Abstract: Background: The advent of immunotherapy renewed the interest in immune monitoring to identify determinants of treatment response. Flow cytometry is widely adopted in immunotherapy-based clinical trials, but manual analysis of multiparameter files poses a challenge to capture full cellular diversity and to provide unbiased reporting in large datasets. Methods: Here, we developed a semi-automated pipeline named "FlowCT" which, starting from compensated data obtained with standardized protocols, allows simultaneous analyses of multiple files and automated cell clustering. FlowCT starts with quality control and data normalization followed by an analytical stage with clustering algorithms, dimensional reduction techniques and cluster identification based on antigen expression. Statistical tools are included for immediate analysis of results. Results: As proof-of-concept, we used FlowCT in three different datasets. First, we applied FlowCT to bone marrow (BM) samples from three multiple myeloma (MM) patients stained with 17-color flow cytometry, to determine the increment in the complexity of analyzing 8 and 17 markers, chosen to characterize T cells. Of note, a single combination of CD3, CD4, CD8, CD45RA, CD56, CCR7, PD1 and TIGIT, allowed the identification of 31 lymphocyte subsets using FlowCT, which increased to 39 different clusters with 17 markers and unveiled a novel population of CD3- CD56- CD8+ CD16+ lymphoid cells in the MM immune microenvironment. Secondly, we applied FlowCT to matched peripheral blood (PB) and BM samples from 10 patients with smoldering MM, to objectively assess if PB represents a good surrogate of T-cell distribution in the BM. Using an 8-color combination to characterize CD4 T cells, up to 26 different subsets were identified, including several CD4 T helper (Th) type subsets. Of note, their distribution within PB CD4 T cells was similar to that found in BM, except for CD4 T CXCR3+CCR4+ effector memory and Th17 central memory subsets that decreased in the BM tumor immune microenvironment. Thirdly, we analyzed 30 BM samples from 10 MM patients studied every year during maintenance therapy, monitored with CD4, CD8, CD25, CD45RA, CD127, CCR7, PD1, and TCRγδ to characterize T cells. FlowCT identified 29 different T-cell populations, including 9 CD4 subsets, 14 CD8 subsets, 4 Tγδ cell subsets and 2 distinct Treg subsets. Longitudinal, semi-automated and unbiased analysis unveiled a significant fluctuation of CD4 naïve and transitional memory cells during maintenance, as well as a significant decrease of CD8 CD127- effector memory and transitional effectors cells after 2 years of maintenance. Conclusions: Here, we presented FlowCT, a pipeline optimized for the analysis of large flow cytometry datasets that could be easily implemented by research laboratories to unveil full cellular diversity, singular patterns of antigen expression, and to provide unbiased reporting in large studies, like clinical trials. Disclosures Puig: Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; The Binding Site: Honoraria; Takeda: Consultancy, Honoraria. Borrello:WindMIL Therapeutics: Equity Ownership, Patents & Royalties, Research Funding; Aduro: Patents & Royalties: intellectual property on allogeneic MM GVAX; BMS: Consultancy; Celgene: Honoraria, Research Funding, Speakers Bureau. Rosinol Dachs:Janssen, Celgene, Amgen and Takeda: Honoraria. Mateos:Janssen, Celgene, Takeda, Amgen, GSK, Abbvie, EDO, Pharmar: Membership on an entity's Board of Directors or advisory committees; Janssen, Celgene, Takeda, Amgen, Adaptive: Honoraria; Amgen Inc, Janssen Biotech Inc: Other: Data and Monitoring Committee; Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Takeda Oncology.: Speakers Bureau; AbbVie Inc, Amgen Inc, Celgene Corporation, Genentech, GlaxoSmithKline, Janssen Biotech Inc, Mundipharma EDO, PharmaMar, Roche Laboratories Inc, Takeda Oncology: Other: Advisory Committee. Lahuerta:Takeda, Amgen, Celgene and Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Bladé:Jansen, Celgene, Takeda, Amgen and Oncopeptides: Honoraria. San-Miguel:Amgen, Bristol-Myers Squibb, Celgene, Janssen, MSD, Novartis, Roche, Sanofi, and Takeda: Consultancy, Honoraria. Paiva:Celgene, Janssen, Sanofi and Takeda: Consultancy; Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche and Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Abstract: 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 & lt; .001). We also determined progression-free survival (HR, 4.09; P & lt; .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.

Abstract: 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 & gt;0.015% versus ≤0.015% CTCs at baseline had a median time-to-progression of 17 months versus not reached (HR, 4.9; P  & lt; 0.001). Presence of & gt;20% BM PCs had no prognostic value in a multivariate analysis that included serum free light-chain ratio & gt;20, & gt;2 g/dL M-protein, and & gt;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.

Abstract: MYD88 L265P occurs in between a normal mutated lymphopoiesis and additional genetic alterations during lymphomagenesis.

