Characteristics of white blood cell count in acute lymphoblastic leukemia: A COST LEGEND phenotype-genotype study.
acute lymphoblastic leukemia (ALL)
genome-wide association studies (GWAS)
genotype
spline functions
white blood cell count (WBC)
Journal
Pediatric blood & cancer
ISSN: 1545-5017
Titre abrégé: Pediatr Blood Cancer
Pays: United States
ID NLM: 101186624
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
revised:
20
12
2021
received:
13
09
2021
accepted:
31
12
2021
pubmed:
23
3
2022
medline:
27
4
2022
entrez:
22
3
2022
Statut:
ppublish
Résumé
White blood cell count (WBC) as a measure of extramedullary leukemic cell survival is a well-known prognostic factor in acute lymphoblastic leukemia (ALL), but its biology, including impact of host genome variants, is poorly understood. We included patients treated with the Nordic Society of Paediatric Haematology and Oncology (NOPHO) ALL-2008 protocol (N = 2347, 72% were genotyped by Illumina Omni2.5exome-8-Bead chip) aged 1-45 years, diagnosed with B-cell precursor (BCP-) or T-cell ALL (T-ALL) to investigate the variation in WBC. Spline functions of WBC were fitted correcting for association with age across ALL subgroups of immunophenotypes and karyotypes. The residuals between spline WBC and actual WBC were used to identify WBC-associated germline genetic variants in a genome-wide association study (GWAS) while adjusting for age and ALL subtype associations. We observed an overall inverse correlation between age and WBC, which was stronger for the selected patient subgroups of immunophenotype and karyotypes (ρ These results indicate that host genome variants do not strongly influence WBC across ALL subsets, and future studies of why some patients are more prone to hyperleukocytosis should be performed within specific ALL subsets that apply more complex analyses to capture potential germline variant interactions and impact on WBC.
Sections du résumé
BACKGROUND
White blood cell count (WBC) as a measure of extramedullary leukemic cell survival is a well-known prognostic factor in acute lymphoblastic leukemia (ALL), but its biology, including impact of host genome variants, is poorly understood.
METHODS
We included patients treated with the Nordic Society of Paediatric Haematology and Oncology (NOPHO) ALL-2008 protocol (N = 2347, 72% were genotyped by Illumina Omni2.5exome-8-Bead chip) aged 1-45 years, diagnosed with B-cell precursor (BCP-) or T-cell ALL (T-ALL) to investigate the variation in WBC. Spline functions of WBC were fitted correcting for association with age across ALL subgroups of immunophenotypes and karyotypes. The residuals between spline WBC and actual WBC were used to identify WBC-associated germline genetic variants in a genome-wide association study (GWAS) while adjusting for age and ALL subtype associations.
RESULTS
We observed an overall inverse correlation between age and WBC, which was stronger for the selected patient subgroups of immunophenotype and karyotypes (ρ
CONCLUSION
These results indicate that host genome variants do not strongly influence WBC across ALL subsets, and future studies of why some patients are more prone to hyperleukocytosis should be performed within specific ALL subsets that apply more complex analyses to capture potential germline variant interactions and impact on WBC.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e29582Informations de copyright
© 2022 The Authors. Pediatric Blood & Cancer published by Wiley Periodicals LLC.
Références
Pui C-H, Yang JJ, Hunger SP, et al. Childhood acute lymphoblastic leukemia: progress through collaboration. J Clin Oncol. 2015;33:2938-2948.
Toft N, Birgens H, Abrahamsson J, et al. Risk group assignment differs for children and adults 1-45 yr with acute lymphoblastic leukemia treated by the NOPHO ALL-2008 protocol. Eur J Haematol. 2013;90:404-412.
Vaitkevičienė G, Forestier E, Hellebostad M, et al. High white blood cell count at diagnosis of childhood acute lymphoblastic leukaemia: biological background and prognostic impact. Results from the NOPHO ALL-92 and ALL-2000 studies. Eur J Haematol. 2011;86:38-46.
Smith M, Arthur D, Camitta B, et al. Uniform approach to risk classification and treatment assignment for children with acute lymphoblastic leukemia. J Clin Oncol. 1996;14:18-24.
Amin HM, Yang Y, Shen Y, et al. Having a higher blast percentage in circulation than bone marrow: clinical implications in myelodysplastic syndrome and acute lymphoid and myeloid leukemias. Leukemia. 2005;19:1567-1572.
Enshaei A, O'Connor D, Bartram J, et al. A validated novel continuous prognostic index to deliver stratified medicine in pediatric acute lymphoblastic leukemia. Blood. 2020;135:1438-1446.
Pain management. In: Pui C-H, ed. Childhood Leukemias. 3rd ed. Cambridge University Press; 2012. https://doi.org/10.1017/CBO9780511977633
Forestier E, Schmiegelow K, Nordic Society of Paediatric Haematology and Oncology NOPHO. The incidence peaks of the childhood acute leukemias reflect specific cytogenetic aberrations. J Pediatr Hematol Oncol. 2006;28:486-495.
