CENTROSOME ASSOCIATED GENES PATTERN FOR RISK SUBSTRATIFICATION IN MULTIPLE MYELOMA

Background
Multiple myeloma (MM) is a lymphoproliferative disease characterized by the clonal expansion of neoplastic plasma cells within the bone marrow. The genome of the malignant plasma cells is extremely unstable characterized by a complex combination of structure and numerical abnormalities. Our previous study defined centrosome associated genes pattern with impact in myeloma pathogenesis. Revealed molecular signature is related to OS as well as to clinical parameters and ISS staging (Kryukov et al, 2013).

Aims
The objective of our study was to create and validate risk stratification model based on previously described centrosome associated genes pattern in MM.

Methods

One hundred and fifty patients were evaluated for this study. The patients’ baseline characteristics were as follows: males/females 79/71, median age of 67 years (range 38-90 years). Newly diagnosed (90/150) and relapsed (60/150) patients were included in this study; most of them had advanced stage of MM (DS II/III n =110; ISS II/III n=144). Interphase FISH with cytoplasmic immunoglobulin light chain staining (cIg FISH) and gene expression profiling (GEP) were performed on CD138+ plasma cells separated by MACS. Training and validation cohort includes 72 and 78 pts, resp.

Results

Using multinomial logistic regression 12 candidate genes (BUB1, BUB1B|PAK6, RAD51, PLK1, BRCA1, CENPA, BARD1, AURKA, MAD2L1, CENPH, XRCC2 and CDC25C|FAM53C) for patients, risk stratification were defined. Based on expression of these genes, patients can be stratified into three groups - 'High-', 'Mediate-' and 'Low expressed'. The overall survival of patients in “High” and “Medium” subgroups was significantly worse than in patients in 'Low' subgroup for both training and validation cohorts and was in concordance with previously published results (Kryukov et al. 2013). To characterize the prognostic significance of centrosome associated genes pattern (CAGP), multivariate Cox proportional hazards survival model was used. Significantly worse prognosis was found for united “High & Medium” group (HR=2.182; 3-year OS=28.3%) compared to “Low” group (3-year OS=63.8%, p<0.01).  Besides CAGP, the following parameters were used for multivariate Cox proportional hazards survival model: ISS stadium, β2-microglobulin, del 17p13, t(4;14), amp 1q21. The variables in the multivariate model were the only variables, which remained statistically significant when potential predictors were combined together as well as CAGP, which was forced into the model. Among all subsequently tested combinations of predictors, the best results in risk of death assessment were obtained for CAGP combined with del 17p13 (p<0.001). It is worth to mention that both prognostic factors were independent. United “High & Medium” CAGP subgroup as well as TP53 deletion had significantly higher risk of death assessment (HR=3.189 and HR=3.699 resp. p<0.005). Survival characteristics for different risk groups are presented in the Table 1.

Summary

We have created a new GEP-based model for classification of every patient into one of three prognostic subgroups, which can be easily used in routine practice. This approach can be used independently as well as in combination with other factors. Best results in risk of death assessment are obtained when the new stratification model is used in combination with detection of TP53 loss analyzed by FISH. Thus, the new model can be used for substratification of prognosis in patients with TP53 loss. These findings need to be confirmed on larger cohort with longer follow-up.

Acknowledgments This work was supported by the Moravian-Silesian Region - grant no. (MSK 02680/2014/RRC and MSK 02692/2014/RRC); grants by MH CZ - DRO - FNOs/2014 and FNBr, 65269705; by The Ministry of Education, Youth and Sports (Specific university research of the Faculty of Medicine, University of Ostrava) project no. (SGS01/LF/2014-2015, SGS02/LF/2014-2015, SGS03/LF/2015-2016); and by grant IGA of The Ministry of Health (NT 13190-3 and NT 14575)

Keyword(s): Gene expression profile, Multiple myeloma, Risk factor

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