Bone Marrow Mononuclear Cells Samples Research Articles

High Dimensional Profiling of Patient-Derived Multiple Myeloma Bone Marrow Specimens Treated with an FcRH5-Targeted Bispecific Ex Vivo

Background: Cevostamab is an FcRH5 (Fc receptor-homolog 5) targeted T cell-dependent bispecific (TDB) currently being evaluated in the clinic, with promising activity and favorable safety profile as a monotherapy in patients (pts) with heavily pre-treated Relapsed/Refractory multiple myeloma (MM) (NCT03275103, Trudel et al. ASH 2021). Cevostamab selectively engages and activates T cells in the proximity of MM cells in the bone marrow (BM) to drive tumor cell killing. To dissect the transcriptional and functional impact of cevostamab treatment on the MM BM tumor microenvironment (TME) and identify factors linked to enhanced killing, we established an ex vivo TDB killing assay with MM pt BMMC (BM mononuclear cells) samples followed by detailed characterization of immune and tumor cells. Methods: We tested commercially procured frozen BMMC samples from 7 pts with Stage I or Stage III Newly Diagnosed Multiple Myeloma (NDMM) with no known prior treatments (except a paired, longitudinally collected sample) and median age of 67.5 years. BMMC samples containing endogenous T and MM cells were treated ex vivo with 10 ug/mL of either a non-targeted TDB (considered as baseline) or a research grade cevostamab surrogate (FcRH5 TDB) for 16 to 48 hours (hr). T cell enumeration and MM killing (CD138 + CD38 +) was evaluated at 48 hours by flow cytometry. Ex vivo response was normalized by T cell quantity and defined as % tumor cell killing per 10 3 T cells present in the assay, which was used to classify samples as Responders (R) (>= 10%) or Suboptimal Responders (SoR) (< 10%). Single cell RNA-seq (scRNA-seq) was performed at 16, 24 and 48 hr post treatment. Tumor-specific genes enriched in SoRs at baseline were investigated for prognostic value by examining their bulk gene expression profile in baseline tumor samples (CD138-enriched) from pts (N=1150) treated with immunomodulatory drug, proteasome inhibitors, or combinations thereof (NCT01454297, MMRF CoMMpass IA22). Results: We first examined the T cell compartment by scRNA-seq profiling. Upon FcRH5 TDB treatment, clustering analysis suggested that CD4 and CD8 T cells undergo extensive transcriptional changes compared to control TDB (Fig 1A). A TNF and LTA-expressing CD4 memory cluster was enriched upon treatment in both SoRs and Rs, but to a greater extent in Rs. A similar trend was observed with an IFNG and CCL4-enriched CD8 effector-like proliferating T cell cluster. Transcriptional profiles of MM cells suggested inter- and intra-pt tumor heterogeneity at baseline. MM cells in SoRs at baseline had higher expression of genes like CD44, SRGN, CCND2 and SLAMF1. Of these genes, higher expression of CD44 or SRGN was found to be significantly associated with worse outcome to 1L treatments. Exposure to FcRH5 TDB altered tumor cells from all pts (Figure 1B), resulting in significant upregulation of genes such as HLA-E, SOCS1, and TNFSF10. Increase in CXCL9 expression upon treatment was found to be higher in Rs than SoRs. Discussion: Our work provides a novel ex vivo approach for unbiased, high dimensional pharmacodynamic profiling of functional and transcriptomic changes in pt samples by combining TDB treatment with scRNA-seq. Consistent with the expected mechanism of action and preliminary biomarker analysis reported for cevostamab (Nakamura et al. ASH 2020), FcRH5 TDB led to increased expression of genes in T cells associated with infiltration, activation and effector function. It also induced transcriptional changes in MM cells regardless of the ex vivo response status, including upregulation of genes associated with T and NK cell infiltration and antigen presentation. For certain genes, a greater magnitude of these changes was observed in Rs. These identified genes, and enriched clusters will be examined in clinical RNAseq data from cevostamab-treated pts. It remains to be determined whether our results reflect treatment effects that are applicable to other targeted bispecifics in MM. Overall, our assay setup can serve as a platform to generate novel hypotheses, corroborate clinical observations, identify pt-specific features driving improved or dampened response, and test novel combinations for improved killing. Figure 1 : UMAP (Uniform Manifold Approximation and Projection) plot of all (A) T cells and (B) tumor cells shows distinct clustering of cells by treatment type for all pts (red: FcRH5 TDB; blue: non-targeted TDB)

