL productive process by using instruments, technologies, and quality/safety standards required for blood bank activities and products. The quality and improved selection of the starting material (i.e., the whole blood), together with the improvement of the production process, guarantee a product characterized by higher content and quality of growth factors as well as a reduction in batch-to-batch variability. By increasing the number of freeze/thaw cycles from one (hPL1c) to four (hPL4c), we obtained a favorable effect on the release of growth factors from platelet α granules. Those changes have directly translated into biological effects leading to a decreasing doubling time (DT) of MSC expansion at 7 days (49.41 ± 2.62 vs. 40.61 ± 1.11 h, p < 0.001). Furthermore, mass spectrometry (MS)-based evaluation has shown that the proliferative effects of hPL4c are also combined with a lower batch-to-batch variability (10-15 vs. 21-31%) at the proteomic level. In conclusion, we have considered lot-to-lot hPL variability, and by the strict application of blood bank standards, we have obtained a standardized, reproducible, safe, cheap, and ready-to-use product.
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Affiliation(s)
- Andrea Bianchetti
- Laboratory for Stem Cells Manipulation and Cryopreservation, Blood Bank, Department of Transfusion Medicine, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Clizia Chinello
- Clinical Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, Irvine, CA, United States
| | - Simona Braga
- Laboratory for Stem Cells Manipulation and Cryopreservation, Blood Bank, Department of Transfusion Medicine, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Arabella Neva
- Laboratory for Stem Cells Manipulation and Cryopreservation, Blood Bank, Department of Transfusion Medicine, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Rosanna Verardi
- Laboratory for Stem Cells Manipulation and Cryopreservation, Blood Bank, Department of Transfusion Medicine, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Giovanna Piovani
- Biology and Genetics Division, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Lisa Pagani
- Clinical Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Gina Lisignoli
- IRCCS, Istituto Ortopedico Rizzoli, SC Laboratorio di Immunoreumatologia e Rigenerazione Tissutale, Bologna, Italy
| | - Fulvio Magni
- Clinical Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Domenico Russo
- Chair of Hematology, Unit of Blood Diseases and Stem Cell Transplantation, University of Brescia, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Camillo Almici
- Laboratory for Stem Cells Manipulation and Cryopreservation, Blood Bank, Department of Transfusion Medicine, ASST Spedali Civili of Brescia, Brescia, Italy
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10
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Affiliation(s)
- Xiao Li
- Personalized Healthcare Genentech, Inc. South San Francisco CA USA
| | | | - Chaan S. Ng
- Department of Diagnostic Radiology The University of Texas MD Anderson Cancer Center Houston TX USA
| | - Brian P. Hobbs
- Dell Medical School The University of Texas at Austin Austin TX USA
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11
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Hart B, Guindani M, Malone S, Fiecas M. A nonparametric Bayesian model for estimating spectral densities of resting-state EEG twin data. Biometrics 2020; 78:313-323. [PMID: 33058149 DOI: 10.1111/biom.13393] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 09/16/2020] [Accepted: 10/05/2020] [Indexed: 11/27/2022]
Abstract
Electroencephalography (EEG) is a noninvasive neuroimaging modality that captures electrical brain activity many times per second. We seek to estimate power spectra from EEG data that ware gathered for 557 adolescent twin pairs through the Minnesota Twin Family Study (MTFS). Typically, spectral analysis methods treat time series from each subject separately, and independent spectral densities are fit to each time series. Since the EEG data were collected on twins, it is reasonable to assume that the time series have similar underlying characteristics, so borrowing information across subjects can significantly improve estimation. We propose a Nested Bernstein Dirichlet prior model to estimate the power spectrum of the EEG signal for each subject by smoothing periodograms within and across subjects while requiring minimal user input to tuning parameters. Furthermore, we leverage the MTFS twin study design to estimate the heritability of EEG power spectra with the hopes of establishing new endophenotypes. Through simulation studies designed to mimic the MTFS, we show our method out-performs a set of other popular methods.
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Affiliation(s)
- Brian Hart
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Michele Guindani
- Department of Statistics, University of California Irvine, Irvine, California, USA
| | - Stephen Malone
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Mark Fiecas
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA
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12
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Abstract
There is a strong interest in the neuroscience community to measure brain connectivity and develop methods that can differentiate connectivity across patient groups and across different experimental stimuli. The development of such statistical tools is critical to understand the dynamics of functional relationships among brain structures supporting memory encoding and retrieval. However, the challenge comes from the need to incorporate within-condition similarity with between-conditions heterogeneity in modeling connectivity, as well as how to provide a natural way to conduct trial- and condition-level inference on effective connectivity. A Bayesian hierarchical vector autoregressive (BH-VAR) model is proposed to characterize brain connectivity and infer differences in connectivity across conditions. Within-condition connectivity similarity and between-conditions connectivity heterogeneity are accounted for by the priors on trial-specific models. In addition to the fully Bayesian framework, an alternative two-stage computation approach is also proposed which still allows straightforward uncertainty quantification of between-trial conditions via MCMC posterior sampling, but provides a fast approximate procedure for the estimation of trial-specific VAR parameters. A novel aspect of the approach is the use of a frequency-specific measure, partial directed coherence (PDC), to characterize effective connectivity under the Bayesian framework. More specifically, PDC allows inferring directionality and explaining the extent to which the present oscillatory activity at a certain frequency in a sender channel influences the future oscillatory activity in a specific receiver channel relative to all possible receivers in the brain network. The proposed model is applied to a large electrophysiological dataset collected as rats performed a complex sequence memory task. This unique dataset includes local field potentials (LFPs) activity recorded from an array of electrodes across hippocampal region CA1 while animals were presented with multiple trials from two main conditions. The proposed modeling approach provided novel insights into hippocampal connectivity during memory performance. Specifically, it separated CA1 into two functional units, a lateral and a medial segment, each showing stronger functional connectivity to itself than to the other. This approach also revealed that information primarily flowed in a lateral-to-medial direction across trials (within-condition), and suggested this effect was stronger on one trial condition than the other (between-conditions effect). Collectively, these results indicate that the proposed model is a promising approach to quantify the variation of functional connectivity, both within- and between-conditions, and thus should have broad applications in neuroscience research.
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Affiliation(s)
- Lechuan Hu
- Department of Statistics, University of California, Irvine,
USA
| | | | - Norbert J. Fortin
- Department of Neurobiology and Behavior, University of
California, Irvine, USA
| | - Hernando Ombao
- Statistics Program, King Abdullah University of Science and
Technology (KAUST), Saudi Arabia
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13
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Denti F, Guindani M, Leisen F, Lijoi A, Wadsworth WD, Vannucci M. Two-group Poisson-Dirichlet mixtures for multiple testing. Biometrics 2020; 77:622-633. [PMID: 32535900 DOI: 10.1111/biom.13314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 05/21/2020] [Accepted: 05/22/2020] [Indexed: 11/26/2022]
Abstract
The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Here, we investigate employing mixtures of two-parameter Poisson-Dirichlet Processes instead, and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs nonlocal prior densities to allow separation between the two mixture components. We obtain a closed-form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward Markov Chain Monte Carlo implementation. We compare the performance of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer data set and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate-to-severe diarrhea.
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Affiliation(s)
- Francesco Denti
- Department of Statistics, University of California, Irvine, California
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, California
| | - Fabrizio Leisen
- School of Mathematics, Statistics and Actuarial Sciences, University of Kent, Canterbury, UK
| | - Antonio Lijoi
- Department of Decision Sciences, Bocconi University, Milan, Italy.,Bocconi Institute of Data Science and Analytics (BIDSA), Milan, Italy
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14
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Chang S, Guindani M, Morahan P, Magrane D, Newbill S, Helitzer D. Increasing Promotion of Women Faculty in Academic Medicine: Impact of National Career Development Programs. J Womens Health (Larchmt) 2020; 29:837-846. [PMID: 32466701 PMCID: PMC7307676 DOI: 10.1089/jwh.2019.8044] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background: Three national career development programs (CDPs)-Early and Mid-Career Programs sponsored by the Association of American Medical Colleges and the Hedwig van Ameringen Executive Leadership in Academic Medicine sponsored by Drexel University-seek to expand gender diversity in faculty and institutional leadership of academic medical centers. Over 20 years of success and continued need are evident in the sustained interest and investment of individuals and institutions. However, their impact on promotion in academic rank remains unknown. The purpose of the study is to compare promotion rates of women CDP participants and other faculty of similar institutional environment and initial career stage. Methods: The study examined retrospective cohorts of 2,719 CDP participants, 12,865 nonparticipant women, and 26,810 men, from the same institutions, with the same degrees, and first years of appointment in rank. Rates of promotion to Associate and Full Professor ranks in respective cohorts of Assistant and of Associate Professors were compared using Kaplan-Meier survival curves and log-rank tests, and logistic regression adjusting for other predictors of academic success. Results: In adjusted analyses, participants were more likely than men and non-participant women to be promoted to Associate Professor and as likely as men and more likely than non-participant women to be promoted to Full Professor within 10 years. Within 5 years, CDP participants were more likely than nonparticipant women to be promoted to Associate Professor and as likely as to be promoted to Full Professor; in the same interval, participants were promoted to both higher ranks at the same rates as men. For both intervals, nonparticipant women were significantly less likely than men to be promoted to either rank. Conclusions: The higher rates of promotion for women participating in national CDPs support the effectiveness of these programs in building capacity for academic medicine.
