Anagnostou, Theodora

Abstract The role and immunological constitution of the tumor microenvironment (TME) in splenic marginal zone lymphoma (sMZL) is largely unknown and studies on the phenotype and subset composition of intratumoral T cells are lacking. In the present study, we evaluated 36 sMZL spleen specimens by single cell analysis to gain a better understanding of the TME in sMZL. Using mass cytometry (CyTOF), we observed that the TME in sMZL is distinct from that of control non-malignant reactive spleen (rSP). We found that the number of TFH cells varied greatly in sMZL, ICOS+ TFH cells were more abundant in sMZL than rSP, and TFH cells positively correlated with increased numbers of memory B cells. Treg cell analysis revealed that TIGIT+ Treg cells are enriched in sMZL and correlate with suppression of TH17 and TH22 cells. Intratumoral CD8+ T cells were comprised of subsets of short-lived, exhausted and late-stage differentiated cells, thereby functionally impaired. We observed that T-cell exhaustion was present in sMZL and TIM-3 expression on PD-1low cells identified cells with severe immune dysfunction. Gene expression profiling by CITE-seq analysis validated this finding. Taken together, our data sugget that the TME as a whole, and T-cell population specifically, are heterogenous in sMZL and immune exhaustion is one of the major factors impairing T-cell function.

Abstract Purpose: Regulatory T-cells (Treg) are essential to Tregs homeostasis and modulate the antitumor immune response in patients with lymphoma. However, the biology and prognostic impact of Tregs in splenic marginal zone lymphoma (SMZL) have not been studied. Experimental Design: Biopsy specimens from 24 patients with SMZL and 12 reactive spleens (rSP) from individuals without lymphoma were analyzed by using CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing), CyTOF (mass cytometry) analysis, and flow cytometry to explore the phenotype, transcriptomic profile, and clinical significance of intratumoral Tregs and their subsets. The biological characteristics and cell signaling pathways of intratumoral Treg subsets were confirmed by in vitro functional assays. Results: We found that Tregs are more abundant in SMZL patients' spleens than rSP, and Tregs from patients with SMZL and rSP can be separated into CD161+Treg and CD26+Treg subsets. CD161+Tregs are increased in SMZL but have dysregulated immune function. We found that CD161+Treg and CD26+Tregs have unique gene expression and phenotypic profiles and are differentially correlated with patient outcomes. Specifically, increased CD161+Tregs are significantly associated with a favorable prognosis in patients with SMZL, whereas CD26+Tregs are associated with a poor prognosis. Furthermore, activation of the IL2/STAT5 pathway contributes to the induction of CD26+Tregs and can be reversed by STAT5 inhibition. Conclusions: IL2/STAT5-mediated expansion of CD26+Tregs contributes to a poor clinical outcome in SMZL and may represent a therapeutic opportunity in this disease.

7022 Background: Philadelphia-like Acute Lymphoblastic Leukemia (Ph-like ALL) is a high-risk subset of adult ALL. Until recently, there has not been a systematic platform to recognize this entity in clinical practice. Furthermore, data regarding the role of allogeneic hematopoietic cell transplantation (allo-HCT) is lacking. We conducted this study to identify patients with Ph-like ALL and describe their outcomes in comparison to Ph+ and Ph- ALL with emphasis on the role of allo-HCT. Methods: To identify cases of Ph-like ALL, available diagnostic cytogenetic pellets for patients in the Mayo Clinic ALL cohort (N=365) were tested using a targeted fluorescence in situ hybridization (FISH) panel developed by the Mayo Clinic Genomics Laboratory and includes probes to detect Ph-like-specific rearrangements (i.e., ABL1, ABL2, PDGFRB, JAK2 and CRLF2). Results: Thirty-three (9%) patients with Ph-like ALL were identified, the remaining patients were classified as Ph+ (N=132, 36%) or Ph- ALL (N=200, 55%). Patients with Ph-like ALL were younger (Median: 39 vs. 50 vs. 49 years, P=.01), had higher WBC (Median: 27.9 vs. 21.5 vs. 4.5 x109/L, P<.001), were less likely to achieve CR (91% vs. 99% vs. 96%, P=.02), more likely to be MRD+ (64% vs. 34% vs. 36%, P=.03), had a higher relapse rate (5-year: 39% vs. 24% vs. 38%, P=.01) and lower OS (5-year: 41% vs. 64% vs. 49%, P=.02), see Table. Patients who achieved MRD negativity had better OS (MRD+ vs MRD-, P=.01). Importantly, no statistically significant difference in OS, relapse or non-relapse mortality were noted between the 3 groups in patients who underwent allo-HCT in CR1. Conclusions: Ph-like ALL is a high risk subgroup with increased prevalence in younger adults. Allo-HCT appears to offset the poor prognosis associated with this entity. A targeted FISH panel offers timely recognition of this entity in a clinical setting.[Table: see text]

