Literature DB >> 21986844

Gene expression profiling in multiple myeloma--reporting of entities, risk, and targets in clinical routine.

Tobias Meissner1, Anja Seckinger, Thierry Rème, Thomas Hielscher, Thomas Möhler, Kai Neben, Hartmut Goldschmidt, Bernard Klein, Dirk Hose.   

Abstract

PURPOSE: Multiple myeloma is an incurable malignant plasma cell disease characterized by survival ranging from several months to more than 15 years. Assessment of risk and underlying molecular heterogeneity can be excellently done by gene expression profiling (GEP), but its way into clinical routine is hampered by the lack of an appropriate reporting tool and the integration with other prognostic factors into a single "meta" risk stratification. EXPERIMENTAL
DESIGN: The GEP-report (GEP-R) was built as an open-source software developed in R for gene expression reporting in clinical practice using Affymetrix microarrays. GEP-R processes new samples by applying a documentation-by-value strategy to the raw data to be able to assign thresholds and grouping algorithms defined on a reference cohort of 262 patients with multiple myeloma. Furthermore, we integrated expression-based and conventional prognostic factors within one risk stratification (HM-metascore).
RESULTS: The GEP-R comprises (i) quality control, (ii) sample identity control, (iii) biologic classification, (iv) risk stratification, and (v) assessment of target genes. The resulting HM-metascore is defined as the sum over the weighted factors gene expression-based risk-assessment (UAMS-, IFM-score), proliferation, International Staging System (ISS) stage, t(4;14), and expression of prognostic target genes (AURKA, IGF1R) for which clinical grade inhibitors exist. The HM-score delineates three significantly different groups of 13.1%, 72.1%, and 14.7% of patients with a 6-year survival rate of 89.3%, 60.6%, and 18.6%, respectively.
CONCLUSION: GEP reporting allows prospective assessment of risk and target gene expression and integration of current prognostic factors in clinical routine, being customizable about novel parameters or other cancer entities. ©2011 AACR.

Entities:  

Mesh:

Year:  2011        PMID: 21986844     DOI: 10.1158/1078-0432.CCR-11-1628

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  17 in total

1.  The 7p15.3 (rs4487645) association for multiple myeloma shows strong allele-specific regulation of the MYC-interacting gene CDCA7L in malignant plasma cells.

Authors:  Niels Weinhold; Tobias Meissner; David C Johnson; Anja Seckinger; Jérôme Moreaux; Asta Försti; Bowang Chen; Jolanta Nickel; Daniel Chubb; Andrew C Rawstron; Chi Doughty; Nasrin B Dahir; Dil B Begum; Kwee Young; Brian A Walker; Per Hoffmann; Marcus M Nöthen; Faith E Davies; Bernard Klein; Hartmut Goldschmidt; Gareth J Morgan; Richard S Houlston; Dirk Hose; Kari Hemminki
Journal:  Haematologica       Date:  2014-12-05       Impact factor: 9.941

2.  [Clincal features and treatment of multiple myeloma].

Authors:  E K Mai; H Goldschmidt
Journal:  Radiologe       Date:  2014-06       Impact factor: 0.635

Review 3.  IMWG consensus on risk stratification in multiple myeloma.

Authors:  W J Chng; A Dispenzieri; C-S Chim; R Fonseca; H Goldschmidt; S Lentzsch; N Munshi; A Palumbo; J S Miguel; P Sonneveld; M Cavo; S Usmani; B G M Durie; H Avet-Loiseau
Journal:  Leukemia       Date:  2013-08-26       Impact factor: 11.528

4.  Understanding the multiple biological aspects leading to myeloma.

Authors:  Eileen M Boyle; Faith E Davies; Xavier Leleu; Gareth J Morgan
Journal:  Haematologica       Date:  2014-04       Impact factor: 9.941

5.  A Molecular Predictor Reassesses Classification of Human Grade II/III Gliomas.

Authors:  Thierry Rème; Jean-Philippe Hugnot; Ivan Bièche; Valérie Rigau; Fanny Burel-Vandenbos; Vincent Prévot; Marc Baroncini; Denys Fontaine; Hugues Chevassus; Sophie Vacher; Rosette Lidereau; Hugues Duffau; Luc Bauchet; Dominique Joubert
Journal:  PLoS One       Date:  2013-06-21       Impact factor: 3.240

6.  Detection of Cross-Sample Contamination in Multiple Myeloma Samples and Sequencing Data.

Authors:  Owen W Stephens; Tobias Meißner; Niels Weinhold
Journal:  Methods Mol Biol       Date:  2018

7.  In vivo treatment with epigenetic modulating agents induces transcriptional alterations associated with prognosis and immunomodulation in multiple myeloma.

Authors:  Ken Maes; Eva De Smedt; Alboukadel Kassambara; Dirk Hose; Anja Seckinger; Els Van Valckenborgh; Eline Menu; Bernard Klein; Karin Vanderkerken; Jérôme Moreaux; Elke De Bruyne
Journal:  Oncotarget       Date:  2015-02-20

8.  The glycome of normal and malignant plasma cells.

Authors:  Thomas M Moehler; Anja Seckinger; Dirk Hose; Mindaugas Andrulis; Jèrôme Moreaux; Thomas Hielscher; Martina Willhauck-Fleckenstein; Anette Merling; Uta Bertsch; Anna Jauch; Hartmut Goldschmidt; Bernard Klein; Reinhard Schwartz-Albiez
Journal:  PLoS One       Date:  2013-12-26       Impact factor: 3.240

9.  Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System.

Authors:  Rowan Kuiper; Mark van Duin; Martin H van Vliet; Annemiek Broijl; Bronno van der Holt; Laila El Jarari; Erik H van Beers; George Mulligan; Hervé Avet-Loiseau; Walter M Gregory; Gareth Morgan; Hartmut Goldschmidt; Henk M Lokhorst; Pieter Sonneveld
Journal:  Blood       Date:  2015-09-01       Impact factor: 22.113

10.  Targeting UCHL1 Induces Cell Cycle Arrest in High-Risk Multiple Myeloma with t(4;14).

Authors:  Parin Kamseng; Teerapong Siriboonpiputtana; Teeraya Puavilai; Suporn Chuncharunee; Karan Paisooksantivatana; Takol Chareonsirisuthigul; Mutita Junking; Wannasiri Chiraphapphaiboon; Pa-Thai Yenchitsomanus; Budsaba Rerkamnuaychoke
Journal:  Pathol Oncol Res       Date:  2021-03-31       Impact factor: 3.201

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