SECURE: A Prospective Long-term Observational Study in Patients With Monoclonal Gammopathy of Undetermined Significance
Study Details
Study Description
Brief Summary
Multiple Myeloma (MM) is a rare blood cancer affecting over 5000 people a year in the UK. All cases of myeloma start with a condition called monoclonal gammopathy of undetermined significance (MGUS). MGUS occurs in approximately 3.2% of people aged 50 and over. Only a small proportion of these people - around 1% each year - will develop myeloma. Most people with MGUS have no symptoms, but a small number of people will suffer complications. This group are referred to as having monoclonal gammopathy of clinical significance (MGCS).
People with myeloma frequently experience long delays in diagnosis; the delays are longer than for any other cancer. Although we know that MGUS leads to myeloma, most cases of MGUS are only found 'incidentally' when the person is having blood tests for something else. And the people who have MGUS do not have consistent testing or follow up. This situation means that 80 - 90% of people who are diagnosed with myeloma did not have an earlier MGUS diagnosis.
Earlier diagnosis of myeloma might be possible with better understanding MGUS and how it should be monitored. The SECURE study will help with this. It will help confirm the rate at which people with MGUS progress to a diagnosis of myeloma. It will further understanding of screening, diagnosis, and monitoring patterns of people with MGUS and MGCS in the UK.
The study aims to find out more about the role of family history and demographic factors in the development of MGUS. It will also find out more about the psychological impact of an MGUS diagnosis and individual quality of life.
Patients with MGUS will be identified by their clinical care team and invited to participate in the SECURE study. Participants will be required to answer surveys and questionnaires annually for a period of 5 years or until their disease changes. The study will recruit participants from 20 NHS sites in the UK. Some will be asked to provide blood samples. SECURE is funded by Cancer Research UK (CRUK) and the National Institute for Health Research (NIHR).
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
|
Detailed Description
Myeloma affects 5820 people/year in the UK and is the advanced stage of a clonal plasma cell disorder with a distinct precursor state, termed monoclonal gammopathy of undetermined significance (MGUS). The term monoclonal gammopathy refers to the aberrant amounts of monoclonal immunoglobulin produced by the dysregulated plasma cell clone. MGUS is heterogenous in its clinical presentation, with varying levels of both aberrant and suppression of normal immunoglobulins. Myeloma is the only clinical state currently offered therapy, although a minority of MGUS patients experience complications such as amyloidosis, kidney disorders, osteoporosis, and skin and nervous system manifestations. MGUS patients with these clinical complications are referred to as having monoclonal gammopathy of clinical significance (MGCS). In MGCS, the morbidity is driven by secreted protein rather than clone size which often is significantly smaller than in myeloma patients. Myeloma care costs are substantial relative to the overall cancer chemotherapy spend worldwide. Earlier detection is a high priority for patients and improves survival: 84% of people with myeloma survive for >5 years if diagnosed at the earliest stage, compared with only 26% if diagnosed at advanced stage. Despite the widespread availability of diagnostic serological techniques, myeloma is most frequently diagnosed late (>3-6 months post symptom presentation) and has the longest diagnostic delay of any cancer, with emergency presentations in >30% of newly diagnosed myeloma patients who have shortened survival. Most avoidable delays occur in primary care for reasons including inconsistent MGUS testing and follow-up, highlighting the need for improved connectivity between primary care (screening), immunology (initial investigations) and haematology (ongoing management) for effective diagnosis and management of myeloma and precursor states. Because MGUS precedes all myelomas, an early diagnosis strategy is to regularly monitor people with MGUS for progression to myeloma. Progression risk is ~1%/year with a general MGUS population prevalence of 3.2% in individuals >50 years. Unfortunately, MGUS is often diagnosed incidentally and 80-90% of myelomas are diagnosed without first receiving an MGUS diagnosis. Given population-level MGUS screening would be impractical and expensive, research is required to understand clinical symptoms. However, the need to regularly monitor a higher number of patients with MGUS would place a huge burden on GPs. There is a lack of well-defined prediction models for the MGUS-MGCS/ myeloma transition that can be applied in clinical care. Although the size of the aberrant monoclonal protein and subtype (non-IgG) does broadly prognosticate progression to myeloma, only 20-30% of MGUS patients belong to this group.