Multimodal Atlas of Paired Diagnosis and Relapse AML Samples Enables Novel Therapeutic Targeting of Surface Antigens

Note: A.H. and M.U. share co-first authorship. AML is an aggressive clonal malignancy characterized by combinations of chromosomal abnormalities, gene mutations, and cell surface antigen (Ag) expression profiles. This heterogeneity contributes to refractory or relapsed disease that presents a major challenge when treating AML. Previous single cell sequencing studies of primary AML samples have provided remarkable insight into the clonal architecture of AML cell populations and how their mutational, transcriptomic, and surface Ag profiles vary between patients. However, information addressing clonal shifts during progression from diagnosis to relapse in large cohorts is lacking. Here, we adapted single cell RNA sequencing with surface Ag feature barcoding to analyze more than 450,000 cells from 28 paired AML patient bone marrow mononuclear cell samples collected at diagnosis and relapse (56 samples). To our knowledge, it is the largest and most comprehensive single cell AML atlas to date. This atlas contains rich clinical metadata including cytogenetics, mutation status of canonical AML genes, treatment history, and survival information. We leveraged these data to identify potential correlations with clonal heterogeneity during progression to relapse and to propose novel strategies for targeting the surface of AML cells. The feature barcoding panel consists of a comprehensive list of 81 surface Ags reported in clinicaltrials.gov or mined from literature, including large proteomics mass spectrometry datasets of AML patient bone marrow samples (deBoer et. al. Cancer Cell 2018; Jayavelu et al, Cancer Cell 2022). Feature barcoding produces antibody derived tag (ADT) counts which are interpreted as relative values but alone does not indicate absolute number of Ags per cell. We addressed this limitation by measuring absolute Ag density of AML Ags, CD33, CLL1, CD123, and EMR2 in patient samples using the flow cytometric QuantiBRITE assay. Focusing on these four Ags, we modeled the relationship between ADT expression and antibodies per cell (ABC) Ag density from QuantiBRITE. The model was applied to impute absolute Ag density for the remaining 77 Ags in all samples. UMAP visualization after sample transcriptome integration revealed distinct clustering of CD45-dim myeloblasts, T cells, B cells, and erythroid cells. Longitudinal analysis identified 28 surface Ags that were differentially expressed (absolute log 2FC > 1, p < 0.01) between relapse and diagnosis myeloblasts across multiple patients (Fig. 1A). An additional 19 Ags were differentially expressed in no more than one patient while the other 19 had no significant change in any patient, highlighting the inter-and intra- heterogeneity of AML at diagnosis and relapse in these patients. Targeting multiple antigens simultaneously may help address the issue of antigen heterogeneity of tumor cells and help avoid potential antigen escape. Because multi-targeting therapies are a potential solution to this, weleveraged the single-cell resolution of our atlas to systematically identify Ags that in combination provide maximum coverage and are significantly co-expressed in myeloblasts. From this analysis, we found 48 and 52 co-expressed Ag pairs in diagnosis and relapse blasts, respectively (r > 0.4, p < 0.01; Fig. 1B). Of note, CD44 and EMR2 were co-expressed at both diagnosis (r = 0.49 p < 0.01) and relapse (r = 0.3, p < 0.01) with >90% of their blasts expressing CD44, EMR2, or both at targetable antigen levels of >1000 Ags/cell. Furthermore, 10 samples had >40% of their blasts co-expressing both Ags simultaneously at >1000 Ags/cell at diagnosis or relapse. 6 of these patients had a >10% increase in co-expression at relapse, 3 had >10% decrease at relapse, and one patient had no co-expression change. Our comprehensive profiling of AML has provided high resolution insight on cell surface at diagnosis and during disease progression and produced novel hypotheses about targetable combinations of surface Ags based on their expression changes between timepoints, co-expression, and surface densities. Furthermore, our reference atlas can be further dissected to characterize the transcriptome and mutational profiles of myeloblasts at diagnosis and relapse thereby prodding an important resource to create surface antigen protein - transcript correlations within AML blasts and to trace molecular signatures during disease progression.

