More Than 2% of Circulating Tumor Plasma Cells Defines Plasma Cell Leukemia-Like Multiple Myeloma.

PURPOSE: Primary plasma cell leukemia (PCL) is the most aggressive monoclonal gammopathy. It was formerly characterized by ≥ 20% circulating plasma cells (CTCs) until 2021, when this threshold was decreased to ≥ 5%. We hypothesized that primary PCL is not a separate clinical entity, but rather that it represents ultra-high-risk multiple myeloma (MM) characterized by elevated CTC levels. METHODS: We assessed the levels of CTCs by multiparameter flow cytometry in 395 patients with newly diagnosed transplant-ineligible MM to establish a cutoff for CTCs that identifies the patients with ultra-high-risk PCL-like MM. We tested the cutoff on 185 transplant-eligible patients with MM and further validated on an independent cohort of 280 transplant-ineligible patients treated in the GEM-CLARIDEX trial. The largest published real-world cohort of patients with primary PCL was used for comparison of survival. Finally, we challenged the current 5% threshold for primary PCL diagnosis. RESULTS: Newly diagnosed transplant-ineligible patients with MM with 2%-20% CTCs had significantly shorter progression-free survival (3.1 v 15.6 months; P < .001) and overall survival (14.6 v 33.6 months; P = .023) than patients with < 2%. The 2% cutoff proved to be applicable also in transplant-eligible patients with MM and was successfully validated on an independent cohort of patients from the GEM-CLARIDEX trial. Most importantly, patients with 2%-20% CTCs had comparable dismal outcomes with primary PCL. Moreover, after revealing a low mean difference between flow cytometric and morphologic evaluation of CTCs, we showed that patients with 2%-5% CTCs have similar outcomes as those with 5%-20% CTCs. CONCLUSION: Our study uncovers that ≥ 2% CTCs is a biomarker of hidden primary PCL and supports the assessment of CTCs by flow cytometry during the diagnostic workup of MM.

Circulating Tumor and Immune Cells for Minimally Invasive Risk Stratification of Smoldering Multiple Myeloma.

PURPOSE: Early intervention in smoldering multiple myeloma (SMM) requires optimal risk stratification to avoid under- and overtreatment. We hypothesized that replacing bone marrow (BM) plasma cells (PC) for circulating tumor cells (CTC), and adding immune biomarkers in peripheral blood (PB) for the identification of patients at risk of progression due to lost immune surveillance, could improve the International Myeloma Working Group 20/2/20 model. EXPERIMENTAL DESIGN: We report the outcomes of 150 patients with SMM enrolled in the iMMunocell study, in which serial assessment of tumor and immune cells in PB was performed every 6 months for a period of 3 years since enrollment. RESULTS: Patients with >0.015% versus ≤0.015% CTCs at baseline had a median time-to-progression of 17 months versus not reached (HR, 4.9; P < 0.001). Presence of >20% BM PCs had no prognostic value in a multivariate analysis that included serum free light-chain ratio >20, >2 g/dL M-protein, and >0.015% CTCs. The 20/2/20 and 20/2/0.015 models yielded similar risk stratification (C-index of 0.76 and 0.78). The combination of the 20/2/0.015 model with an immune risk score based on the percentages of SLAN+ and SLAN- nonclassical monocytes, CD69+HLADR+ cytotoxic NK cells, and CD4+CXCR3+ stem central memory T cells, allowed patient' stratification into low, intermediate-low, intermediate-high, and high-risk disease with 0%, 20%, 39%, and 73% rates of progression at 2 years. CONCLUSIONS: This study showed that CTCs outperform BM PCs for assessing tumor burden. Additional analysis in larger series are needed to define a consensus cutoff of CTCs for minimally invasive stratification of SMM.

Circulating Tumor Cells for the Staging of Patients With Newly Diagnosed Transplant-Eligible Multiple Myeloma.

