Influence of predictor genes of TC classification on clinical outcome in multiple myeloma

Abstract

A set of TC classification signature genes with prognostic expression value for Multiple Myeloma (MM) were investigated in a cohort of 156 patients of MM with a median follow-up of 11 months (range 0 to 33 months). The patients were classified as ISS stages I (10.25%), II (17.3%), III (72.4%), and their response to treatment with Thalidomide/Lenalidomide/Bortezomib based therapies was examined. MM specific aCGH arrays were analyzed among 82 out of 156 patients while Taqman based quantitative Real Time PCR assays were performed on all the patients using Quant studio 12K Flex. Expression levels of a set of 10 genes (WHSC1, CCND1, CCND3, MAFB, CX3CR1, FGFR3, CCND2, MAF, ITGB7, CKS1B) and a house keeping gene (GAPDH) were studied among MM patients along with cDNA from a pool of sorted plasma cells obtained from patients with Hodgkin's disease which served as controls. Data obtained was co-analyzed in terms of its correlation with a CGH profiles, response to treatment and progression free survival (PFS). An attempt has been made to investigate gene expression based predictive analyses for TC classification in analogy with international standards and algorithms. The main observations include (i) 46% patients were cytogenetically HD while 54% NHD as per a CGH analyses (ii) patients with gene expressions reflective of 4p16 and 11q13 TC classification were most frequent, (iii) analyses of FGFR3 or MMSET (reflective of 4p16) or CCND1 (for 11q13) or MAFB (20q12) or CCND3 (for 6p21) at individual expression levels did not indicate any significant correlation with PFS; in contrast, patients with both predicted 4p16 as well as relatively high expression of CCND3, in particular, indicated a cumulative unfavourable response with significantly poor PFS outcome, and (iv) patients with higher MAF expression levels showed poor outcome in terms of PFS. Based on above, and as expected, these set of genes can be used as important predictive biomarkers for prognosis of MM.