Impact of Gamma-Secretase Inhibition on the Multiple Myeloma Immune Microenvironment

Background: Therapy with monoclonal antibodies, chimeric antigen receptor (CAR) T-cells, and bispecific T-cell engagers (BiTE) has significantly changed the treatment landscape for multiple myeloma (MM). B-cell maturation antigen (BCMA), a cell surface protein highly expressed in malignant plasma cells, has been established as an effective MM cell target, however, achieving long-term responses in relapsed or refractory patients remains a challenge. Resistance to BCMA targeting may result from the enzymatic cleavage of BCMA by surface membrane gamma-secretase resulting in reduced target antigen density on MM cells, and from the soluble BCMA ectodomain that may hinder tumor cell recognition through binding to circulating therapeutic antibodies or to CAR T-cells. Methods: We have previously demonstrated that gamma-secretase inhibitors (GSIs) abrogate enzymatic function and increase BCMA surface density on plasma cells with a concomitant decrease in soluble BCMA concentrations [Pont, MJ. et al. Blood, 134(2019)]. In a phase I, first-in-human clinical trial (n=18; relapsed/refractory MM) combining the GSI, crenigacestat, with anti-BCMA CAR T-cell therapy (FCARH143), we recently demonstrated that plasma cell BCMA antibody-binding capacity increased a median of 12-fold among 17/18 (94%) of participants after they received a 5-day GSI “run-in” (25 mg orally administered QOD for 3 doses) [Cowan AJ, et al. Lancet Oncology. 24(7) 2023]. Overall, 78% of participants experienced a very good partial response or better to GSI with FCAR143, but the duration of response was significantly longer in patients without prior anti-BCMA exposure. Here, we report results from an analysis of the bone marrow aspirates collected at enrollment and again within 12 hours of the third “run-in” dose. We leveraged single-cell RNA sequencing (scRNAseq) coupled with the single-cell assay for transposase-accessible chromatin sequencing (scATACseq) to profile the transcriptome and chromatin accessibility of individual cells within the bone marrow microenvironment. Results: Single-cell multi-omic analysis was performed on 32 bone marrow mononuclear cell samples from 16 patients collected before and within 12 hours after the third oral dose of GSI. Six patients had previously been treated with BCMA-directed therapy, including antibody-drug conjugates, CAR T-cells, or BiTEs. After quality filtering, including compensation for ambient RNA, 142,498 cells were analyzed (median 6,373 cells/sample, range 613-17,150 cells/sample). Cells were automatically classified into 92 immune cell types using Celltypist and a reference dataset trained on immune sub-populations from 20 tissues in 18 studies. Comparing samples pre- and post-GSI exposure revealed a significant decrease in the abundance of plasmablasts, non-classical monocytes, germinal center B cells, mid-erythroid cells, and central memory/naïve cytotoxic T cells after GSI treatment. Additionally, gene expression analysis detected increased expression of Notch signaling genes, NEURL1 and HES1, in non-classical monocytes following GSI treatment. Although RNA expression of TNFRSF17 ( BCMA) did not significantly change, chromatin accessibility was considerably higher in patients without a history of prior BCMA-directed therapy following GSI treatment. In non-plasma cells, we observed increased chromatin accessibility after GSI treatment involving genes encoding proteins known to be cleaved by gamma-secretase, such as ERBB4, NOTCH1, NOTCH2, and TNFRSF17. Accessibility of CD38, the target of daratumumab, was significantly increased in B cells, and SLAMF7, the target of elotuzumab, was significantly increased in plasma cells. Conclusions: BCMA cleavage from myeloma cells' surface is a putative resistance mechanism to BCMA-targeting immunotherapy. This study assessed the single-cell transcriptome and chromatin accessibility in the bone marrow environment of 16 patients given GSI monotherapy to ultimately enhance the efficacy of subsequent anti-BCMA CAR T-cell therapy. We found that prior BCMA-targeted therapy resulted in reduced chromatin accessibility within the BCMA epigenome. Surprisingly, GSI administration also modified the epigenome of other gamma-secretase sensitive genes. These data provide insight into the molecular consequences of prior BCMA-targeted therapy and GSI exposure that may inform future studies.

