35 research outputs found
A polygenic risk score for multiple myeloma risk prediction
There is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53â4.69, p = 3.55 Ă 10â15 for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34â4.33, p = 1.62 Ă 10â13 for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population
Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients
Canadian Institutes of Health Research, Grant/
Award Number: 81274; Huntsman Cancer
Institute Pilot Funds; Leukemia Lymphoma
Society, Grant/Award Number: 6067-09; the
National Institute of Health/National Cancer
Institute, Grant/Award Numbers: 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, Grant/Award
Numbers: 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
and DKFZThe authors thank all site investigators that contributed to the studies
within the Multiple Myeloma Working Group (Interlymph Consortium),
staff involved at each site and, most importantly, the study participants
for their contributions that made our study possible. This work was partially
supported by intramural funds of University of Pisa and DKFZ. This
work was supported in part by the National Institute of Health/National
Cancer Institute (R25 CA092049, P30 CA016672, R01 CA134674, P30
CA042014, R01 CA186646, R21 CA155951, U54 CA118948, P30
CA13148, R25 CA47888, R01 CA235026, R01 CA107476, R01
CA168762, P50 CA186781 and the NCI Intramural Research Program),
Leukemia Lymphoma Society (6067-09), Huntsman Cancer Institute
Pilot Funds, Utah PopulationDatabase, Utah Cancer Registry, Huntsman
Cancer Center Support Grant, Utah StateDepartment of Health, University
of Utah, Canadian Institutes of Health Research (Grant number
81274), VicHealth, Cancer Council Victoria, Australian National Health
and Medical Research Council (Grants 209057, 396414, 1074383), Victorian
Cancer Registry, Australian Institute of Health and Welfare,
Australian National Death Index, Australian Cancer Database and the
Mayo Clinic Cancer Center.Open Access funding enabled and organized
by ProjektDEAL.The data that support the findings of this study are available on
request from the corresponding author. The data are not publicly
available due to privacy or ethical restrictions.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.Canadian Institutes of Health Research (CIHR)
81274Huntsman Cancer Institute Pilot FundsLeukemia and Lymphoma Society
6067-09United 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 CA118948Utah Population Database, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah State Department of Health, University of UtahVicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council
1074383
209057
396414Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer DatabaseMayo Clinic Cancer CenterUniversity of PisaHelmholtz Associatio
Polymorphisms within autophagy-related genes as susceptibility biomarkers for multiple myeloma: a meta-analysis of three large cohorts and functional characterization
Functional data used in this project have been meticulously catalogued and archived in the BBMRI-NL data infrastructure (https://hfgp.bbmri.nl/, accessed on 12 February 2020) using the MOLGENIS open-source platform for scientific data.Multiple myeloma (MM) arises following malignant proliferation of plasma cells in the
bone marrow, that secrete high amounts of specific monoclonal immunoglobulins or light chains,
resulting in the massive production of unfolded or misfolded proteins. Autophagy can have a
dual role in tumorigenesis, by eliminating these abnormal proteins to avoid cancer development,
but also ensuring MM cell survival and promoting resistance to treatments. To date no studies
have determined the impact of genetic variation in autophagy-related genes on MM risk. We
performed meta-analysis of germline genetic data on 234 autophagy-related genes from three independent study populations including 13,387 subjects of European ancestry (6863 MM patients and
6524 controls) and examined correlations of statistically significant single nucleotide polymorphisms
(SNPs; p < 1 Ă 10â9) with immune responses in whole blood, peripheral blood mononuclear
cells (PBMCs), and monocyte-derived macrophages (MDM) from a large population of healthy
donors from the Human Functional Genomic Project (HFGP). We identified SNPs in six loci, CD46,
IKBKE, PARK2, ULK4, ATG5, and CDKN2A associated with MM risk (p = 4.47 Ă 10â4â5.79 Ă 10â14).
