Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients
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John Wiley & Sons
eQTLGenetic polymorphismsMultiple myelomaOverall survivalProgression-free survival
Macauda A, Piredda C, Clay- Gilmour AI, et al. Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients. Int. J. Cancer. 2021;1–10. [https://doi.org/10.1002/ijc.33547]
SponsorshipCanadian Institutes of Health Research (CIHR) 81274; Huntsman Cancer Institute Pilot Funds; Leukemia and Lymphoma Society 6067-09; United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) P30 CA016672 P30 CA042014 P30 CA13148 P50 CA186781 R01 CA107476 R01 CA134674 R01 CA168762 R01 CA186646 R01 CA235026 R21 CA155951 R25 CA092049 R25 CA47888 U54 CA118948; Utah Population Database, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah State Department of Health, University of Utah; VicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council 1074383 209057 396414; Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer Database; Mayo Clinic Cancer Center; University of Pisa; Helmholtz Association
Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10(-7) either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.