PURPOSE: Patients with multiple myeloma (MM) may show patchy bone marrow (BM) infiltration and extramedullary disease. Notwithstanding, quantification of plasma cells (PCs) continues to be performed in BM since the clinical translation of circulating tumor cells (CTCs) remains undefined. PATIENTS AND METHODS: CTCs were measured in peripheral blood (PB) of 374 patients with newly diagnosed MM enrolled in the GEM2012MENOS65 and GEM2014MAIN trials. Treatment included bortezomib, lenalidomide, and dexamethasone induction followed by autologous transplant, consolidation, and maintenance. Next-generation flow cytometry was used to evaluate CTCs in PB at diagnosis and measurable residual disease (MRD) in BM throughout treatment. RESULTS: CTCs were detected in 92% (344 of 374) of patients with newly diagnosed MM. The correlation between the percentages of CTCs and BM PCs was modest. Increasing logarithmic percentages of CTCs were associated with inferior progression-free survival (PFS). A cutoff of 0.01% CTCs showed an independent prognostic value (hazard ratio: 2.02; 95% CI, 1.3 to 3.1; P = .001) in multivariable PFS analysis including the International Staging System, lactate dehydrogenase levels, and cytogenetics. The combination of the four prognostic factors significantly improved risk stratification. Outcomes according to the percentage of CTCs and depth of response to treatment showed that patients with undetectable CTCs had exceptional PFS regardless of complete remission and MRD status. In all other cases with detectable CTCs, only achieving MRD negativity (and not complete remission) demonstrated a statistically significant increase in PFS. CONCLUSION: Evaluation of CTCs in PB outperformed quantification of BM PCs. The detection of ≥ 0.01% CTCs could be a new risk factor in novel staging systems for patients with transplant-eligible MM.

Biological Characterization and Clinical Relevance of Circulating Tumor Cells: Opening the Pandora's Box of Multiple Myeloma.

Bone marrow (BM) aspirates are the gold standard for patient prognostication and genetic characterization in multiple myeloma (MM). However, they represent an important limitation for periodic disease monitoring because they entail an aggressive procedure. Moreover, recent findings show that a single BM aspirate is unable to reflect the complex MM heterogeneity. Recent advances in flow cytometry, microfluidics, and "omics" technologies have opened Pandora's box of MM: The detection and isolation of circulating tumor cells (CTCs) offer a promising and minimally invasive alternative for tumor assessment and metastasis study. CTCs are detectable in premalignant and active MM states, and their enumeration has strong prognostic value, to the extent that it is challenging current stratification systems. In addition, CTCs reflect with high precision both intra- and extra-medullary disease at the phenotypic, genomic, and transcriptomic levels. Despite this high resemblance between tumor clones in distinct locations, some subtle (not random) differences might shed some light on the metastatic process. Thus, it has been suggested that a hypoxic and pro-inflammatory microenvironment could induce an arrest in proliferation forcing tumor cells to recirculate. Herein, we summarize data on the characterization of MM CTCs as well as their clinical and research potential.

Preneoplastic somatic mutations including MYD88L265P in lymphoplasmacytic lymphoma.

Normal cell counterparts of solid and myeloid tumors accumulate mutations years before disease onset; whether this occurs in B lymphocytes before lymphoma remains uncertain. We sequenced multiple stages of the B lineage in elderly individuals and patients with lymphoplasmacytic lymphoma, a singular disease for studying lymphomagenesis because of the high prevalence of mutated MYD88. We observed similar accumulation of random mutations in B lineages from both cohorts and unexpectedly found MYD88L265P in normal precursor and mature B lymphocytes from patients with lymphoma. We uncovered genetic and transcriptional pathways driving malignant transformation and leveraged these to model lymphoplasmacytic lymphoma in mice, based on mutated MYD88 in B cell precursors and BCL2 overexpression. Thus, MYD88L265P is a preneoplastic event, which challenges the current understanding of lymphomagenesis and may have implications for early detection of B cell lymphomas.

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.

FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology.

Large-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge when attempting to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large data sets. It includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering, and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T-cell compartment in bone marrow (BM) with peripheral blood (PB) from patients with smoldering multiple myeloma (SMM), identify minimally invasive immune biomarkers of progression from smoldering to active MM, define prognostic T-cell subsets in the BM of patients with active MM after treatment intensification, and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation were identified in 150 patients with SMM (hazard ratio [HR], 1.7; P < .001). We also determined progression-free survival (HR, 4.09; P < .0001) and overall survival (HR, 3.12; P = .047) in 100 patients with active MM. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM and PB, and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality control, analyze high-dimensional data, unveil cellular diversity, and objectively identify biomarkers in large immune monitoring studies. These trials were registered at www.clinicaltrials.gov as #NCT01916252 and #NCT02406144.

Immunological Biomarkers of Fatal COVID-19: A Study of 868 Patients.

