Gene Expression Profiling for Melanoma Risk Stratification: Where do we Stand?

Integrating Gene and Mir Expression Profiles and Regulatory Network Structures to Define Aberrent Feed Forward Loops with Functional and Clinical Implications in Myeloma.

Genome-scale profiling of gene expression in hepatocellular carcinoma: Classification, survival prediction, and identification of therapeutic targets

New technological developments have frequently preceded major advances in biomedical research and medicine [1]. For example, the development of fluorescent DNA sequencing techniques made it possible to establish the large-scale high-throughput technology needed for human genome sequencing. Polymerase chain reaction (PCR), fluorescent DNA sequencing, and other techniques have enabled the discovery of about 1700 mendelian disease genes [2]. The advent of the DNA microarray based technologies has now made it possible to measure simultaneously the expression of tens of thousands of genes in different tissues under a variety of conditions. This high-throughput technology has afforded biomedical scientists a unique opportunity to integrate the descriptive characteristics (i.e. ‘phenotype’) of a biological system under study with the genomic readout (i.e. gene expression). The opportunity to contemplate the integrated view of biological systems has provoked a shift in biological sciences away from the classical reductionism to systems biology [1,3,4]. The systems approach to a disease is based on the hypothesis that disease processes perturb a regulatory network of genes and proteins in a way that differs from the respective normal counterpart. Consequently, by using multi-parametric measurements it may be possible to transform current diagnostic and therapeutic approaches and enable a predictive and preventive personalized medicine [4]. The application of microarray technologies to characterize tumors at the gene expression level has significantly impacted clinical oncology [5,6]. Global gene expression analysis of various human tumors has resulted in

BackgroundAcute Myeloid Leukemia (AML) is characterized by various cytogenetic and molecular abnormalities. Detection of these abnormalities is important in the risk-classification of patients but requires laborious experimentation. Various studies showed that gene expression profiles (GEP), and the gene signatures derived from GEP, can be used for the prediction of subtypes in AML. Similarly, successful prediction was also achieved by exploiting DNA-methylation profiles (DMP). There are, however, no studies that compared classification accuracy and performance between GEP and DMP, neither are there studies that integrated both types of data to determine whether predictive power can be improved.ApproachHere, we used 344 well-characterized AML samples for which both gene expression and DNA-methylation profiles are available. We created three different classification strategies including early, late and no integration of these datasets and used them to predict AML subtypes using a logistic regression model with Lasso regularization.ResultsWe illustrate that both gene expression and DNA-methylation profiles contain distinct patterns that contribute to discriminating AML subtypes and that an integration strategy can exploit these patterns to achieve synergy between both data types. We show that concatenation of features from both data sets, i.e. early integration, improves the predictive power compared to classifiers trained on GEP or DMP alone. A more sophisticated strategy, i.e. the late integration strategy, employs a two-layer classifier which outperforms the early integration strategy.ConclusionWe demonstrate that prediction of known cytogenetic and molecular abnormalities in AML can be further improved by integrating GEP and DMP profiles.

The progressing clinical utility of DNA microarrays.

Molecular subtypes of breast cancer with relevant biological and clinical features have been defined recently, notably ERBB2-overexpressing, basal-like, and luminal-like subtypes. To investigate the ability of mass spectrometry-based proteomics technologies to analyze the molecular complexity of human breast cancer, we performed a SELDI-TOF MS-based protein profiling of human breast cell lines (BCLs). Triton-soluble proteins from 27 BCLs were incubated with ProteinChip arrays and subjected to SELDI analysis. Unsupervised global hierarchical clustering spontaneously discriminated two groups of BCLs corresponding to "luminal-like" cell lines and to "basal-like" cell lines, respectively. These groups of BCLs were also different in terms of estrogen receptor status as well as expression of epidermal growth factor receptor and other basal markers. Supervised analysis revealed various protein biomarkers with differential expression in basal-like versus luminal-like cell lines. We identified two of them as a carboxyl terminus-truncated form of ubiquitin and S100A9. In a small series of frozen human breast tumors, we confirmed that carboxyl terminus-truncated ubiquitin is observed in primary breast samples, and our results suggest its higher expression in luminal-like tumors. S100A9 up-regulation was found as part of the transcriptionally defined basal-like cluster in DNA microarrays analysis of human tumors. S100A9 association with basal subtypes as well as its poor prognosis value was demonstrated on a series of 547 tumor samples from early breast cancer deposited in a tissue microarray. Our study shows the potential of integrated genomics and proteomics profiling to improve molecular knowledge of complex tumor phenotypes and identify biomarkers with valuable diagnostic or prognostic values.

Zebrafish: A Model System to Study Heritable Skin Diseases

Prognostic Gene Expression Signatures Can Be Measured in Tissues Collected in RNAlater Preservative

Genome-wide expression profiling reveals new candidate genes associated with osteoarthritis

AimsTo characterise a histologically unusual paediatric uveal melanoma by gene expression and karyotypic profiling and assess prognosis.MethodsThe tumour was studied by histopathology, karyotype analysis, single nucleotide polymorphism and gene expression...