Key Points Systemic immune microenvironment signatures in CMML indicate an altered T- and natural killer cell balance. CMML bone marrow dendritic cell aggregates associate with disease progression and systemic regulatory T-cell phenotypic switch.

Introduction: Current classifications of low-grade B-cell lymphomas (LGL), including splenic marginal zone lymphomas (SMZL), nodal marginal zone lymphomas (NMZL), and Lymphoplasmacytic lymphomas (LPL) are based on a mixture of clinical features and morphologic, immunophenotypic, and genetic findings from the tumor biopsy specimen. While this approach to classification makes pathologic diagnosis more precise, the corresponding clinical impact for the timing and choice of treatment is limited, and differentiating between cases can be challenging. Although LGL are considered indolent and the 10-year overall survival (OS) is about 80%, 70% of cases will eventually require treatment and approximately 30% of patients display a more aggressive phenotype and have a poor prognosis. Consequently, further investigations into the driving genetic, biological, and immune mechanisms of LGL are essential for early identification of high-risk patients and design of personalized treatments. Materials and Methods: RNA-seq was performed on 63 newly diagnosed LGL patient samples from the Mayo Clinic/University of Iowa Lymphoma SPORE: SMZL (N=48), NMZL (N=6), LPL (N=5), MALT (N=2), and BCL (N=2) as well as 5 normal memory B cell controls (CD19+CD27+). For identification of biologic clusters, filtered RNA-seq data was analyzed using the Non-negative Matrix Factorization (NMF) clustering tool from the Broad Institute against the normal samples. Each cluster was analyzed for differential gene expression. This analysis generated cluster-specific T values for each gene. Genes that were significantly associated with a cluster (FDR<0.05) were analyzed for genetic ontology. Cibersort was used to deconvolute the tumor microenvironment (TME). We performed whole exome sequencing (WES) analyses on 60/63 matched LGL to characterize mutations and copy number alterations. Results: WES revealed the presence of previously reported mutations in genes such as MYD88, SPEN, NOTCH, and KLF2 as well as copy number alterations 7q31.2, BCL6, TNFAIP3, and BCL2. NMF clustering of RNA-seq data resulted in a best fit of 5 clusters, named LGL1 through LGL4 and "Normals". Pathologic subtypes were not exclusive to a specific cluster. Differential RNA expression analysis resulted in gene sets that were differentially expressed in each cluster. A cluster signature of the top 1% of associated genes (N=94) was created for each. LGL1 genes were associated with higher BCR signaling. LGL2 was characterized by regulatory dysfunction, particularly concerning mitochondrial integrity. LGL3 genes were associated with high TME and NOTCH and STAT signaling components. Specifically, LGL3 was associated with high CD8+ T-cell and M2 macrophage TME presence. LGL4 was distinguished by the presence of aggressive genetic programs related to B-cell activation such as NF-kB and IRF4. Consequently, LGL4 patients had significantly poorer rates of event-free survival (EFS) (P=0.029) and OS (P=0.006) when compared against the other groups. The top 1% gene signature (N=94) was next validated against an independent cohort of 84 LGL samples with gene expression data (SMZL [N=34], NZML [N=24], and LPL [N=24]). A similar cluster pattern emerged, with pathological subtypes distributing across LGL classifications based on respective transcriptomic signatures. Association of DNA variants within each cluster was also analyzed, but due to the low frequency of genomic variants detected in LGL, nothing was significantly associated. However, we did see an enrichment of BCL2, BCL6, and TNFAIP3 alterations in LGL4 and an overall lower driver gene mutation frequency in LGL2. Conclusions: Gaining a greater understanding of LGL based on their genetic, biologic, and immune profiles will establish a valuable platform for clinicians to identify high-risk cases and make better therapeutic decisions. Using a large cohort of well-annotated cases we identify novel clusters of LGL that present unique genomic and clinical profiles. Our study lays the groundwork for a precision therapy approach in LGL in which DNA or RNA profiles can be used to identify patients early in treatment who may not benefit from the current standard of care and who would benefit from closer monitoring and targeted agents. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding.