Further, risk factors for MGUS-MGCS/myeloma progression have been difficult to define, leading to largely non-standardised approaches to detection, risk stratification and ongoing monitoring, contributing to the diagnostic delay. Patients presenting with myeloma report bone pain as the most common symptom at diagnosis and >80% have bone lesions on imaging at diagnosis. Patients diagnosed with MGUS show significantly higher incidence of death due to co-morbidities such as fractures (including all hospital-related morbidities from long-term hospital admission such as hospital-acquired infection), thrombi formation, organ failure and infection, compared with non-MGUS controls. Further, >18% of MGUS patients incidentally diagnosed and with no prior history of osteoporosis will suffer from a vertebral fracture.
This project is an observational study, whereby investigators hope to understand, via qualitative analysis, the existing screening and monitoring patterns, along with current routes for MGUS and MGCS diagnosis, in the UK. Investigators also hope to improve the understanding of demographic associations and family linkage, as there has been recent evidence that supports higher risk and earlier MGUS progression in Black people and evidence of higher risk in those with immediate relatives with the disease.
Furthermore, as there is limited information available to understand the psychological needs after diagnosis and during the progression of the disease, the study will also focus on quality of life of patients and requirements for psychological services provided to participants. Investigators hope to use both standardised and non-standardised scales during baseline and annual follow-ups.
Metabolomics studies will be undertaken in the Phenome Centre-Birmingham (PC-B); a £8M facility applied for the large scale targeted and untargeted study of metabolites present in human biofluids and tissues, with a significant focus on precision/stratified medicine. A number of studies have applied metabolomics to the study of plasma cell dyscrasias. A 2013 study used Proton NMR-based metabolite analyses of archived serial paired serum and urine samples from MM patients at different stages of disease. The study showed discrimination between active disease at diagnosis, remission and relapsed disease and identified elevated acetyl carnitine as a novel marker of active disease. A more recent study identified that elevated levels of 2-hydroxyglutarate (2-HG) in the blood are associated with higher levels of c-MYC expression in MM and a shorter time to progression. More recently we have analysed serial samples obtained in Birmingham from 12 MGUS patients before and after their progression to MM and in a second cohort, serial samples from MM patients at diagnosis (before treatment), during treatment and in remission. Statistical analysis identified a number of metabolites which were altered in their relative concentration between pre- and post-MM diagnosis. For example, serum sphingosine-1-phosphate (S1P) levels were higher in MGUS individuals prior to their progression to MM (p<0.05, mean fold change of 7.5) and this change in S1P levels was progressively reversed during MM therapy and in first remission. SECURE study serum samples will be analysed using ultra-performance liquid chromatography-mass spectrometry (Ultimate3000 UPLC system coupled to an electrospray ionisation Q Exactive Plus mass spectrometer).
Characterisation of germline genetic variants in study participants will be undertaken using methodology that is state-of-the art at the end of the planned observation period to identify variants and risk scores associated with and potentially predictive of development of MGUS or progression to MM. The myeloma group in the Division of Genetics and Epidemiology at The Institute of Cancer Research (ICR) has been pioneering the discovery of germline genetic risk variants for MM, their functional annotation, as well as development of polygenic risk scores. An ongoing program of activities is focused on functional characterisation, in particular also investigating the interplay with non-coding mutations in the tumour genome, which will inform interpretation of findings generated via SECURE and support development of individualised risk stratification tools.
Colleagues from the Mayo clinic have shown post translational modification of light chains is a biomarker of progression from MGUS to myeloma, in a screened MGUS cohort. In addition, a recent paper has shown N-lined glycosylation transcriptional programs are significantly upregulated in plasma cells from patients with AL amyloidosis in comparison to patients with myeloma and normal controls. Within SECURE, investigators will be using a mass spectrometry platform established by Binding site to longitudinally profile glycosylation patterns in light chains of MGUS patients and test whether this is a potential biomarker for progression to myeloma and or amyloidosis.
Clinical impact: A factual understanding of progression of Monoclonal gammopathy to Myeloma in a UK population is needed. This study will provide additional information on diagnostic routes, screening for MGCS, monitoring patterns and both psychological impact as well as health resource utilisation of this patient population. This will allow us to risk stratified monitoring of patients with MGUS, and streamline pathways with intent to early diagnosis of MGCS. Additional data on family linkage, QoL and HRU helps develop a framework for enhanced clinical management of MGUS.
