scholarly journals MB-31 * MicroRNA EXPRESSION PATTERNS IN TISSUE AND CEREBROSPINAL FLUID AID IN MOLECULAR CLASSIFICATION OF PEDIATRIC MEDULLOBLASTOMA

2015 ◽  
Vol 17 (suppl 3) ◽  
pp. iii26-iii27
Author(s):  
R. Lulla ◽  
J. Laskowski ◽  
Y. Bi ◽  
S. Goldman ◽  
J. Fangusaro ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Expression Patterns ◽  
Molecular Classification ◽  
Microrna Expression ◽  
Pediatric Medulloblastoma

scholarly journals Accurate Molecular Classification of Renal Tumors Using MicroRNA Expression

2010 ◽  
Vol 12 (5) ◽  
pp. 687-696 ◽  
Author(s):  
Eddie Fridman ◽  
Zohar Dotan ◽  
Iris Barshack ◽  
Miriam Ben David ◽  
Avital Dov ◽  
...  
Keyword(s):  
Molecular Classification ◽  
Microrna Expression ◽  
Renal Tumors

MicroRNA expression profiling for Molecular Classification of pediatric brain tumors

2011 ◽  
Vol 57 (1) ◽  
pp. 183-184 ◽  
Author(s):  
Simone Treiger Sredni ◽  
Chiang-Ching Huang ◽  
Maria de Fátima Bonaldo ◽  
Tadanori Tomita
Keyword(s):  
Brain Tumors ◽  
Expression Profiling ◽  
Molecular Classification ◽  
Pediatric Brain Tumors ◽  
Microrna Expression ◽  
Pediatric Brain

Molecular Classification of Multiple Myeloma (MM) Based on Gene Expression Profiling (GEP) and Fluorescence In Situ Hybridisation (FISH) Is an Independent Predictor for Event Free Survival (EFS).

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 507-507
Author(s):  
Dirk Hose ◽  
Jean-François Rossi ◽  
Carina Ittrich ◽  
John De Vos ◽  
Thierry Rème ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
In Situ Hybridisation ◽  
Molecular Classification ◽  
Rank Test ◽  
High Dose ◽  
Fluorescence In Situ Hybridisation ◽  
Prognostic Parameters ◽  
Log Rank Test

Abstract AIM was to test the new molecular classification of MM based on changes in global gene expression attributable to cytogenetic aberrations detected by interphase FISH (iFISH) in order to (i) predict EFS in a group of 100 MM-patients treated by high dose chemotherapy, and (ii) to investigate whether the classification represents an independent prognostic factor. PATIENTS AND METHODS. Bone marrow aspirates from MM-patients and normal donors were CD138-purified by magnetic activated cell sorting. RNA was in-vitro transcribed and hybridised to Affymetrix HG U133 A+B GeneChip (TG) and HG U133 2.0 plus array (VG). CCND1, CCND2 and FGFR3 expression was verified by real time RT-PCR and western blotting. iFISH was performed on purified MM-cells using probes for chromosomes 11q23, 13q14, 17p13 and the IgH-translocations t(4;14)(p16;q32.3) and t(11;14)(q13;q32.3). Expression data were normalised (Bioconductor package gcrma), and nearest shrunken centroids (NSC) applied to calculate and cross validate a predictor on a training group (TG) of 40 patients in whom a comprehensive iFISH panel combined with data on CCND overexpression were available. The ExpressMiner tool of the HUSAR bioinformatics laboratory was used to analyze genes differentially expressed between GEP-defined groups. A log-rank test and a Cox proportional hazard model were used to test the influence of prognostic parameters in combination with the predicted groups. RESULTS. Four groups were distinguished: (1.1) CCND1 (11q13) overexpression and trisomy 11q13, (1.2) CCND1 overexpression and translocations involving 11q13, i.e. t(11;14), (2.1) CCND2 overexpression without 11q13+, t(11;14), t(4;14), (2.2) CCND2 overexpression with t(4;14) and FGFR3 upregulation. A predictor of 6 genes correctly classified all 40 patients of the TG (estimated cross validated error rate 0%). An independent validation group (VG) of 65 patients was used. Distribution of clinical parameters (i.e. beta2M, Durie Salmon stages, ISS) was not significantly different between the 4 groups. The groups defined by the predictor have a significantly different EFS after autologous stem cell transplantation according to the GMMG-HD3 protocol (n=100; median: 26 /not reached /22 /6 months in groups 1.1 /1.2 /2.1 /2.2, respectively). A model testing the combination of the predicted group and B2M (above or below 3.5 mg/dl) showed a significant (p<0.006, log-rank-test) correlation with EFS. The distribution of del(13q14) (n=118) was (1.1) 34.0%, (1.2) 60.8%, (2.1) 46.4% and (2.2) 94%. The presence of a del(13q14) either in a subclone (<60% of analyzed nuclei) or major clone had no significant influence on EFS. CONCLUSION. Gene expression and iFISH allow a molecular classification of MM which can be predicted by GEP. Groups in the classification have distinct gene expression patterns as well as statistically significant different EFS. GEP-defined groups and B2M represent independent prognostic parameters.