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Affiliation(s)
- Shine Chang
- Division of Cancer Prevention and Population Sciences, Department of Epidemiology, Cancer Prevention Research Training Program, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, Irvine, California, USA
| | - Page Morahan
- Academic Medicine (ELAM) Program for Women, Philadelphia, Pennsylvania, USA
- Foundation for Advancement of International Medical Education and Research (FAIMER) Institutes, Philadelphia, Pennsylvania, USA
- Microbiology and immunology at Drexel, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA
| | - Diane Magrane
- Academic Medicine Program, Obstetrics and Gynecology, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA
| | - Sharon Newbill
- Folkstone: Evaluation Anthropology, Pensacola, Florida, USA
| | - Deborah Helitzer
- College of Health Solutions, Arizona State University, Phoenix, Arizona, USA
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15
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Nardo L, Abdelhafez YG, Acquafredda F, Schirò S, Wong AL, Sarohia D, Maroldi R, Darrow MA, Guindani M, Lee S, Zhang M, Moawad AW, Elsayes KM, Badawi RD, Link TM. Qualitative evaluation of MRI features of lipoma and atypical lipomatous tumor: results from a multicenter study. Skeletal Radiol 2020; 49:1005-1014. [PMID: 31965239 DOI: 10.1007/s00256-020-03372-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/25/2019] [Accepted: 01/01/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES The objectives of the study are (1) to distinguish lipoma (L) from atypical lipomatous tumor (ALT) using MRI qualitative features, (2) to assess the value of contrast enhancement, and (3) to evaluate the reproducibility and confidence level of radiological readings. MATERIALS AND METHODS Patients with pathologically proven L or ALT, who underwent MRI within 3 months from surgical excision were included in this retrospective multicenter international study. Two radiologists independently reviewed MRI centrally. Impressions were recorded as L or ALT. A third radiologist was consulted for discordant readings. The two radiologists re-read all non-contrast sequences; impression was recorded; then post-contrast images were reviewed and any changes were recorded. RESULTS A total of 246 patients (135 females; median age, 59 years) were included. ALT was histopathologically confirmed in 70/246 patients. In multivariable analysis, in addition to the lesion size, deep location, proximal lower limb lesions, demonstrating incomplete fat suppression, or increased architectural complexity were the independent predictive features of ALT; but not the contrast enhancement. Post-contrast MRI changed the impression in a total of 5 studies (3 for R1 and 4 for R2; 2 studies are common); all of them were incorrectly changed from Ls to ALTs. Overall, inter-reader kappa agreement was 0.42 (95% CI 0.39-0.56). Discordance between the two readers was statistically significant for both pathologically proven L (p < 0.001) and ALT (p = 0.003). CONCLUSION Most qualitative MR imaging features can help distinguish ALTs from BLs. However, contrast enhancement may be limited and occasionally misleading. Substantial discordance on MRI readings exists between radiologists with a relatively high false positive and negative rates.
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Affiliation(s)
- Lorenzo Nardo
- Department of Radiology, University of California Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817, USA.
| | - Yasser G Abdelhafez
- Department of Radiology, University of California Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817, USA
| | | | - Silvia Schirò
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.,Section of Radiology, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Andrew L Wong
- Department of Radiology, University of California Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817, USA
| | - Dani Sarohia
- Department of Radiology, University of California Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817, USA
| | - Roberto Maroldi
- Scienze Radiologiche, Università degli Studi di Brescia, Brescia, Italy
| | - Morgan A Darrow
- Pathology and Laboratory Medicine, University of California Davis, Sacramento, CA, USA
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, CA, USA
| | - Sonia Lee
- Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Michelle Zhang
- Department of Diagnostic Radiology, McGill University Health Center, Montreal, Canada
| | - Ahmed W Moawad
- Department of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Khaled M Elsayes
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ramsey D Badawi
- Department of Radiology, University of California Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
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16
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Tonello S, Bianchetti A, Braga S, Almici C, Marini M, Piovani G, Guindani M, Dey K, Sartore L, Re F, Russo D, Cantù E, Francesco Lopomo N, Serpelloni M, Sardini E. Impedance-Based Monitoring of Mesenchymal Stromal Cell Three-Dimensional Proliferation Using Aerosol Jet Printed Sensors: A Tissue Engineering Application. Materials (Basel) 2020; 13:E2231. [PMID: 32413993 PMCID: PMC7287852 DOI: 10.3390/ma13102231] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/10/2020] [Accepted: 05/11/2020] [Indexed: 12/12/2022]
Abstract
One of the main hurdles to improving scaffolds for regenerative medicine is the development of non-invasive methods to monitor cell proliferation within three-dimensional environments. Recently, an electrical impedance-based approach has been identified as promising for three-dimensional proliferation assays. A low-cost impedance-based solution, easily integrable with multi-well plates, is here presented. Sensors were developed using biocompatible carbon-based ink on foldable polyimide substrates by means of a novel aerosol jet printing technique. The setup was tested to monitor the proliferation of human mesenchymal stromal cells into previously validated gelatin-chitosan hybrid hydrogel scaffolds. Reliability of the methodology was assessed comparing variations of the electrical impedance parameters with the outcomes of enzymatic proliferation assay. Results obtained showed a magnitude increase and a phase angle decrease at 4 kHz (maximum of 2.5 kΩ and -9 degrees) and an exponential increase of the modeled resistance and capacitance components due to the cell proliferation (maximum of 1.5 kΩ and 200 nF). A statistically significant relationship with enzymatic assay outcomes could be detected for both phase angle and electric model parameters. Overall, these findings support the potentiality of this non-invasive approach for continuous monitoring of scaffold-based cultures, being also promising in the perspective of optimizing the scaffold-culture system.
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Affiliation(s)
- Sarah Tonello
- Department of Information Engineering, University of Padova, 35131 Padua, Italy
| | - Andrea Bianchetti
- Laboratory for Stem Cells Manipulation and Cryopreservation, Department of Transfusion Medicine, ASST Spedali Civili, 25123 Brescia, Italy; (A.B.); (S.B.); (C.A.); (M.M.)
| | - Simona Braga
- Laboratory for Stem Cells Manipulation and Cryopreservation, Department of Transfusion Medicine, ASST Spedali Civili, 25123 Brescia, Italy; (A.B.); (S.B.); (C.A.); (M.M.)
| | - Camillo Almici
- Laboratory for Stem Cells Manipulation and Cryopreservation, Department of Transfusion Medicine, ASST Spedali Civili, 25123 Brescia, Italy; (A.B.); (S.B.); (C.A.); (M.M.)
| | - Mirella Marini
- Laboratory for Stem Cells Manipulation and Cryopreservation, Department of Transfusion Medicine, ASST Spedali Civili, 25123 Brescia, Italy; (A.B.); (S.B.); (C.A.); (M.M.)
| | - Giovanna Piovani
- Biology and Genetics Division, Department of Molecular and Translational Medicine, University of Brescia, 25123 Brescia, Italy;
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, CA 92697-1250, USA;
| | - Kamol Dey
- Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy; (K.D.); (L.S.)
| | - Luciana Sartore
- Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy; (K.D.); (L.S.)
| | - Federica Re
- Department of Clinical and Experimental Sciences, University of Brescia, Bone Marrow Transplant Unit, ASST Spedali Civili, 25123 Brescia, Italy; (F.R.); (D.R.)
| | - Domenico Russo
- Department of Clinical and Experimental Sciences, University of Brescia, Bone Marrow Transplant Unit, ASST Spedali Civili, 25123 Brescia, Italy; (F.R.); (D.R.)
| | - Edoardo Cantù
- Department of Information Engineering, University of Brescia, 25123 Brescia, Italy; (E.C.); (N.F.L.); (M.S.); (E.S.)
| | - Nicola Francesco Lopomo
- Department of Information Engineering, University of Brescia, 25123 Brescia, Italy; (E.C.); (N.F.L.); (M.S.); (E.S.)
| | - Mauro Serpelloni
- Department of Information Engineering, University of Brescia, 25123 Brescia, Italy; (E.C.); (N.F.L.); (M.S.); (E.S.)
| | - Emilio Sardini
- Department of Information Engineering, University of Brescia, 25123 Brescia, Italy; (E.C.); (N.F.L.); (M.S.); (E.S.)