The outcomes of lymphoma patients treated with PD1 inhibitors (PD1i) vary significantly across different histologies with objective responses rates that are around 70% in classical Hodgkin lymphoma, but significantly lower, around 10%, in indolent non-Hodgkin lymphomas (iNHL). This may be due in part to the inability of PD1i to restore the function of exhausted T-cells (Tex) in the tumor microenvironment (TME). PD1 is expressed on various T-cell subsets, including effector cells and Tex. In this study we sought to identify markers of exhaustion beyond PD1 that define T-cell subsets with high reactivation potential in marginal zone lymphoma (MZL). To define different stages of exhaustion in MZL, we developed and validated a mass cytometry (CyTOF) panel which integrates T-cell markers of lineage, differentiation, activation, suppression, cytokine production and transcription factors associated with exhaustion. We analyzed 35 MZL spleens obtained at diagnosis and 6 reactive spleens. We identified a population of CD8 cells in MZL that express TIM3 without PD1, compose 1.5% (range 0.14-4.88%) of total CD8 cells and expand with activation through the TCR (6.2% of CD8, range 0.6-14.2%). Prolonged cytokine exposure has been shown to induce exhaustion in healthy T-cells, and so to determine whether PD1-TIM3+ cells are Tex, we treated healthy donor T-cells with activating beads and IL12 and analyzed them by flow cytometry. We showed that PD1-TIM3+ cells expand with repetitive activation and IL12 treatment suggesting that they are Tex. Since Tex have been shown to have a distinct transcription profile compared to functional effector T-cells, we analyzed the expression of several transcription factors (TFs) that are upregulated in Tex by CyTOF and compared them to other subsets of T-cells. PD1-TIM3+ cells expressed higher levels of Eomes, Tbet, TOX and Helios compared to activated effector cells, confirming that they are Tex, but lower levels levels of the same TFs compared to PD1+TIM3+ cells, consistent with a less severely exhausted phenotype. To characterize the function of PD1-TIM3+ cells, we manually gated on subsets of CD8+ non-naïve cells that were stained with the CyTOF panel and compared the different subsets. We showed that PD1-TIM3+ cells arise from effector cells, maintain their polyfunctionality by producing cytokines and chemokines and have a unique cytokine production profile, including perforin, granzyme B and proinflammatory cytokines, such as IL17A. Importantly, PD1-TIM3+ cells produced higher levels of most cytokines compared to more severely Tex expressing both PD1 and TIM3 (Fig 1). We then performed tSNE analysis where similar objects are modeled by nearby points. We determined that PD1-TIM3+ cells cluster together, suggesting that they are phenotypically homogeneous cells in terms of function and expression of activation markers and are significantly different from PD1+TIM3+ cells (Fig 2). We finally blocked PD1 or TIM3 and analyzed the effect of each antibody blockade on function. We showed that TIM3 blockade was able to restore the production of most cytokines to higher degrees than PD1 blockade. In addition, TIM3 blockade led to upregulation of CD28, which is critical for the rescue of Tex through costimulation, while PD1 blockade did not. We then analyzed the clinical outcomes of all 34 MZL patients. The frequency of PD1-TIM3+ cells was lower among observed patients who failed to achieve an event-free survival (EFS) of 12 months (EFS12) (0.8% vs 1.7%, p=0.02) and among treated patients that failed an EFS of 24 months (EFS24) (0.9 vs 1.7%, p=0.03), when compared to patients that reached these endpoints. Higher levels of PD1-TIM3+ cells were also associated with a trend towards improved overall survival (OS). Five-year OS was 88% vs. 69% in patients with PD1-TIM3+ levels >2% and <2% of total CD8 cells respectively (p=0.1) (Fig 3). Our study provides insights into the mechanisms of failure of PD1i in MZL and identifies a novel population of Tex with high reactivation potential which is clinically relevant and could serve as a target for future immunotherapies in iNHL. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding.