Study Design
Outcome Measures
Primary Outcome Measures
- Progression rate to MM [Through study completion, assessed up to 60 months]
Progression rate to MM during observation
Secondary Outcome Measures
- MGUS monitoring patterns as determined via questionnaire [Through study completion, an average of 5 years]
Qualitative analysis to determine the monitoring patterns of patients with MGUS and the rationale behind them
- MGCS screening as determined via questionnaire [Through study completion, an average of 5 years]
Qualitative analysis to determine the screening of patients with MGCS
- Understanding routes to MGUS diagnosis as determined via questionnaire [Baseline]
Qualitative analysis to determine routes to MGUS diagnosis Screening vs Incidental
- To understand family linkage in relation to MGUS as determined via questionnaire [Baseline]
To understand family linkage in relation to MGUS as determined via questionnaire
- Quality of Life measured by the EQ-5D-3L Questionnaire [Through study completion, an average of 5 years]
The EQ-5D-3L descriptive system comprises the following five dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Each dimension is scored on a scale of 1 to 3: 1 (no problems), 2 (some problems), and 3 (extreme problems). Higher score equates to a worse outcome. As stated in the official EQ-5D user guide, patient responses to the 5 questions were converted into a single index value as per Dolan P (1997). Modeling valuations for EuroQol health states. Med Care 35(11):1095-108. These index values, with country specific value sets, facilitate the calculation of quality-adjusted life years (QALYs) that are used to inform economic evaluations of health care interventions. In the UK, the values range from -0.594 to +1.
- Determine if S1P has value as a predictive biomarker for progression to MM [Through study completion, assessed up to 60 months]
Determine if S1P has value as a predictive biomarker for progression to MM
- Determine if acetyl carnitine has value as a predictive biomarker for progression to MM [Through study completion, assessed up to 60 months]
Determine if acetyl carnitine has value as a predictive biomarker for progression to MM
- Identify germline genetic variants associated with risk of developing MGUS and/or MM [Through study completion, assessed up to 60 months]
Assessed through germline genetic profiling
Eligibility Criteria
Criteria
Inclusion Criteria:
• Any individual with a confirmed or suspected case of MGUS
Exclusion Criteria:
-
Those who are unable or unwilling to give informed consent
-
Patients under the age of 18
-
Patients with no evidence of MGUS
-
Patients with a light chain ratio of 0.3 to 3.0 without a monoclonal protein on serum electrophoresis or immunofixation
-
Patients with rapidly rising paraprotein or serum free light chains of progressive disease at time of diagnosis or inclusion into study
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Secure Study | Oxford | Oxfordshire | United Kingdom | OX3 7LE |
Sponsors and Collaborators
- Oxford University Hospitals NHS Trust
Investigators
- Principal Investigator: Karthik Ramasamy, Oxford University Hospitals NHS Trust
Study Documents (Full-Text)
None provided.More Information
Additional Information:
Publications
- Alameda D, Goicoechea I, Vicari M, Arriazu E, Nevone A, Rodriguez S, Lasa M, Puig N, Cedena MT, Alignani D, Garate S, Lara-Astiaso D, Vilas-Zornoza A, Sarvide S, Ocio EM, Lecumberri R, Garcia de Coca A, Labrador J, Gonzalez ME, Palomera L, Gironella M, Cabañas V, Casanova M, Oriol A, Krsnik I, Perez-Montaña A, de la Rubia J, de la Puerta JE, de Arriba F, Fazio VM, Martinez-Lopez J, Lahuerta JJ, Mateos MV, Odero MD, Prosper F, Weiner A, Amit I, Nuvolone M, San Miguel JF, Paiva B. Tumor cells in light-chain amyloidosis and myeloma show distinct transcriptional rewiring of normal plasma cell development. Blood. 2021 Oct 28;138(17):1583-1589. doi: 10.1182/blood.2020009754.
- Breitkopf SB, Yuan M, Helenius KP, Lyssiotis CA, Asara JM. Triomics Analysis of Imatinib-Treated Myeloma Cells Connects Kinase Inhibition to RNA Processing and Decreased Lipid Biosynthesis. Anal Chem. 2015 Nov 3;87(21):10995-1006. doi: 10.1021/acs.analchem.5b03040. Epub 2015 Oct 12.
- Broderick P, Chubb D, Johnson DC, Weinhold N, Försti A, Lloyd A, Olver B, Ma Y, Dobbins SE, Walker BA, Davies FE, Gregory WA, Childs JA, Ross FM, Jackson GH, Neben K, Jauch A, Hoffmann P, Mühleisen TW, Nöthen MM, Moebus S, Tomlinson IP, Goldschmidt H, Hemminki K, Morgan GJ, Houlston RS. Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk. Nat Genet. 2011 Nov 27;44(1):58-61. doi: 10.1038/ng.993.
