References

Kuiper R et al. A gene expression signature for high-risk multiple myeloma. Leukemia 2012 26:2406-13
 

Hofste op Bruinink D et al. Differential effect of upfront intensification treatment in genetically defined myeloma risk groups - a combined analysis of ISS, del17p and SKY92 scores in the EMN-02/Hovon-95 MM trial. ASH 2018 3186
 

Ubels J et al. Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects. Nature Communications 2018
 

Kuiper R et al. RNA-Seq based risk stratification in multiple myeloma patients validates SKY92 as a high risk marker in the COMPASS trial. Abstract EHA 2018 PF528
 

Kuiper R et al. SKY92 risk stratification at relapse provides additional prognostic information for standard-risk multiple myeloma patients. Abstract EHA 2018 PS1295
 

Sherbone AL et al. Identifying ultra-high risk myeloma by integrated molecular genetic and gene expression profiling. ASH 2016 Abstract 4407
 

Van Vliet MH et al. Risk stratification by SKY92+ISS outperforms iFISH markers t(4;14) and del17p in multiple myeloma. EHA 2016 P283
 

Van Vliet MH et al. Precision as part of the analytical validation of the SKY92 high risk signature and the MMprofiler assay. EHA 2016 P282
 

Van Vliet MH et al. The SKY92 prognostic marker is validated in eight multiple myeloma clinical datasets. EHA 2016 P276
 

Van Vliet MH et al. Robustness of the prognostic value of the SKY92 marker versus FISH markers acros nine multiple myeloma cohorts. EHA 2016 E1262
 

Van Duin M et al. Validation of the EMC92/SKY92 signature in HOVON-87/NMSG-18: gene expression based prognostication is applicable in elderly patients with newly diagnosed multiple myeloma. ASH 2015. Blood 2015 126:2967 Abstract 84350. 
 

Van Vliet MH et al. The combination of SKY92 and ISS provides a powerful tool to identify both high risk and low risk multiple myeloma cases, validation in two independent cohorts. ASH 2015. Blood 2015 126:2970
 

Kuiper R et al. Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System. Blood 2015 126(17): 1996-2004
 

Van beers EH et al. SKY92 GEP, iFISH and ISS comparisons for risk stratification in multiple myeloma. EHA 2015 Haematologica 2015 Abstract P661
 

Van Vliet MH et al. Prognostic and predictive gene expression profiling (GEP) markers confirmed in carfilzomib, lenalidomide and dexamethasone (KRd) treated newly diagnosed multiple myeloma (NDMM) patients. ASH 2014 Abstract 2141
 

Van Vliet MH et al. Single sample application of the EMC92/SKY92 signature using the MMprofiler. ASH 2014 Abstract 2026. Blood 2014 124:2026
 

Van Vliet MH et al. Proteasome inhibitor treatment response can be predicted by gene expession profiling in multiple myeloma. EHA 2014 Haematologica 2014. Abstract S1286
 

jasielec J et al. Predictors of treatment outcome with the combination of carfilzomib, lenalidomide and low-dose dexamethasone (CRd) in newly diagnosed multiple myeloma (NDMM). ASH 2013 Blood 2013 122:3220
 

Broyl A et al. High cereblon expression is associated with better survival in patients with newly diagnosed multiple myeloma treated with thalidomide maintenance. Blood 2013 Jan 24;121(4):624-7
 

Broyl A et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Blood 2010 Oct 7;116(14):2543-53
 

Sonneveld P et al. Treatment of multiple myeloma with high-risk cytogenetics: a consensus of the International Myeloma Working Group. Blood 2016 Jun 16;127(24):2955-62
 

Chang H et al. 1p21 deletions are strongly associated with 1q21 gains and are an independent adverse prognostic factor for the outcome of high-dose chemotherapy in patients with multiple myeloma. Bone Marrow Transplant 2010 Jan;45(1):117-21
 

Boyd KD et al. A novel prognostic model in myeloma based on co-segregating adverse FISH lesions and the ISS: analysis of patients treated in the MRC Myeloma IX trial. Leukemia 2012 Feb;26(2):349-55
 

Kumar SK et al. Impact of gene expression profiling-based risk stratification in patients with myeloma receiving initial therapy with lenalidomide and dexamethasone. Blood 2011 Oct 20;118(16):4359-62
 