Information on the immunopathobiology of coronavirus disease 2019 (COVID-19) is rapidly increasing; however, there remains a need to identify immune features predictive of fatal outcome. This large-scale study characterized immune responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection using multidimensional flow cytometry, with the aim of identifying high-risk immune biomarkers. Holistic and unbiased analyses of 17 immune cell-types were conducted on 1,075 peripheral blood samples obtained from 868 COVID-19 patients and on samples from 24 patients presenting with non-SARS-CoV-2 infections and 36 healthy donors. Immune profiles of COVID-19 patients were significantly different from those of age-matched healthy donors but generally similar to those of patients with non-SARS-CoV-2 infections. Unsupervised clustering analysis revealed three immunotypes during SARS-CoV-2 infection; immunotype 1 (14% of patients) was characterized by significantly lower percentages of all immune cell-types except neutrophils and circulating plasma cells, and was significantly associated with severe disease. Reduced B-cell percentage was most strongly associated with risk of death. On multivariate analysis incorporating age and comorbidities, B-cell and non-classical monocyte percentages were independent prognostic factors for survival in training (n=513) and validation (n=355) cohorts. Therefore, reduced percentages of B-cells and non-classical monocytes are high-risk immune biomarkers for risk-stratification of COVID-19 patients.

Impact of measurable residual disease by decentralized flow cytometry: a PETHEMA real-world study in 1076 patients with acute myeloid leukemia.

The role of decentralized assessment of measurable residual disease (MRD) for risk stratification in acute myeloid leukemia (AML) remains largely unknown, and so it does which methodological aspects are critical to empower the evaluation of MRD with prognostic significance, particularly if using multiparameter flow cytometry (MFC). We analyzed 1076 AML patients in first remission after induction chemotherapy, in whom MRD was evaluated by MFC in local laboratories of 60 Hospitals participating in the PETHEMA registry. We also conducted a survey on technical aspects of MRD testing to determine the impact of methodological heterogeneity in the prognostic value of MFC. Our results confirmed the recommended cutoff of 0.1% to discriminate patients with significantly different cumulative-incidence of relapse (-CIR- HR:0.71, P < 0.001) and overall survival (HR: 0.73, P = 0.001), but uncovered the limited prognostic value of MFC based MRD in multivariate and recursive partitioning models including other clinical, genetic and treatment related factors. Virtually all aspects related with methodological, interpretation, and reporting of MFC based MRD testing impacted in its ability to discriminate patients with different CIR. Thus, this study demonstrated that "real-world" assessment of MRD using MFC is prognostic in patients at first remission, and urges greater standardization for improved risk-stratification toward clinical decisions in AML.

Deep MRD profiling defines outcome and unveils different modes of treatment resistance in standard- and high-risk myeloma.

Patients with multiple myeloma (MM) carrying standard- or high-risk cytogenetic abnormalities (CAs) achieve similar complete response (CR) rates, but the later have inferior progression-free survival (PFS). This questions the legitimacy of CR as a treatment endpoint and represents a biological conundrum regarding the nature of tumor reservoirs that persist after therapy in high-risk MM. We used next-generation flow (NGF) cytometry to evaluate measurable residual disease (MRD) in MM patients with standard- vs high-risk CAs (n = 300 and 90, respectively) enrolled in the PETHEMA/GEM2012MENOS65 trial, and to identify mechanisms that determine MRD resistance in both patient subgroups (n = 40). The 36-month PFS rates were higher than 90% in patients with standard- or high-risk CAs achieving undetectable MRD. Persistent MRD resulted in a median PFS of âˆ¼3 and 2 years in patients with standard- and high-risk CAs, respectively. Further use of NGF to isolate MRD, followed by whole-exome sequencing of paired diagnostic and MRD tumor cells, revealed greater clonal selection in patients with standard-risk CAs, higher genomic instability with acquisition of new mutations in high-risk MM, and no unifying genetic event driving MRD resistance. Conversely, RNA sequencing of diagnostic and MRD tumor cells uncovered the selection of MRD clones with singular transcriptional programs and reactive oxygen species-mediated MRD resistance in high-risk MM. Our study supports undetectable MRD as a treatment endpoint for patients with MM who have high-risk CAs and proposes characterizing MRD clones to understand and overcome MRD resistance. This trial is registered at www.clinicaltrials.gov as #NCT01916252.

Immunologic characterization of COVID-19 patients with hematological cancer.

Not available.

Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma.