- Chubb D, Weinhold N, Broderick P, Chen B, Johnson DC, Försti A, Vijayakrishnan J, Migliorini G, Dobbins SE, Holroyd A, Hose D, Walker BA, Davies FE, Gregory WA, Jackson GH, Irving JA, Pratt G, Fegan C, Fenton JA, Neben K, Hoffmann P, Nöthen MM, Mühleisen TW, Eisele L, Ross FM, Straka C, Einsele H, Langer C, Dörner E, Allan JM, Jauch A, Morgan GJ, Hemminki K, Houlston RS, Goldschmidt H. Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk. Nat Genet. 2013 Oct;45(10):1221-1225. doi: 10.1038/ng.2733. Epub 2013 Aug 18.
- Cook R. Economic and clinical impact of multiple myeloma to managed care. J Manag Care Pharm. 2008 Sep;14(7 Suppl):19-25. Review.
- Dispenzieri A, Larson DR, Rajkumar SV, Kyle RA, Kumar SK, Kourelis T, Arendt B, Willrcih M, Dasari S, Murray D. N-glycosylation of monoclonal light chains on routine MASS-FIX testing is a risk factor for MGUS progression. Leukemia. 2020 Oct;34(10):2749-2753. doi: 10.1038/s41375-020-0940-8. Epub 2020 Jun 27.
- Dona AC, Jiménez B, Schäfer H, Humpfer E, Spraul M, Lewis MR, Pearce JT, Holmes E, Lindon JC, Nicholson JK. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem. 2014 Oct 7;86(19):9887-94. doi: 10.1021/ac5025039. Epub 2014 Sep 16.
- Du H, Wang L, Liu B, Wang J, Su H, Zhang T, Huang Z. Analysis of the Metabolic Characteristics of Serum Samples in Patients With Multiple Myeloma. Front Pharmacol. 2018 Aug 22;9:884. doi: 10.3389/fphar.2018.00884. eCollection 2018.
- Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R; Human Serum Metabolome (HUSERMET) Consortium. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011 Jun 30;6(7):1060-83. doi: 10.1038/nprot.2011.335.
- Gonsalves WI, Ramakrishnan V, Hitosugi T, Ghosh T, Jevremovic D, Dutta T, Sakrikar D, Petterson XM, Wellik L, Kumar SK, Nair KS. Glutamine-derived 2-hydroxyglutarate is associated with disease progression in plasma cell malignancies. JCI Insight. 2018 Jan 11;3(1). pii: 94543. doi: 10.1172/jci.insight.94543. eCollection 2018 Jan 11.
- Graziani G, Herget GW, Ihorst G, Zeissig M, Chaidos A, Auner HW, Duyster J, Wäsch R, Engelhardt M. Time from first symptom onset to the final diagnosis of multiple myeloma (MM) - possible risks and future solutions: retrospective and prospective 'Deutsche Studiengruppe MM' (DSMM) and 'European Myeloma Network' (EMN) analysis(.). Leuk Lymphoma. 2020 Apr;61(4):875-886. doi: 10.1080/10428194.2019.1695051. Epub 2019 Nov 28.
- Jones DR, Wu Z, Chauhan D, Anderson KC, Peng J. A nano ultra-performance liquid chromatography-high resolution mass spectrometry approach for global metabolomic profiling and case study on drug-resistant multiple myeloma. Anal Chem. 2014 Apr 1;86(7):3667-75. doi: 10.1021/ac500476a. Epub 2014 Mar 20.
- Kaufmann H, Ackermann J, Odelga V, Sagaster V, Nösslinger T, Pfeilstöcker M, Keck A, Ludwig H, Gisslinger H, Drach J. Cytogenetic patterns in multiple myeloma after a phase of preceding MGUS. Eur J Clin Invest. 2008 Jan;38(1):53-60. doi: 10.1111/j.1365-2362.2007.01903.x.
- Koshiaris C, Oke J, Abel L, Nicholson BD, Ramasamy K, Van den Bruel A. Quantifying intervals to diagnosis in myeloma: a systematic review and meta-analysis. BMJ Open. 2018 Jun 22;8(6):e019758. doi: 10.1136/bmjopen-2017-019758.
