scholarly journals Gene expression signatures in polyarticular juvenile idiopathic arthritis demonstrate disease heterogeneity and offer a molecular classification of disease subsets

2009 ◽  
Vol 60 (7) ◽  
pp. 2113-2123 ◽  
Author(s):  
Thomas A. Griffin ◽  
Michael G. Barnes ◽  
Norman T. Ilowite ◽  
Judyann C. Olson ◽  
David D. Sherry ◽  
...  
Keyword(s):  
Gene Expression ◽  
Juvenile Idiopathic Arthritis ◽  
Molecular Classification ◽  
Polyarticular Juvenile Idiopathic Arthritis ◽  
Disease Heterogeneity ◽  
Gene Expression Signatures

scholarly journals Molecular classification of high grade endometrioid and clear cell ovarian cancer using TCGA gene expression signatures

2016 ◽  
Vol 141 (1) ◽  
pp. 95-100 ◽  
Author(s):  
Boris Winterhoff ◽  
Habib Hamidi ◽  
Chen Wang ◽  
Kimberly R. Kalli ◽  
Brooke L. Fridley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Clear Cell ◽  
Molecular Classification ◽  
High Grade ◽  
Gene Expression Signatures

Abstract 5597: Molecular classification of glioblastoma multiforme using isoform-level gene expression signatures.

2013 ◽  
Author(s):  
Sharmistha Pal ◽  
Yingtao Bi ◽  
Lukasz Macyszyn ◽  
Louise C. Showe ◽  
Donald M. O'Rourke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Glioblastoma Multiforme ◽  
Molecular Classification ◽  
Gene Expression Signatures

Molecular Classification of Light Chain (Al) Amyloidosis by Gene Expression Profiling

2004 ◽  
pp. 58-60
Author(s):  
R Abraham ◽  
R Fonseca ◽  
M Manske ◽  
T Price-Troska ◽  
M Gertz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Light Chain ◽  
Expression Profiling ◽  
Molecular Classification ◽  
Al Amyloidosis

scholarly journals Molecular Classification of Parathyroid Neoplasia by Gene Expression Profiling

2004 ◽  
Vol 165 (2) ◽  
pp. 565-576 ◽  
Author(s):  
Carl Morrison ◽  
William Farrar ◽  
Jeff Kneile ◽  
Nita Williams ◽  
Yiwen Liu-Stratton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Molecular Classification ◽  
Parathyroid Neoplasia

A New Molecular Classification of Multiple Myeloma Using Gene Expression Profiling and Fluorescence In Situ Hybridisation as Predictor for Event Free Survival.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 73-73 ◽  
Author(s):  
Dirk Hose ◽  
Jean-Francois Rossi ◽  
Carina Ittrich ◽  
John deVos ◽  
Axel Benner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Molecular Classification ◽  
Differentially Expressed ◽  
Event Free Survival ◽  
Free Survival ◽  
Cytogenetic Aberrations

Abstract AIM was to establish a new molecular classification of Multiple Myeloma (MM) based on changes in global gene expression attributable to cytogenetic aberrations detected by interphase FISH (iFISH) in order to (i) predict event free survival (EFS) and (ii) investigate differentially expressed genes as basis for a group specific and risk adapted therapy. PATIENTS AND METHODS. Bone marrow aspirates of 105 newly diagnosed MM-patients (65 trial (TG) / 40 independent validation group (VG)) and 7 normal donors (ND) 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 arrays (VG). CCND1 and CCND2 expression was verified by real time RT-PCR. iFISH was performed on purified MM-cells using probes for chromosomes 11q23, 11q13, 13q14, 17p13 and the IgH-translocations t(4;14) and t(11;14). Expression data were normalised (Bioconductor package gcrma) and nearest shrunken centroids (NSC) applied to calculate and cross validate a predictor on 40 patients of the TG with a comprehensive iFISH panel available combined with CCND overexpression. Differentially expressed genes were identified using empirical Bayes statistics for pairwise comparison. RESULTS. Overexpression of a D-type cyclin (D1 or D2) was found in 61/65 patients with MM compared to ND. CCND3 overexpression only appeared concomitantly with CCND2 overexpression. Four groups could be 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 to 566 genes correctly classifies all 40 patients of the TG (estimated cross validated error rate 0%). An independent VG of 40 patients was used. Genes with highest scores in NSC are: (1.1) CCND1, ribosomal proteins (e.g. RPL 28, 29), GPX1, CCRL2, (1.2) CCND1, TGIF, and NCAM (non-overexpression), (2.1) CCND2, (2.2) FGFR3, WHSC1, CCND2, IRTA2, SELL, and MAGED4. Distribution of clinical parameters (i.e. β2M, Durie Salmon stages, ISS) was not significantly different between the groups. The distribution of del(13)(q14q14) was (1.1) 31.5%, (1.2) 37.5%, (2.1) 37.5% and (2.2) 100%. (p<0.01). I.e. HGF, DKK1, VCAM, CD163 are differentially expressed between all 4 groups and ND (adjusted p<0.001). The groups defined by the predictor show a significantly different EFS after autologous stem cell transplantation according to the GMMG-HD3 protocol (median: (1.1) 18 / (1.2) not reached (no event) / (2.1) 22 / (2.2) 6 months; log-rank-test: p=0.004). CONCLUSION. CCND1 or CCND2 overexpression is nearly ubiquitous in MM and attributable to defined cytogenetic aberrations. Gene expression and iFISH allow a molecular classification of MM which can be predicted by gene expression profiling alone. Groups in the classification show a distinctive pattern in gene expression as well as a different EFS interpretable as risk stratification and indicator of therapeutic targets.

Sign in / Sign up

Export Citation Format

Share Document