Multiple myeloma (MM) patients undergo repetitive bone marrow (BM) aspirates for genetic characterization. Circulating tumor cells (CTCs) are detectable in peripheral blood (PB) of virtually all MM cases and are prognostic, but their applicability for noninvasive screening has been poorly investigated. Here, we used next-generation flow (NGF) cytometry to isolate matched CTCs and BM tumor cells from 53 patients and compared their genetic profile. In eight cases, tumor cells from extramedullary (EM) plasmacytomas were also sorted and whole-exome sequencing was performed in the three spatially distributed tumor samples. CTCs were detectable by NGF in the PB of all patients with MM. Based on the cancer cell fraction of clonal and subclonal mutations, we found that ~22% of CTCs egressed from a BM (or EM) site distant from the matched BM aspirate. Concordance between BM tumor cells and CTCs was high for chromosome arm-level copy number alterations (≥95%) though not for translocations (39%). All high-risk genetic abnormalities except one t(4;14) were detected in CTCs whenever present in BM tumor cells. Noteworthy, ≥82% mutations present in BM and EM clones were detectable in CTCs. Altogether, these results support CTCs for noninvasive risk-stratification of MM patients based on their numbers and genetic profile.

Transcriptional profiling of circulating tumor cells in multiple myeloma: a new model to understand disease dissemination.

The reason why a few myeloma cells egress from the bone marrow (BM) into peripheral blood (PB) remains unknown. Here, we investigated molecular hallmarks of circulating tumor cells (CTCs) to identify the events leading to myeloma trafficking into the bloodstream. After using next-generation flow to isolate matched CTCs and BM tumor cells from 32 patients, we found high correlation in gene expression at single-cell and bulk levels (r ≥ 0.94, P = 10-16), with only 55 genes differentially expressed between CTCs and BM tumor cells. CTCs overexpressed genes involved in inflammation, hypoxia, or epithelial-mesenchymal transition, whereas genes related with proliferation were downregulated in CTCs. The cancer stem cell marker CD44 was overexpressed in CTCs, and its knockdown significantly reduced migration of MM cells towards SDF1-α and their adhesion to fibronectin. Approximately half (29/55) of genes differentially expressed in CTCs were prognostic in patients with newly-diagnosed myeloma (n = 553; CoMMpass). In a multivariate analysis including the R-ISS, overexpression of CENPF and LGALS1 was significantly associated with inferior survival. Altogether, these results help understanding the presence of CTCs in PB and suggest that hypoxic BM niches together with a pro-inflammatory microenvironment induce an arrest in proliferation, forcing tumor cells to circulate in PB and seek other BM niches to continue growing.

The PHES battery does not detect all cirrhotic patients with early neurological deficits, which are different in different patients.

BACKGROUND AND AIMS: The psychometric hepatic encephalopathy score (PHES) is the "gold standard" for minimal hepatic encephalopathy (MHE) diagnosis. Some reports suggest that some cirrhotic patients "without" MHE according to PHES show neurological deficits and other reports that neurological alterations are not homogeneous in all cirrhotic patients. This work aimed to assess whether: 1) a relevant proportion of cirrhotic patients show neurological deficits not detected by PHES; 2) cirrhotic patients with mild neurological deficits are a homogeneous population or may be classified in sub-groups according to specific deficits. METHODS: Cirrhotic patients "without" (n = 56) or "with" MHE (n = 41) according to PHES and controls (n = 52) performed psychometric tests assessing attention, concentration, mental processing speed, working memory and bimanual and visuomotor coordination. Heterogeneity of neurological alterations was analysed using Hierarchical Clustering Analysis. RESULTS: PHES classified as "with" MHE 42% of patients. Around 40% of patients "without" MHE according to PHES fail two psychometric tests. Oral SDMT, d2, bimanual and visuo-motor coordination tests are failed by 54, 51, 51 and 43% of patients, respectively. The earliest neurological alterations are different for different patients. Hierarchical clustering analysis shows that patients "without" MHE according to PHES may be classified in clusters according to the tests failed. In some patients coordination impairment appear before cognitive impairment while in others concentration and attention deficits appear before. CONCLUSIONS: PHES is not sensitive enough to detect early neurological alterations in a relevant proportion of cirrhotic patients. Oral SDMT, d2 and bimanual and visuo-motor coordination tests are more sensitive. The earliest neurological alterations are different in different cirrhotic patients. These data also have relevant clinical implications. Patients classified as "without MHE" by PHES belonging to clusters 3 and 4 in our study have a high risk of suffering clinical complications, including overt HE and must be diagnosed and clinically followed.