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17
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Versace F, Frank DW, Stevens EM, Deweese MM, Guindani M, Schembre SM. The reality of "food porn": Larger brain responses to food-related cues than to erotic images predict cue-induced eating. Psychophysiology 2019; 56:e13309. [PMID: 30556253 PMCID: PMC6446735 DOI: 10.1111/psyp.13309] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 11/02/2018] [Accepted: 11/05/2018] [Indexed: 11/28/2022]
Abstract
While some individuals can defy the lure of temptation, many others find appetizing food irresistible. The goal of this study was to investigate the neuropsychological mechanisms that increase individuals' vulnerability to cue-induced eating. Using ERPs, a direct measure of brain activity, we showed that individuals with larger late positive potentials in response to food-related cues than to erotic images are more susceptible to cue-induced eating and, in the presence of a palatable food option, eat more than twice as much as individuals with the opposite brain reactivity profile. By highlighting the presence of individual brain reactivity profiles associated with susceptibility to cue-induced eating, these findings contribute to the understanding of the neurobiological basis of vulnerability to obesity.
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Affiliation(s)
- Francesco Versace
- Department of Behavioral ScienceThe University of Texas MD Anderson Cancer CenterHoustonTexas
| | - David W. Frank
- Department of Behavioral ScienceThe University of Texas MD Anderson Cancer CenterHoustonTexas
| | - Elise M. Stevens
- Oklahoma Tobacco Research CenterThe University of Oklahoma Health Sciences CenterOklahoma CityOklahoma
| | - Menton M. Deweese
- Department of Teaching and LearningVanderbilt UniversityNashvilleTennessee
| | - Michele Guindani
- Department of StatisticsThe University of California, IrvineIrvineCalifornia
| | - Susan M. Schembre
- Department of Family and Community MedicineUniversity of Arizona, College of Medicine–TucsonTucsonArizona
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18
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Galloway-Peña J, Guindani M. Editorial: Novel Approaches in Microbiome Analyses and Data Visualization. Front Microbiol 2018; 9:2274. [PMID: 30356851 PMCID: PMC6190871 DOI: 10.3389/fmicb.2018.02274] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Accepted: 09/06/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Jessica Galloway-Peña
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, United States.,Department of Infectious Diseases, Infection Control, and Employee Health, MD Anderson Cancer Center, Houston, TX, United States
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, Irvine, CA, United States
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19
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Li Q, Cassese A, Guindani M, Vannucci M. Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data. Biometrics 2018; 75:183-192. [DOI: 10.1111/biom.12962] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 05/01/2018] [Accepted: 07/01/2018] [Indexed: 02/01/2023]
Affiliation(s)
- Qiwei Li
- Department of Clinical SciencesUniversity of Texas Southwestern Medical Center Dallas Texas U.S.A
| | - Alberto Cassese
- Department of Methodology and StatisticsFaculty of Psychology and NeuroscienceMaastricht University Maastricht, The Netherlands
| | - Michele Guindani
- Department of StatisticsUniversity of California Irvine California U.S.A
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20
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Kitkungvan D, Yusuf SW, Moudgil R, Palaskas N, Guindani M, Juhee S, Hassan S, Sanchez L, Banchs J. Echocardiographic measures associated with the presence of left ventricular thrombus in patients with chemotherapy-related cardiac dysfunction. Echocardiography 2018; 35:1512-1518. [PMID: 30005128 DOI: 10.1111/echo.14087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Previous studies have not evaluated the prevalence and specific risk factors for the development of left ventricular (LV) thrombus in patients with severely reduced left ventricular dysfunction due to chemotherapy-related cardiomyopathy. We sought to evaluate the prevalence and potential markers of LV thrombus in this patient population. METHODS From January 2009 to December 2013, patients with chemotherapy-related severe LV dysfunction (LV ejection fraction [LVEF] ≤ 30%) identified from MD Anderson Cancer Center database were reviewed. Patient characteristics and echocardiographic parameters were analyzed to determine potential risk factors for LV thrombus. RESULTS A total of 121 patients met inclusion criteria (age 54.8 ± 15.2 years; female 63.6%; LVEF 26.3 ± 4%). LV thrombus was present in 9 patients (7.4%). Patients with LV thrombus have significantly lower LVEF compared to those without (18.7 ± 3.8% vs 26.9 ± 3.4%, P < .0001). Prevalence of LV thrombus increased as LVEF decreased and was the highest in patients with LVEF < 20%. By univariate analysis, decreased LVEF, particularly LVEF < 20% (OR 36.30, 95% CI 7.35-179.25, P < .0001) and restrictive LV filling pattern (OR 18.13, 95% CI 4.17-78.89, P = .0001) were associated with presence of LV thrombus. CONCLUSION In patients with severely reduced LV systolic function due to chemotherapy-induced cardiomyopathy, LV thrombus was found in 7.4% of subjects. Severely decreased LVEF (<20%) and restrictive LV filling pattern were associated with the presence of LV thrombus.
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Affiliation(s)
- Danai Kitkungvan
- Division of Cardiovascular Medicine, The University of Texas Health and Science Center at Houston, Houston, TX, USA
| | - Syed W Yusuf
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rohit Moudgil
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nicolas Palaskas
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michele Guindani
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Song Juhee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Saamir Hassan
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Liza Sanchez
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jose Banchs
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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21
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22
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Warnick R, Guindani M, Erhardt E, Allen E, Calhoun V, Vannucci M. A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data. J Am Stat Assoc 2018; 113:134-151. [PMID: 30853734 PMCID: PMC6405235 DOI: 10.1080/01621459.2017.1379404] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 08/01/2017] [Indexed: 01/22/2023]
Abstract
Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad-hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks. Our method utilizes a hidden Markov model for classification of latent cognitive states, achieving estimation of the networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We apply our method to simulated task-based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on an individual healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.
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Affiliation(s)
- Ryan Warnick
- Department of Statistics, Rice University, Houston, TX
| | - Michele Guindani
- Department of Statistics, University of California at Irvine, Irvine, CA
| | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM
| | - Elena Allen
- Research Scientist, Medici Technologies, Albuquerque, NM
| | - Vince Calhoun
- Distinguished Professor, Departments of Electrical and Computer Engineering, University of New Mexico
| | - Marina Vannucci
- Noah Harding Professor and Chair, Department of Statistics, Rice University
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23
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Li X, Guindani M, Ng CS, Hobbs BP. Spatial Bayesian modeling of GLCM with application to malignant lesion characterization. J Appl Stat 2018; 46:230-246. [PMID: 31439980 PMCID: PMC6706247 DOI: 10.1080/02664763.2018.1473348] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 04/27/2018] [Indexed: 01/20/2023]
Abstract
The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.
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Affiliation(s)
- Xiao Li
- Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Chaan S Ng
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Brian P Hobbs
- Quantitative Health Sciences and Taussig Cancer Institute, Cleveland Clinic, Cleveland, USA
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Affiliation(s)
- Bernardo Nipoti
- School of Computer Science and Statistics; Trinity College; Dublin Ireland
| | - Alejandro Jara
- Department of Statistics; Pontificia Universidad Católica de Chile; Santiago Chile
| | - Michele Guindani
- Department of Statistics; The University of California; Irvine CA USA
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25
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Branco D, Taylor P, Zhang X, Li H, Guindani M, Followill D. An Anthropomorphic Head and Neck Quality Assurance Phantom for Credentialing of Intensity-Modulated Proton Therapy. Int J Part Ther 2018; 4:40-47. [PMID: 31773010 DOI: 10.14338/ijpt-17-00005.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 11/15/2017] [Indexed: 11/21/2022] Open
Abstract
Purpose To design and commission a head and neck (H&N) anthropomorphic phantom that the Imaging and Radiation Oncology Core Houston (IROC-H) can use to verify the quality of intensity-modulated proton therapy H&N treatments for institutions participating in National Cancer Institute-sponsored clinical trials. Materials and Methods The phantom design was based on a generalized oropharyngeal tumor, including critical H&N structures (parotid glands and spinal cord). Radiochromic film and thermoluminescent dosimeter (TLD)-100 capsules were embedded in the phantom and used to evaluate dose delivery. A spot-scanning treatment plan with typical clinical constraints for H&N cancer was created by using the Eclipse analytic algorithm. The treatment plan was approved by a radiation oncologist and the phantom was irradiated 4 times. The measured dose distribution using a ±7%/4 mm gamma analysis (85% of pixels passing) and point doses were compared with the treatment planning system calculations. The prescribed target dose was 6 Gy (RBE) with 646.2 cGy (RBE) and 648.6 cGy (RBE) planned to the superior and inferior TLD, respectively. Results For point dosimetry, the average measured-to-calculated dose ratios were 0.984 and 0.986 for the superior and inferior target TLD, respectively. Dose values for the superior and inferior target TLDs were 636.1 cGy and 639.6 cGy, respectively. For the relative dose comparison, the pixel passing rates for the axial and sagittal films, respectively, were 95.5% and 94.2% for trial 1, 97.3% and 93.2% for trial 2, 93.4% and 90.0% for trial 3, and 96.2% and 92.7% for trial 4. Conclusion The anthropomorphic H&N phantom was successfully designed so that TLD measured-to-calculated ratios were within IROC-H's 7% acceptance criteria, 1.6% and 1.4% lower than expected for the superior and inferior target TLDs, respectively. All trials passed the 85% pixel passing criteria established at IROC-H for the relative dose comparison performed when using a gamma index of ±7%/4 mm.