Visscher PM, Wray NR, Zhang Q, et al. 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101:5-22.
Sud A, Kinnersley B, Houlston RS, Genome-wide association studies of cancer: current insights and future perspectives. Nat Rev Cancer. 2017;17:692-704.
McCarthy MI, Abecasis GR, Cardon LR, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356-369.
Bao EL, Cheng AN, Sankaran VG. The genetics of human hematopoiesis and its disruption in disease. EMBO Mol Med. 2019;11:e10316.
Vuckovic D, Bao EL, Akbari P, et al. The polygenic and monogenic basis of blood traits and diseases. Cell. 2020;182:1214-1231.e11.
Crosslin DR, McDavid A, Weston N, et al. Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network. Hum Genet. 2012;131:639-652.
Schmiegelow K. Nordic Society of Paediatric Haematology and Oncology (NOPHO) ALL 2008 final protocol version 3a. Treatment protocol for children (1.0-17.9 years of age) and young adults (18-45 years of age) with acute lymphoblastic leukemia. Nordic Society of Paediatric Haematology and Oncology; 2008. Accessed November 26, 2020. https://www.nopho.org
R Core Team. R: A Language and Environment for Statistical Computing. R Core Team; 2021.
Josse J, Husson F, missMDA: a package for handling missing values in multivariate data analysis. J Stat Softw. 2016;70:1-31. https://doi.org/10.18637/jss.v070.i01
Rayner W Strand home: genotyping chips strand and build files. Accessed April 22, 2020. https://www.well.ox.ac.uk/~wrayner/strand/index.html
Anderson CA, Pettersson FH, Clarke GM, et al. Data quality control in genetic case-control association studies. Nat Protoc. 2010;5:1564-1573.
Chang CC, Chow CC, Tellier LC, et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.
Gauthier J, Wu QV, Gooley TA, Cubic splines to model relationships between continuous variables and outcomes: a guide for clinicians. Bone Marrow Transplant. 2020;55:675-680.
James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning. Springer US; 2021. https://doi.org/10.1007/978-1-0716-1418-1
Hjalgrim LL, Rostgaard K, Schmiegelow K, et al. Age- and sex-specific incidence of childhood leukemia by immunophenotype in the Nordic countries. J Natl Cancer Inst. 2003;95:1539-1544.
Iacobucci I, Mullighan CG, Genetic basis of acute lymphoblastic leukemia. J Clin Oncol. 2017;35:975-983.
Toft N, Birgens H, Abrahamsson J, et al. Results of NOPHO ALL2008 treatment for patients aged 1-45 years with acute lymphoblastic leukemia. Leukemia. 2018;32:606-615.
Hu W, Zhang P, Su Q, et al. Peripheral leukocyte counts vary with lipid levels, age and sex in subjects from the healthy population. Atherosclerosis. 2020;308:15-21.
Turner S. qqman: Q-Q and Manhattan plots for GWAS data. 2017.
McLaren W, Gil L, Hunt SE, et al. The Ensembl variant effect predictor. Genome Biol. 2016;17:122.
Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54:1.30.1-1.30.33.
Lonsdale J, Thomas J, Salvatore M, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580-585.
Aguet F, Anand S, Ardlie KG, et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318-1330.
Boyle AP, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790-1797.
Dong S, Boyle AP. Predicting functional variants in enhancer and promoter elements using RegulomeDB. Hum Mutat. 2019;40:1292-1298.
Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Nat Acad Sci U S A. 2005;102:15545-15550.
Liberzon A, Subramanian A, Pinchback R, et al. Molecular Signatures Database (MSigDB) 3.0. Bioinformatics. 2011;27:1739-1740.
Liberzon A, Birger C, Thorvaldsdóttir H, et al. The Molecular Signatures Database hallmark gene set collection. Cell Syst. 2015;1:417-425.
Pathway Solutions. Accessed May 19, 2021. https://www.pathway.jp/
Reactome Pathway Database. Accessed May 19, 2021. https://reactome.org/
Godec J, Tan Y, Liberzon A, et al. Compendium of immune signatures identifies conserved and species-specific biology in response to inflammation. Immunity. 2016;44:194-206.
Pui CH, Nichols KE, Yang JJ. Somatic and germline genomics in paediatric acute lymphoblastic leukaemia. Nat Rev Clin Oncol. 2019;16:227-240.
GSE39820_CTRL_VS_TGFBETA3_IL6_IL23A_CD4_TCELL_DN. Broad Institute Inc. Accessed May 19, 2021. http://www.gsea-msigdb.org/gsea/msigdb/geneset_page.jsp?geneSetName=GSE39820_CTRL_VS_TGFBETA3_IL6_IL23A_CD4_TCELL_DN
Lee Y, Awasthi A, Yosef N, et al. Induction and molecular signature of pathogenic TH17 cells. Nat Immunol. 2012;13:991-999.
