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Updated: May 28, 2026
An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells
Published on: July 15, 2015
Gene expression profiling in multiple myeloma--reporting of entities, risk, and targets in clinical routine
Tobias Meissner1, Anja Seckinger, Thierry Rème
1Medizinische Klinik V, Universitätsklinikum Heidelberg, Germany.
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.
Insights
Gene expression profiling (GEP) reporting software (GEP-R) integrates multiple myeloma prognostic factors into a novel HM-metascore. This tool enhances clinical risk stratification and aids in personalized treatment strategies for patients.
Area of Science:
- Hematology
- Oncology
- Bioinformatics
Background:
- Multiple myeloma is a fatal plasma cell malignancy with variable prognoses.
- Gene expression profiling (GEP) offers insights into molecular heterogeneity but lacks clinical integration.
- Current risk stratification methods need improvement for clinical routine.
Purpose of the Study:
- To develop an open-source software tool (GEP-R) for gene expression reporting in multiple myeloma.
- To integrate GEP data with conventional prognostic factors for a unified risk score (HM-metascore).
- To improve clinical risk stratification and treatment decisions in multiple myeloma.
Main Methods:
- Developed GEP-R using R for Affymetrix microarray data analysis.
- Applied a documentation-by-value strategy for threshold and grouping algorithms.
- Integrated GEP-based scores (UAMS, IFM), proliferation, ISS stage, t(4;14), and target genes (AURKA, IGF1R) into the HM-metascore.
Main Results:
- GEP-R includes quality control, sample identity, biologic classification, risk stratification, and target gene assessment.
- The HM-metascore stratified patients into three distinct risk groups with significantly different 6-year survival rates (89.3%, 60.6%, 18.6%).
- Identified prognostic target genes with existing clinical-grade inhibitors.
Conclusions:
- GEP reporting, via GEP-R, enables prospective risk assessment and target gene evaluation in clinical practice.
- The HM-metascore provides a comprehensive risk stratification by integrating diverse prognostic factors.
- GEP-R is customizable for novel parameters and adaptable to other cancer types.