ALBERT

Description: The sand rat Psammomys obesus is a gerbil species native to deserts of North Africa and the Middle East, and is constrained in its ecology because high carbohydrate diets induce obesity and type II diabetes that, in extreme cases, can lead to pancreatic failure and death. We report the sequencing of the sand rat genome and discovery of an unusual, extensive, and mutationally biased GC-rich genomic domain. This highly divergent genomic region encompasses several functionally essential genes, and spans the ParaHox cluster which includes the insulin-regulating homeobox gene Pdx1. The sequence of sand rat Pdx1 has been grossly affected by GC-biased mutation, leading to the highest divergence observed for this gene across the Bilateria. In addition to genomic insights into restricted caloric intake in a desert species, the discovery of a localized chromosomal region subject to elevated mutation suggests that mutational heterogeneity within genomes could influence the course of evolution.

Description: BACKGROUND: Loss of immune surveillance is critical in the pathogenesis of multiple myeloma (MM) and the progression from smoldering to symptomatic MM. To date, no clear efficacy signal has been observed with programmed-death 1 and programmed death ligand-1 inhibitors in patients with MM. General immune dysfunction in MM is well documented, but the evolving immune landscape in relapsed/refractory MM (RRMM) vs newly diagnosed MM (NDMM) is less well characterized. This study aimed to characterize immune profiles in peripheral blood and bone marrow from patients with NDMM and RRMM. METHODS: Peripheral blood samples were collected from 35 NDMM and 146 RRMM patients and 36 age-matched healthy volunteers (HVs). Cell surface and intracellular antigen staining using fluorochrome labeled antibodies was performed on a BD FACSCanto II flow cytometer. Bone marrow aspirates were collected from 26 NDMM and 73 RRMM patients, and the transcriptome was assessed by mRNA-Seq. RESULTS: In peripheral blood, T-cell populations differed between HVs and NDMM and RRMM patients. Absolute numbers of lymphocytes were higher in HVs than in NDMM and RRMM, regardless of the MM disease state. Absolute numbers of total CD4+ T cells and naïve CD4+ T cells were lower in RRMM patients, whereas CD4+ effector memory T cells as a proportion of total CD4+ T cells were increased in RRMM patients. Blood from RRMM patients also contained increased levels of proliferating CD4+ T cells, as evidenced by Ki67, ICOS, and HLA-DR, compared with blood from NDMM patients; HVs had values much closer to those from NDMM than from RRMM patients, suggesting a trend influenced by disease state or therapeutic intervention. In bone marrow, immunologic gene expression signatures were elevated in NDMM vs RRMM patients; the differences were similar to those in peripheral blood. Using limma to model the differential expression of all measured genes between NDMM and RRMM, we identified 367 genes that were elevated in NDMM patients vs 52 in RRMM patients. Gene set analyses using Molecular Signatures Database immunologic signatures (C7) applied to those 367 genes showed that naïve T-cell genes were increased in the bone marrow of NDMM vs RRMM patients. Gene set enrichment analysis with limma, using 489 gene sets from xCell representing 64 cell types and controlling for differences in tumor burden, indicated that macrophage, monocyte, and neutrophil genes were upregulated and T cells, particularly naïve CD4+ T cells, were downregulated in RRMM patients. Immunohistochemistry results from bone marrow biopsies showed increased programmed death-ligand 1 expression on tumor and infiltrating immune cells and increased CD8 infiltration into bone marrow in RRMM vs NDMM patients. Multiparameter immunofluorescence is underway to confirm these findings and further understand the tumor immune microenvironment in patient subsets. As expected, baseline RRMM immune cell populations depended on prior lines of therapy. Daratumumab-exposed RRMM patients had elevated total CD8+ T cells in peripheral blood but decreased CD38+, CD4+, and CD8+ T cells, as well as decreased total natural killer cells, compared with the daratumumab-naïve patients. Transcriptome analyses of bone marrow from daratumumab-exposed RRMM patients revealed increased T-cell gene expression signatures relative to marrow from daratumumab-naïve patients. Additionally, pomalidomide-exposed RRMM patients had increased activated CD4+ and CD8+ T cells vs pomalidomide-naïve patients. CONCLUSIONS: These data indicate that RRMM patients have peripheral blood and bone marrow environments with highly differentiated T-cell populations, whereas NDMM patients show elevated T-cell levels with proliferative capacity. Furthermore, the bone marrow of RRMM patients is enriched with neutrophils and macrophages; investigation is ongoing to determine if these cell types contribute to an immunosuppressive tumor microenvironment. Understanding immune system function based on disease progression, patient segments, and prior lines of therapy is imperative as treatment of MM improves, and it may inform the administration and sequence of next generation immunotherapeutics and identify predictive biomarkers for optimal treatment selection. Disclosures Pietz: Celgene Corporation: Employment. Tometsko:Celgene Corporation: Employment, Equity Ownership. Copeland:Celgene Corporation: Employment, Equity Ownership. Whalen:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Thompson:Celgene Corporation: Employment, Equity Ownership. Agarwal:Celgene Corporation: Employment, Equity Ownership. Foy:Celgene Corporation: Employment, Equity Ownership. Buchholz:Celgene Corporation: Employment. Komashko:Celgene Corporation: Employment. Dell'Aringa:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment.