Abstract: The genetic heterogeneity of multiple myeloma (MM) makes it unlikely that established or novel chemotherapy could be equally effective in all genetic subgroups. Therefore, genetics alone is insufficient to fully capture different disease outcomes, and there is growing body of evidence showing that detection of minimal residual disease (MRD), using immunophenotypic or molecular-based approaches, also provides powerful independent prognostic information particularly among transplant-eligible patients. However, it is perhaps in elderly MM, the major patient subgroup and in which optimal balance between efficacy and toxicity is critical, that sensitive response assessment could help to tailor patients’ treatment. Here, we used for the first time sensitive 8-color multidimensional flow cytometry (cut-off of 10-5) to monitor MRD among elderly MM patients included in the PETHEMA/GEM2010MAS65 trial (sequential chemotherapy with 9 cycles of bortezomib-melphalan-prednisone (VMP) followed by 9 cycles of lenalidomide-low dose dexamethasone (Rd), or alternating cycles of VMP and Rd up to 18 cycles). A single 8-color antibody combination (CD45-PacB/CD138-OC515/CD38-FITC/CD56-PE/CD27-PerCPCy5.5/CD19-PECy7/CD117-APC/CD81-APCH7) was used to detect phenotypically aberrant clonal plasma cells (PCs), and MRD-negativity was defined when & lt;20 clonal PCs were detected among ≥2.000.000 leukocytes ( & lt;0.001%). MRD assessment was centralized in three PETHEMA/GEM laboratory-cores, cytometrists were blinded to all clinical data, and results were prospectively uploaded into a locked intranet dataset. Median follow-up of the series was 27 months, and time-to-progression (TTP) / overall survival (OS) was measured from the moment of MRD assessment. First, we evaluated the MRD status at cycle 9 of chemotherapy (n=117), and no significant differences were observed for MRD-negative rates between the sequential vs alternating regimens (23% vs 25%; P = .86). However, when we focused on patients in complete response (CR; n=41) and compared the quality of CR achieved in each arm according to patients’ MRD status, we found significantly higher frequencies of MRD-negative rates after the sequential vs alternating schema (75% vs 40%; P = .03). Patients in CR attaining MRD-negativity at cycle 9 showed a significantly prolonged TTP (100% vs 41% at 2-years; P = .001) as well as OS (100% vs 71% at 2-years; P= .03) as compared to patients in CR but with persistent MRD cells. To understand the kinetics of MDR response with sequential vs alternating 18 cycles of chemotherapy, we focused on 72 patients with paired Flow-MRD assessments at cycles 9 and 18. No MRD-negative patients at cycle 9 turned into MRD-positive at cycle 18; however, 21% of MRD-positive patients at cycle 9 became MRD-negative at cycle 18, with no significant differences between rates of transformation after sequential vs alternating regimens (P = .23). At the end of cycle 18, MRD-negative rates among patients randomized to the sequential vs alternating schema were of 48% vs 31% (P = .08), and the quality of CR (according to patients’ MRD status) was slightly but not significantly superior in the sequential vs alternating arm (66% vs 48%; P = .16). Again, patients in CR at cycle 18 attaining MRD-negativity showed superior TTP as compared to those in CR with persistent MRD: TTP at 2-years of 83% vs 56% (P= .06). We also compared the impact of Flow-MRD among cytogenetically defined standard- and high-risk [+1q, t(4;14), t(14;16), and/or del(17p)] patient subgroups (n=125). As expected, standard-risk patients attaining MRD-negativity had significantly prolonged TTP as compared to MRD-positive patients (94% vs 58% at 2-years; P = .035); however, also high-risk cytogenetic patients achieving Flow-CR showed significantly superior TTP (median not reached vs 10 months; P= .001). In summary, we unravel the clinical impact of sensitive Flow-MRD monitoring (10-5) among elderly MM patients in which attaining MRD-negativity, particularly early in therapy, translated into virtually relapse-free intervals at 2-years. In parallel, we also show the value of sensitive MRD kinetics to understand the benefit of additional (sequential or alternating) chemotherapy to further reduce MRD levels, as well as the significance of Flow-MRD among cytogenetically defined standard- and high-risk patents. Disclosures Paiva: Millenium: Honoraria; Celgene: Honoraria; Janssen: Honoraria. Ocio:Array Biopharma: Honoraria, Research Funding. Rosiñol:Janssen: Honoraria; Celgene: Honoraria. Oriol:Celgene Corporation: Consultancy. Gutierrez:Celgene: Honoraria; Janssen: Honoraria. Blade:Janssen: Honoraria; Celgene: Honoraria. Lahuerta:Janssen: Honoraria; Celgene: Honoraria. Mateos:Celgene: Honoraria; Janssen: Honoraria. San Miguel:Janssen: Honoraria; Celgene: Honoraria.

Abstract: MRD monitoring is one of the most relevant prognostic factors in elderly MM patients, irrespective of age or cytogenetic risk. Second-generation MFC immune profiling concomitant to MRD monitoring also helped to identify patients with different outcomes.