- Koshiaris C, Van den Bruel A, Oke JL, Nicholson BD, Shephard E, Braddick M, Hamilton W. Early detection of multiple myeloma in primary care using blood tests: a case-control study in primary care. Br J Gen Pract. 2018 Sep;68(674):e586-e593. doi: 10.3399/bjgp18X698357. Epub 2018 Aug 13.
- Kyle RA, Durie BG, Rajkumar SV, Landgren O, Blade J, Merlini G, Kröger N, Einsele H, Vesole DH, Dimopoulos M, San Miguel J, Avet-Loiseau H, Hajek R, Chen WM, Anderson KC, Ludwig H, Sonneveld P, Pavlovsky S, Palumbo A, Richardson PG, Barlogie B, Greipp P, Vescio R, Turesson I, Westin J, Boccadoro M; International Myeloma Working Group. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering (asymptomatic) multiple myeloma: IMWG consensus perspectives risk factors for progression and guidelines for monitoring and management. Leukemia. 2010 Jun;24(6):1121-7. doi: 10.1038/leu.2010.60. Epub 2010 Apr 22. Review.
- Kyle RA, Gertz MA, Witzig TE, Lust JA, Lacy MQ, Dispenzieri A, Fonseca R, Rajkumar SV, Offord JR, Larson DR, Plevak ME, Therneau TM, Greipp PR. Review of 1027 patients with newly diagnosed multiple myeloma. Mayo Clin Proc. 2003 Jan;78(1):21-33.
- Kyle RA, Therneau TM, Rajkumar SV, Larson DR, Plevak MF, Offord JR, Dispenzieri A, Katzmann JA, Melton LJ 3rd. Prevalence of monoclonal gammopathy of undetermined significance. N Engl J Med. 2006 Mar 30;354(13):1362-9.
- Landgren O, Kyle RA, Pfeiffer RM, Katzmann JA, Caporaso NE, Hayes RB, Dispenzieri A, Kumar S, Clark RJ, Baris D, Hoover R, Rajkumar SV. Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study. Blood. 2009 May 28;113(22):5412-7. doi: 10.1182/blood-2008-12-194241. Epub 2009 Jan 29.
- Li N, Johnson DC, Weinhold N, Studd JB, Orlando G, Mirabella F, Mitchell JS, Meissner T, Kaiser M, Goldschmidt H, Hemminki K, Morgan GJ, Houlston RS. Multiple myeloma risk variant at 7p15.3 creates an IRF4-binding site and interferes with CDCA7L expression. Nat Commun. 2016 Nov 24;7:13656. doi: 10.1038/ncomms13656.
- Lodi A, Tiziani S, Khanim FL, Günther UL, Viant MR, Morgan GJ, Bunce CM, Drayson MT. Proton NMR-based metabolite analyses of archived serial paired serum and urine samples from myeloma patients at different stages of disease activity identifies acetylcarnitine as a novel marker of active disease. PLoS One. 2013;8(2):e56422. doi: 10.1371/journal.pone.0056422. Epub 2013 Feb 19.
- Lyratzopoulos G, Neal RD, Barbiere JM, Rubin GP, Abel GA. Variation in number of general practitioner consultations before hospital referral for cancer: findings from the 2010 National Cancer Patient Experience Survey in England. Lancet Oncol. 2012 Apr;13(4):353-65. doi: 10.1016/S1470-2045(12)70041-4. Epub 2012 Feb 24.
- Mitchell JS, Li N, Weinhold N, Försti A, Ali M, van Duin M, Thorleifsson G, Johnson DC, Chen B, Halvarsson BM, Gudbjartsson DF, Kuiper R, Stephens OW, Bertsch U, Broderick P, Campo C, Einsele H, Gregory WA, Gullberg U, Henrion M, Hillengass J, Hoffmann P, Jackson GH, Johnsson E, Jöud M, Kristinsson SY, Lenhoff S, Lenive O, Mellqvist UH, Migliorini G, Nahi H, Nelander S, Nickel J, Nöthen MM, Rafnar T, Ross FM, da Silva Filho MI, Swaminathan B, Thomsen H, Turesson I, Vangsted A, Vogel U, Waage A, Walker BA, Wihlborg AK, Broyl A, Davies FE, Thorsteinsdottir U, Langer C, Hansson M, Kaiser M, Sonneveld P, Stefansson K, Morgan GJ, Goldschmidt H, Hemminki K, Nilsson B, Houlston RS. Genome-wide association study identifies multiple susceptibility loci for multiple myeloma. Nat Commun. 2016 Jul 1;7:12050. doi: 10.1038/ncomms12050.
