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author:("Kim, since")
1.  Evaluation of Diabetic Patients with Breast Cancer Treated with Metformin during Adjuvant Radiotherapy 
Purpose. The purpose of this study was to evaluate acute locoregional toxicity in patients with breast cancer receiving concurrent metformin plus radiation therapy. Methods and Materials. Diabetic breast cancer patients receiving concurrent metformin and radiation therapy were matched with nondiabetic patients and diabetic patients using an alternative diabetes medication. Primary endpoints included the presence of a treatment break and development of dry or moist desquamation. Results. There was a statistically significant increase in treatment breaks for diabetic patients receiving concurrent metformin when compared to the nondiabetic patients (P value = 0.02) and a trend toward significance when compared to diabetic patients receiving an alternate diabetes medication (P value = 0.08). Multiple logistic regression analysis demonstrated concurrent metformin use as being associated with a trend toward the predictive value of determining the incidence of developing desquamation in diabetic patients receiving radiation therapy compared to diabetic patients receiving an alternate diabetes medication (P value = 0.06). Conclusions. Diabetic patients treated with concurrent metformin and radiation therapy developed increased acute locoregional toxicity in comparison with diabetic patients receiving an alternate diabetes medication and nondiabetic patients. Further clinical investigation should be conducted to determine the therapeutic ratio of metformin in combination with radiation therapy.
doi:10.1155/2013/659723
PMCID: PMC3876696  PMID: 24416595
2.  Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models 
Bayesian analysis (Online)  2009;4(4):707-732.
We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment conditions. A key feature of our method is the use of a product of spiked distributions, i.e., mixtures of a point-mass and continuous distributions, as the centering distribution for the DP prior. Adopting these spiked centering priors readily accommodates sharp null hypotheses and allows for the estimation of the posterior probabilities of such hypotheses. Dirichlet process mixture models naturally borrow information across objects through model-based clustering while inference on single hypotheses averages over clustering uncertainty. We demonstrate via a simulation study that our method yields increased sensitivity in multiple hypothesis testing and produces a lower proportion of false discoveries than other competitive methods. While our modeling framework is general, here we present an application in the context of gene expression from microarray experiments. In our application, the modeling framework allows simultaneous inference on the parameters governing differential expression and inference on the clustering of genes. We use experimental data on the transcriptional response to oxidative stress in mouse heart muscle and compare the results from our procedure with existing nonparametric Bayesian methods that provide only a ranking of the genes by their evidence for differential expression.
doi:10.1214/09-BA426
PMCID: PMC3741668  PMID: 23950766
Bayesian nonparametrics; differential gene expression; Dirichlet process prior; DNA microarray; mixture priors; model-based clustering; multiple hypothesis testing
3.  Development and Validation of a qRT-PCR Classifier for Lung Cancer Prognosis 
Purpose
This prospective study aimed to develop a robust and clinically-applicable method to identify high-risk early stage lung cancer patients and then to validate this method for use in future translational studies.
Patients and Methods
Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model and random survival forest (RSF) analysis. A final set of 91 genes was selected and tested as a predictor of survival using a qRT-PCR-based assay utilizing an independent cohort of 101 lung adenocarcinomas.
Results
The RSF model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index (MRI) was significantly related to survival (Cox model p < 0.00001) and separated all patients into low, medium, and high-risk groups (HR = 1.00, 2.82, 4.42). The MRI was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low, medium, and high-risk groups (HR = 1.00, 3.29, 3.77).
Conclusions
The development and validation of this robust qRT-PCR platform allows prediction of patient survival with early stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of lung cancer patients and improving patient survival.
doi:10.1097/JTO.0b013e31822918bd
PMCID: PMC3167380  PMID: 21792073
Lung cancer; qRT-PCR; Prognosis
4.  Cell Therapy with Bone Marrow Cells for Myocardial Regeneration 
Antioxidants & redox signaling  2009;11(8):1897-1911.
Cell therapy has tremendous potential for the damaged heart, which has limited self-renewing capability. Bone marrow (BM) cells are attractive for cell therapy, as they contain diverse stem and progenitor cell populations that can give rise to various cell types, including cardiomyocytes, endothelial cells, and smooth muscle cells. Studies have shown BM cells to be safe and efficacious in the treatment of myocardial infarction. Possible therapeutic mechanisms mediated by both host and transplanted cells include cardiomyogenesis, neovascularization, and attenuation of adverse remodeling. In this review, different stem and progenitor cells in the bone marrow and their application in cell therapy are reviewed, and evidence for their therapeutic mechanisms is discussed
doi:10.1089/ARS.2009.2486
PMCID: PMC2788115  PMID: 19203213
5.  Cell Therapy with Bone Marrow Cells for Myocardial Regeneration 
Antioxidants & Redox Signaling  2009;11(8):1897-1911.
