ALBERT

Description: Patients with multiple myeloma (MM) carrying high-risk cytogenetic abnormalities (CA) have inferior outcome despite achieving similar complete response (CR) rates when compared to cases with standard-risk CA. This questions the legitimacy of CR as treatment endpoint for high-risk MM, and represents a biological conundrum regarding the nature of tumor reservoirs persisting after therapy in patients with standard- and high-risk CA. Here, we used next-generation flow (NGF) to evaluate measurable residual disease (MRD) in MM patients with standard- (N=300) vs high-risk CA (N=90) enrolled in the PETHEMA/GEM2012MENOS65 trial (NCT01916252), and to identify mechanisms determining MRD resistance in both patient subgroups (N=40). The 36-month progression-free and overall survival rates were higher than 90% in patients with undetectable MRD, with no significant differences (P≥0.202) between cases having standard- vs high-risk CA. Persistent MRD resulted in median progression-free survival of approximately three and two years in patients with standard- and high-risk CA, respectively (P

Description: In MM patients relapsing after MRD-negativity, the disease could reemerge from immature cells or from undetectable MRD. However, it remains unknown if immature cells have the same genetic background as MM plasma cells (PCs), as well as the amount of MRD that persists below the limit of detection (LOD) of next-generation techniques. To obtain further insight, we compared the biological landscape of MM PCs at diagnosis to that of CD34 progenitors, B cells and normal PCs isolated from patients with negative MRD by next-generation flow (NGF) after treatment. We performed whole-exome sequencing (WES, mean depth: 90x) with the 10XGenomics Exome Solution for low DNA-input as well as deep NGS of B-cell receptor immunoglobulin (BcR IG) gene rearrangements (mean, 69,975 sequences), in a total of 68 cell-samples isolated from the bone marrow (BM) of 7 MM patients with MRD-negativity by EuroFlow NGF after induction with VRD and auto-transplant (GEM2012MENOS65 trial). Patients with negative MRD were intentionally selected to avoid contamination with MM PCs during sorting of CD34 progenitors, B-cell precursors, mature B cells and normal PCs after induction and transplant. We investigated in these populations the presence of somatic mutations and clonotypic BcR Ig rearrangements detectable in MM PCs sorted at diagnosis, using peripheral blood T cells as germline control. We also performed WES in matched diagnostic MM PCs and MRD cells persisting after VRD induction in 14 cases as control. In another 6 patients with untreated MM, we performed single-cell RNA and BcR IG sequencing (scRNA/BcRIGseq) of total BM B cells and PCs (n=16,380) to investigate before treatment, if the clonotypic BcR IG sequence of MM PCs was detectable in other B cell stages defined by their molecular phenotype. We used multidimensional flow cytometry (MFC) to investigate the frequency of B cell clonality in BM samples from a larger series of 195 newly-diagnosed MM patients, prospectively enrolled in the GEM-CLARIDEX trial. Somatic mutations present in diagnostic MM PCs were detectable in the lymphopoiesis of 5/7 patients achieving MRD-negativity after treatment. In one case, out of 55 mutations present in diagnostic MM PCs, a single mutation in PCSK1N (VAF: 0.30) was detectable in normal PCs. In the other four patients, a total of 85 mutations were present in MM PCs and up to 10 (median VAF, 0.16) were found all the way from CD34 progenitors into B-cell precursors, mature B cells and normal PCs, but not in T cells. Of note, most mutations were reproducibly detected in each cell type after induction and after transplant. All somatic mutations shared by MM PCs and normal cells were non-recurrent, and genes recurrently mutated in MM (eg. ACTG1, ATM, DIS3, FAM46C, KRAS, LTB, MAX, TRAF3) were found in MM PCs but never in normal cells. Copy number alterations (CNA) were found only in MM PCs. By contrast, up to 513/827 (62%) mutations and 48/67 (72%) CNA were detectable in matched diagnostic MM PCs and persistent MRD cells, indicating that the few somatic variants present in normal cells were unlikely related to contaminating MRD below NGF's LOD. Accordingly, MM clonotypic BcR IG rearrangements were detectable in normal PCs (4/7patients) and in immature B cells (5/7 patients) but at much lower frequencies (mean of 0.02% in both). Of note, 9 additional clonotypes (mean 8.4%) were found in MM PCs of 5/7 patients (range, 1-3). scRNR/BcRIGseq unveiled that clonotypic cells were confined mostly but not entirely within PC clusters, and that in 1 patient another clonotype was detectable in mature B cells. Accordingly, using MFC we found in a larger series that 25/195 (13%) of newly-diagnosed MM patients display B-cell clonality (median of 0.7% BM clonal B cells, range 0.02%-6.3%). In conclusion, we show for the first time that MM patients bear somatic mutations in CD34 progenitors that specifically differentiate into the B cell lineage, likely before the disease onset. Because diagnostic, MRD (and relapse) MM PCs display great genetic similarity, these results suggest that undetectable MRD

Description: 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.