Jacobus SJ et al. Impact of high-risk classification by FISH: an eastern cooperative oncology group (ECOG) study E4A03. Br J Haematol 2011 Nov;155(3):340-8
 

Kumar SK et al. Trisomies in multiple myeloma: impact on survival in patients with high-risk cytogenetics. Blood 2012 Mar 1;119(9):2100-5
 

Kröger N et al. Impact of high-risk cytogenetics and achievement of molecular remission on long-term freedom from disease after autologous-allogeneic tandem transplantation in patients with multiple myeloma. Biol Blood Marrow Transplant 2013 Mar;19(3):398-404
 

Biran N et al. Patients with newly diagnosed multiple myeloma and chromosome 1 amplification have poor outcomes despite the use of novel triplet regimens. Am J Hematol 2014 Jun;89(6):616-20
 

Kazmi SM et al. Outcome among high-risk and standard-risk multiple myeloma patients treated with high-dose chemotherapy and autologous hematopoietic stem-cell transplantation. Clin Lymphoma Myeloma Leuk 2015 Nov;15(11):687-93


Kaufman GP et al. Impact of cytogenetic classification on outcomes following early high-dose therapy in multiple myeloma. Leukemia 2016 Mar;30(3):633-9
 

Franssen LE et al. Outcome of allogeneic transplantation in newly diagnosed and relapsed/refractory multiple myeloma: long-term follow-up in a single institution. Eur J Haematol 2016 Nov;97(5):479-488
 

Scott EC et al. Post-transplant outcomes in high-risk compared with non-high-risk multiple myeloma: a CIBMTR analysis. Biol Blood Marrow Transplant 2016 Oct;22(10):1893-1899
 

Merz M et al. Baseline characteristics, chromosomal alterations, and treatment affecting prognosis of deletion 17p in newly diagnosed myeloma. Am J Hematol 2016 Nov;91(11):E473-E477
 

Perrot A et al. Risk stratification and targets in multiple myeloma: from genomics to the bedside. Am Soc Clin Oncol Educ Book 2018 May 23;(38):675-680
 

Walker BA et al. A high-risk, double-hit, group of newly diagnosed myeloma identified by genomic analysis. Leukemia 2019 Jan;33(1):159-170
 

Bolli N et al. Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups. Leukemia 2018 Dec;32(12):2604-2616
 

Avet-Loiseau H et al. Optimizing therapy for del(17p) multiple myeloma. Oncotarget 2017 Dec 6;8(66):109859-109860
 

Avet-Loiseau H et al. Ixazomib significantly prolongs progression-free survival in high-risk relapsed/refractory myeloma patients. Blood 2017 Dec 14;130(24):2610-2618
 

Moreau P et al. ESMO Guidelines Committee. Multiple myeloma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2017 Jul 1;28(suppl_4):iv52-iv61
 

Robiou du Pont S et al. Genomics of multiple myeloma. J Clin Oncol 2017 Mar 20;35(9):963-967
 

Dispenzieri A. Myeloma: management of the newly diagnosed high-risk patient. Hematology Am Soc Hematol Educ Program 2016 Dec 2;2016(1):485-494. Review.
 

Palumbo A et al. Revised International Staging System for multiple myeloma: a report from International Myeloma Working Group. J Clin Oncol 2015 Sep 10;33(26):2863-9
 

Stadtmauer EA et al. Autologous transplantation, consolidation and maintenance therapy in multiple myeloma. J Clin Oncol 2019 Jan 17;JCO1800685
 

Kaur G et al. Clinical impact of chromothriptic complex chromosomal rearrangements in newly diagnosed multiple myeloma: comparative effectiveness analysis of modern induction regimes on outcome. Leuk Res 2019 Jan;76:58-64
 

Paquin AR et al. Overall survival of transplant eligible patients with newly diagnosed multiple myeloma: comparative analysis of modern induction regimes on outcome. Blood Cancer J 2018 Dec 11;8(12):125
 

Goldschmidt H et al. Navigating the treatment landscape in multiple myeloma: which combinations to use and when? Ann Hematol 2019 Jan;98(1):1-18
 

Senín A et al. Study of the frequency and reasons for discontinuation of different lines of treatment in patients with multiple myeloma. Ann Hematol 2019 Jan 23