Molecular Signature of 18F-FDG PET Biomarkers in Newly Diagnosed Multiple Myeloma Patients: A Genome-Wide Transcriptome Analysis from the CASSIOPET Study

Visual Abstract The International Myeloma Working Group recently fully incorporated 18F-FDG PET into multiple myeloma (MM) diagnosis and response evaluation. Moreover, a few studies demonstrated the prognostic value of several biomarkers extracted from this imaging at baseline. Before these 18F-FDG PET biomarkers could be fully endorsed as risk classifiers by the hematologist community, further characterization of underlying molecular aspects was necessary. Methods: Reported prognostic biomarkers (18F-FDG avidity, SUVmax, number of focal lesions, presence of paramedullary disease [PMD] or extramedullary disease) were extracted from 18F-FDG PET imaging at baseline in a group of 139 patients from CASSIOPET, a companion study of the CASSIOPEIA cohort (ClinicalTrials.gov identifier NCT02541383). Transcriptomic analyses using RNA sequencing were realized on sorted bone marrow plasma cells from the same patients. An association with a high-risk gene expression signature (IFM15), molecular classification, progression-free survival, a stringent clinical response, and minimal residual disease negativity were explored. Results: 18F-FDG PET results were positive in 79.4% of patients; 14% and 11% of them had PMD and extramedullary disease, respectively. Negative 18F-FDG PET results were associated with lower levels of expression of hexokinase 2 (HK2) (fold change, 2.1; adjusted P = 0.04) and showed enrichment for a subgroup of patients with a low level of bone disease. Positive 18F-FDG PET results displayed 2 distinct signatures: either high levels of expression of proliferation genes or high levels of expression of GLUT5 and lymphocyte antigens. PMD and IFM15 were independently associated with a lower level of progression-free survival, and the presence of both biomarkers defined a group of “double-positive” patients at very high risk of progression. PMD and IFM15 were related neither to minimal residual disease assessment nor to a stringent clinical response. Conclusion: Our study confirmed and extended the association between imaging biomarkers and transcriptomic programs in MM. The combined prognostic value of PMD and a high-risk IFM15 signature may help define MM patients with a very high risk of progression.

[1]  C. Nanni,et al.  Glucose Metabolism Quantified by SUVmax on Baseline FDG-PET/CT Predicts Survival in Newly Diagnosed Multiple Myeloma Patients: Combined Harmonized Analysis of Two Prospective Phase III Trials , 2020, Cancers.

[2]  P. Moreau,et al.  FDG-PET/CT, a Promising Exam for Detecting High-Risk Myeloma Patients? , 2020, Cancers.

[3]  P. Sonneveld,et al.  Evaluation of the Prognostic Value of Positron Emission Tomography-Computed Tomography (PET-CT) at Diagnosis and Follow-up in Transplant-Eligible Newly Diagnosed Multiple Myeloma (TE NDMM) Patients Treated in the Phase 3 Cassiopeia Study: Results of the Cassiopet Companion Study , 2019, Blood.

[4]  P. Sonneveld,et al.  Bortezomib, thalidomide, and dexamethasone with or without daratumumab before and after autologous stem-cell transplantation for newly diagnosed multiple myeloma (CASSIOPEIA): a randomised, open-label, phase 3 study , 2019, The Lancet.

[5]  D. Auclair,et al.  Multiple myeloma immunoglobulin lambda translocations portend poor prognosis , 2019, Nature Communications.

[6]  Hiroki Kobayashi,et al.  Low hexokinase-2 expression-associated false-negative 18F-FDG PET/CT as a potential prognostic predictor in patients with multiple myeloma , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[7]  Pingping Qu,et al.  A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis , 2018, Leukemia.

[8]  M. Wuest,et al.  Expression and function of hexose transporters GLUT1, GLUT2, and GLUT5 in breast cancer—effects of hypoxia , 2018, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[9]  O. Stephens,et al.  Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing , 2017, Nature Communications.

[10]  Caleb K. Stein,et al.  Low expression of hexokinase-2 is associated with false-negative FDG-positron emission tomography in multiple myeloma. , 2017, Blood.

[11]  Bart Barlogie,et al.  Role of 18F-FDG PET/CT in the diagnosis and management of multiple myeloma and other plasma cell disorders: a consensus statement by the International Myeloma Working Group. , 2017, The Lancet. Oncology.

[12]  H. Goldschmidt,et al.  International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. , 2016, The Lancet. Oncology.

[13]  S. Iwata,et al.  Structure and mechanism of the mammalian fructose transporter GLUT5 , 2015, Nature.

[14]  B. Barlogie,et al.  Extramedullary disease portends poor prognosis in multiple myeloma and is over-represented in high-risk disease even in the era of novel agents , 2012, Haematologica.

[15]  P. Sonneveld,et al.  Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. , 2010, Blood.

[16]  Cheng Li,et al.  Prognostic significance of copy-number alterations in multiple myeloma. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  Laurence Lodé,et al.  Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the Intergroupe Francophone du Myélome. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  R. Bataille,et al.  Genetic abnormalities and survival in multiple myeloma: the experience of the Intergroupe Francophone du Myélome. , 2007, Blood.

[19]  Yongsheng Huang,et al.  A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. , 2006, Blood.

[20]  John Crowley,et al.  The molecular classification of multiple myeloma. , 2006, Blood.