Matrix Metalloproteinase-9 (MMP-9) polymorphisms in patients with cutaneous malignant melanoma

BACKGROUND: Cutaneous Malignant Melanoma causes over 75% of skin cancer-related deaths, and it is clear that many factors may contribute to the outcome. Matrix Metalloproteinases (MMPs) play an important role in the degradation and remodeling of the extracellular matrix and basement membrane that, in turn, modulate cell division, migration and angiogenesis. Some polymorphisms are known to influence gene expression, protein activity, stability, and interactions, and they were shown to be associated with certain tumor phenotypes and cancer risk. METHODS: We tested seven polymorphisms within the MMP-9 gene in 1002 patients with melanoma in order to evaluate germline genetic variants and their association with progression and known risk factors of melanoma. The polymorphisms were selected based on previously published reports and their known or potential functional relevance using in-silico methods. Germline DNA was then genotyped using pyrosequencing, melting temperature profiles, heteroduplex analysis, and fragment size analysis. RESULTS: We found that reference alleles were present in higher frequency in patients who tend to sunburn, have family history of melanoma, higher melanoma stage, intransit metastasis and desmoplastic melanomas among others. However, after adjustment for age, sex, phenotypic index, moles, and freckles only Q279R, P574R and R668Q had significant associations with intransit metastasis, propensity to tan/sunburn and primary melanoma site. CONCLUSION: This study does not provide strong evidence for further investigation into the role of the MMP-9 SNPs in melanoma progression

Statistical methods for analysis of structured genomic data

Partially motivated by analysis of high dimensional genomic data, high dimensional statistics, especially high dimensional regression analysis, have been an active research area in the last decades. Besides high dimensionality of the genomic data, another important feature is that the genomic data often have certain structure such as time course measurements and group or graphical structures. How to incorporate such structure information into analysis of numerical data raises interesting statistical challenges. This dissertation develops statistical methods for two problems motivated by genomic data analysis. The first problem is related to variable selection for high dimensional varying coefficients models, where we develop a regularization method for variable selection and estimation. We use basis function expansion to model the time-dependent regression coefficient functions and a combination of smoothness and group-level penalty to achieve both smooth function estimation and coefficient function selection. We apply the methods for analysis of microarray time course gene expression data in order to identify the transcription factors that regulate expression changes over times. Our results show that the varying coefficients model provides better power in identifying the relevant transcription factors than simple time-wise analysis. The second problem considers variable selection for graph-structured group variables, where we assume that the variables are grouped and also have a graphical structure. Such examples include genes in a collection of pathways and single nucleotide polymorphisms (SNP) in genes. We introduce a new penalty that is a combination of group Lasso and a graph-constrained smoothness penalty within groups in order to perform both group-level variable selection and to impose some smoothness of the regression coefficients with respect to the graph structures. Simulation results have shown that the new method gives better variable selection and also prediction when such group and graphical structure information exists. We apply this method to analysis of two real data sets: an analysis of a glioblastoma gene expression data to identify several KEGG pathways that are potentially related to survival time of glioblastoma; and an analysis of a SNP data to identify genes that are associated with patient HDL level

Statistical methods for analysis of structured genomic data

Partially motivated by analysis of high dimensional genomic data, high dimensional statistics, especially high dimensional regression analysis, have been an active research area in the last decades. Besides high dimensionality of the genomic data, another important feature is that the genomic data often have certain structure such as time course measurements and group or graphical structures. How to incorporate such structure information into analysis of numerical data raises interesting statistical challenges. This dissertation develops statistical methods for two problems motivated by genomic data analysis. The first problem is related to variable selection for high dimensional varying coefficients models, where we develop a regularization method for variable selection and estimation. We use basis function expansion to model the time-dependent regression coefficient functions and a combination of smoothness and group-level penalty to achieve both smooth function estimation and coefficient function selection. We apply the methods for analysis of microarray time course gene expression data in order to identify the transcription factors that regulate expression changes over times. Our results show that the varying coefficients model provides better power in identifying the relevant transcription factors than simple time-wise analysis. The second problem considers variable selection for graph-structured group variables, where we assume that the variables are grouped and also have a graphical structure. Such examples include genes in a collection of pathways and single nucleotide polymorphisms (SNP) in genes. We introduce a new penalty that is a combination of group Lasso and a graph-constrained smoothness penalty within groups in order to perform both group-level variable selection and to impose some smoothness of the regression coefficients with respect to the graph structures. Simulation results have shown that the new method gives better variable selection and also prediction when such group and graphical structure information exists. We apply this method to analysis of two real data sets: an analysis of a glioblastoma gene expression data to identify several KEGG pathways that are potentially related to survival time of glioblastoma; and an analysis of a SNP data to identify genes that are associated with patient HDL level