Polo-like kinase 1 Decrease During Induction Therapy Could Indicate Good Treatment Response, Favorable Risk Stratification, and Prolonged Survival in Pediatric Acute Lymphoblastic Leukemia.

Polo-like kinase 1 (PLK1) modulates leukemia cell apoptosis, proliferation, and cell cycle arrest in the progression of acute lymphoblastic leukemia (ALL). This study intended to investigate the dysregulation of PLK1 and its association with induction therapy response and prognosis in pediatric ALL patients. Bone marrow mononuclear cell samples were collected from 90 pediatric ALL patients at baseline and on the 15th day of induction therapy (D15), as well as from 20 controls after enrollment, for the detection of PLK1 by reverse transcription-quantitative polymerase chain reaction. PLK1 was increased in pediatric ALL patients compared with controls ( P <0.001). In pediatric ALL patients, PLK1 decreased from baseline to D15 ( P <0.001). Lower PLK1 at baseline was associated with a good prednisone response ( P =0.002), while decreased PLK1 at D15 was related to good prednisone response ( P =0.001), better bone marrow response ( P =0.025), and favorable risk stratification ( P =0.014). In addition, reduced PLK1 at baseline was linked with better event-free survival (EFS) ( P =0.046), and decreased PLK1 at D15 was related to prolonged EFS ( P =0.027) and overall survival (OS) ( P =0.047). Moreover, PLK1 decline ≥25% was linked to favorable EFS ( P =0.015) and OS ( P =0.008). Further multivariate Cox proportional regression analysis revealed that PLK1 decline ≥25% was independently linked with prolonged EFS (hazard ratio (HR)=0.324, P =0.024) and OS (HR=0.211, P =0.019). The reduction of PLK1 after induction therapy reflects a good treatment response and correlates with a favorable survival profile in pediatric ALL patients.