Abstract Introduction: The development of immune checkpoint inhibitors (ICPis) has ushered in a new paradigm for the treatment of both solid and liquid malignancies. However, these therapies, which promote an activated T cell phenotype, can cause a unique spectrum of autoimmune toxicities known as immune-related adverse events (IRAEs), which can affect a variety of organ systems. Autoimmune hemolytic anemia (AIHA) has been described as a rare complication of ICPis, but existing data are mostly limited to isolated case reports. Further, guidelines for the diagnosis and treatment of these patients are lacking. We describe the clinical and laboratory features of 14 patients who developed ICPi-associated AIHA. Methods: We contacted hematology departments at 18 academic medical centers across the U.S. to inquire about potential cases of ICPi-associated AIHA. Nine centers contributed a total of 14 cases to the current series. We defined AIHA as an abrupt decrease in hemoglobin ≥2g/dL, the presence of at least two laboratory features of hemolysis (elevated serum lactate dehydrogenase [LDH], elevated reticulocyte percentage, low or absent serum haptoglobin, and spherocytes), and the exclusion of other causes of anemia. We collected data on demographics and comorbidities, features of AIHA, and response to treatment. We defined complete recovery (CR) and partial recovery (PR) of hemoglobin as an increase from the nadir hemoglobin to within 1 and 2 g/dL of the baseline value, respectively, in the absence of recent transfusion. Results: The clinical features of the 14 patients in our cohort with ICPi-associated AIHA are summarized in the Figure (panel A). Underlying malignancies included melanoma (n = 9), non-small cell lung cancer (n = 3), colorectal cancer (n = 1), and acute myelogenous leukemia (n = 1). Four patients had a pre-existing diagnosis of a lymphoproliferative disorder, and two of these patients were known to have a positive direct antiglobulin test (DAT) prior to initiation of ICPi. ICPis used included ipilimumab, nivolumab, a combination of ipilimumab and nivolumab, and pembrolizumab. Representative hemoglobin and LDH trends are shown in the Figure (panels B and C). The median (interquartile range [IQR]) interval from initiation of ICPis to AIHA was 55 (22-110) days. Eight patients (57%) were DAT positive: 3 were positive for both IgG and C3; 3 were positive for IgG alone; 2 were positive for C3 alone; and 1 was weakly positive on polyspecific testing. Five patients (36%) were DAT negative, and in one patient a DAT was not performed. The median (IQR) hemoglobin nadir was 6.3 (6.1-8.0) g/dL,and 11 patients (79%) required red blood cell transfusion. All patients were treated with glucocorticoids, with 3 requiring additional immunosuppressive therapy. All patients achieved a CR or PR of hemoglobin in response to treatment (CR, n = 11; PR, n =3). The median (IQR) time from the hemoglobin nadir to CR or PR was 47 (30-62) days. Seven patients (50%) experienced at least one other IRAE. Seven patients (50%) were re-challenged with ICPis, and one patient developed recurrent AIHA with re-challenge. Conclusion: ICPi-associated AIHA is a recently-described phenomenon, which will be encountered more frequently with the widespread use of ICPis. ICPi-associated AIHA shares many clinical features with other forms of AIHA; however, a unique aspect of ICPi-associated AIHA appears to be a relatively high incidence of DAT negativity (36% in our series, compared with 3-11% in other forms of AIHA). It is possible that more advanced DAT testing - i.e., "super-Coombs" - which was not performed in any of the patients in this series, would have yielded different results. Glucocorticoids appear to be an effective first-line regimen in most patients with ICPi-associated AIHA. Furthermore, we found that AIHA does not necessarily recur with ICPi re-challenge. Additional studies are needed to better understand the mechanisms underlying ICPi-associated AIHA and optimal treatment strategies. Disclosures Anagnostou: ASH Research Training Award for Fellows 2018: Research Funding; Mayo Clinic SPORE: Research Funding; Mayo Clinic Immune Monitoring Core: Research Funding; Mayo Clinic Hematology Research Division: Research Funding. Salama:BMS: Research Funding; Celldex: Research Funding; Dynavax: Research Funding; Genentech: Research Funding; Merck: Research Funding; Reata: Research Funding; Incyte: Research Funding; Immunocore: Research Funding; Array: Consultancy; Merck: Consultancy; Biocryst: Consultancy; BMS: Speakers Bureau.