Donadieu J, Auclerc MF, Baruchel A, et al. Prognostic study of continuous variables (white blood cell count, peripheral blast cell count, haemoglobin level, platelet count and age) in childhood acute lymphoblastic leukaemia. Analysis of a population of 1545 children treated by FRALLE. Br J Cancer. 2000;83:1617-1622. https://doi.org/10.1054/bjoc.2000.1504
Chmielewski PP, Strzelec B. Elevated leukocyte count as a harbinger of systemic inflammation, disease progression, and poor prognosis: a review. Folia Morphol (Warsz). 2018;77:171-178.
Hulstaert F, Hannet I, Deneys V, et al. Age-related changes in human blood lymphocyte subpopulations: II. Varying kinetics of percentage and absolute count measurements. Clin Immunol Immunopathol. 1994;70:152-158.
Valiathan R, Ashman M, Asthana D. Effects of ageing on the immune system: infants to elderly. Scand J Immunol. 2016;83:255-266.
Vrooman LM, Blonquist TM, Harris MH, et al. Refining risk classification in childhood b acute lymphoblastic leukemia: results of DFCI ALL consortium protocol 05-001. Blood Adv. 2018;2:1449-1458.
Schmiegelow K, Forestier E, Hellebostad M, et al. Long-term results of NOPHO ALL-92 and ALL-2000 studies of childhood acute lymphoblastic leukemia. Leukemia. 2010;24:345-354.
Li J, Dai Y, Wu L, et al. Emerging molecular subtypes and therapeutic targets in B-cell precursor acute lymphoblastic leukemia. Front Med. 2021;15:347-371. https://doi.org/10.1007/s11684-020-0821-6
Lilljebjörn H, Fioretos T. New oncogenic subtypes in pediatric B-cell precursor acute lymphoblastic leukemia. Blood. 2017;130:1395-1401.
Storti F, Moorman AV. New and emerging prognostic and predictive genetic biomarkers in B-cell precursor acute lymphoblastic leukemia. Haematologica. 2016;101:407-416.
Cipriano R, Miskimen KLS, Bryson BL, et al. Conserved oncogenic behavior of the FAM83 family regulates MAPK signaling in human cancer. Mol Cancer Res. 2014;12:1156-1165.
Snijders AM, Lee SY, Hang B, Hao W, Bissell MJ, Mao JH. FAM83 family oncogenes are broadly involved in human cancers: an integrative multi-omics approach. Mol Oncol. 2017;11:167-179.
Maurice D, Costello P, Sargent M, Treisman R. ERK signaling controls innate-like CD8+ T cell differentiation via the ELK4 (SAP-1) and ELK1 transcription factors. J Immunol. 2018;201:1681-1691.
Hao P, Yu J, Ward R, et al. Eukaryotic translation initiation factors as promising targets in cancer therapy. Cell Commun Signal. 2020;18:175.
Felipe-Abrio B, Carnero A. The tumor suppressor roles of MYBBP1A, a major contributor to metabolism plasticity and stemness. Cancers (Basel). 2020;12:254.
Brotto DB, Diogenes Siena AD, de Barros II, et al. Contributions of HOX genes to cancer hallmarks: enrichment pathway analysis and review. Tumor Biol. 2020;42:1010428320918050. https://doi.org/10.1177/1010428320918050
Ali MU, Ur Rahman MS, Jia Z, Jiang C. Eukaryotic translation initiation factors and cancer. Tumor Biol. 2017;39:1010428317709805. https://doi.org/10.1177/1010428317709805
Alharbi RA, Pettengell R, Pandha HS, Morgan R. The role of HOX genes in normal hematopoiesis and acute leukemia. Leukemia. 2012;27:1000-1008.
Bhatlekar S, Fields JZ, Boman BM. Role of HOX genes in stem cell differentiation and cancer. Stem Cells Int. 2018;2018:3569493.
Lawrence HJ, Fishbach NA, Largman C. HOX genes: not just myeloid oncogenes any more. Leukemia. 2005;19:1328-1330.
Luo Z, Rhie SK, Farnham PJ. The enigmatic HOX genes: can we crack their code? Cancers (Basel). 2019;11:323.
Gough SM, Slape CI, Aplan PD. NUP98 gene fusions and hematopoietic malignancies: common themes and new biologic insights. Blood. 2011;118:6247-6257.
Wu Y, Zhang L, Wang Y, et al. Long noncoding RNA HOTAIR involvement in cancer. Tumor Biol. 2014;35:9531-9538.
Deng Y, Cai S, Shen J, Peng H. Tetraspanins: novel molecular regulators of gastric cancer. Front Oncol. 2021;11:2389.
Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer; 2009.
Balding DJ, A tutorial on statistical methods for population association studies. Nat Rev Genet. 2006;7:781-791.
Nicholls HL, John CR, Watson DS, et al. Reaching the end-game for GWAS: machine learning approaches for the prioritization of complex disease loci. Front Genet. 2020;11:350.
Ho DSW, Schierding W, Wake M, Saffery R, O'Sullivan J. Machine learning SNP based prediction for precision medicine. Front Genet. 2019;10:267.
Upstill-Goddard R, Eccles D, Fliege J, Collins A. Machine learning approaches for the discovery of gene-gene interactions in disease data. Brief Bioinform. 2013;14:251-260.