Metascoring and Gene Expression Profiling in Clinical Routine in Multiple Myeloma,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3940-3940
Author(s):  
Tobias Meißner ◽  
Anja Seckinger ◽  
Thierry Rème ◽  
Thomas Hielscher ◽  
Thomas Möhler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Prognostic Factors ◽  
Risk Stratification ◽  
Gene Expression Profiling ◽  
Open Source ◽  
Expression Profiling ◽  
Target Genes ◽  
Molecular Heterogeneity ◽  
Clinical Routine

Abstract Abstract 3940 BACKGROUND. Multiple myeloma is characterized by molecular heterogeneity transmitting to survival ranging from several months to over 15 years. Gene expression profiling allows assessment of biological entities, risk, and targets. Its translation into clinical routine alongside conventional prognostic factors has been prevented by lack of appropriated reporting tools and the integration with other prognostic factors into a single risk stratification (metascoring). METHODS. We present here a non-commercial open source software-framework developed in the open source language R (GEP-report) containing a graphic user interphase based on Gtk2. Affymetrix microarray raw-data and a documentation-by-value strategy to directly apply thresholds and grouping-algorithms from a reference cohort of 262 myeloma patients are used. Gene expression-based and conventional prognostic factors are integrated within one risk-stratification (HM-metascore) and the final report is created as a PDF-file. RESULTS. The GEP-report comprises i) quality control, ii) test of sample identity, iii) biological classifications of multiple myeloma, iv) risk stratification, v) assessment of target-genes, and vi) integration of expression-based and clinical risk factors within one metascore. This HM-metascore sums over the weighted factors gene-expression based risk-assessment (UAMS-, IFM-score), proliferation, ISS-stage, t(4;14), and expression of prognostic target-genes (AURKA, IGF1R) for which clinical grade inhibitors exist. It delineates three significantly different groups of 13.1, 72.1 and 14.7% of patients with a 6-year survival of 89.3, 60.6 and 18.6%, respectively. CONCLUSION. GEP-reporting allows prospective assessment of target gene expression and integration of current prognostic factors within one risk stratification (metascoring), being customizable regarding novel parameters or other cancer entities. Disclosures: No relevant conflicts of interest to declare.

scholarly journals The role of fluorescence in situ hybridization and gene expression profiling in myeloma risk stratification

2011 ◽  
Vol 139 (suppl. 2) ◽  
pp. 84-89 ◽  
Author(s):  
Dirk Hose ◽  
Anja Seckinger ◽  
Anna Jauch ◽  
Thierry Reme ◽  
Jerome Moreaux ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Assessment ◽  
Multiple Myeloma ◽  
Prognostic Factors ◽  
In Situ Hybridization ◽  
High Risk ◽  
Risk Stratification ◽  
Gene Expression Profiling ◽  
Expression Profiling

Multiple myeloma patients? survival under treatment varies from a few months to more than 15 years. Clinical prognostic factors, especially beta2-microglobulin (B2M) and the international staging system (ISS), allow risk assessment to a certain extent, but do not identify patients at very high risk. As malignant plasma cells are characterized by a variety of chromosomal aberrations and changes in gene expression, a molecular characterization of CD138-purified myeloma cells by interphase fluorescence in situ hybridization (iFISH) and gene expression profiling (GEP) can be used for improved risk assessment. iFISH allows a risk stratification with presence of a translocation t(4;14) and/or deletion of 17p13 being the best documented adverse prognostic factors. A deletion of 13q14 is no longer considered to define adverse risk. Patients harbouring a t(4;14) seems to benefit from a bortezomib- or lenalidomide containing regimen, whereas patients with deletion 17p13 seem only to benefit from a high dose therapy approach using long term bortezomib (in induction and maintenance) and autologous tandem-transplantation as used in the GMMG-HD4 trial, or the total therapy 3 concept. Gene expression profiling allows the assessment of high risk scores (IFM, UAMS), remaining prognostic despite treatment with novel agents, and prognostic surrogates of biological factors (e.g. proliferation) and (prognostic) target gene expression (e.g. Aurora-kinase A). Thus, assessment of B2M and ISS-stage, iFISH, and GEP is considered extended routine diagnostics in therapy requiring multiple myeloma patients for risk assessment and, even now, to a certain extent selection of treatment.

