Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Angelica Macauda; Chiara Piredda; Alyssa I Clay-Gilmour; Juan Sainz; Gabriele Buda; Miroslaw Markiewicz; Torben Barington; Elad Ziv; Michelle A T Hildebrandt; Alem A Belachew; Judit Varkonyi; Witold Prejzner; Agnieszka Druzd-Sitek; John Spinelli; Niels Frost Andersen; Jonathan N Hofmann; Marek Dudziński; Joaquin Martinez-Lopez; Elzbieta Iskierka-Jazdzewska; Roger L Milne; Grzegorz Mazur; Graham G Giles; Lene Hyldahl Ebbesen; Marcin Rymko; Krzysztof Jamroziak; Edyta Subocz; Rui Manuel Reis; Ramon Garcia-Sanz; Anna Suska; Eva Kannik Haastrup; Daria Zawirska; Norbert Grzasko; Annette Juul Vangsted; Charles Dumontet; Marcin Kruszewski; Magdalena Dutka; Nicola J Camp; Rosalie G Waller; Waldemar Tomczak; Matteo Pelosini; Małgorzata Raźny; Herlander Marques; Niels Abildgaard; Marzena Wątek; Artur Jurczyszyn; Elizabeth E Brown; Sonja Berndt; Aleksandra Butrym; Celine M Vachon; Aaron D Norman; Susan L Slager; Federica Gemignani; Federico Canzian; Daniele Campa

doi:10.1002/ijc.33547

Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Int J Cancer. 2021 Jul 15;149(2):327-336. doi: 10.1002/ijc.33547. Epub 2021 Mar 30.

Authors

Angelica Macauda^{1

2}, Chiara Piredda², Alyssa I Clay-Gilmour³, Juan Sainz^{4

5}, Gabriele Buda⁶, Miroslaw Markiewicz⁷, Torben Barington⁸, Elad Ziv⁹, Michelle A T Hildebrandt¹⁰, Alem A Belachew¹⁰, Judit Varkonyi¹¹, Witold Prejzner¹², Agnieszka Druzd-Sitek¹³, John Spinelli^{14

15}, Niels Frost Andersen¹⁶, Jonathan N Hofmann¹⁷, Marek Dudziński¹⁸, Joaquin Martinez-Lopez¹⁹, Elzbieta Iskierka-Jazdzewska²⁰, Roger L Milne^{21

22

23}, Grzegorz Mazur²⁴, Graham G Giles^{21

22

23}, Lene Hyldahl Ebbesen¹⁶, Marcin Rymko²⁵, Krzysztof Jamroziak²⁶, Edyta Subocz²⁷, Rui Manuel Reis^{28

29}, Ramon Garcia-Sanz³⁰, Anna Suska³¹, Eva Kannik Haastrup³², Daria Zawirska³³, Norbert Grzasko^{34

35}, Annette Juul Vangsted³², Charles Dumontet³⁶, Marcin Kruszewski³⁷, Magdalena Dutka¹², Nicola J Camp³⁸, Rosalie G Waller³⁸, Waldemar Tomczak³⁹, Matteo Pelosini⁶, Małgorzata Raźny⁴⁰, Herlander Marques²⁹, Niels Abildgaard⁴¹, Marzena Wątek⁴², Artur Jurczyszyn³¹, Elizabeth E Brown⁴³, Sonja Berndt¹⁷, Aleksandra Butrym²⁴, Celine M Vachon⁴⁴, Aaron D Norman⁴⁴, Susan L Slager⁴⁴, Federica Gemignani², Federico Canzian¹, Daniele Campa²