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Affiliation(s)
- Daniela Branco
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paige Taylor
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiaodong Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michele Guindani
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David Followill
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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26
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Chiang S, Vankov ER, Yeh HJ, Guindani M, Vannucci M, Haneef Z, Stern JM. Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity. PLoS One 2018; 13:e0190220. [PMID: 29320526 PMCID: PMC5761874 DOI: 10.1371/journal.pone.0190220] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 12/11/2017] [Indexed: 12/24/2022] Open
Abstract
Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.
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Affiliation(s)
- Sharon Chiang
- Department of Statistics, Rice University, Houston, Texas, United States of America
- Baylor College of Medicine, School of Medicine, Houston, Texas, United States of America
| | - Emilian R. Vankov
- Department of Statistics, Rice University, Houston, Texas, United States of America
- Baker Institute for Public Policy, Rice University, Houston, Texas, United States of America
| | - Hsiang J. Yeh
- Department of Neurology, University of California at Los Angeles, Los Angeles, California, United States of America
| | - Michele Guindani
- Department of Statistics, Uniersity of California at Irvine, Irvine, California, United States of America
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, Texas, United States of America
- Neurology Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, United States of America
| | - John M. Stern
- Department of Neurology, University of California at Los Angeles, Los Angeles, California, United States of America
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27
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Chiang S, Guindani M, Yeh HJ, Dewar S, Haneef Z, Stern JM, Vannucci M. A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection. Front Neurosci 2017; 11:669. [PMID: 29259537 PMCID: PMC5723403 DOI: 10.3389/fnins.2017.00669] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 11/17/2017] [Indexed: 01/19/2023] Open
Abstract
We develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population. The modeling strategy we adopt allows the identification of patient subgroups characterized by latent pathologies differentially associated to the clinical outcome of interest. It also identifies imaging biomarkers characterizing the pathological states of the subjects. In the data application, we identify a subgroup of TLE patients at high risk for post-surgical seizure recurrence after anterior temporal lobe resection, together with a set of discriminatory brain regions that can be used to distinguish the latent subgroups. We show that the proposed method achieves high cross-validated accuracy in predicting post-surgical seizure recurrence.
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Affiliation(s)
- Sharon Chiang
- Department of Statistics, Rice University, Houston, TX, United States.,School of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, Irvine, CA, United States
| | - Hsiang J Yeh
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sandra Dewar
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - John M Stern
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, TX, United States
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28
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Thall PF, Mueller P, Xu Y, Guindani M. Bayesian nonparametric statistics: A new toolkit for discovery in cancer research. Pharm Stat 2017; 16:414-423. [PMID: 28677272 PMCID: PMC5681362 DOI: 10.1002/pst.1819] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 04/20/2017] [Accepted: 06/03/2017] [Indexed: 11/10/2022]
Abstract
Many commonly used statistical methods for data analysis or clinical trial design rely on incorrect assumptions or assume an over-simplified framework that ignores important information. Such statistical practices may lead to incorrect conclusions about treatment effects or clinical trial designs that are impractical or that do not accurately reflect the investigator's goals. Bayesian nonparametric (BNP) models and methods are a very flexible new class of statistical tools that can overcome such limitations. This is because BNP models can accurately approximate any distribution or function and can accommodate a broad range of statistical problems, including density estimation, regression, survival analysis, graphical modeling, neural networks, classification, clustering, population models, forecasting and prediction, spatiotemporal models, and causal inference. This paper describes 3 illustrative applications of BNP methods, including a randomized clinical trial to compare treatments for intraoperative air leaks after pulmonary resection, estimating survival time with different multi-stage chemotherapy regimes for acute leukemia, and evaluating joint effects of targeted treatment and an intermediate biological outcome on progression-free survival time in prostate cancer.
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Affiliation(s)
- Peter F. Thall
- Department of Biostatistics, University of Texas, M. D. Anderson Cancer Center
| | - Peter Mueller
- Department of Mathematics, University of Texas at Austin
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Johns Hopkins University
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29
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Prokhorov AV, Khalil GE, Foster DW, Marani SK, Guindani M, Espada JP, Gonzálvez MT, Idrisov B, Galimov A, Arora M, Tewari A, Isralowitz R, Lapvongwatana P, Chansatitporn N, Chen X, Zheng H, Sussman S. Testing the nicotine dependence measure mFTQ for adolescent smokers: A multinational investigation. Am J Addict 2017; 26:689-696. [PMID: 28708935 DOI: 10.1111/ajad.12583] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 06/19/2017] [Accepted: 06/25/2017] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND OBJECTIVES As a measure of nicotine dependence among adolescent smokers, the modified Fagerström Tolerance Questionnaire (mFTQ; seven items), has been successfully used in the United States (USA). Nonetheless, the validity and reliability of mFTQ at the international level is still needed. The current study is the first to test the validity and reliability of mFTQ in four countries: Thailand, Spain, the USA, and Russia. METHODS In a cross-sectional survey, mFTQ, risk factors of nicotine dependence, and sociodemographic characteristics were assessed. Risk factors included age of first cigarette, frequency of alcohol use, frequency of marijuana use, and number of cigarettes smoked yesterday. Salivary cotinine was also obtained in Thailand and Spain. RESULTS For all four countries, mFTQ exhibited a single factor structure, as supported by previous work in the USA. For all studied countries except Thailand, mFTQ presented acceptable internal reliability. Overall, risk factors of nicotine dependence have predicted mFTQ scores across countries. Frequency of alcohol use in the USA and frequency of marijuana use in Thailand and Spain were not associated with mFTQ scores. DISCUSSION AND CONCLUSIONS mFTQ is a single-factor measure of nicotine dependence that shows acceptable internal consistency and validity across countries. Further work can advance the scale and tailor it to different cultures. SCIENTIFIC SIGNIFICANCE mFTQ can be a clinically practical international measure of nicotine dependence. This study provides initial support for the utility of the mFTQ among Thai, Spanish, American, and Russian adolescents. Further research is needed to test and advance mFTQ across cultures. (Am J Addict 2017;26:689-696).
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Affiliation(s)
- Alexander V Prokhorov
- Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Georges E Khalil
- Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Dawn W Foster
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Salma K Marani
- Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Michele Guindani
- Department of Statistics, University of California Irvine, Irvine, California
| | | | | | - Bulat Idrisov
- Bashkir State Medical University, Ufa, Bashkortostan
| | - Artur Galimov
- Bashkir State Medical University, Ufa, Bashkortostan
| | - Monika Arora
- Health Promotion and Tobacco Control Public Health Foundation of India, New Delhi.,HRIDAY, Health Related Information Dissemination Among Youth, New Delhi
| | - Abha Tewari
- Health Promotion and Tobacco Control Public Health Foundation of India, New Delhi.,HRIDAY, Health Related Information Dissemination Among Youth, New Delhi
| | | | | | | | - Xinguang Chen
- University of Florida, Gainesville, Florida.,Wuhan Center for Disease Prevention and Control, Hubei Sheng, China
| | - Hong Zheng
- WestEd, San Francisco, San Francisco, California
| | - Steve Sussman
- University of Southern California, Los Angeles, California
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30
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Li Q, Guindani M, Reich BJ, Bondell HD, Vannucci M. A Bayesian mixture model for clustering and selection of feature occurrence rates under mean constraints. Stat Anal Data Min 2017. [DOI: 10.1002/sam.11350] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Qiwei Li
- Department of Statistics; Rice University; Houston Texas
| | - Michele Guindani
- Department of Statistics; University of California; Irvine California
| | - Brian J. Reich
- Department of Statistics; North Carolina State University; Raleigh North Carolina
| | - Howard D. Bondell
- Department of Statistics; North Carolina State University; Raleigh North Carolina
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31
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Lutgendorf SK, Shinn E, Carter J, Leighton S, Baggerly K, Guindani M, Fellman B, Matzo M, Slavich GM, Goodman MT, Tew W, Lester J, Moore KM, Karlan BY, Levine DA, Sood AK. Quality of life among long-term survivors of advanced stage ovarian cancer: A cross-sectional approach. Gynecol Oncol 2017; 146:101-108. [PMID: 28527672 DOI: 10.1016/j.ygyno.2017.05.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 05/05/2017] [Accepted: 05/07/2017] [Indexed: 11/28/2022]
Abstract
PURPOSE Long-term survival of women with advanced-stage ovarian cancer is relatively rare. Little is known about quality of life (QOL) and survivorship concerns of these women. Here, we describe QOL of women with advanced-stage ovarian cancer surviving for 8.5 years or longer and compare women with 0-1 recurrence to those with multiple recurrences. METHODS Participants (n=56) recruited from 5 academic medical centers and the Ovarian Cancer Research Fund Alliance completed surveys regarding QOL (FACT-O), mood (CESD), social support (SPS), physical activity (IPAQ-SF), diet, and clinical characteristics. Median survival was 14.0 years (range 8.8-33.3). RESULTS QOL and psychological adjustment of long-term survivors was relatively good, with mean FACT-G scores (multiple recurrences: 80.81±13.95; 0-1 recurrence: 89.05 ±10.80) above norms for healthy community samples (80.1±18.1). Survivors with multiple recurrences reported more compromised QOL in domains of physical and emotional well-being (p <.05), and endorsed a variety of physical and emotional concerns compared to survivors with 0-1 recurrence. Difficulties in sexual functioning were common in both groups. Almost half (43%) of the survivors reported low levels of physical activity. CONCLUSIONS Overall, women with advanced-stage ovarian cancer who have survived at least 8.5 years report good QOL and psychological adjustment. QOL of survivors with multiple recurrences is somewhat impaired compared to those with 0-1 recurrence. Limitations include a possible bias towards participation by healthier survivors, thus under-representing the level of compromise in long-term survivors. Health care practitioners should be alert to psychosocial issues faced by these long-term survivors to provide interventions that enhance QOL.