AbstractImmune checkpoint inhibitors (ICPis) are a novel class of immunotherapeutic agents that have revolutionized the treatment of cancer; however, these drugs can also cause a unique spectrum of autoimmune toxicity. Autoimmune hemolytic anemia (AIHA) is a rare, but often severe, complication of ICPis. We identified 14 patients from nine institutions across the United States who developed ICPi‐AIHA. The median interval from ICPi initiation to development of AIHA was 55 days (interquartile range [IQR], 22‐110 days). Results from the direct antiglobulin test (DAT) were available for 13 of 14 patients: 8 patients (62%) had a positive DAT and 5 (38%) had a negative DAT. The median pretreatment and nadir hemoglobin concentrations were 11.8 g/dL (IQR, 10.2‐12.9 g/dL) and 6.3 g/dL (IQR, 6.1‐8.0 g/dL), respectively. Four patients (29%) had a preexisting lymphoproliferative disorder, and two (14%) had a positive DAT prior to initiation of ICPi therapy. All patients were treated with glucocorticoids, with three requiring additional immunosuppressive therapy. Complete and partial recoveries of hemoglobin were achieved in 12 (86%) and 2 (14%) patients, respectively. Seven patients (50%) were rechallenged with ICPis, and one (14%) developed recurrent AIHA. Clinical and laboratory features of ICPi‐AIHA were similar in DAT positive and negative patients. ICPi‐AIHA shares many clinical features with primary AIHA; however, a unique aspect of ICPi‐AIHA is a high incidence of DAT negativity. Glucocorticoids are an effective first‐line treatment in the majority of patients with ICPi‐AIHA, and most patients who are rechallenged with an ICPi do not appear to develop recurrence of AIHA.

8034 Background: Multiple myeloma (MM), a monoclonal plasma cell disorder, is one of the most common hematologic malignancies in the US. In preclinical studies, metformin demonstrated plasma cells cytotoxicity. However, there is lack of studies translating the effect of metformin into the clinical setting. Therefore, we assessed the clinical effect of metformin in patients (pts) with MM. Methods: All MM pts who underwent stem cell transplant (SCT) at the Mayo Clinic Rochester from 2007 to 2012 were reviewed. Patients were grouped based on metformin use. Initial diagnosis at our institution and ≥12 months of follow up were required. Kaplan-Meier method and Cox regression were used for time-to-event and multivariate analysis. Results: Out of 687 pts, 78 (11.4%) were using metformin at the time of MM diagnosis. Baseline characteristics in the metformin (Mt) and no-metformin (NMt) groups were similar (Table). Median (M) metformin dose was 2000 mg daily and m duration of metformin use from MM diagnosis was 22 months. Pts on the Mt group achieved higher rates of CR after SCT (41% vs. 29%, p<0.02). Median PFS after SCT was longer in the Mt group, 31.3 months (95% CI: 10.4-52.2) vs. 16.6 months in the NMt group (95%CI: 14.5-18.7) p<0.04. There was a trend towards longer OS in the Mt group, but it was not statistically significant (170 vs. 106 months, p<0.10). In a multivariate analysis of metformin use, age, ISS, LDH, and cytogenetics/FISH, the former was an independent predictor of PFS after SCT (OR: 0.38, 95%CI: 0.20-0.68, p<0.001). Conclusions: Metformin use was associated with a better PFS and higher CR after SCT in our MM cohort. A trend towards better OS was also noted in the Mt group. Larger studies are needed to enhance our understanding of the clinic effect of metformin on MM. [Table: see text]