Predictors of unsustained measurable residual disease negativity in transplant-eligible patients with multiple myeloma.

ABSTRACT: The role of measurable residual disease (MRD) negativity as a biomarker to stop treatment is being investigated in transplant-eligible patients with multiple myeloma (MM). Thus, it is important to identify risk factors of MRD resurgence and/or progressive disease (PD) among patients achieving undetectable MRD to avoid undertreating them. Here, we studied 267 newly diagnosed transplant-eligible patients with MM enrolled in the GEM2012MENOS65 and GEM2014MAIN clinical trials who achieved MRD negativity by next-generation flow cytometry. After a median follow-up of 73 months since the first MRD negative assessment, 111 of the 267 (42%) patients showed MRD resurgence and/or PD. The only prognostic factors at diagnosis that predicted MRD resurgence and/or PD were an International Staging System (ISS) 3 and the presence of ≥0.01% circulating tumor cells (CTCs). Failure to achieve MRD negativity after induction also predicted higher risk of MRD resurgence and/or PD. Patients having 0 vs 1 vs ≥2 risk factors (ISS 3, ≥0.01% CTCs, and late MRD negativity) showed 5-year rates of MRD resurgence and/or PD of 16%, 33%, and 57%, respectively (P < .001). Thus, these easily measurable risk factors could help refine the selection of patients for whom treatment cessation after MRD negativity is being investigated in clinical trials. This trial was registered at www.clinicaltrials.gov as NCT01916252 and NCT02406144.

Definition and Clinical Significance of the Monoclonal Gammopathy of Undetermined Significance-Like Phenotype in Patients With Monoclonal Gammopathies.

PURPOSE: The existence of patients with multiple myeloma (MM) and light-chain (AL) amyloidosis who present with a monoclonal gammopathy of undetermined significance (MGUS)-like phenotype has been hypothesized, but methods to identify this subgroup are not standardized and its clinical significance is not properly validated. PATIENTS AND METHODS: An algorithm to identify patients having MGUS-like phenotype was developed on the basis of the percentages of total bone marrow (BM) plasma cells (PC) and of clonal PC within the BM PC compartment, determined at diagnosis using flow cytometry in 548 patients with MGUS and 2,011 patients with active MM. The clinical significance of the algorithm was tested and validated in 488 patients with smoldering MM, 3,870 patients with active MM and 211 patients with AL amyloidosis. RESULTS: Patients with smoldering MM with MGUS-like phenotype showed significantly lower rates of disease progression (4.5% and 0% at 2 years in two independent series). There were no statistically significant differences in time to progression between treatment versus observation in these patients. In active newly diagnosed MM, MGUS-like phenotype retained independent prognostic value in multivariate analyses of progression-free survival (PFS; hazard ratio [HR], 0.49; P = .001) and overall survival (OS; HR, 0.56; P = .039), together with International Staging System, lactate dehydrogenase, cytogenetic risk, transplant eligibility, and complete remission status. Transplant-eligible patients with active MM with MGUS-like phenotype showed PFS and OS rates at 5 years of 79% and 96%, respectively. In this subgroup, there were no differences in PFS and OS according to complete remission and measurable residual disease status. Application of the algorithm in two independent series of patients with AL predicted for different survival. CONCLUSION: We developed an open-access algorithm for the identification of MGUS-like patients with distinct clinical outcomes. This phenotypic classification could become part of the diagnostic workup of MM and AL amyloidosis.

A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable MRD and Survival Outcomes in Multiple Myeloma.

PURPOSE: Undetectable measurable residual disease (MRD) is a surrogate of prolonged survival in multiple myeloma. Thus, treatment individualization based on the probability of a patient achieving undetectable MRD with a singular regimen could represent a new concept toward personalized treatment, with fast assessment of its success. This has never been investigated; therefore, we sought to define a machine learning model to predict undetectable MRD at the onset of multiple myeloma. EXPERIMENTAL DESIGN: This study included 487 newly diagnosed patients with multiple myeloma. The training (n = 152) and internal validation cohorts (n = 149) consisted of 301 transplant-eligible patients with active multiple myeloma enrolled in the GEM2012MENOS65 trial. Two external validation cohorts were defined by 76 high-risk transplant-eligible patients with smoldering multiple myeloma enrolled in the Grupo Español de Mieloma(GEM)-CESAR trial, and 110 transplant-ineligible elderly patients enrolled in the GEM-CLARIDEX trial. RESULTS: The most effective model to predict MRD status resulted from integrating cytogenetic [t(4;14) and/or del(17p13)], tumor burden (bone marrow plasma cell clonality and circulating tumor cells), and immune-related biomarkers. Accurate predictions of MRD outcomes were achieved in 71% of cases in the GEM2012MENOS65 trial (n = 214/301) and 72% in the external validation cohorts (n = 134/186). The model also predicted sustained MRD negativity from consolidation onto 2 years maintenance (GEM2014MAIN). High-confidence prediction of undetectable MRD at diagnosis identified a subgroup of patients with active multiple myeloma with 80% and 93% progression-free and overall survival rates at 5 years. CONCLUSIONS: It is possible to accurately predict MRD outcomes using an integrative, weighted model defined by machine learning algorithms. This is a new concept toward individualized treatment in multiple myeloma. See related commentary by Pawlyn and Davies, p. 2482.

Immune biomarkers to predict SARS-CoV-2 vaccine effectiveness in patients with hematological malignancies.

There is evidence of reduced SARS-CoV-2 vaccine effectiveness in patients with hematological malignancies. We hypothesized that tumor and treatment-related immunosuppression can be depicted in peripheral blood, and that immune profiling prior to vaccination can help predict immunogenicity. We performed a comprehensive immunological characterization of 83 hematological patients before vaccination and measured IgM, IgG, and IgA antibody response to four viral antigens at day +7 after second-dose COVID-19 vaccination using multidimensional and computational flow cytometry. Health care practitioners of similar age were the control group (n = 102). Forty-four out of 59 immune cell types were significantly altered in patients; those with monoclonal gammopathies showed greater immunosuppression than patients with B-cell disorders and Hodgkin lymphoma. Immune dysregulation emerged before treatment, peaked while on-therapy, and did not return to normalcy after stopping treatment. We identified an immunotype that was significantly associated with poor antibody response and uncovered that the frequency of neutrophils, classical monocytes, CD4, and CD8 effector memory CD127low T cells, as well as naive CD21+ and IgM+D+ memory B cells, were independently associated with immunogenicity. Thus, we provide novel immune biomarkers to predict COVID-19 vaccine effectiveness in hematological patients, which are complementary to treatment-related factors and may help tailoring possible vaccine boosters.