Description: Leukemia relapse occurring in donor cells, so called donor cell leukemia (DCL) after allogeneic hematopoietic stem cell transplantation has been previously reported in the literature. Some authors have suggested that the development of DCL is perhaps a more common occurrence than traditionally thought. Donor cell myeloma (DCM) seems to be less frequent than DCL. This 46-year old male when first seen in 2000 was diagnosed with stage IIIa multiple myeloma. A monoclonal IgA kappa spike was recorded at diagnosis. Treatment with melphalan and prednisone was delivered every four to six weeks for a total of 22 courses. Fourty months after the initial diagnosis, an M2 acute myelogenous leukemia was identified. Treatment with chemotherapy resulted in complete remission. Matched UCB cells were localized at the London Cord Blood Bank. The UCB belonged to a male product of a white western European mother and a black Nigerian father who was a carrier of hemoglobin S. Hemoglobins A, F and S were detected in the UCB, consonant with sickle cell trait. The patient was allografted employing the "Mexican" NST conditioning regimen, granulocyte count recovered to more than 0.5 x 109/L on day 14, with the platelet count never dropping below 20 x 109/L. On day +40, the polymorphic microsatellite markers revealed mixed chimerism. The hemoglobin S gene was identified on day +20 and on day +60, full chimerism was shown. Cyclosporine A was stopped on day +350. The patient returned 170 months after the transplant with low back pain and the bone marrow aspiration disclosed 80% abnormal plasma cells, an IgA kappa monoclonal spike of 3.1 gr/dl, and complete chimerism. Malignant plasma cells were sorted by means of flow cytometry before genetic fingerprinting; cells were stained with an admixture of fluorescent monoclonal antibodies and cells co-expressing dim CD45, bright CD38 and CD56 were sorted out to ≥99% purity. Sorted cells were shown to have donor origin (Figure 1). The patient was treated with thalidomide, dexamethasone and bortezomib and the monoclonal spike disappeared; an autologous stem cell transplant is planned. Most people consider that the development of a malignancy in the cells of the donor is a rare event and very few prospective studies have analyzed the real prevalence of this phenomenon. Prospectively, we have found that 7% (95% CI 2.9 to 13.6%) of patients with leukemic activity after an allogeneic graft do have a donor cell-derived leukemia; this figure contrasts with those described elsewhere in non-prospective studies. A major problem in the analysis of donor cell derived malignancies is that demonstration of the donor cell origin of malignant activity. In this case, the demonstration of DNA of the donor in the fluorescence-activated sorted malignant plasma cells is indicative of the origin of the myeloma cells. Interestingly, the immunoglobulin type produced by the initial myeloma cells is the same as that of the donor-cell myeloma; Despite being two myelomas producing the same immunoglobulin subtype, both should be considered as de novomalignancies and as such, treated; we have previously shown that donor cell leukemias do have a response when treated as de novo, non-secondary leukemias. To our best knowledge, this is the second report of DCM following allogeneic HSCT. Prior to this case, Kim et al reported a DCM after an allogeneic transplant in a patient with refractory anemia with ringed sideroblasts. Previously, two cases have been reported of donor-origin MM, but they occurred in patients who underwent solid organ transplantation of the kidney and heart-lung. Kumar et alreported a case of DCM developing after unrelated allogeneic HSCT in the both donor and recipient but they did not conducted a comprehensive molecular cytogenetic study. In the case published by Maestas et al, an abnormal proliferation of plasma cells was identified in the donor, thus making possible that a malignant plasma cell clone was already present in the donor stem cells. In summary, we have clearly shown that this patient has had three different malignancies: 1) De novomultiple myeloma, 2) Secondary acute myelogenous leukemia and 3) De novodonor cell-derived multiple myeloma. The mechanisms involved in these episodes could be useful to better understand tumorigenesis. Disclosures No relevant conflicts of interest to declare.