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Affiliation(s)
- Susan K Lutgendorf
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA; Department of Obstetrics and Gynecology, University of Iowa, Iowa City, IA, USA; Department of Urology, University of Iowa, Iowa City, IA, USA; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA.
| | - Eileen Shinn
- Department of Behavioral Science, Division of OVP, Cancer Prevention and Population Sciences, The University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Jeanne Carter
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Gynecology Service, Department of Psychiatry and Surgery Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Susan Leighton
- Ovarian Cancer Research Fund Alliance, Washington, DC, United States
| | - Keith Baggerly
- Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Michele Guindani
- Department of Biostatistics, Division of Quantitative Sciences, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Bryan Fellman
- Department of Biostatistics, Division of Quantitative Sciences, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Marianne Matzo
- College of Family Medicine, Stephenson Oklahoma Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - George M Slavich
- Cousins Center for Psychoneuroimmunology, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Marc T Goodman
- Cancer Prevention and Genetics Program, Samuel Oschin Comprehensive Cancer Institute, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - William Tew
- Gynecologic Medical Oncology Service, Memorial Sloan Kettering Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Jenny Lester
- Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kathleen M Moore
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Stephenson Oklahoma Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Beth Y Karlan
- Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Douglas A Levine
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anil K Sood
- Department of Gynecologic Oncology, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA; Department of Cancer Biology, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA; Center for RNA Interference and Noncoding RNA, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
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32
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Galloway-Peña JR, Smith DP, Sahasrabhojane P, Wadsworth WD, Fellman BM, Ajami NJ, Shpall EJ, Daver N, Guindani M, Petrosino JF, Kontoyiannis DP, Shelburne SA. Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients. Genome Med 2017; 9:21. [PMID: 28245856 PMCID: PMC5331640 DOI: 10.1186/s13073-017-0409-1] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 02/11/2017] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Understanding longitudinal variability of the microbiome in ill patients is critical to moving microbiome-based measurements and therapeutics into clinical practice. However, the vast majority of data regarding microbiome stability are derived from healthy subjects. Herein, we sought to determine intra-patient temporal microbiota variability, the factors driving such variability, and its clinical impact in an extensive longitudinal cohort of hospitalized cancer patients during chemotherapy. METHODS The stool (n = 365) and oral (n = 483) samples of 59 patients with acute myeloid leukemia (AML) undergoing induction chemotherapy (IC) were sampled from initiation of chemotherapy until neutrophil recovery. Microbiome characterization was performed via analysis of 16S rRNA gene sequencing. Temporal variability was determined using coefficients of variation (CV) of the Shannon diversity index (SDI) and unweighted and weighted UniFrac distances per patient, per site. Measurements of intra-patient temporal variability and patient stability categories were analyzed for their correlations with genera abundances. Groups of patients were analyzed to determine if patients with adverse outcomes had significantly different levels of microbiome temporal variability. Potential clinical drivers of microbiome temporal instability were determined using multivariable regression analyses. RESULTS Our cohort evidenced a high degree of intra-patient temporal instability of stool and oral microbial diversity based on SDI CV. We identified statistically significant differences in the relative abundance of multiple taxa amongst individuals with different levels of microbiota temporal stability. Increased intra-patient temporal variability of the oral SDI was correlated with increased risk of infection during IC (P = 0.02), and higher stool SDI CVs were correlated with increased risk of infection 90 days post-IC (P = 0.04). Total days on antibiotics was significantly associated with increased temporal variability of both oral microbial diversity (P = 0.03) and community structure (P = 0.002). CONCLUSIONS These data quantify the longitudinal variability of the oral and gut microbiota in AML patients, show that increased variability was correlated with adverse clinical outcomes, and offer the possibility of using stabilizing taxa as a method of focused microbiome repletion. Furthermore, these results support the importance of longitudinal microbiome sampling and analyses, rather than one time measurements, in research and future clinical practice.
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Affiliation(s)
- Jessica R Galloway-Peña
- Department of Infectious Disease, Infection Control and Employee Health, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Daniel P Smith
- The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Pranoti Sahasrabhojane
- Department of Infectious Disease, Infection Control and Employee Health, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | | | - Bryan M Fellman
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Nadim J Ajami
- The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Elizabeth J Shpall
- Department of Stem Cell Transplantation, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Naval Daver
- Department of Leukemia, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, CA, 92697, USA
| | - Joseph F Petrosino
- The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Dimitrios P Kontoyiannis
- Department of Infectious Disease, Infection Control and Employee Health, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Samuel A Shelburne
- Department of Infectious Disease, Infection Control and Employee Health, MD Anderson Cancer Center, Houston, TX, 77030, USA. .,Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, 77030, USA.
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Wadsworth WD, Argiento R, Guindani M, Galloway-Pena J, Shelburne SA, Vannucci M. An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data. BMC Bioinformatics 2017; 18:94. [PMID: 28178947 PMCID: PMC5299727 DOI: 10.1186/s12859-017-1516-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 01/31/2017] [Indexed: 12/19/2022] Open
Abstract
Background The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. Results In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. Conclusions Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1516-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Raffaele Argiento
- ESOMAS Department, University of Torino and Collegio Carlo Alberto, Torino, Italy
| | - Michele Guindani
- Department of Statistics, University of California, Irvine, CA, USA
| | - Jessica Galloway-Pena
- Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA
| | - Samuel A Shelburne
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, TX, USA.
