Noncoding mutations cause super-enhancer retargeting resulting in protein synthesis dysregulation during B cell lymphoma progression.

Whole-genome sequencing of longitudinal tumor pairs representing transformation of follicular lymphoma to high-grade B cell lymphoma with MYC and BCL2 rearrangements (double-hit lymphoma) identified coding and noncoding genomic alterations acquired during lymphoma progression. Many of these transformation-associated alterations recurrently and focally occur at topologically associating domain resident regulatory DNA elements, including H3K4me3 promoter marks located within H3K27ac super-enhancer clusters in B cell non-Hodgkin lymphoma. One region found to undergo recurrent alteration upon transformation overlaps a super-enhancer affecting the expression of the PAX5/ZCCHC7 gene pair. ZCCHC7 encodes a subunit of the Trf4/5-Air1/2-Mtr4 polyadenylation-like complex and demonstrated copy number gain, chromosomal translocation and enhancer retargeting-mediated transcriptional upregulation upon lymphoma transformation. Consequently, lymphoma cells demonstrate nucleolar dysregulation via altered noncoding 5.8S ribosomal RNA processing. We find that a noncoding mutation acquired during lymphoma progression affects noncoding rRNA processing, thereby rewiring protein synthesis leading to oncogenic changes in the lymphoma proteome.

Glioma tumor proteomics: clinically useful protein biomarkers and future perspectives.

Introduction: Despite being rare cancers, gliomas account for a significant number of cancer-related deaths. Identification and treatment of these tumors at an early stage would greatly improve the therapeutic outcomes. There is an urgent need for diagnostic and prognostic markers, which can identify disease early and discriminate the subtypes of these tumors thereby improving the existing treatment modalities.Areas covered: In this article, we have reviewed published literature on proteomics biomarkers for gliomas and their importance in diagnosis or prognosis. Proteomic studies for the discovery of protein, autoantibody biomarkers, and biological pathway alterations in serum, CSF and tumor biopsies have been discussed in this review.Expert opinion: The rapid development in the field of mass spectrometry and increased sensitivity and reproducibility in assays has led to the identification and quantification of large number of proteins very precisely. Though genomic markers are the prime focus in the classification of gliomas, incorporating protein markers would further improve the existing classification. In this regard, data mining and studies on large cohorts of glioma patients would help in the identification of diagnostic and prognostic markers ultimately translating to the clinics.

Quantitative mass spectrometry analysis reveals a panel of nine proteins as diagnostic markers for colon adenocarcinomas.

Adenocarcinomas are cancers originating from the gland forming cells of the colon and rectal lining, and are known to be the most common type of colorectal cancers. The current diagnosis strategies for colorectal cancers include biopsy, laboratory tests, and colonoscopy which are time consuming. Identification of protein biomarkers could aid in the detection of colon adenocarcinomas (CACs). In this study, tissue proteome of colon adenocarcinomas (n = 11) was compared with the matched control specimens (n = 11) using isobaric tags for relative and absolute quantitation (iTRAQ) based liquid chromatography-mass spectrometry (LC-MS/MS) approach. A list of 285 significantly altered proteins was identified in colon adenocarcinomas as compared to its matched controls, which are associated with growth and malignancy of the tumors. Protein interaction analysis revealed the association of altered proteins in colon adenocarcinomas with various transcription factors and their targets. A panel of nine proteins was validated using multiple reaction monitoring (MRM). Additionally, S100A9 was also validated using immunoblotting. The identified panel of proteins may serve as potential biomarkers and thereby aid in the detection of colon adenocarcinomas.

Subventricular zone involvement in Glioblastoma - A proteomic evaluation and clinicoradiological correlation.

