Citations

Abstract Multiple Myeloma (MM) is a hematologic malignancy marked by uncontrolled clonal expansion of plasma cells. Previous research has examined single-cell transcriptome profiles of Monoclonal gammopathy of undetermined significance (MGUS) and MM tumor microenvironment (TME) and found that natural killer (NK) cell abundance is elevated in the early stages and correlated with altered chemokine receptor expression. This study suggested the critical role of immune cells on myeloma progression from asymptomatic MGUS to symptomatic MM. Up to date, however, there are no published studies comprehensively comparing tumor and immune populations differences between MM NON-progressors (NPs) and FAST-progressors (FPs) and investigating how clonal plasma cells affect disease progression in a large cohort. Therefore, understanding how tumor and immune cells influence disease progression within symptomatic MM is of great interest.Here, we subjected CD138-negative Bone Marrow Mononuclear cells (BMMC) samples from 418 MM patients to scRNA-seq. From MMRF CoMMpass study (NCT01454297), we also have whole exome sequencing (WES) and bulk RNA-seq from CD138-positive fraction of BMMC samples. Based on time to progressive disease (TTPD), we classified patients into 2 categories. Patients with TTPD less than 18 months were classified as FAST-progressors (FPs), whereas patients with TTPD more than 5 years were classified as NON-progressors (NPs). By analyzing patient genomic alterations and its association with progression, we found that there was a significant association of slow MM progression with t(11;14) (p = 0.048), consistent with previous study. In our preliminary analysis, we profiled 83 CD138-sorted MM bone marrow samples using scRNA-seq. Interestingly, we found plasma cells from samples with the same genetic alterations tend to cluster together, highlighting the important role of genetic drivers in transcriptome profiles of plasma cells. Moreover, integrated analysis of bone marrow samples from 83 MM patients and 4 healthy donors revealed an atypical naïve-B cell subset with enrichment of cells from fast-progressors and partial expression of MS4A1. Differentially expressed genes for this naïve-B cell subset includes KLF9, BCL2L11, JOSD1, and IRS2, etc. Overall, as part of MMRF immune profiling research, this study will help to interrogate how genetic alterations and disease progression interplay MM tumor and TME and provide a sufficiently broad and valuable dataset for systematically characterizing MM at single-cell resolution. Hopefully, this study could identify novel targets for MM immunotherapies, and ultimately identify patients with high risk of fast progression for early intervention in the clinic. Citation Format: Lijun Yao, Tianjiao Wang, Kazuhiro Sato, Reyka Jayasinghe, I-Ling Chiang, Darwin D'souza, William Pilcher, Edgar Gonzalez-Kozlova, Yered Pita-Juarez, Taxiarchis Kourelis, Deon Bryant Doxie, Beena Thomas, Brian Lee, Swati Sharma Bhasin, Upadhyaya Bhaskar, Mark Fiala, Julie Fortier, Travis Dawson, John Leech, Shaji Kumar, Hearn Cho, Seunghee Kim-Schulze, Bee Raj, Stephen Oh, the MMRF Immune Profiling Research Team, John Dipersio, Ravi Vij, Adeeb Rahman, Ionnis Vlachos, Shaadi Mehr, Mark Hamilton, Daniel Auclair, Surendra Dasari, David Avigan, Madhav Dhodapkar, Sacha Gnjatic, Manoj Bhasin, Li Ding. Single-cell transcriptome profiling of multiple myeloma bone marrow samples suggests that disease progression interplays with tumor and tumor microenvironment in The MMRF CoMMpass Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1729.