Description: Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

Description: Introduction Relapsed and refractory multiple myeloma (RRMM) remains a challenging disease to treat due to its heterogeneity and complexity. There is an urgent need for novel combination strategies, including immunotherapy. The study of the tumour and immune microenvironment before and after treatment with combination therapy is a crucial part of understanding the underpinning of disease response. Methods Longitudinal samples of bone marrow aspirates and whole blood were collected from a phase II clinical trial, MEDI4736-MM-003 (NCT02807454) where daratumumab and durvalumab naïve patients were exposed simultaneously to both these drugs. A combination of mass cytometry (CyTOF), RNAseq and flow cytometry were performed on a subset of samples from these subjects. Specifically, paired bone marrow mononuclear cells (BMMC) samples from nine patients taken at screening and six weeks post-treatment were analysed by mass cytometry (CyTOF) using a 37-marker pan-immune panel that included both lineage and functional intracellular/extracellular markers. In addition, whole blood sample specimens were collected at screening and on treatment (8, 15, 30, and 45 days after treatment) and analysed by flow cytometry. Flow cytometry panels were designed to allow interrogation of the abundance and activation status of immune cell subsets. Finally, RNA from bone marrow aspirates at screening and C2D15 were analysed by RNA sequencing. Expression profiles from the aspirates were used to estimate cell proportions by computational deconvolution. Individual cell types in these microenvironments were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results In a heavily pre-treated population with RRMM, treatment with durvalumab and daratumumab leads to shifts in a number of key immunological populations when compared to pre-treatment. In the bone marrow, CD8 and CD4 populations rise (by CyTOF and RNAseq), while NK, DC and B cell populations fall (by CyTOF). In the bone marrow within CD8+ T lymphocyte populations, we observed a post-treatment rise in markers of degranulation (granzyme p=0.0195, perforin p=0.0078, Wilcoxon signed-rank test). This is also accompanied by a fall in PD1 expression (p=0.0078) and rise in the co-stimulatory receptor DNAM1 (p=0.0273). These changes are most marked on cells with an effector memory CD45RA+ CD8+ T cell phenotype. In the blood, similar to the bone marrow, CD8+ T cells proliferate over the course of treatment (flow cytometry). A fall in both naïve and active NK cell populations is seen following treatment in bone marrow. NK cells express high levels of CD38 and are therefore depleted by daratumumab. Those NK cells which remain have an active phenotype with increased expression of TNFa (p=0.0039) and IFNg (p=0.0195) following treatment. Across the time points sampled in peripheral blood, NK cells were also decreased and those that remained were proliferating. Dendritic cells with a tolerogenic phenotype can be identified prior to treatment and are seen to fall in abundance following treatment with durvalumab and daratumumab. Conclusions The combination of durvalumab and daratumumab leads to several immune microenvironment changes that biologically portend clinical effect. We see increases in the abundance of cell populations with functional anti-tumour activity, including granzyme B+ CD8 T cells and a reduction in PD1high T cells. Despite the treatment expectedly reducing NK cell numbers, many functionally competent NK cells remain, as evidenced by the presence of anti-tumour cytokines. This combination strategy also reduces immunosuppressive tolerogenic DCs, which suppress CD4 and CD8 T cell activity. Taken together, this suggests that this chemotherapy free, doublet treatment has the potential to up-regulate anti-tumour immunological responses, which may restore immunosurveillance mechanisms critically needed in these highly refractory patients. Disclosures Seymour: Celgene: Research Funding. Young:Celgene Corporation: Employment, Equity Ownership. Tometsko:Celgene Corporation: Employment, Equity Ownership. Cavenagh:Celgene: Honoraria, Research Funding, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Takeda: Research Funding, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Thompson:Celgene Corporation: Employment, Equity Ownership. Whalen:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene Corporation: Employment, Equity Ownership. Foy:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Gribben:Acerta Pharma: Honoraria, Research Funding; Cancer Research UK: Research Funding; TG Therapeutics: Honoraria; Roche: Honoraria; NIH: Research Funding; Medical Research Council: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria; Kite: Honoraria; Pharmacyclics: Honoraria; Novartis: Honoraria; Janssen: Honoraria, Research Funding; Wellcome Trust: Research Funding; Unum: Equity Ownership.

