Identification of a Maturation Plasma Cell Index through a Highly Sensitive Droplet Digital PCR Assay Gene Expression Signature Validation in Newly Diagnosed Multiple Myeloma Patients.

DNA microarrays and RNA-based sequencing approaches are considered important discovery tools in clinical medicine. However, cross-platform reproducibility studies undertaken so far have highlighted that microarrays are not able to accurately measure gene expression, particularly when they are expressed at low levels. Here, we consider the employment of a digital PCR assay (ddPCR) to validate a gene signature previously identified by gene expression profile. This signature included ten Hedgehog (HH) pathways' genes able to stratify multiple myeloma (MM) patients according to their self-renewal status. Results show that the designed assay is able to validate gene expression data, both in a retrospective as well as in a prospective cohort. In addition, the plasma cells' differentiation status determined by ddPCR was further confirmed by other techniques, such as flow cytometry, allowing the identification of patients with immature plasma cells' phenotype (i.e., expressing CD19+/CD81+ markers) upregulating HH genes, as compared to others, whose plasma cells lose the expression of these markers and were more differentiated. To our knowledge, this is the first technical report of gene expression data validation by ddPCR instead of classical qPCR. This approach permitted the identification of a Maturation Index through the integration of molecular and phenotypic data, able to possibly define upfront the differentiation status of MM patients that would be clinically relevant in the future.

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.

Large T cell clones expressing immune checkpoints increase during multiple myeloma evolution and predict treatment resistance.

Tumor recognition by T cells is essential for antitumor immunity. A comprehensive characterization of T cell diversity may be key to understanding the success of immunomodulatory drugs and failure of PD-1 blockade in tumors such as multiple myeloma (MM). Here, we use single-cell RNA and T cell receptor sequencing to characterize bone marrow T cells from healthy adults (n = 4) and patients with precursor (n = 8) and full-blown MM (n = 10). Large T cell clones from patients with MM expressed multiple immune checkpoints, suggesting a potentially dysfunctional phenotype. Dual targeting of PD-1 + LAG3 or PD-1 + TIGIT partially restored their function in mice with MM. We identify phenotypic hallmarks of large intratumoral T cell clones, and demonstrate that the CD27- and CD27+ T cell ratio, measured by flow cytometry, may serve as a surrogate of clonal T cell expansions and an independent prognostic factor in 543 patients with MM treated with lenalidomide-based treatment combinations.

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.

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.