Abstract: Background: MM and AL are the two most common malignant monoclonal gammopathies. Both diseases result from the accumulation of clonal PCs, but their clinical behavior is significantly different suggesting fundamental differences in disease biology. Previous attempts to identify genetic hallmarks that could explain such differences have been unsuccessful. Furthermore, it is unknown if MM and AL arise from the same or different normal PC counterparts. Aim: To define a transcriptional atlas of the normal PC development in peripheral blood (PB) and bone marrow (BM) for comparison with the transcriptional programs of clonal PCs in MM and AL. Methods: A total of 93 subjects were studied. In 7 healthy adults (HA), PB PCs were phenotypically sorted according to heavy-chain isotypes (IgG, IgA and IgM). In addition, 5 different BM PCs subsets were isolated based on the differential expression of CD19, CD39, CD81 and CD56, due to their ascribed role in dissecting unique BM PC differentiation states. Clonal PCs from patients with MM (n=38) and AL (n=41) were isolated by FACS according to patient-specific aberrant phenotypes. Due to small numbers of PCs sorted from each subset in HA and clonal PCs in AL patients, we used an RNAseq method optimized for limited cell numbers. Differential expression across all pairwise comparisons between groups was analyzed with Deseq2 R package followed by k-means clustering of genes in R. Single-cell RNAseq (scRNAseq, 10xGenomics) was performed in a total of 35,910 PCs from 3 HA, 2 MM and 2 AL. We used Seurat R package to remove batch effect followed by canonical correlation to perform an integrated analysis of all single PCs from HA, MM and AL subjects. Results: Principal component analysis of RNAseq data unveiled two major clusters of normal PCs: those in PB and those in BM (with some transcriptional diversity between CD19+ and CD19- PCs), whereas the CD19+CD39+CD81+CD56- BM subset co-localized with PB and CD39- BM PCs (Panel A). Clonal PCs from MM and AL patients clustered together, and both displayed some transcriptional variance related to the spatial location of normal PCs (i.e. PB or BM). In total, 2174 genes were found significantly deregulated after cross-comparing the 10 PC groups (adj.p-value 〈 0.01, logFC 〉 1) and semi-supervised k-means clustering unveiled 8 transcriptional modules (Panel B). Namely, the transition from PB into BM PCs was characterized by genes related to proliferation (clusters 1 & 2), whereas CD39+ and CD39- BM PC subsets differed on the expression of genes associated with proliferation, homing, and metabolism (1, 2, 4 & 6). Thus, CD19+CD39+CD81+CD56- BM PCs emerged as a novel subset that bridges new-born PB with long-lived (CD39-) BM PCs. Interestingly, clonal PCs from MM and AL shared transcriptional programs related to quiescence (5 & 6) with long-lived BM PCs; however, skewing of polyclonal immunoglobulin gene expression (3) and active gene transcription (8) emerged as hallmarks of the neoplastic transformation from normal, long-lived PCs into clonal PCs. That notwithstanding, the later displayed expression levels of the proliferation and homing transcriptional modules (1 & 4) similar to new-born PB and CD39+ BM PCs. Of note, a small transcriptional cluster of genes related to ribosome biogenesis (7) was significantly more expressed in MM than AL. These findings led us to integrate scRNAseq profiles of normal and clonal BM PCs from MM and AL patients, to define PC clusters based on their transcriptional program rather than their normal vs malignant status (Panel C). This strategy unveiled 11 different PC clusters with unequal distribution between groups. Thus, more than half of clonal PCs in MM and AL were assigned to a cluster that is also predominant in normal PCs (1). By contrast, other clusters with a transcriptional program similar to that of new-born PCs (2 & 5) became rarer in MM and AL. Furthermore, a cluster of PCs with an immature-like phenotype (6) was detectable in MM but almost absent in AL. Conclusions: This is the first integrated analysis of the transcriptional programs of normal PC subsets and clonal PCs in MM and AL, both at the bulk and single-cell levels. Our results unveil shared and exclusive transcriptional states in normal and clonal PCs, together with unique differences between clonal PCs in MM and AL. Thus, we provide here a fundamental resource to understand normal PC development and the cellular origin of both malignant monoclonal gammopathies. Figure Figure. Disclosures Puig: Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Ocio:Pharmamar: Consultancy; AbbVie: Consultancy; Janssen: Consultancy, Honoraria; Seattle Genetics: Consultancy; BMS: Consultancy; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Sanofi: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Mundipharma: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Array Pharmaceuticals: Research Funding. Oriol:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Martinez Lopez:Bristol Myers Squibb: Research Funding, Speakers Bureau; Janssen: Research Funding, Speakers Bureau; Novartis: Research Funding, Speakers Bureau; Celgene: Research Funding, Speakers Bureau. Mateos:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees. Lahuerta:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. San-Miguel:Sanofi: Consultancy; Takeda: Consultancy; Novartis: Consultancy; MSD: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Brystol-Myers Squibb: Consultancy; Amgen: Consultancy; Roche: Membership on an entity's Board of Directors or advisory committees.

Abstract: 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 ∼3 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.