Objectives/Goals: Neuroendocrine malignancies are heterogeneous cancers with varied clinical outcomes, yet the molecular landscape driving this heterogeneity has not been fully characterized. Here, we investigate the gene expression and mutational profiles of neuroendocrine malignancies to better understand the underlying biology and therapeutic targets. Methods/Study Population: Patients with neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs) treated at Cleveland Clinic (2000–2022) with molecular profiling (n = 66) were identified. Mutational and gene expression profiles were abstracted from electronic health records (EHR). Clinico-pathological characteristics and overall survival (OS) were obtained from EHR. Statistical analyses were performed by R v.4.0.5 and R package Limma for differential gene expression, as well as Chi-square, Fisher’s exact, and Wilcoxon rank sum tests. Results/Anticipated Results: The cohort consisted of 38 cases with NEC, 18 NET g3, and 10 NET g1/2. EZH2 and cyclin E1 were differentially over-expressed in NEC vs. NET (p < 0.05), while PTEN and MSLN were differentially under-expressed in NEC vs. NET (p < 0.005). Several recurrent alterations co-segregated with aggressive histology (NEC vs. NET): TP53 (p 60. Also, there was no difference in gene expression profiles between the two age groups among NETs or NECs. Discussion/Significance of Impact: This study explores the molecular landscape of NETs and NECs, revealing distinct gene expression and mutation profiles related to clinical outcomes. High expressions of cyclin D1 and EGFR were significantly associated with improved 2-year OS in NECs, highlighting potential therapeutic targets. Future studies are needed to validate these findings.

Successful Application of Microarray Technology to Microdissected Formalin-Fixed, Paraffin-Embedded Tissue

Introduction: Diffuse Intrinsic Pontine Glioma (DIPG) is a highly morbid form of pediatric brainstem glioma. Molecular characterization is limited due to lack of tissue. Recent investigations suggest possible molecular subtypes may account for the historical poor response to therapy. We previously generated protein profiles of CSF and formalin fixed DIPG tumor specimens to characterize patterns of protein expression. Here, we present the first comprehensive tissue proteome of fresh frozen DIPG tumor specimens (n=16) and normal brain tissue (n=10). We characterize differential protein expression in DIPG tumor specimens, and compare these to gene expression and DNA methylation profiles of the same tissue. Methods: Normal brain and tumor tissue was collected intraoperatively or post-mortem. Extracted total tissue protein was quantified by mass spectrometry (MS/MS) via LTQ-Orbitrap-XL and database search using the Sequest algorithm. Gene expression profiles were detected using whole-genome Human HT-4 v12 Gene Expression Bead Chips. DNA methylation profiles were characterized after bisulphite conversion using Infinium HumanMethylation 450K BeadChip arrays. Quantitative and statistical analysis was performed with Genome Studio, ProteoIQ, and Partek Genomics Suite. Functional analysis was performed using Ingenuity Pathways Analysis. Gene and protein expression was validated via western blot and immunohistochemical staining of tumor and normal brain tissue. Results: 1,918 differentially expressed genes were detected in DIPG tumor tissue (ANOVA, p&amp;lt;0.05, FC &amp;gt;2 or &amp;lt;-2). Unsupervised clustering revealed two distinct subgroups with differential ShH activity (GLI1 z-score -2.000 vs 2.000) and expression of GLI1, GLIPR1, PTCHD1 and SMO. Protein profiling revealed high expression of TLN1, CLU, EEF2 (FC &amp;gt;2), with differential ShH pathway (GLI1 z-score -0.626 vs. 2.254) and protein expression COL1A2, LMNA, MAP4, NES, NRCAM, STMN1, and TNC between subgroups (ANOVA, p&amp;lt;0.05, FC &amp;gt;2 or &amp;lt;-2). Concordant differences in DNA methylation were detected in related genes, including GLI1, FOXF1, SMO, SHH, and SUFU (ANOVA, p&amp;lt;0.05, FC&amp;lt;-2 or &amp;gt;2). Conclusions: We present the first comprehensive protein profile of DIPG fresh frozen tumor tissue and correlate to tissue gene expression profiles, which suggest differential activity of SHh pathway. This may in part be explained by differential methylation patterns of key genes. Proteomic analysis of DIPG tumor tissue reveals protein expression profiles reflective of this differential pathway activation, and hence may be useful tool for elucidating mechanisms of brainstem gliomagenesis in what likely is a heterogeneous tumor population. Biomarkers identified through proteomic analysis may in turn serve to more accurately diagnose patients with DIPG and measure response to therapy. Citation Format: Amanda Saratsis, Sridevi Yadavilli, Madhuri Kambhampati, Eric Raabe, Suresh Magge, Javad Nazarian. Four dimensional molecular analysis of pediatric diffuse intrinsic pontine glioma. [abstract]. In: Proceedings of the Third AACR International Conference on Frontiers in Basic Cancer Research; Sep 18-22, 2013; National Harbor, MD. Philadelphia (PA): AACR; Cancer Res 2013;73(19 Suppl):Abstract nr C30.

Quantitative high-throughput gene expression profiling of human striatal development to screen stem cell–derived medium spiny neurons

Profiling the gene signature of endometrial receptivity: clinical results