Abstract Multiple Myeloma (MM) is the second most common hematologic malignancy, marked by uncontrolled clonal expansion of plasma cells. MM genome is complex and heterogeneous, with a high frequency of structural variants (SVs) and copy-number abnormalities (CNAs). Single-cell sequencing technologies offer advantages over traditional bulk methods in cancer genomics research for evaluating cellular heterogeneity and investigating evolution of cellular subpopulations within the tumor. Thus, there is an impetus to translate the growing understanding of the genomic landscape of MM into dissecting the tumor heterogeneity using scRNA-seq. So far, there are no reported studies systematically comparing tumor and immune populations differences between MM NON-progressors (NPs) and RAPID-progressor (RPs) and investigating how clonal plasma cells contribute to progression in a large cohort. In addition, the bone marrow microenvironment plays an important role in the evolution of premalignant MM and MM progression. A previous study of single-cell transcriptomics analysis of Monoclonal gammopathy of undetermined significance (MGUS) and MM tumor microenvironment (TME) revealed that natural killer (NK) cell abundance is frequently increased in the early stages and associated with altered chemokine receptor expression. This study shed the light on the role of immune cells on disease progression from asymptomatic MGUS to symptomatic MM. However to date, how immune cells influence disease progression within symptomatic MM is still unclear. Here, we subjected 344 CD138-negative Bone Marrow Mononuclear cells (BMMC) samples to scRNA-seq. To control for the first line therapy, 272 patients were treated with RVD and Autologous stem cell transplant (ASCT) and were stratified into 3 groups based on progression: 200 NON-progressors (NPs) (PFS> 5 years), 7 Intermediate Progressors (IPs, PFS 2-4 years) and 38 RAPID-progressors (RPs) (PFS<=18 months). In addition, there are 72 subjects treated with RVD but without ASCT, including 34 NPs, 2 IPs and 36 RPs. From MMRF CoMMpass study (NCT01454297), we also have whole exome sequencing (WES) and bulk RNA-seq from CD138-positive fraction of BMMC samples. Our preliminary analysis of scRNA-seq of 6 samples treated by the same first-line therapies (RVD but without ASCT) revealed that plasma cells (PCs) from two patients (MMRF2550 and MMRF2562) with chromosome 13q deletion clustered together, whereas PCs from a patient (MMRF2187) with t(8;14) Myc translocation formed a distinct cluster. Interestingly, PCs from a patient (MMRF2271) with both chromosome 13q deletion and t(8;14) formed another independent cluster. These observations highlight the important role of genetic drivers in transcriptome profiles of plasma cells. In addition, we further investigated how progression features correlate with the immune microenvironment and identified differentially expressed genes between NK cells from RPs versus those from NPs, which could potentially serve as MM progression markers. Interestingly, several cytotoxicity genes are significantly upregulated in FPs, such as GZMB and KLRB1 (log Fold Change=1.44, and 8.41 respectively). Overall, our preliminary results provide a small glimpse of the interconnected nature of driver genetic alterations and progression features in MM tumor and TME. This study will provide a sufficiently broad, deep, and diverse vast dataset for accurately characterizing MM at single-cell resolution to help interrogate how genetic alterations and disease progression interplay MM tumor and TME. We hope this study could identify novel candidate targets for therapeutic approaches, and ultimately stratify patients by risk of progression for early intervention in the clinic. Disclosures Kumar: Roche-Genentech: Consultancy, Research Funding; Merck: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Novartis: Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Beigene: Consultancy; Carsgen: Research Funding; Bluebird Bio: Consultancy; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Tenebio: Research Funding; BMS: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Oh: Abbvie: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Membership on an entity's Board of Directors or advisory committees; Celgene Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Constellation: Membership on an entity's Board of Directors or advisory committees; CTI Biopharma: Membership on an entity's Board of Directors or advisory committees; Disc Medicine: Membership on an entity's Board of Directors or advisory committees; Geron: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Kartos Therapeutics: Membership on an entity's Board of Directors or advisory committees; PharamaEssentia: Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Membership on an entity's Board of Directors or advisory committees. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Avigan: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Kite Pharma: Consultancy, Research Funding; Juno: Membership on an entity's Board of Directors or advisory committees; Partner Tx: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Aviv MedTech Ltd: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Legend Biotech: Membership on an entity's Board of Directors or advisory committees; Chugai: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy; Parexcel: Consultancy; Takeda: Consultancy; Sanofi: Consultancy.

