Impact of Genetic Abnormalities on the Prognoses and Clinical Parameters of Patients with Multiple Myeloma

Abstract

BackgroundWe reviewed patients with multiple myeloma (MM) in order to assess the incidence of genetic abnormalities and their associations with clinical parameters, risk groups, and prognosis.MethodsA total of 130 patients with MM were enrolled. The incidences of genetic abnormalities were determined in all patients. The relationships of the genetic abnormalities and clinical parameters were investigated. In addition, a survival analysis was performed.ResultsAbnormal karyotypes were detected in 42.3% (N=55) of the patients, and this was increased to 63.1% (N=82) after including the results determined with interphase FISH. Hypodiploidy was observed in 7.7% (N=10) of the patients, and all were included in the group with complex karyotypes (30.8%, N=40). The 14q32 rearrangements were detected in 29.2% (N=38) of the patients, and these most commonly included t(11;14), which was followed by t(4;14) and t(14;16) (16.2%, 11.5%, and 0.8%, respectively). Abnormal karyotypes and complex karyotypes were associated with disease progression markers, including low hemoglobin levels, low platelet counts, high plasma cell burden, high β2-microglobulin, and high international staging system stages. A high free light chain (FLC) ratio and FLC difference were associated with abnormal karyotypes, complex karyotypes, and higher plasma cell burden. Hypodiploidy and low platelet counts were significant independent prognostic factors and were more important in patient outcome than any single abnormality.ConclusionsGenetic abnormalities were associated with disease progression markers and prognosis of MM patients.

Interphase Fluorescence in Situ Hybridization Analysis of Cytogenetic Abnormalities in Egyptian Patients with Plasma Cell Myeloma

“…It stratifies patients by their albumin and β2-microglobulin levels [13] . Regarding patients' clinical staging in the present study according to the international staging system, 7 patients (22.6%) were in stage Ι, which was nearly similar to Jekarl et al (2013) (21.5%) [4] , however it was lower than that reported in the Chinese study by Chen et al (2007) (45.8%) [3] , and Shin et al (2015) (32.4%) [13] in their recent Korean study. On the contrary, it was higher than that detected by Liu et al (2015) (10.5%) [14] whose…”

“…Regarding stage II, 10 patients (32.3%) were detected in this study which was nearly similar to that reported by Chen et al (2007) (33.3%) [3] , while it was marginally lower than that observed by Liu et al (2015) (37.1%) [14] , Shin et al (2015) (39.7%) [13] , and Jekarl et al (2013) (43.9%) [4] . In the current study, 14 patients (45.2%) were in stage III, which was higher than that reported by Jekarl et al…”

“…(2013) (34.6%) [4], Shin et al (2015) (27.9%) [13] , and Chen et al (2007) (20.8%) [3] , while it was less than that mentioned by Liu et al (2015) (52.4%) [14] .…”

“…Because of relatively low rate of proliferation of plasma cells in plasma cell myeloma, many genetic abnormalities couldn't be detected by conventional cytogenetics. Also some of the most important abnormalities may be cryptic even among patients harboring abnormal karyotypes, however interphase fluorescence in situ hybridization (iFISH) is independent of plasma cell division overcoming the limitations of conventional cytogenetics and thus has higher detection rate of these genetic abnormalities [4] .…”

Proteomic Characterization of Plasma Cells from Patients with Multiple Myeloma

“…However, to date the advance of proteomics in MM research lags significantly behind the other mentioned technologies. Proteomic studies in myeloma has been largely limited to serum profiling [10][11][12][13]. The clinical applicability of the few published reports on cellular profiling of MM cells are hampered at least in part by the following: 1) the different techniques and statistical platforms used by different investigators, hence not allowing cross-validation; 2) the lack of standard procedures for sample collection, storage, and processing, hence causing background noises and lowered sensitivity of the test; and 3) limited sample numbers, hence lacking statistical power for clinical correlation analysis.…”