Comparison of Data from Fresh and Frozen AML Samples for Functional Drug Testing

Functional testing on cancer patient samples is common in cancer research, but the methodology and type of samples used can vary considerably between studies. In 25 recent studies, where functional testing was performed on blood cancers, half used fresh samples, while others included only frozen or both sample types. Here, we investigated how sample handling influences functional testing in primary acute myeloid leukemia (AML) patient samples. To look for systematic changes in cell population induced by sample handling, we first compared cellular function between frozen (n=70) and freshly isolated (n=112) bone marrow mononuclear cell samples from patients with AML using a high-throughput functional drug testing assay with 528 clinically applied and emerging drugs. We then specifically collected paired samples (n=10) before and after cryopreservation using drug testing and flow cytometry as readouts, including a flow cytometry-based drug testing assay for selected compounds.The Z'-factor, an assay quality indicator, was similar between frozen and fresh sample groups. However, overall cell viability and growth rate were significantly lower in the frozen samples. Interestingly, in fresh samples, delays from sampling to the start of the assay had no effect up to 72h. When comparing overall drug efficacies, most of the drugs tested correlated highly between sample types, but 29 out of 528 drugs showed a notable systematic difference in drug sensitivity. Samples that had been frozen were more sensitive to drugs such as kinase inhibitors (amcasertib, dinaciclib), as well as apoptotic modulators (S-63845), and proteasome inhibitors (VLX1570). Additionally, fresh samples were more sensitive to paclitaxel than frozen ones across the paired samples.The cell composition of paired samples was also affected by cryopreservation, with a reduction in the frequency of cells expressing c-KIT (CD117) and a reduction of cells with high granularity (side scatter). Similar effects could be observed for the flow cytometry-based assay where frozen samples showed a cryopreservation-potentiated reduction in CD34+,CD11b+ and CD56+ cells after proteasome inhibitor treatment, and a reduction in CD56+ cells after taxane treatment. In conclusion, drug responses are in general highly correlated between fresh and frozen samples for about 95% of the drugs tested. However, careful consideration should be given to the specific drugs and cell populations of interest before deciding on sample handling methodology. This may be of particular importance when investigating specific drugs and phenotypes affecting cell proliferation and maturation stage, as sample handling may induce systematic differences and confound interpretation.

Identifying the Cells of Origin and Risk-Stratification Strategy By Single-Cell Sequencing in Multiple Myeloma

Background: With the advent of novel agents, poor prognosis remains an unsolved problem for some patients. Identifying patients with super-high-risk and offering novel therapeutic strategy is essential. The aim of our study is to infer the identity of cells of origin in MM and dig out the novel strategy for risk sub-stratification and treatment. Method: We applied single cell RNA sequencing to fresh bone marrow mononuclear cell samples from 7 healthy donors and 12 newly diagnosed MM patients utilizing 10x Chromium platform. Results: Firstly, we separated plasma cells (PCs) in silico and identified 10 cellular subpopulations in total. Via the CNV score and pseudotime trajectory analysis, we distinguished normal PCs and observed the differentiation trajectory from normal PCs to malignant PCs. We identified cluster 4 as the starting point of malignant transition and possessed plasmablast-like gene signature, which displayed high expression of B-cell gene signatures and specifically high level of CD24. Additionally, cluster 4 was featured by high expression of high-risk genes and drug-resistance genes. Gene enrichment analysis implicated that Wnt, Notch, stem cell differentiation and Hedgehog pathway were enriched in cluster4. Notably, we identified the top upregulated differentially expressed genes in cluster 4 including LILRB4, CRIP1, etc. We next explored their biological function in MM cell lines. We found that both LILRB4 and CRIP1 could promote the proliferation of MM cells, indicating their potential novel therapeutic target in MM. Finally, we constructed a 7-Star stratification model, which was validated to better facilitate discrimination of super-high-risk MM patients when combined with International Staging System (ISS). Conclusion: Our study inferred the cells of origin in MM at single cell resolution. We suggested targeting marker genes of cells of origin as potential novel therapeutic strategy, and our 7-Star stratification model as a powerful tool for discrimination of super-high-risk MM patients when combined with ISS stage.

DNA Replication Stress Due to Loss of R-Loops in Myelodysplastic Syndromes with SF3B1 Mutation