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Hassan SA, Yusuf SW, Sharma J, Khan J, Guindani M, Valero V, Chavez-McGregor M, Banchs J. Predictors of left ventricular systolic function recovery in the setting of sinus tachycardia in patients with cancer. Echocardiography 2017; 34:29-36. [DOI: 10.1111/echo.13372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Affiliation(s)
- Saamir A. Hassan
- Department of Cardiology; Division of Medicine; MD Anderson Cancer Center; Houston Texas
| | - Syed Wamique Yusuf
- Department of Cardiology; Division of Medicine; MD Anderson Cancer Center; Houston Texas
| | - Jyoti Sharma
- Division of Cardiology; Department of Medicine; University of Texas Health Science Center; Houston Texas
| | - Jasmine Khan
- Division of Cardiology; Department of Medicine; University of Texas Health Science Center; Houston Texas
| | - Michele Guindani
- Department of Biostatistics; MD Anderson Cancer Center; Houston Texas
| | - Vicente Valero
- Department of Cardiology; Division of Medicine; MD Anderson Cancer Center; Houston Texas
| | | | - Jose Banchs
- Department of Cardiology; Division of Medicine; MD Anderson Cancer Center; Houston Texas
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Ehrmann JM, Taylor KA, Korman AJ, Graziano RF, Page D, Sanchez K, Ballesteros-Merino C, Martel M, Bifulco C, Urba W, Fox B, Patel SP, De Macedo MP, Qin Y, Reuben A, Spencer C, Guindani M, Bassett R, Wargo J, Racolta A, Kelly B, Jones T, Polaske N, Theiss N, Robida M, Meridew J, Habensus I, Zhang L, Pestic-Dragovich L, Tang L, Sullivan RJ, Logan T, Khushalani N, Margolin K, Koon H, Olencki T, Hutson T, Curti B, Roder J, Blackmon S, Roder H, Stewart J, Amin A, Ernstoff MS, Clark JI, Atkins MB, Kaufman HL, Sosman J, Weber J, McDermott DF, Weber J, Kluger H, Halaban R, Snzol M, Roder H, Roder J, Asmellash S, Steingrimsson A, Blackmon S, Sullivan RJ, Wang C, Roman K, Clement A, Downing S, Hoyt C, Harder N, Schmidt G, Schoenmeyer R, Brieu N, Yigitsoy M, Madonna G, Botti G, Grimaldi A, Ascierto PA, Huss R, Athelogou M, Hessel H, Harder N, Buchner A, Schmidt G, Stief C, Huss R, Binnig G, Kirchner T, Sellappan S, Thyparambil S, Schwartz S, Cecchi F, Nguyen A, Vaske C. 31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016): part one. J Immunother Cancer 2016. [PMCID: PMC5123387 DOI: 10.1186/s40425-016-0172-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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Chiang S, Guindani M, Yeh HJ, Haneef Z, Stern JM, Vannucci M. Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data. Hum Brain Mapp 2016; 38:1311-1332. [PMID: 27862625 DOI: 10.1002/hbm.23456] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 10/13/2016] [Accepted: 10/25/2016] [Indexed: 11/05/2022] Open
Abstract
In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Sharon Chiang
- Department of Statistics, Rice University, Houston, Texas
| | - Michele Guindani
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hsiang J Yeh
- Department of Neurology, University of California Los Angeles, Los Angeles, California
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, Texas
| | - John M Stern
- Department of Neurology, University of California Los Angeles, Los Angeles, California
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Chekouo T, Stingo FC, Guindani M, Do KA. A Bayesian predictive model for imaging genetics with application to schizophrenia. Ann Appl Stat 2016. [DOI: 10.1214/16-aoas948] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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38
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Edwards BJ, Sun M, West DP, Guindani M, Lin YH, Lu H, Hu M, Barcenas C, Bird J, Feng C, Saraykar S, Tripathy D, Hortobagyi GN, Gagel R, Murphy WA. Incidence of Atypical Femur Fractures in Cancer Patients: The MD Anderson Cancer Center Experience. J Bone Miner Res 2016; 31:1569-76. [PMID: 26896384 DOI: 10.1002/jbmr.2818] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 02/08/2016] [Accepted: 02/17/2016] [Indexed: 12/23/2022]
Abstract
Atypical femoral fractures (AFFs) are rare adverse events attributed to bisphosphonate (BP) use. Few cases of AFF in cancer have been described; the aim of this study is to identify the incidence and risk factors for AFF in a large cancer center. This retrospective study was conducted at the MD Anderson Cancer Center. The incidence rate of AFF among BP users was calculated from January 1, 2004 through December 31, 2013. The control group (n = 51) included 2 or 3 patients on BPs matched for age (≤1 year) and gender. Logistic regression analysis was used to assess the relationship between clinical characteristics and AFF. Twenty-three AFF cases were identified radiographically among 10,587 BP users, the total BP exposure was 53,789 months (4482 years), and the incidence of AFF in BP users was 0.05 cases per 100,000 person-years. Meanwhile, among 300,553 patients who did not receive BPs there were 2 cases of AFF as compared with the 23 cases noted above. The odds ratio (OR) of having AFF in BP users was 355.58 times higher (95% CI, 84.1 to 1501.4, p < 0.0001) than the risk in non-BP users. The OR of having AFF in alendronate users was 5.54 times greater (OR 5.54 [95% CI, 1.60 to 19.112, p = 0.007]) than the odds of having AFF among other BP users. Patients who were on zoledronic acid (ZOL) had smaller odds of developing AFF compared with other BP users in this matched case control sample. AFFs are rare, serious adverse events that occur in patients with cancer who receive BP therapy. Patients with cancer who receive BPs for prior osteoporosis therapy or for metastatic cancer are at higher risk of AFF. © 2016 American Society for Bone and Mineral Research.
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Affiliation(s)
- Beatrice J Edwards
- Bone Program of Texas, Division of Internal Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ming Sun
- Bone Program of Texas, Division of Internal Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dennis P West
- Departments of Dermatology and Pediatrics, and Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA
| | - Michele Guindani
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yan Heather Lin
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Huifang Lu
- Bone Program of Texas, Division of Internal Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mimi Hu
- Bone Program of Texas, Division of Internal Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carlos Barcenas
- Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Justin Bird
- Department of Orthopaedic Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chun Feng
- Department of Medication Management & Analytics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Smita Saraykar
- Bone Program of Texas, Division of Internal Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Debasish Tripathy
- Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gabriel N Hortobagyi
- Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Robert Gagel
- Bone Program of Texas, Division of Internal Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - William A Murphy
- Division of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Rubinstein A, Kingsley C, Melancon A, Tailor R, Pollard J, Guindani M, Followill D, Hazle J, Court L. TU-H-CAMPUS-TeP2-01: A Comparison of Noninvasive Techniques to Assess Radiation-Induced Lung Damage in Mice. Med Phys 2016. [DOI: 10.1118/1.4957689] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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40
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Rubinstein AE, Liao Z, Melancon AD, Guindani M, Followill DS, Tailor RC, Hazle JD, Court LE. Technical Note: A Monte Carlo study of magnetic-field-induced radiation dose effects in mice. Med Phys 2016; 42:5510-6. [PMID: 26328998 DOI: 10.1118/1.4928600] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Magnetic fields are known to alter radiation dose deposition. Before patients receive treatment using an MRI-linear accelerator (MRI-Linac), preclinical studies are needed to understand the biological consequences of magnetic-field-induced dose effects. In the present study, the authors sought to identify a beam energy and magnetic field strength combination suitable for preclinical murine experiments. METHODS Magnetic field dose effects were simulated in a mouse lung phantom using various beam energies (225 kVp, 350 kVp, 662 keV [Cs-137], 2 MV, and 1.25 MeV [Co-60]) and magnetic field strengths (0.75, 1.5, and 3 T). The resulting dose distributions were compared with those in a simulated human lung phantom irradiated with a 6 or 8 MV beam and orthogonal 1.5 T magnetic field. RESULTS In the human lung phantom, the authors observed a dose increase of 45% and 54% at the soft-tissue-to-lung interface and a dose decrease of 41% and 48% at the lung-to-soft-tissue interface for the 6 and 8 MV beams, respectively. In the mouse simulations, the magnetic fields had no measurable effect on the 225 or 350 kVp dose distribution. The dose increases with the Cs-137 beam for the 0.75, 1.5, and 3 T magnetic fields were 9%, 29%, and 42%, respectively. The dose decreases were 9%, 21%, and 37%. For the 2 MV beam, the dose increases were 16%, 33%, and 31% and the dose decreases were 9%, 19%, and 30%. For the Co-60 beam, the dose increases were 19%, 54%, and 44%, and the dose decreases were 19%, 42%, and 40%. CONCLUSIONS The magnetic field dose effects in the mouse phantom using a Cs-137, 3 T combination or a Co-60, 1.5 or 3 T combination most closely resemble those in simulated human treatments with a 6 MV, 1.5 T MRI-Linac. The effects with a Co-60, 1.5 T combination most closely resemble those in simulated human treatments with an 8 MV, 1.5 T MRI-Linac.
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Affiliation(s)
- Ashley E Rubinstein
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and The University of Texas Graduate School of Biomedical Sciences, Houston, Texas 77030
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Adam D Melancon
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Michele Guindani
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - David S Followill
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Ramesh C Tailor
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - John D Hazle
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - Laurence E Court
- Departments of Radiation Physics and Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
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Rubinstein A, Tailor R, Melancon A, Pollard J, Guindani M, Followill D, Hazle J, Court L. TH-CD-BRA-01: BEST IN PHYSICS (THERAPY): -Field-Induced Dose Effects in a Mouse Lung Phantom: Monte Carlo and Experimental Assessments. Med Phys 2016. [DOI: 10.1118/1.4958143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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42
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Zhang L, Guindani M, Versace F, Engelmann JM, Vannucci M. A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data. Ann Appl Stat 2016. [DOI: 10.1214/16-aoas926] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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43
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Branco D, Taylor P, Frank S, Li H, Zhang X, Mehrens H, Guindani M, Followill D. SU-F-T-168: Development and Implementation of An Anthropomorphic Head & Neck Phantom for the Assessment of Proton Therapy Treatment Procedures. Med Phys 2016. [DOI: 10.1118/1.4956305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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44
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Fronczyk KM, Guindani M, Hobbs BP, Ng CS, Vannucci M. A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization. Cancer Inform 2016; 14:151-62. [PMID: 27279730 PMCID: PMC4886897 DOI: 10.4137/cin.s31933] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Revised: 03/20/2016] [Accepted: 03/20/2016] [Indexed: 11/05/2022] Open
Abstract
Computed tomography perfusion (CTp) is an emerging functional imaging technology that provides a quantitative assessment of the passage of fluid through blood vessels. Tissue perfusion plays a critical role in oncology due to the proliferation of networks of new blood vessels typical of cancer angiogenesis, which triggers modifications to the vasculature of the surrounding host tissue. In this article, we consider a Bayesian semiparametric model for the analysis of functional data. This method is applied to a study of four interdependent hepatic perfusion CT characteristics that were acquired under the administration of contrast using a sequence of repeated scans over a period of 590 seconds. More specifically, our modeling framework facilitates borrowing of information across patients and tissues. Additionally, the approach enables flexible estimation of temporal correlation structures exhibited by mappings of the correlated perfusion biomarkers and thus accounts for the heteroskedasticity typically observed in those measurements, by incorporating change-points in the covariance estimation. This method is applied to measurements obtained from regions of liver surrounding malignant and benign tissues, for each perfusion biomarker. We demonstrate how to cluster the liver regions on the basis of their CTp profiles, which can be used in a prediction context to classify regions of interest provided by future patients, and thereby assist in discriminating malignant from healthy tissue regions in diagnostic settings.