Mechanistically, we found that the ULK4rs6599175 SNP correlated with circulating concentrations
of vitamin D3 (p = 4.0 Ă 10â4), whereas the IKBKErs17433804 SNP correlated with the number of
transitional CD24+CD38+ B cells (p = 4.8 Ă 10â4) and circulating serum concentrations of Monocyte
hemoattractant Protein (MCP)-2 (p = 3.6 Ă 10â4). We also found that the CD46rs1142469 SNP corre lated with numbers of CD19+ B cells, CD19+CD3â B cells, CD5+ IgDâ cells, IgMâ cells, IgDâIgMâ
cells, and CD4âCD8â PBMCs (p = 4.9 Ă 10â4â8.6 Ă 10â4
) and circulating concentrations of interleukin (IL)-20 (p = 0.00082). Finally, we observed that the CDKN2Ars2811710 SNP correlated with levels
of CD4+EMCD45RO+CD27â cells (p = 9.3 Ă 10â4
). These results suggest that genetic variants within
these six loci influence MM risk through the modulation of specific subsets of immune cells, as well
as vitamin D3â, MCP-2â, and IL20-dependent pathways.This work was supported by the European Unionâs Horizon 2020 research and innovation program, N° 856620 and by grants from the Instituto de Salud Carlos III and FEDER (Madrid, Spain; PI17/02256 and PI20/01845), ConsejerĂa de TransformaciĂłn EconĂłmica, Industria, Conocimiento y Universidades and FEDER (PY20/01282), from the CRIS foundation against cancer, from the Cancer Network of Excellence (RD12/10 Red de CĂĄncer), from the Dietmar Hopp Foundation and the German Ministry of Education and Science (BMBF: CLIOMMICS [01ZX1309]), and from National Cancer Institute of the National Institutes of Health under award numbers: R01CA186646, U01CA249955 (EEB).This work was also funded d by Portuguese National funds, through the Foundation for Science and Technology (FCT)âproject UIDB/50026/2020 and UIDP/50026/2020 and by the project NORTE-01-0145-FEDER-000055, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)
Polymorphisms within Autophagy-Related Genes as Susceptibility Biomarkers for Multiple Myeloma: A Meta-Analysis of Three Large Cohorts and Functional Characterization
Multiple myeloma (MM) arises following malignant proliferation of plasma cells in the bone marrow, that secrete high amounts of specific monoclonal immunoglobulins or light chains, resulting in the massive production of unfolded or misfolded proteins. Autophagy can have a dual role in tumorigenesis, by eliminating these abnormal proteins to avoid cancer development, but also ensuring MM cell survival and promoting resistance to treatments. To date no studies have determined the impact of genetic variation in autophagy-related genes on MM risk. We performed meta-analysis of germline genetic data on 234 autophagy-related genes from three independent study populations including 13,387 subjects of European ancestry (6863 MM patients and 6524 controls) and examined correlations of statistically significant single nucleotide polymorphisms (SNPs; p < 1 Ă 10â9) with immune responses in whole blood, peripheral blood mononuclear cells (PBMCs), and monocyte-derived macrophages (MDM) from a large population of healthy donors from the Human Functional Genomic Project (HFGP). We identified SNPs in six loci, CD46, IKBKE, PARK2, ULK4, ATG5, and CDKN2A associated with MM risk (p = 4.47 Ă 10â4â5.79 Ă 10â14). Mechanistically, we found that the ULK4rs6599175 SNP correlated with circulating concentrations of vitamin D3 (p = 4.0 Ă 10â4), whereas the IKBKErs17433804 SNP correlated with the number of transitional CD24+CD38+ B cells (p = 4.8 Ă 10â4) and circulating serum concentrations of Monocyte Chemoattractant Protein (MCP)-2 (p = 3.6 Ă 10â4). We also found that the CD46rs1142469 SNP correlated with numbers of CD19+ B cells, CD19+CD3â B cells, CD5+IgDâ cells, IgMâ cells, IgDâIgMâ cells, and CD4âCD8â PBMCs (p = 4.9 Ă 10â4â8.6 Ă 10â4) and circulating concentrations of interleukin (IL)-20 (p = 0.00082). Finally, we observed that the CDKN2Ars2811710 SNP correlated with levels of CD4+EMCD45RO+CD27â cells (p = 9.3 Ă 10â4). These results suggest that genetic variants within these six loci influence MM risk through the modulation of specific subsets of immune cells, as well as vitamin D3â, MCP-2â, and IL20-dependent pathways.This work was supported by the European Unionâs Horizon 2020 research and innovation program, N° 856620 and by grants from the Instituto de Salud Carlos III and FEDER (Madrid, Spain; PI17/02256 and PI20/01845), ConsejerĂa de TransformaciĂłn EconĂłmica, Industria, Conocimiento y Universidades and FEDER (PY20/01282), from the CRIS foundation against cancer, from the Cancer Network of Excellence (RD12/10 Red de CĂĄncer), from the Dietmar Hopp Foundation and the German Ministry of Education and Science (BMBF: CLIOMMICS [01ZX1309]), and from National Cancer Institute of the National Institutes of Health under award numbers: R01CA186646, U01CA249955 (EEB). This work was also funded d by Portuguese National funds, through the Foundation for Science and Technology (FCT)âproject UIDB/50026/2020 and UIDP/50026/2020 and by the project NORTE-01-0145-FEDER-000055, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).Peer reviewe