Affiliations

¹ Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
² Department of Biology, University of Pisa, Pisa, Italy.
³ Department of Epidemiology & Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.
⁴ Genomic Oncology Area, GENYO. Centre for Genomics and Oncological Research: Pfizer, University of Granada/Andalusian Regional Government, Granada, Spain.
⁵ Hematology department, Virgen de las Nieves University Hospital, Granada, Spain.
⁶ Clinical and Experimental Medicine, Section of Hematology, University of Pisa, Pisa, Italy.
⁷ Department of Hematology and Bone Marrow Transplantation, SPSKM Hospital, Katowice, Poland.
⁸ Department of Clinical Immunology, Odense University Hospital, Odense, Denmark.
⁹ Department of Medicine, Division of General Internal Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA.
¹⁰ Department of Epidemiology, Division of Cancer Prevention and Population Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
¹¹ Third Department of Internal Medicine, Semmelweis University, Budapest, Hungary.
¹² Department of Hematology and Transplantation, Medical University of Gdansk, Gdansk, Poland.
¹³ Department of Lymphoid Malignacies, Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland.
¹⁴ Cancer Control Research, BC Cancer Agency, Vancouver, British Columbia, Canada.
¹⁵ School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.
¹⁶ Department of Hematology, Aarhus University Hospital, Aarhus, Denmark.
¹⁷ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
¹⁸ Department of Hematology, Institute of Medical Sciences, College of Medical Sciences, University of Rzeszow, Rzeszow, Poland.
¹⁹ Complutense University, CNIO, CIBERONC, Hospital 12 de Octubre, Madrid, Spain.
²⁰ Department of Haematology, Copernicus Memorial Hospital, Lodz, Poland.
²¹ Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.
²² Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
²³ Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
²⁴ Department of Internal and Occupational Diseases, Hypertension and Clinical Oncology, Wroclaw Medical University, Wroclaw, Poland.
²⁵ Department of Hematology, N. Copernicus Town Hospital, Torun, Poland.
²⁶ Department of Hematology, Institute of Hematology and Transfusion Medicine, Warsaw, Poland.
²⁷ Department of Haematology, Military Institute of Medicine, Warsaw, Poland.
²⁸ Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal.
²⁹ Molecular Oncology Research Center, Barretos, São Paulo, Brazil.
³⁰ Department of Hematology, University Hospital of Salamanca, IBSAL, Salamanca, Spain.
³¹ Department of Hematology, Jagiellonian University Medical College, Cracow, Poland.
³² Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
³³ Department of Hematology, University Hospital of Cracow, Cracow, Poland.
³⁴ Department of Experimental Hematooncolog, Medical University of Lublin, Lublin, Poland.
³⁵ Department of Hematology, St. John's Cancer Center, Lublin, Poland.
³⁶ Cancer Research Center of Lyon/Hospices Civils de Lyon, Lyon, France.
³⁷ Department of Hematology, University Hospital Bydgoszcz, Bydgoszcz, Poland.
³⁸ University of Utah, Salt Lake City, Utah, USA.
³⁹ Medical University of Lublin, Lublin, Poland.
⁴⁰ Department of Hematology, Rydygier Specialistic Hospital, Cracow, Poland.
⁴¹ Department of Hematology, Odense University Hospital, Odense, Denmark.
⁴² Hematology Clinic, Holycross Cancer Center, Kielce, Poland.
⁴³ Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
⁴⁴ Genetic Epidemiology and Risk Assessment Program, Mayo Clinic Comprehensive Cancer Center, and Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.

Abstract

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.

Keywords: eQTL; genetic polymorphisms; multiple myeloma; overall survival; progression-free survival.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Apyrase / genetics*
Female
Gene Expression Profiling / methods*
Genetic Association Studies
Germ-Line Mutation
Humans
Male
Middle Aged
Mitochondrial Proteins / genetics*
Multiple Myeloma / genetics
Multiple Myeloma / mortality*
Polymorphism, Single Nucleotide*
Quantitative Trait Loci*
RNA-Binding Proteins / genetics*
Survival Analysis

Substances

Mitochondrial Proteins
RNA-Binding Proteins
TBRG4 protein, human
Apyrase
ENTPD1 protein, human

Abstract

Publication types

MeSH terms

Substances

Grants and funding