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Affiliation(s)
- Kassandra M. Fronczyk
- Research Staff Member, Operational Evaluation Division, Institute for Defense Analyses, Alexandria, VA, USA
| | - Michele Guindani
- Assistant Professor, Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Brian P. Hobbs
- Assistant Professor, Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chaan S. Ng
- Professor, Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marina Vannucci
- Professor, Department of Statistics, Rice University, Houston, TX, USA
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Galloway-Peña JR, Smith DP, Sahasrabhojane P, Ajami NJ, Wadsworth WD, Daver NG, Chemaly RF, Marsh L, Ghantoji SS, Pemmaraju N, Garcia-Manero G, Rezvani K, Alousi AM, Wargo JA, Shpall EJ, Futreal PA, Guindani M, Petrosino JF, Kontoyiannis DP, Shelburne SA. The role of the gastrointestinal microbiome in infectious complications during induction chemotherapy for acute myeloid leukemia. Cancer 2016; 122:2186-96. [PMID: 27142181 DOI: 10.1002/cncr.30039] [Citation(s) in RCA: 106] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 02/08/2016] [Accepted: 03/17/2016] [Indexed: 12/28/2022]
Abstract
BACKGROUND Despite increasing data on the impact of the microbiome on cancer, the dynamics and role of the microbiome in infection during therapy for acute myelogenous leukemia (AML) are unknown. Therefore, the authors sought to determine correlations between microbiome composition and infectious outcomes in patients with AML who were receiving induction chemotherapy (IC). METHODS Buccal and fecal specimens (478 samples) were collected twice weekly from 34 patients with AML who were undergoing IC. Oral and stool microbiomes were characterized by 16S ribosomal RNA V4 sequencing using an Illumina MiSeq system. Microbial diversity and genera composition were associated with clinical outcomes. RESULTS Baseline stool α-diversity was significantly lower in patients who developed infections during IC compared with those who did not (P = .047). Significant decreases in both oral and stool microbial α-diversity were observed over the course of IC, with a linear correlation between α-diversity change at the 2 sites (P = .02). Loss of both oral and stool α-diversity was associated significantly with the receipt of a carbapenem P < 0.001. Domination events by the majority of genera were transient (median duration, 1 sample), whereas the number of domination events by pathogenic genera increased significantly over the course of IC (P = .002). Moreover, patients who lost microbial diversity over the course of IC were significantly more likely to contract a microbiologically documented infection within the 90 days after IC neutrophil recovery (P = .04). CONCLUSIONS The current data present the largest longitudinal analyses to date of oral and stool microbiomes in patients with AML and suggest that microbiome measurements could assist with the mitigation of infectious complications of AML therapy. Cancer 2016;122:2186-96. © 2016 American Cancer Society.
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Affiliation(s)
- Jessica R Galloway-Peña
- Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Daniel P Smith
- The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - Pranoti Sahasrabhojane
- Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Nadim J Ajami
- The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - W Duncan Wadsworth
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Statistics, Rice University, Houston, Texas
| | - Naval G Daver
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Roy F Chemaly
- Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lisa Marsh
- Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Shashank S Ghantoji
- Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Naveen Pemmaraju
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Amin M Alousi
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jennifer A Wargo
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Elizabeth J Shpall
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Phillip A Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Michele Guindani
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Joseph F Petrosino
- The Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas
| | - Dimitrios P Kontoyiannis
- Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Samuel A Shelburne
- Department of Infectious Disease, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Trevino V, Cassese A, Nagy Z, Zhuang X, Herbert J, Antzack P, Clarke K, Davies N, Rahman A, Campbell MJ, Guindani M, Bicknell R, Vannucci M, Falciani F. A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells. PLoS Comput Biol 2016; 12:e1004884. [PMID: 27124473 PMCID: PMC4849722 DOI: 10.1371/journal.pcbi.1004884] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 03/24/2016] [Indexed: 11/19/2022] Open
Abstract
The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems. In the current era of cancer research, stimulated by the release of the entire human genome, it has become increasingly clear that to understand cancer we need to understand how the many thousands of genes and proteins involved interact. Modern techniques have enabled the collection of unprecedented amounts of high quality data describing the state of these molecules during cancer development. In cancer research particularly, this strategy has been particularly successful, leading to the discovery of new drugs able to target key factors promoting cancer growth. However, a large body of research suggests that in complex organs, the interaction between cancer and its surrounding environment is an essential part of the biology of both diseased and healthy tissues, therefore it is of paramount importance that this process is further investigated. Here we report a strategy designed to reveal communication signals between cancer cells and adjacent cell types. We apply the strategy to prostate cancer and find that normal cells surrounding the tumour do exert an anti-tumour activity on prostate cancer cells. By using a statistical model which integrates multiple levels of genetic data, we show that cell-to-cell communication genes are controlled by DNA alterations and have potential prognostic value.
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Affiliation(s)
- Victor Trevino
- Catedra de Bioinformatica, Escuela de Medicina, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico
| | - Alberto Cassese
- Department of Methodology and Statistics, Maastricht University, Maastricht, Netherlands
| | - Zsuzsanna Nagy
- School of Experimental and Clinical Medicine, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Xiaodong Zhuang
- School of Immunity and Infection, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - John Herbert
- Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Philipp Antzack
- Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Kim Clarke
- Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Nicholas Davies
- School of Cancer Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Ayesha Rahman
- School of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, United Kingdom
| | - Moray J. Campbell
- Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, New York, United States of America
| | - Michele Guindani
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Roy Bicknell
- School of Immunity and Infection, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - Francesco Falciani
- Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- * E-mail:
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Chang S, Morahan PS, Magrane D, Helitzer D, Lee HY, Newbill S, Peng HL, Guindani M, Cardinali G. Retaining Faculty in Academic Medicine: The Impact of Career Development Programs for Women. J Womens Health (Larchmt) 2016; 25:687-96. [PMID: 27058451 DOI: 10.1089/jwh.2015.5608] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND For more than two decades, national career development programs (CDPs) have addressed underrepresentation of women faculty in academic medicine through career and leadership curricula. We evaluated CDP participation impact on retention. METHODS We used Association of American Medical Colleges data to compare 3268 women attending CDPs from 1988 to 2008 with 17,834 women and 40,319 men nonparticipant faculty similar to CDP participants in degree, academic rank, first year of appointment in rank, and home institution. Measuring from first year in rank to departure from last position held or December 2009 (study end date), we used Kaplan-Meier curves; Cox survival analysis adjusted for age, degree, tenure, and department; and 10-year rates to compare retention. RESULTS CDP participants were significantly less likely to leave academic medicine than their peers for up to 8 years after appointment as Assistant and Associate Professors. Full Professor participants were significantly less likely to leave than non-CDP women. Men left less often than non-CDP women at every rank. Participants attending more than one CDP left less often than those attending one, but results varied by rank. Patterns of switching institutions after 10 years varied by rank; CDP participants switched significantly less often than men at Assistant and Associate Professor levels and significantly less often than non-CDP women among Assistant Professors. Full Professors switched at equal rates. CONCLUSION National CDPs appear to offer retention advantage to women faculty, with implications for faculty performance and capacity building within academic medicine. Intervals of retention advantage for CDP participants suggest vulnerable periods for intervention.