Abstract
Cell therapy has tremendous potential for the damaged heart, which has limited self-renewing capability. Bone marrow (BM) cells are attractive for cell therapy, as they contain diverse stem and progenitor cell populations that can give rise to various cell types, including cardiomyocytes, endothelial cells, and smooth muscle cells. Studies have shown BM cells to be safe and efficacious in the treatment of myocardial infarction. Possible therapeutic mechanisms mediated by both host and transplanted cells include cardiomyogenesis, neovascularization, and attenuation of adverse remodeling. In this review, different stem and progenitor cells in the bone marrow and their application in cell therapy are reviewed, and evidence for their therapeutic mechanisms is discussed. Antioxid. Redox Signal. 11, 1897–1911.
doi:10.1089/ars.2009.2486
PMCID: PMC2788115  PMID: 19203213
6.  Clinically Relevant Characterization of Lung Adenocarcinoma Subtypes Based on Cellular Pathways: An International Validation Study 
PLoS ONE  2010;5(7):e11712.
Lung adenocarcinoma (AD) represents a predominant type of lung cancer demonstrating significant morphologic and molecular heterogeneity. We sought to understand this heterogeneity by utilizing gene expression analyses of 432 AD samples and examining associations between 27 known cancer-related pathways and the AD subtype, clinical characteristics and patient survival. Unsupervised clustering of AD and gene expression enrichment analysis reveals that cell proliferation is the most important pathway separating tumors into subgroups. Further, AD with increased cell proliferation demonstrate significantly poorer outcome and an increased solid AD subtype component. Additionally, we find that tumors with any solid component have decreased survival as compared to tumors without a solid component. These results lead to the potential to use a relatively simple pathological examination of a tumor in order to determine its aggressiveness and the patient's prognosis. Additional results suggest the ability to use a similar approach to determine a patient's sensitivity to targeted treatment. We then demonstrated the consistency of these findings using two independent AD cohorts from Asia (N = 87) and Europe (N = 89) using the identical analytic procedures.
doi:10.1371/journal.pone.0011712
PMCID: PMC2908611  PMID: 20661423
7.  Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data 
Affinity purification followed by mass spectrometry (AP-MS) has become a common approach for identifying protein–protein interactions (PPIs) and complexes. However, data analysis and visualization often rely on generic approaches that do not take advantage of the quantitative nature of AP-MS. We present a novel computational method, nested clustering, for biclustering of label-free quantitative AP-MS data. Our approach forms bait clusters based on the similarity of quantitative interaction profiles and identifies submatrices of prey proteins showing consistent quantitative association within bait clusters. In doing so, nested clustering effectively addresses the problem of overrepresentation of interactions involving baits proteins as compared with proteins only identified as preys. The method does not require specification of the number of bait clusters, which is an advantage against existing model-based clustering methods. We illustrate the performance of the algorithm using two published intermediate scale human PPI data sets, which are representative of the AP-MS data generated from mammalian cells. We also discuss general challenges of analyzing and interpreting clustering results in the context of AP-MS data.
doi:10.1038/msb.2010.41
PMCID: PMC2913403  PMID: 20571534
clustering; mass spectrometry; protein complexes; protein–protein interaction; spectral counts
8.  Bone Marrow Mononuclear Cells Have Neurovascular Tropism and Improve Diabetic Neuropathy 
Stem cells (Dayton, Ohio)  2009;27(7):1686-1696.
Bone marrow-derived mononuclear cells (BMNCs) have been shown to effectively treat ischemic cardiovascular diseases. Because diabetic neuropathy (DN) is causally associated with impaired angiogenesis and deficiency of angiogenic and neurotrophic factors in the nerves, we investigated whether DN can be ameliorated by local injection of BMNCs. Severe peripheral neuropathy, characterized by a significant decrease in the motor and sensory nerve conduction velocities (NCVs), developed 12 weeks after the induction of diabetes with streptozotocin in rats. The injection of BMNCs restored motor and sensory NCVs to normal levels and significantly improved vascular density and blood flow in diabetic nerves over 4 weeks. Fluorescent microscopic observation revealed that DiI-labeled BMNCs preferentially engrafted in sciatic nerves. Whole-mount fluorescent imaging and confocal microscopic evaluation demonstrated that many of the BMNCs localized following the course of the vasa nervorum in close proximity to blood vessels without incorporation into vasa nervorum as endothelial cells at a detectable level. Real-time reverse transcription-polymerase chain reaction analysis showed that the levels of angiogenic and neurotrophic factors were significantly increased in the nerves by BMNC injection. Local transplantation of BMNCs improved experimental DN by augmenting angiogenesis and increasing angiogenic and neurotrophic factors in peripheral nerves. These findings suggest that BMNC transplantation may represent a novel therapeutic option for treating DN.
doi:10.1002/stem.87
PMCID: PMC2746563  PMID: 19544451
Bone marrow mononuclear cells; Diabetes; Diabetic neuropathy; Angiogenesis; Angiogenic factors; Neurotrophic factors

Results 1-8 (8)