A Clinically Usable Gene Expression Report for Risk Assessment and Identification of Possible Therapeutic Targets in Multiple Myeloma.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1829-1829
Author(s):  
Tobias Meiβner ◽  
Anja Seckinger ◽  
Thomas Hielscher ◽  
Thierry Rème ◽  
Jerome Moreaux ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Risk Stratification ◽  
Open Source ◽  
Dna Microarrays ◽  
Conflicts Of Interest ◽  
Therapeutic Targets ◽  
Proliferation Index ◽  
Final Report ◽  
Clinical Routine

Abstract Abstract 1829 Poster Board I-855 Introduction In addition to current clinical and cytogenetic risk factors, several highly predictive gene expression based risk stratifications have been proposed in multiple myeloma. At the same time, putative drugable targets have been identified which are only expressed in a subpopulation of myeloma patients (e.g. AURKA). Whereas assessment of both works well within a clinical trial or an experimental setting, they can currently not readily be applied to clinical routine. Methods As reference a group of 300 Affymetrix U133 Plus 2.0 DNA microarrays from patients with multiple myeloma is preprocessed using GC-RMA. Quality control of the DNA microarrays is implemented according to the MACQ-Project. Gene expression based prediction of sex, immunoglobulin- and light chain type is used as sample identity-test within a multicenter-setting. Gene expression based risk stratification (IFM-score, 70-gene high risk score, gene expression based proliferation index) and molecular classifications are assessed as published, as are individual target genes e.g. AURKA. To classify a patient within a prospective clinical routine setting, the documentation by value strategy (Kostka & Spang, 2008) was adapted for GC-RMA preprocessing and is used for documenting the quantitative preprocessing information of the reference group. The gene expression based report is developed in the open source language R, containing a GUI based on Gtk2, and the final report is created as a PDF-file. Results We present here our publicly available (http://code.google.com/p/gep-r) open source software-framework (GEP-R) that allows creating a gene expression based report from Affymetrix raw-data. The risk stratification of an individual patient is assessed and based on saved preprocessing information of a reference cohort by treating the individual patient's expression data as being part of this group, assuring comparable risk stratification. Results can be interpreted and commented within the report and a PDF based document be created. The generation of the report can be performed within short time on a standard computer. Conclusion Gene expression reporting allows validated assessment of risk and of individual therapeutic targets in myeloma patients within a clinical routine setting. Disclosures No relevant conflicts of interest to declare.

scholarly journals Gene Expression Profiling in Multiple Myeloma—Reporting of Entities, Risk, and Targets in Clinical Routine

2011 ◽  
Vol 17 (23) ◽  
pp. 7240-7247 ◽  
Author(s):  
Tobias Meißner ◽  
Anja Seckinger ◽  
Thierry Rème ◽  
Thomas Hielscher ◽  
Thomas Möhler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Clinical Routine

scholarly journals The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications

2021 ◽  
Vol 22 (21) ◽  
pp. 12070
Author(s):  
Anna Puła ◽  
Paweł Robak ◽  
Damian Mikulski ◽  
Tadeusz Robak
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Complex Disease ◽  
Plasma Cells ◽  
Response To Therapy ◽  
Driver Mutations ◽  
Molecular Heterogeneity

Multiple myeloma (MM) is a genetically complex disease that results from a multistep transformation of normal to malignant plasma cells in the bone marrow. However, the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown. A fundamental step needed to understand the oncogenesis of MM and its response to therapy is the identification of driver mutations. The introduction of gene expression profiling (GEP) in MM is an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Since some mutations in myeloma occur in non-coding regions, studies based on the analysis of mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology. In this review, we discuss the role of gene expression profiling in understanding the biology of multiple myeloma together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions.

scholarly journals The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications

2021 ◽  
Author(s):  
Anna Puła ◽  
Paweł Robak ◽  
Damian Mikulski ◽  
Tadeusz Robak
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Complex Disease ◽  
Plasma Cells ◽  
Response To Therapy ◽  
Driver Mutations ◽  
Molecular Heterogeneity

Multiple myeloma (MM) is a genetically complex disease that results from a multistep transformation of normal to malignant plasma cells in the bone marrow. However, the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown. A fundamental step needed to understand the oncogenesis of MM and its response to therapy is the identification of driver mutations. The introduction of gene expression profiling (GEP) in MM was an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Since some mutations in myeloma occur in non-coding regions, studies based on the analysis of mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology. In this review, we discuss the role of gene expression profiling in understanding the biology of multiple myeloma together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions.

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2543-2553 ◽  
Author(s):  
Annemiek Broyl ◽  
Dirk Hose ◽  
Henk Lokhorst ◽  
Yvonne de Knegt ◽  
Justine Peeters ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Medical Science ◽  
Protein Tyrosine Phosphatases ◽  
Molecular Classification ◽  
Newly Diagnosed ◽  
Expression Of Genes

Abstract To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.

Sign in / Sign up

Export Citation Format

Share Document