Gender Disparities in the Tumor Genetics and Clinical Outcome of Multiple Myeloma
ABSTRACT Several cancer types have differences in incidence and clinical outcome dependent on gender, but these are not well described in myeloma. The aim of this study was to characterize gender disparities in myeloma.
We investigated the association of gender with the prevalence of tumor genetic lesions and the clinical outcome of 1,960 patients enrolled in the phase III clinical trial MRC Myeloma IX. Genetic lesions were characterized by FISH.
Disparities were found in the prevalence of primary genetic lesions with immunoglobulin heavy chain gene (IGH) translocations being more common in women (50% of female patients vs. 38% of male patients, P < 0.001) and hyperdiploidy being more common in men (50% female vs. 62% male, P < 0.001). There were also differences in secondary genetic events with del(13q) (52% female vs. 41% male, P < 0.001) and +1q (43% female vs. 36% male, P = 0.042) being found more frequently in female myeloma patients. Female gender was associated with inferior overall survival (median: 44.8 months female vs. 49.9 months male, P = 0.020).
We found gender-dependent differences in the prevalence of the primary genetic events of myeloma, with IGH translocations being more common in women and hyperdiploidy more common in men. This genetic background may impact subsequent genetic events such as +1q and del(13q), which were both more frequent in women. The higher prevalence of lesions associated with poor prognosis in the female myeloma population, such as t(4;14), t(14;16) and +1q, may adversely affect clinical outcome.
These differences suggest that gender influences the primary genetic events of myeloma.
- SourceAvailable from: Lauren I Aronson[Show abstract] [Hide abstract]
ABSTRACT: Myeloma is a malignancy of the antibody-producing plasma cells and, as such, these cells synthesize large quantities of unfolded or misfolded immunoglobulin. The build-up of excess protein triggers a number of downstream signal transduction cascades, including endoplasmic reticulum stress and autophagy. As a result, myeloma cells are uniquely reliant on these and other protein handling pathways for their survival. Strategies aimed at targeting this vulnerability have proved successful with the proteasome inhibitor, bortezomib, already licensed for clinical use. In addition to the proteasome, various other points within the protein handling pathways are also the subject of drug discovery projects, with some already progressing into clinical trials. These include compounds directed against heat shock proteins, the unfolded protein response and pathways both upstream and downstream of the proteasome. More recently, the role of autophagy has been recognized in myeloma. In this review, we discuss the various pathways used by myeloma cells for survival, with particular emphasis on the emerging role and conundrum of autophagy, as well as highlighting pre-clinical research on novel inhibitors targeting protein handling pathways.Haematologica 05/2012; 97(8):1119-30. DOI:10.3324/haematol.2012.064923 · 5.87 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Myeloma develops due to the accumulation of multiple pathological genetic events, many of which have been defined. Hyperdiploidy and reciprocal translocations centered on the immunoglobulin heavy chain variable region constitute primary genetic lesions. These primary lesions co-operate with secondary genetic events including chromosomal deletions and gains, gene mutations and epigenetic modifiers such as DNA methylation to produce the malignant phenotype of myeloma. Some of these events have been linked with distinct clinical outcome and can be used to define patient groups. This review explores the molecular biology of myeloma and identifies how genetic lesions can be used to define high- and low-risk patient groups, and also defines potential targets for therapy. The authors also explore how this information can be used to guide therapeutic decision-making and the design and interpretation of clinical trials, both now and in the future.Expert Review of Hematology 12/2012; 5(6):603-617. DOI:10.1586/ehm.12.51 · 2.14 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: BACKGROUND: Risk of developing multiple myeloma (MM) rises with age and is greater among men and blacks than among women and whites, respectively, and possibly increased among obese persons. Other risk factors remain poorly understood. By pooling data from two complementary epidemiologic studies, we assessed whether obesity, smoking, or alcohol consumption alters MM risk and whether female reproductive history might explain the lower occurrence of MM in females than in males. METHODS: The Los Angeles County MM Case-Control Study (1985-1992) included 278 incident cases and 278 controls, matched on age, sex, race, and neighborhood of residence at case's diagnosis. We estimated MM risk using conditional logistic regression to calculate odds ratios (ORs) and 95 % confidence intervals (CIs). In the prospective California Teachers Study (CTS), 152 women were diagnosed with incident MM between 1995 and 2009; we calculated hazard ratios using Cox proportional hazards analysis. Data from the two studies were pooled using a stratified, nested case-control sampling scheme (10:1 match) for the CTS; conditional logistic regression among 430 cases and 1,798 matched controls was conducted. RESULTS: Obesity and smoking were not associated with MM risk in the individual or combined studies. Alcohol consumption was associated with decreased MM risk among whites only (pooled OR = 0.66, 95 % CI = 0.49-0.90) for ever versus never drinking. Higher gravidity and parity were associated with increased MM risk, with pooled ORs of 1.38 (95 % CI = 1.01-1.90) for ≥3 versus 1-2 pregnancies and 1.50 (95 % CI = 1.09-2.06) for ≥3 versus 1-2 live births. CONCLUSIONS: Female reproductive history may modestly alter MM risk, but appears unlikely to explain the sex disparity in incidence. Further investigation in consortial efforts is warranted.Cancer Causes and Control 04/2013; DOI:10.1007/s10552-013-0206-0 · 2.96 Impact Factor