Description: Background: Orva-cel, a B-cell maturation antigen (BCMA)-targeted chimeric antigen receptor (CAR) T cell therapy, has shown promising preliminary efficacy and a favorable safety profile in patients with relapsed/refractory multiple myeloma in the ongoing phase 1/2 EVOLVE study (NCT03430011). Patients enrolled in the trial were heavily pretreated (median of 6 prior lines of therapy) and refractory to their last regimen per International Myeloma Working Group criteria. Five orva-cel dose levels (50, 150, 300, 450, and 600 × 106 CAR+ T cells) have been evaluated in 115 patients as of May 1, 2020. Here, we present the serum soluble BCMA (sBCMA) results and levels of immune-related serum factors associated with study safety and efficacy endpoints. Methods: Baseline and postinfusion levels of sBCMA (preinfusion through disease progression) and 39 immune-related serum factors (preinfusion through Day 29) were quantitated in serum by immunoassay (MesoScale Discovery, Rockville, MD). Linear regression models were used to evaluate the effect of orva-cel dose on biomarkers. Logistic regression models were used to evaluate the association between biomarkers and binary clinical response and safety endpoints. All patients with ≥1-month follow-up were included in the safety analyses (n = 115); all patients with ≥3-months potential follow-up were included in the efficacy analyses (n = 102). For assessment of sBCMA levels at nadir, only patients with sampling at Day 75 or later were considered (n = 91). Results & Conclusions: Baseline sBCMA levels (median [range], 480 [3.4-5126.5] ng/mL) correlated with established clinical measures of tumor burden, including percentage of malignant plasma cells in the bone marrow, paraprotein levels, and serum free light chain concentrations. Baseline sBCMA levels also correlated with laboratory measures predictive of prognosis, including β2-microglobulin, lactate dehydrogenase, and ferritin levels. As high tumor burden and high levels of these laboratory measures are both associated with poorer outcomes and increased frequency of adverse events (AEs), baseline sBCMA may provide a useful composite marker of disease burden and prognostic indicators. Patients with higher baseline sBCMA levels were more likely to experience AEs, including cytokine release syndrome (CRS) and neurological events (NEs) (P 〈 0.05). Overall response rate (ORR) was not influenced by baseline sBCMA levels, indicating that circulating sBCMA did not interfere with the ability of orva-cel to bind to and kill tumor cells expressing BCMA such that patients achieved at least a short-term response. Despite a similar ORR, patients with high baseline sBCMA levels were less likely to have an ongoing response at Month 6 (P 〈 0.05). These associations between baseline sBCMA levels and safety and efficacy endpoints appeared to hold within each dose level cohort, despite lacking power to detect a statistical difference. sBCMA levels decreased after orva-cel infusion in all responding patients (n = 86), with nadir reached within 2-3 months (median [range] time to minimum concentration, 2.8 [0.4-17.9] months). A dose effect was observed, with a 34% decrease in sBCMA nadir, on average, for every 150 × 106 CAR+ T cell dose increase (P 〈 0.05). sBCMA levels at nadir were associated with ORR, complete response rate, and Month 6 response rate (P 〈 0.05). sBCMA concentrations within the first month postinfusion allowed for stratification of Month 6 responders vs nonresponders. The significance of this stratification increased from Days 4-5 through Month 2, indicating that sBCMA may serve as an important early biomarker of durable response. Baseline levels of immune-related serum factors did not correlate with safety or efficacy endpoints in the current data set. A subset of serum factors characteristic of CAR T cell activation increased after orva-cel infusion; the peak levels of some biomarkers were associated with the orva-cel dose, including interleukin (IL)-2 and IL-6. Peak postinfusion levels of several inflammatory factors were associated with CRS (eg, IL-6 and IL-8) and NE (eg, tumor necrosis factor-α) grades (P 〈 0.05). Correlative analysis is ongoing, and updated results will be presented. Disclosures Piasecki: Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb Company: Current equity holder in publicly-traded company. Devries:Bristol-Myers Squibb Company: Current equity holder in publicly-traded company; Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment. Radhakrishnan:Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb Company: Current equity holder in publicly-traded company. Li:Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb Company: Current equity holder in publicly-traded company. Heipel:Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb Company: Current equity holder in publicly-traded company. Fox:Bristol-Myers Squibb Company: Current Employment, Current equity holder in publicly-traded company. Beckett:Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb Company: Current equity holder in publicly-traded company. Cota Stirner:Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb Company: Current equity holder in publicly-traded company. Conte:Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb Company: Current equity holder in publicly-traded company. Doerr:Juno Therapeutics, a Bristol-Myers Squibb Company: Current Employment; Bristol-Myers Squibb Company: Current equity holder in publicly-traded company. Mailankody:PleXus Communications: Honoraria; Takeda Oncology: Research Funding; Janssen Oncology: Research Funding; Allogene Therapeutics: Research Funding; Juno Therapeutics, a Bristol-Myers Squibb Company: Research Funding; Physician Education Resource: Honoraria. Wong:Fortis: Research Funding; Amgen: Consultancy; Bristol Myers Squibb: Research Funding; Janssen: Research Funding; Roche: Research Funding; GSK: Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees. McCarthy:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Magenta: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Karyopharm: Consultancy, Honoraria; Juno Therapeutics, a Bristol-Myers Squibb Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board , Research Funding is to Roswell Park, Research Funding; Starton: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Genentech: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Advisory Board.