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Affiliation(s)
- Shine Chang
- 1 Department of Epidemiology, The University of Texas MD Anderson Cancer Center , Houston, Texas
| | - Page S Morahan
- 2 Executive Leadership in Academic Medicine, Drexel University College of Medicine , Philadelphia, Pennsylvania
| | - Diane Magrane
- 2 Executive Leadership in Academic Medicine, Drexel University College of Medicine , Philadelphia, Pennsylvania
| | - Deborah Helitzer
- 3 Department of Faculty and Community Medicine, University of New Mexico School of Medicine , Albuquerque, New Mexico
| | - Hwa Young Lee
- 1 Department of Epidemiology, The University of Texas MD Anderson Cancer Center , Houston, Texas
| | - Sharon Newbill
- 3 Department of Faculty and Community Medicine, University of New Mexico School of Medicine , Albuquerque, New Mexico
| | - Ho-Lan Peng
- 1 Department of Epidemiology, The University of Texas MD Anderson Cancer Center , Houston, Texas
| | - Michele Guindani
- 1 Department of Epidemiology, The University of Texas MD Anderson Cancer Center , Houston, Texas
| | - Gina Cardinali
- 3 Department of Faculty and Community Medicine, University of New Mexico School of Medicine , Albuquerque, New Mexico
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Lee J, Teo I, Guindani M, Reece GP, Markey MK, Fingeret MC. Associations between psychosocial functioning and smiling intensity in patients with head and neck cancer. PSYCHOL HEALTH MED 2016; 20:469-76. [PMID: 25159529 DOI: 10.1080/13548506.2014.951371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Increasing attention is being given to developing quantitative measures of facial expression. This study used quantitative facial expression analysis to examine associations between smiling intensity and psychosocial functioning in patients with head and neck cancer (HNC). Smiling intensity of 95 HNC patients was measured using 48 quantitative measures calculated from facial photographs with and without a smile. We computed a composite smiling intensity score for each patient representing the degree of similarity to healthy controls. A lower composite score indicates that the person is less expressive, on average, than healthy controls. Patients also completed self-report measures assessing domains of body image and quality of life (QOL). Spearman rank correlations were computed to examine relationships between composite scores and psychosocial functioning. Composite scores were significantly correlated with multiple measures of body image and QOL. Specifically, decreased smiling intensity was associated with feelings of dissatisfaction with one's body, perceived negative social impact of body image, increased use of avoidance as a body image-coping strategy, reduced functional well-being, and greater head and neck cancer-specific issues. To the best of our knowledge, this is the first study to demonstrate associations between an objectively quantified facial expression (i.e. smiling) and psychosocial functioning. Most previous studies have measured facial expression qualitatively. These findings indicate that smiling intensity may serve as an important clinical indicator of psychosocial well-being and warrants further clinical investigation.
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Affiliation(s)
- Juhun Lee
- a Electrical and Computer Engineering , The University of Texas at Austin , Austin , TX , USA
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Teo I, Fronczyk KM, Guindani M, Vannucci M, Ulfers SS, Hanasono MM, Fingeret MC. Salient body image concerns of patients with cancer undergoing head and neck reconstruction. Head Neck 2016; 38:1035-42. [PMID: 26970013 DOI: 10.1002/hed.24415] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/29/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Patients with cancer undergoing head and neck reconstruction can experience significant distress from alterations in appearance and bodily functioning. We sought to delineate salient dimensions of body image concerns in this patient population preparing for reconstructive surgery. METHODS Participants completed self-report questionnaires evaluating numerous aspects of body image. We used Bayesian factor analysis modeling methods to identify latent factors emerging from the data. RESULTS We identified 2 latent factors: appearance distress and functional difficulties. The highest level of preoperative body image concerns were related to distress about appearance changes and its perceived social consequences. Appearance distress items displayed greater variability compared with functional difficulties. CONCLUSION Appearance and functional changes to body image are important areas of concern for patients with head and neck cancer as they prepare for reconstructive surgery. Knowledge regarding specific body image issues can be used to guide psychosocial assessments and intervention to enhance patient care. © 2016 Wiley Periodicals, Inc. Head Neck 38: 1035-1042, 2016.
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Affiliation(s)
- Irene Teo
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kassandra M Fronczyk
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Statistics, Rice University, Houston, Texas
| | - Michele Guindani
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Sara S Ulfers
- Department of Occupational Therapy, Washington University School of Medicine, St. Louis, Missouri
| | - Matthew M Hanasono
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Michelle Cororve Fingeret
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Zand B, Previs RA, Zacharias NM, Rupaimoole R, Mitamura T, Nagaraja AS, Guindani M, Dalton HJ, Yang L, Baddour J, Achreja A, Hu W, Pecot CV, Ivan C, Wu SY, McCullough CR, Gharpure KM, Shoshan E, Pradeep S, Mangala LS, Rodriguez-Aguayo C, Wang Y, Nick AM, Davies MA, Armaiz-Pena G, Liu J, Lutgendorf SK, Baggerly KA, Eli MB, Lopez-Berestein G, Nagrath D, Bhattacharya PK, Sood AK. Role of Increased n-acetylaspartate Levels in Cancer. J Natl Cancer Inst 2016; 108:djv426. [PMID: 26819345 DOI: 10.1093/jnci/djv426] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 12/16/2015] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND The clinical and biological effects of metabolic alterations in cancer are not fully understood. METHODS In high-grade serous ovarian cancer (HGSOC) samples (n = 101), over 170 metabolites were profiled and compared with normal ovarian tissues (n = 15). To determine NAT8L gene expression across different cancer types, we analyzed the RNA expression of cancer types using RNASeqV2 data available from the open access The Cancer Genome Atlas (TCGA) website (http://www.cbioportal.org/public-portal/). Using NAT8L siRNA, molecular techniques and histological analysis, we determined cancer cell viability, proliferation, apoptosis, and tumor growth in in vitro and in vivo (n = 6-10 mice/group) settings. Data were analyzed with the Student's t test and Kaplan-Meier analysis. Statistical tests were two-sided. RESULTS Patients with high levels of tumoral NAA and its biosynthetic enzyme, aspartate N-acetyltransferase (NAT8L), had worse overall survival than patients with low levels of NAA and NAT8L. The overall survival duration of patients with higher-than-median NAA levels (3.6 years) was lower than that of patients with lower-than-median NAA levels (5.1 years, P = .03). High NAT8L gene expression in other cancers (melanoma, renal cell, breast, colon, and uterine cancers) was associated with worse overall survival. NAT8L silencing reduced cancer cell viability (HEYA8: control siRNA 90.61% ± 2.53, NAT8L siRNA 39.43% ± 3.00, P < .001; A2780: control siRNA 90.59% ± 2.53, NAT8L siRNA 7.44% ± 1.71, P < .001) and proliferation (HEYA8: control siRNA 74.83% ± 0.92, NAT8L siRNA 55.70% ± 1.54, P < .001; A2780: control siRNA 50.17% ± 4.13, NAT8L siRNA 26.52% ± 3.70, P < .001), which was rescued by addition of NAA. In orthotopic mouse models (ovarian cancer and melanoma), NAT8L silencing reduced tumor growth statistically significantly (A2780: control siRNA 0.52 g ± 0.15, NAT8L siRNA 0.08 g ± 0.17, P < .001; HEYA8: control siRNA 0.79 g ± 0.42, NAT8L siRNA 0.24 g ± 0.18, P = .008, A375-SM: control siRNA 0.55 g ± 0.22, NAT8L siRNA 0.21 g ± 0.17 g, P = .001). NAT8L silencing downregulated the anti-apoptotic pathway, which was mediated through FOXM1. CONCLUSION These findings indicate that the NAA pathway has a prominent role in promoting tumor growth and represents a valuable target for anticancer therapy.Altered energy metabolism is a hallmark of cancer (1). Proliferating cancer cells have much greater metabolic requirements than nonproliferating differentiated cells (2,3). Moreover, altered cancer metabolism elevates unique metabolic intermediates, which can promote cancer survival and progression (4,5). Furthermore, emerging evidence suggests that proliferating cancer cells exploit alternative metabolic pathways to meet their high demand for energy and to accumulate biomass (6-8).
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Affiliation(s)
- Behrouz Zand
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Rebecca A Previs
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Niki M Zacharias
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Rajesha Rupaimoole
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Takashi Mitamura
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Archana Sidalaghatta Nagaraja
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Michele Guindani
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Heather J Dalton
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Lifeng Yang
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Joelle Baddour
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Abhinav Achreja
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Wei Hu
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Chad V Pecot
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Cristina Ivan
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Sherry Y Wu
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Christopher R McCullough
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Kshipra M Gharpure
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Einav Shoshan
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Sunila Pradeep
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Lingegowda S Mangala
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Cristian Rodriguez-Aguayo
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Ying Wang
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Alpa M Nick
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Michael A Davies
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Guillermo Armaiz-Pena
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Jinsong Liu
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Susan K Lutgendorf
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Keith A Baggerly
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Menashe Bar Eli
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Gabriel Lopez-Berestein
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Deepak Nagrath
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Pratip K Bhattacharya
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX
| | - Anil K Sood
- Departments of Gynecologic Oncology and Reproductive Medicine (BZ, RAP, RR, TM, ASN, HJD, WH, CI, SYW, KMG, SP, LSM, AMN, GAP, AKS), Cancer Systems Imaging (NMZ, CRM, PKB), Biostatistics (MG), Cancer Medicine (CVP), Center for RNA Interference and Non-Coding RNA (CI, LSM, CRA, GLB, AKS), Cancer Biology (YS, MBE, GLB, AKS), Experimental Therapeutics (CRA, GLB), Bioinformatics and Computational Biology (YW, KAB), Melanoma Medical Oncology (MAD), and Pathology (JL), University of Texas M. D. Anderson Cancer Center, Houston, TX; Department of Nanomedicine and Bioengineering, UT Health, Houston, TX (GLB, AKS); Departments of Psychology, Urology, and Obstetrics and Gynecology, the University of Iowa, Iowa City, IA (SKL); Laboratory for Systems Biology of Human Diseases (LY, JB, AA, DN), Department of Chemical and Biomolecular Engineering (LY, JB, AA, DN), and Department of Bioengineering (DN), Rice University, Houston, TX.
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