Abstract Background IL-10 polarized macrophages promote tumor growth and survival. The T-cell transcription factor GATA binding protein-3 (GATA3) epigenetically regulates IL10 expression. Therefore, we investigated the extent to which GATA-3 regulates IL-10 production and macrophage polarization in T-cell lymphomas. Methods Monocyte-derived macrophages were generated and polarized with T-cell lymphoma conditioned media ((TCL-CM). Immunophenotypic and functional characteristics associated with IL10-polarized macrophages were examined. GATA3-dependent cytokine production was evaluated in T-cell lymphoma lines by shRNA knockdown. Alternatively polarized macrophages and GATA3 expression were examined in a cohort of patients with peripheral T cell lymphoma, not otherwise specified (PTCL, NOS) (n=76). Comparisons among groups were evaluated using a Student's t test. Progression-free and overall survival (PFS & OS) was estimated using the Kaplan-Meier method and two-tailed log-rank test based on Cox proportional hazards model. Results To investigate the ability of T-cell lymphomas to alternatively polarize macrophages, monocytes from healthy donors were cocultured with TCL-CM. Macrophages were shown to have immunophenotypic characteristics of IL10-polarized macrophages (pSTAT3+/CD16hi/CD163hi/HLA-DR-/lo) by flow cytometry, which was abrogated by IL10, but not IL6, neutralization. TCL-CM polarized macrophages produced abundant IL-10 and were impaired in their ability to stimulate T-cell proliferation. Collectively, these results demonstrate that T-cell lymphomas promote alternative macrophage polarization in an IL-10 dependent manner, and both CD163 and pSTAT3 may aid in their identification in clinical T-cell lymphoma specimens. Therefore, CD163+/pSTAT3+ macrophages were visualized by immunofluorescence in PTCL, NOS specimens. IL-10 polarized macrophages were not identified in normal tissues, but were abundant constituents of the tumor microenvironment in 68% of the PTCL, NOS specimens examined (n=31). STAT3 phosphorylation in response to IL-10 is dependent upon the Janus kinases (JAK) JAK1 and TYK2, both of which are inhibited at nanomolar (and clinically achievable) concentrations by the “JAK2” inhibitor ruxolitinib. Therefore, we sought to determine whether ruxolitinib may prevent IL-10-induced macrophage polarization. As expected, ruxolitinib inhibited IL-10-induced STAT3 phosphorylation. More importantly, macrophages polarized in the presence of ruxolitinib lost the immunophenotype characteristics of IL-10 polarized macrophages, and were CD16-/loCD163-/loHLA-DRhi. Furthermore, IL-10 production was significantly impaired in macrophages polarized in the presence of ruxolitinib, while their ability to stimulate T-cell proliferation was significantly increased. In a separate initiative, we identified GATA3 as the key regulator of IL-10 and Th2 cytokine (IL-4, IL-13 and IL-5) expression in T-cell lymphoma lines by shRNA knockdown assays. Therefore, we examined GATA3 expression by immunohistochemistry in PTCL, NOS specimens. GATA-3 expression was observed in most of the T-cell lymphoma lines and in 47% of PTCL, NOS specimens (n=76). In the cohort of the 76 PTCL, NOS patients, the median PFS and OS observed in GATA3+ PTCL, NOS was 5 months (4-8 months, 95% CI) and 8 months (5-14 months, 95% CI), respectively. In contrast, the median PFS and OS in GATA3- PTCL, NOS was 1.3 years (0.6-1.6, 95% CI) and 1.6 years (0.7-6.9 95% CI), respectively. Conclusions Alternatively polarized macrophages are abundant in T-cell lymphomas and are generated in an IL-10- and STAT3-dependent manner. GATA-3 regulates IL-10 production in these lymphomas, and its expression identified a high-risk subset of PTCL, NOS with distinct clinicopathological features. As IL-10-dependent STAT3 phosphorylation and macrophage polarization were impaired by ruxolitinib, the JAK2 inhibitors may warrant closer scrutiny as immunomodulatory agents. Disclosures: Off Label Use: Ruxolitinib, a JAK2 inhibitor, may warrant closer scrutiny as an immunomodulatory agent in TCL therapeutics.

Key Points Alternatively polarized macrophages are abundant constituents of the tumor microenvironment in T-cell lymphoproliferative disorders. GATA-3 expression identifies a subset of PTCL, NOS with a distinct cytokine profile and inferior survival.