Introduction: Myelodysplastic syndromes (MDS) with SF3B1 mutation represent a subtype of hematopoietic stem cell disorders with bone marrow (BM) erythroid dysplasia and ring sideroblasts. This mutation in a splicing factor gene causes multiple alterations of mRNA maturation. Other mutated splicing factors SRSF2 or U2AF1, produce splicing alterations and formation of unscheduled R-loops, which trigger DNA damage response and genetic instability. Mechanistically, mutant SRSF2 impairs the transcription pause release downstream of TSS allowing invasion of relaxed DNA double helix by nascent RNA. Although previous studies suggested that R-loops inappropriately form in SF3B1 mutant cells, investigations are required to elucidate why MDS with SF3B1 mutation are less prone to genetic instability. Methods: A total of 96 patients including 53 MDS with SF3B1 mutation and 43 other low risk MDS was enrolled in this study. RNA-seq of BM mononuclear cells (MNC) was performed in 21 MDS with and 6 MDS w/o SF3B1 mutation. BM CD34+-derived erythroblasts were expanded from 69 MDS and 22 healthy controls to analyze transcriptome, proteome, R-loop landscape, and DNA replication stress. For global proteome analysis, after protein digestion, peptides were fractionated on SCX column and analyzed by mass spectrometry. R-loops were detected by DNA-RNA immunoprecipitation (DRIP-seq) using S9.6 antibody. Immunofluorescence labeling of pRPA32, 53BP1 and γH2AX, DNA combing, BrdU assay and metabolomics were used to investigate DNA replication stress and DNA biosynthesis in human primary erythroblasts or in murine proerythroblastic (proE) G1E-ER4 Crispr-cas9 Sf3b1K700E/+ cell line. expressing GATA1 under the control of estrogen receptor. Results: In SF3B1-mutated BM MNC samples, DeSeq2 analysis of RNA-seq data revealed 1,058 upregulated genes (log2(FC)>0.6, BH-adj P-value<0.05) involved in DNA replication, cell cycle, nucleotide biosynthesis, DNA repair, and erythroid differentiation. Analysis of splicing identified 3,937 differential splicing events (ΔPSI>0.10, BH-adj P-value<0.05) including 1,256 events of intron retention (IR). Among IR events, 384 loss of IR events correlated with altered expression of 293 genes. In SF3B1-mutated erythroblasts, loss of IR events were more frequent in polychromatophilic erythroblasts (polyE: 383) than in proE/early basophilic erythroblasts (eBasoE: 171) suggesting that gene extinction mediated by IR during normal differentiation was altered. Quantitative proteomics revealed 924 significantly deregulated proteins with little overlap with IR transcripts, consistently involved in DNA damage, DNA repair, and nucleotide biosynthesis pathways. DRIP-seq was performed in proE/eBasoE of 8 MDS w/o or with SF3B1 mutation to investigate R-loop formation. Stringent peak calling identified 2,208 shared peaks in wild-type samples compared to 159 shared peaks in SF3B1-mutated samples, suggesting a loss of R-loops. Integrative analysis and DRIP-qPCR demonstrated that R-loop losses overlapped IR loss events, consistent with the notion that intron excision prevents R-loop formation. Compared to SRSF2 or U2AF1-mutated erythroblasts, SF3B1-mutated cells exhibited a significant increase of DNA synthesis by BrdU labeling and of replication fork speed by DNA combing. Immunofluorescence analysis detected pRPA32 foci marking single strand DNA but no evidence of double strand breaks in contrast to SRSF2 mutant cells in which 53BP1 or γH2AX foci were detected. Compared to isogenic cell lines, Sf3b1K700E/+ proE G1E-ER4 cells demonstrated higher proliferation rate and fork speed in proliferation and in estradiol-induced differentiation conditions, and significantly lower amounts of deoxynucleotides (dATP, dCTP, TTP) suggesting persistence of DNA synthesis during differentiation. Targeting of DNA polymerase by aphidicolin or ribonucleotide reductase by low doses of hydroxyurea partially rescued erythroid differentiation. Conclusion: Our data demonstrated a highly specific signature of DNA replication stress at transcriptomic and proteomic level in SF3B1-mutant cells. High replication fork speed reflects the loss of R-loops in these cells, which may reduce the risk of genetic instability at the expense of normal differentiation. Pharmacological targeting of the DNA replication stress could improve erythropoiesis.

