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

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).

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.

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.

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.

No relevant conflicts of interest to declare.