Statistical methods for analysis of structured genomic data

Partially motivated by analysis of high dimensional genomic data, high dimensional statistics, especially high dimensional regression analysis, have been an active research area in the last decades. Besides high dimensionality of the genomic data, another important feature is that the genomic data often have certain structure such as time course measurements and group or graphical structures. How to incorporate such structure information into analysis of numerical data raises interesting statistical challenges. This dissertation develops statistical methods for two problems motivated by genomic data analysis. The first problem is related to variable selection for high dimensional varying coefficients models, where we develop a regularization method for variable selection and estimation. We use basis function expansion to model the time-dependent regression coefficient functions and a combination of smoothness and group-level penalty to achieve both smooth function estimation and coefficient function selection. We apply the methods for analysis of microarray time course gene expression data in order to identify the transcription factors that regulate expression changes over times. Our results show that the varying coefficients model provides better power in identifying the relevant transcription factors than simple time-wise analysis. The second problem considers variable selection for graph-structured group variables, where we assume that the variables are grouped and also have a graphical structure. Such examples include genes in a collection of pathways and single nucleotide polymorphisms (SNP) in genes. We introduce a new penalty that is a combination of group Lasso and a graph-constrained smoothness penalty within groups in order to perform both group-level variable selection and to impose some smoothness of the regression coefficients with respect to the graph structures. Simulation results have shown that the new method gives better variable selection and also prediction when such group and graphical structure information exists. We apply this method to analysis of two real data sets: an analysis of a glioblastoma gene expression data to identify several KEGG pathways that are potentially related to survival time of glioblastoma; and an analysis of a SNP data to identify genes that are associated with patient HDL level

Additional file 1: of Metastatic EML4-ALK fusion detected by circulating DNA genotyping in an EGFR-mutated NSCLC patient and successful management by adding ALK inhibitors: a case report

Platform and Gene-list for the Multiplex Genotyping of Circulating Tumor DNA. (DOC 45 kb

Identification of Angiogenesis/Metastases Genes Predicting Chemoradiotherapy Response in Patients With Laryngopharyngeal Carcinoma

Purpose To identify genes related to angiogenesis/metastasis that predict locoregional failure in patients with laryngopharyngeal cancer (LPC) undergoing chemoradiotherapy (CRT) treatment. Methods Tumor tissue was collected and snap-frozen from 35 sequential patients with histologically confirmed LPC being treated with CRT. Gene expression analysis was performed using a novel cDNA array consisting of 277 genes functionally associated with angiogenesis (n 152) and/or metastasis (n 125). Locoregional response was correlated to the gene expression profiles to identify genes associated with outcome. These genes were internally validated by real-time reverse transcriptase polymerase chain reaction (RT-PCR) and validated externally by immunohis- tochemistry analysis on an independent set of patients. Results Locoregional failure occurred in nine of 35 patients. Seventeen genes from the cDNA microarray correlated with locoregional failure (two-sample t test, P .05). Seven genes were chosen for additional analysis based on the availability of antibodies for immunohistochemistry. Of these seven genes, real-time RT-PCR validated four genes: MDM2, VCAM-1, erbB2, and H-ras (Wilcoxon rank sum test, P .008, .02, .04, and .04, respectively). External validation by immunohistochem- istry confirmed MDM2 and erbB2 as being predictive of locoregional response. Controlling for stage of disease, positivity for MDM2 or erbB2 was an independent negative predictor of locoregional disease-free survival. Conclusion Genomic screening by cDNA microarray and validation internally by real-time RT-PCR and externally by immunohistochemistry have identified two genes (MDM2 and erbB2) as predictors of locoregional failure in LPC patients treated with CRT. The role of these genes in treatment selection and the functional basis for their activity in CRT response merit additional consideration