Extramedullary myeloma relapses

Gozzetti, Alessandro; Papini, Giulia; Defina, Marzia; Bocchia, Monica

doi:10.1007/s00277-012-1432-3

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…However, the prognosis of EMM patients is usually very poor, especially for patients with extramedullary relapse, which remains a major challenge for the treatment of MM patients even in the era of new drugs [ 8 , 12 , 18 , 19 ]. The standard RRMM treatment regimen based on proteasome inhibitors and immunomodulators does not significantly improve the prognosis of extramedullary relapse patients [ 9 , 11 , 12 , 20 ] because some studies believe that thalidomide and bortezomib can induce the dedifferentiation of bone marrow plasma cells and alter the expression of adhesion molecules, allowing myeloma clones to escape from the marrow microenvironment and thus facilitating extramedullary spread [ 21 ]. Although the second-generation proteasome inhibitor carfizomib has certain efficacy in patients with extramedullary relapse, it still cannot overcome its negative effects [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of High-Risk Extramedullary Relapse Factors in Newly Diagnosed MM Patients

Yue

Zheng

et al. 2022

Cancers

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Extramedullary relapse of multiple myeloma (MM) is often resistant to existing treatments, and has an extremely poor prognosis, but our understanding of extramedullary relapse is still limited. The incidence, clinical characteristics, impact on the prognosis of extramedullary relapse, and the risk factors for extramedullary relapse in NDMM patients were analyzed. Among the 471 NDMM patients, a total of 267 patients had disease relapse during follow-up, including 64 (24.0%) patients with extramedullary relapse. Extramedullary relapse was more common in patients with younger age, IgD subtype, elevated LDH, extensive osteolytic lesions, extramedullary involvement, and spleen enlargement at the time of MM diagnosis. Survival analysis showed that extramedullary relapse patients had significantly worse median OS than patients with relapse but without extramedullary involvement (30.8 months vs. 53.6 months, p = 0.012). Multivariate analysis confirmed that elevated LDH (OR = 2.09, p = 0.023), >2 osteolytic lesions (OR = 3.70, p < 0.001), extramedullary involvement (OR = 3.48, p < 0.001) and spleen enlargement (OR = 2.27, p = 0.011) at the time of MM diagnosis were independent risk factors for extramedullary relapse in NDMM patients. Each of the above four factors was assigned a value of 1 to form the extramedullary relapse prediction score, and the 3-year extramedullary relapse rates of patients in the 0–2 and 3–4 score groups were 9.0 % and 76.7 %, respectively. This study suggested that extramedullary relapse was associated with poor clinical characteristics and poor prognosis in NDMM patients. The extramedullary relapse prediction score model composed of LDH, osteolytic lesions, extramedullary involvement and spleen enlargement has a better ability to predict extramedullary relapse than the existing ISS and R-ISS stages.

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Section: Discussionmentioning

confidence: 99%

Analysis of High-Risk Extramedullary Relapse Factors in Newly Diagnosed MM Patients

Yue

Zheng

et al. 2022

Cancers

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“…Although several new drugs have been introduced into clinical practice, MM is still incurable with dismal clinical outcomes in MM patients [ 18 , 19 ]. Molecular risk stratification basing on hub gene expression of MM has opened an avenue for clinicians to conduct personalized medicine [ 20 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

An interactive nomogram based on clinical and molecular signatures to predict prognosis in multiple myeloma patients

Liu

Dai

et al. 2021

Aging

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Although novel drugs and treatments have been developed and improved, multiple myeloma (MM) is still recurrent and difficult to cure. In the present study, the magenta module containing 400 hub genes was determined from the training dataset of GSE24080 through weighted gene co-expression network analysis (WGCNA). Then, using the least absolute shrinkage and selection operator (Lasso) analysis, a fifteen-gene signature was firstly selected and the predictive performance for overall survival (OS) was favorable, which was identified by Receiver Operating Characteristic (ROC) curves. The risk score model was constructed based on survival-associated fifteen genes from the Lasso model, which classified MM patients into high-risk and low-risk groups. Areas under the curve (AUC) of ROC curve and log-rank test showed that the high-risk group was correlated to the dismal survival outcome of MM patients, which was also identified in testing dataset of GSE9782. The calibration plot, the AUC value of the ROC curve and Concordance-index showed that the interactive nomogram with risk score could favorably predict the probability of multi-year OS of MM patients. Therefore, it may help clinicians make a precise therapeutic decision based on the easy-to-use tool of the nomogram.

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“…As stated above, although several novel agents for patients with MM have been introduced into clinical practice, the disease remained incurable, and the clinical outcome of patients with MM is still poor (2, 3). Thus, it is of vital importance to develop such molecular biomarkers or signature that are significantly correlated with the clinicopathological features and clinical outcome of patients with MM.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a 9-Gene Prognostic Signature in Patients With Multiple Myeloma

Yin

Meng

et al. 2019

Front. Oncol.

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Background: Multiple myeloma (MM) is one of the most common types of hematological malignance, and the prognosis of MM patients remains poor.Objective: To identify and validate a genetic prognostic signature in patients with MM.Methods: Co-expression network was constructed to identify hub genes related with International Staging System (ISS) stage of MM. Functional analysis of hub genes was conducted. Univariate Cox proportional hazard regression analysis was conducted to identify genes correlated with the overall survival (OS) of MM patients. Least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model was used to minimize overfitting and construct a prognostic signature. The prognostic value of the signature was validated in the test set and an independent validation cohort.Results: A total of 758 hub genes correlated with ISS stage of MM patients were identified, and these hub genes were mainly enriched in several GO terms and KEGG pathways involved in cell proliferation and immune response. Nine hub genes (HLA-DPB1, TOP2A, FABP5, CYP1B1, IGHM, FANCI, LYZ, HMGN5, and BEND6) with non-zero coefficients in the LASSO Cox regression model were used to build a 9-gene prognostic signature. Relapsed MM and ISS stage III MM was associated with high risk score calculated based on the signature. Patients in the 9-gene signature low risk group was significantly associated with better clinical outcome than those in the 9-gene signature high risk group in the training set, test, and validation set.Conclusions: We developed a 9-gene prognostic signature that might be an independent prognostic factor in patients with MM.

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Extramedullary myeloma relapses

Cited by 3 publications

References 4 publications

Analysis of High-Risk Extramedullary Relapse Factors in Newly Diagnosed MM Patients

Analysis of High-Risk Extramedullary Relapse Factors in Newly Diagnosed MM Patients

An interactive nomogram based on clinical and molecular signatures to predict prognosis in multiple myeloma patients

Development and Validation of a 9-Gene Prognostic Signature in Patients With Multiple Myeloma

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