- Piot JM, Royer M, Schmidt-Tanguy A, Hoppé E, Gardembas M, Bourrée T, Hunault M, François S, Boyer F, Ifrah N, Renier G, Chevailler A, Audran M, Chappard D, Libouban H, Mabilleau G, Legrand E, Bouvard B. Factors associated with an increased risk of vertebral fracture in monoclonal gammopathies of undetermined significance. Blood Cancer J. 2015 Aug 28;5:e345. doi: 10.1038/bcj.2015.71.
- Puchades-Carrasco L, Lecumberri R, Martínez-López J, Lahuerta JJ, Mateos MV, Prósper F, San-Miguel JF, Pineda-Lucena A. Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission. Clin Cancer Res. 2013 Sep 1;19(17):4770-9. doi: 10.1158/1078-0432.CCR-12-2917. Epub 2013 Jul 19.
- Rajkumar SV. The screening imperative for multiple myeloma. Nature. 2020 Nov;587(7835):S63. doi: 10.1038/d41586-020-03227-y.
- Swann R, Lyratzopoulos G, Rubin G, Pickworth E, McPhail S. The frequency, nature and impact of GP-assessed avoidable delays in a population-based cohort of cancer patients. Cancer Epidemiol. 2020 Feb;64:101617. doi: 10.1016/j.canep.2019.101617. Epub 2019 Dec 3.
- Weinhold N, Johnson DC, Chubb D, Chen B, Försti A, Hosking FJ, Broderick P, Ma YP, Dobbins SE, Hose D, Walker BA, Davies FE, Kaiser MF, Li NL, Gregory WA, Jackson GH, Witzens-Harig M, Neben K, Hoffmann P, Nöthen MM, Mühleisen TW, Eisele L, Ross FM, Jauch A, Goldschmidt H, Houlston RS, Morgan GJ, Hemminki K. The CCND1 c.870G>A polymorphism is a risk factor for t(11;14)(q13;q32) multiple myeloma. Nat Genet. 2013 May;45(5):522-525. doi: 10.1038/ng.2583. Epub 2013 Mar 17.
- Weinhold N, Johnson DC, Rawstron AC, Försti A, Doughty C, Vijayakrishnan J, Broderick P, Dahir NB, Begum DB, Hosking FJ, Yong K, Walker BA, Hoffmann P, Mühleisen TW, Langer C, Dörner E, Jöckel KH, Eisele L, Nöthen MM, Hose D, Davies FE, Goldschmidt H, Morgan GJ, Hemminki K, Houlston RS. Inherited genetic susceptibility to monoclonal gammopathy of unknown significance. Blood. 2014 Apr 17;123(16):2513-7; quiz 2593. doi: 10.1182/blood-2013-10-532283. Epub 2014 Jan 21.
- Went M, Sud A, Försti A, Halvarsson BM, Weinhold N, Kimber S, van Duin M, Thorleifsson G, Holroyd A, Johnson DC, Li N, Orlando G, Law PJ, Ali M, Chen B, Mitchell JS, Gudbjartsson DF, Kuiper R, Stephens OW, Bertsch U, Broderick P, Campo C, Bandapalli OR, Einsele H, Gregory WA, Gullberg U, Hillengass J, Hoffmann P, Jackson GH, Jöckel KH, Johnsson E, Kristinsson SY, Mellqvist UH, Nahi H, Easton D, Pharoah P, Dunning A, Peto J, Canzian F, Swerdlow A, Eeles RA, Kote-Jarai Z, Muir K, Pashayan N, Nickel J, Nöthen MM, Rafnar T, Ross FM, da Silva Filho MI, Thomsen H, Turesson I, Vangsted A, Andersen NF, Waage A, Walker BA, Wihlborg AK, Broyl A, Davies FE, Thorsteinsdottir U, Langer C, Hansson M, Goldschmidt H, Kaiser M, Sonneveld P, Stefansson K, Morgan GJ, Hemminki K, Nilsson B, Houlston RS; PRACTICAL consortium. Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma. Nat Commun. 2018 Sep 13;9(1):3707. doi: 10.1038/s41467-018-04989-w. Erratum in: Nat Commun. 2019 Jan 10;10(1):213.
- PID15967