scholarly journals Determination of a Tumor-Promoting Microenvironment in Recurrent Medulloblastoma: A Multi-Omics Study of Cerebrospinal Fluid

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1350 ◽  
Author(s):  
Bernd Reichl ◽  
Laura Niederstaetter ◽  
Thomas Boegl ◽  
Benjamin Neuditschko ◽  
Andrea Bileck ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Molecular Classification ◽  
Biological Properties ◽  
Neoplastic Disease ◽  
Limited Data ◽  
Beta Oxidation ◽  
Hypoxic Conditions ◽  
Novel Model

Molecular classification of medulloblastoma (MB) is well-established and reflects the cell origin and biological properties of tumor cells. However, limited data is available regarding the MB tumor microenvironment. Here, we present a mass spectrometry-based multi-omics pilot study of cerebrospinal fluid (CSF) from recurrent MB patients. A group of age-matched patients without a neoplastic disease was used as control cohort. Proteome profiling identified characteristic tumor markers, including FSTL5, ART3, and FMOD, and revealed a strong prevalence of anti-inflammatory and tumor-promoting proteins characteristic for alternatively polarized myeloid cells in MB samples. The up-regulation of ADAMTS1, GAP43 and GPR37 indicated hypoxic conditions in the CSF of MB patients. This notion was independently supported by metabolomics, demonstrating the up-regulation of tryptophan, methionine, serine and lysine, which have all been described to be induced upon hypoxia in CSF. While cyclooxygenase products were hardly detectable, the epoxygenase product and beta-oxidation promoting lipid hormone 12,13-DiHOME was found to be strongly up-regulated. Taken together, the data suggest a vicious cycle driven by autophagy, the formation of 12,13-DiHOME and increased beta-oxidation, thus promoting a metabolic shift supporting the formation of drug resistance and stem cell properties of MB cells. In conclusion, the different omics-techniques clearly synergized and mutually supported a novel model for a specific pathomechanism.

Molecular Classification of Adult Renal Epithelial Neoplasms Using MicroRNA Expression and Virtual Karyotyping

2011 ◽  
Vol 20 (2) ◽  
pp. 63-70 ◽  
Author(s):  
Martin P. Powers ◽  
Karla Alvarez ◽  
Hyun-Jung Kim ◽  
Federico A. Monzon
Keyword(s):  
Molecular Classification ◽  
Microrna Expression ◽  
Epithelial Neoplasms

Molecular classification of pituitary adenomas

2015 ◽  
Author(s):  
Antonio Pico ◽  
Laura Sanchez-Tejada ◽  
Ruth Sanchez-Ortiga ◽  
Rosa Camara ◽  
Cristina Lamas ◽  
...  
Keyword(s):  
Pituitary Adenomas ◽  
Molecular Classification

scholarly journals Normal tissue content impact on the GBM molecular classification

2020 ◽  
Author(s):  
Rodrigo Madurga ◽  
Noemí García-Romero ◽  
Beatriz Jiménez ◽  
Ana Collazo ◽  
Francisco Pérez-Rodríguez ◽  
...  
Keyword(s):  
Normal Tissue ◽  
In Silico Analysis ◽  
Molecular Classification ◽  
Gene Signature ◽  
Normal Brain ◽  
Normal Cells ◽  
Final Model ◽  
Tumor Subtypes ◽  
Tissue Content

Abstract Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

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