Glioblastoma multiforme (GBM), the most malignant of all gliomas is characterized by a high degree of heterogeneity and poor response to treatment. The sub-ventricular zone (SVZ) is the major site of neurogenesis in the brain and is rich in neural stem cells. Based on the proximity of the GBM tumors to the SVZ, the tumors can be further classified into SVZ+ and SVZ-. The tumors located in close contact with the SVZ are classified as SVZ+, while the tumors located distantly from the SVZ are classified as SVZ-. To gain an insight into the increased aggressiveness of SVZ+ over SVZ- tumors, we have used proteomics techniques like 2D-DIGE and LC-MS/MS to investigate any possible proteomic differences between the two subtypes. Serum proteomic analysis revealed significant alterations of various acute phase proteins and lipid carrying proteins, while tissue proteomic analysis revealed significant alterations in cytoskeletal, lipid binding, chaperone and cell cycle regulating proteins, which are already known to be associated with disease pathobiology. These findings provide cues to molecular basis behind increased aggressiveness of SVZ+ GBM tumors over SVZ- GBM tumors and plausible therapeutic targets to improve treatment modalities for these highly invasive tumors.

Tissue Proteome Analysis of Different Grades of Human Gliomas Provides Major Cues for Glioma Pathogenesis.

Gliomas are heterogeneous and most commonly occurring brain tumors. Blood-brain barrier restricts the entry of brain tumor proteins into blood stream thus limiting the usage of serum or plasma for proteomic analysis. Our study aimed at understanding the molecular basis of aggressiveness of various grades of brain tumors using isobaric tagging for relative and absolute quantification (iTRAQ) based mass spectrometry. Tissue proteomic analysis of various grades of gliomas was performed using four-plex iTRAQ. We labeled five sets (each set consists of control, grade-II, III, and IV tumor samples) of individual glioma patients using iTRAQ reagents. Significantly altered proteins were subjected to bioinformatics analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID). Various metabolic pathways like glycolysis, TCA-cycle, electron transport chain, lactate metabolism, and blood coagulation pathways were majorly observed to be perturbed in gliomas. Most of the identified proteins involved in redox reactions, protein folding, pre-messenger RNA (mRNA) processing, antiapoptosis, and blood coagulation were found to be upregulated in gliomas. Transcriptomics data of glioblastoma multiforme (GBM), low-grade gliomas (LGGs), and controls were downloaded from The Cancer Genome Atlas (TCGA) data portal and further analyzed using BRB-Array tools. Expression levels of a few significantly altered proteins like lactate dehydrogenase, alpha-1 antitrypsin, fibrinogen alpha chain, nucleophosmin, annexin A5, thioredoxin, ferritin light chain, thymosin beta-4-like protein 3, superoxide dismutase-2, and peroxiredoxin-1 and 6 showed a positive correlation with increasing grade of gliomas thereby offering an insight into molecular basis behind their aggressive nature. Several proteins identified in different grades of gliomas are potential grade-specific markers, and perturbed pathways provide comprehensive overview of molecular cues involved in glioma pathogenesis.

Investigation of serum proteome alterations in human glioblastoma multiforme.

Glioblastoma multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. The present study was conducted to investigate the alterations in the serum proteome in GBM patients compared to healthy controls. Comparative proteomic analysis was performed employing classical 2DE and 2D-DIGE combined with MALDI TOF/TOF MS and results were further validated through Western blotting and immunoturbidimetric assay. Comparison of the serum proteome of GBM and healthy subjects revealed 55 differentially expressed and statistically significant (p <0.05) protein spots. Among the identified proteins, haptoglobin, plasminogen precursor, apolipoprotein A-1 and M, and transthyretin are very significant due to their functional consequences in glioma tumor growth and migration, and could further be studied as glioma biomarkers and grade-specific protein signatures. Analysis of the lipoprotein pattern indicated elevated serum levels of cholesterol, triacylglycerol, and low-density lipoproteins in GBM patients. Functional pathway analysis was performed using multiple software including ingenuity pathway analysis (IPA), protein analysis through evolutionary relationships (PANTHER), database for annotation, visualization and integrated discovery (DAVID), and GeneSpring to investigate the biological context of the identified proteins, which revealed the association of candidate proteins in a few essential physiological pathways such as intrinsic prothrombin activation pathway, plasminogen activating cascade, coagulation system, glioma invasiveness signaling, and PI3K signaling in B lymphocytes. A subset of the differentially expressed proteins was applied to build statistical sample class prediction models for discrimination of GBM patients and healthy controls employing partial least squares discriminant analysis (PLS-DA) and other machine learning methods such as support vector machine (SVM), Decision Tree and Naïve Bayes, and excellent discrimination between GBM and control groups was accomplished.