P548: BEMCENTINIB COMBINED WITH LOW-DOSE CYTARABINE IS EFFICACIOUS AND WELL TOLERATED IN RELAPSED AML PATIENTS UNFIT FOR INTENSIVE CHEMOTHERAPY. UPDATES FROM THE ONGOING PHASE II TRIAL (NCT02488408)

Background: The new standard of care (SOC) in newly-diagnosed AML patients (pts) unfit for intensive chemotherapy (IC) due to age or co-morbidities yields favourable efficacy. However, beyond 1st line, these pts have limited treatment options, with a dismal mOS of 2.4 months at relapse, highlighting a significant unmet need for new treatments in this pt population. Bemcentinib (BEM) is a first-in-class, orally bioavailable, highly selective AXL-inhibitor. AXL (a receptor tyrosine kinase) represents an important target in AML, as its expression on AML cells and stem cell compartment is associated with poor prognosis, resistance to chemotherapy and decreased antitumor immune response. Aims: The ongoing BGBC003 PhII trial studied the safety and efficacy of the BEM+LDAC combination in relapsed (REL) AML pts unfit for IC; in addition, a comprehensive translational biomarker analysis was pursued. Here, we present preliminary efficacy and safety data and include initial translational multiomics data from bone marrow mononuclear cells (BMMNC). Methods: Pts received BEM at 200 mg/OD with 3 loading doses at 400 mg/OD and LDAC at SOC schedule. Study endpoints included overall survival (OS), objective response (OR), clinical benefit rate (CBR) (OR+unchanged [UC]) and exploratory biomarker analyses. Longitudinal BMMNC samples (n=32) from 13 pts underwent scRNA-seq and CITEseq (Chromium 10x genomics; TotalSeq, Biolegend). Pts were stratified by best response: CR, CRi, PR for Responders; UC, PD for Non-Responders. Cell type annotations were inferred from expression of known markers at RNA and protein level and for identification of malignant cells copy number variation was included. Results: As of 13 Dec 2021, 22 REL AML pts (10/22 in 1st relapse) were treated with BEM+LDAC. Median age was 75.5yrs [66-86], median prior lines of therapy 1 [1-8] and adverse cytogenetic risk in 7/22 (32%). Only 19/22 pts were evaluable for efficacy. The overall REL population (n=19) demonstrated a composite CR (CRc=CR+CRi) of 26% (5/19) and CBR of 79% (14/19); AML pts in 1st relapse (10/19) showed a CRc of 30% (3/10) and CBR of 70% (7/10) whereas REL pts with time on treatment (ToT) > 3 months (11/19) achieved CRc of 45% (5/11) and CBR 91% (9/11); median ToT of 12.9 months for CR/CRi pts; 1 pt remains on treatment. Median OS was 6.2 months in the overall REL pts (n=19), 7.4 months in the AML pts in 1st relapse (10/19) and 11.3 months in REL pts with ToT >3 months. Late onset responses suggest an immunological mechanism of action (iMOA) and may contribute to a longer ToT and survival. CITEseq analysis identified differences in the immune compartment, underscoring an iMOA associated with response to BEM+LDAC. Furthermore, increased gene set enrichment of TNFα signalling via NfkB at screening in malignant blasts of responders emphasizes the influence of the TNFα signalling pathway as an interesting field of further research. The safety profile of BEM+LDAC in the REL AML pts (n=19) is comparable with the known safety profile of LDAC. TRAEs of ≥G3 observed in ≥10% of pts were anaemia (33% BEM+LDAC; 22% BEM), and ECG QT prolonged (11% BEM+LDAC; 11% Bem). No G5 TRAEs reported. Summary/Conclusion: BEM+LDAC is efficacious and well tolerated in REL AML pts unfit for IC, with promising survival benefit compared to historical data. Translational research showed activation of CD8+T cells and B/Plasma cells in response to treatment, indicating that BEM elicits activation of two major adaptive immune cell populations responsible for anti-AML immune responses. BEM+LDAC warrants further evaluation in this population.

Abstract 1729: 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 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.

Bemcentinib (Oral AXL Inhibitor) in Combination with Low-Dose Cytarabine Is Well Tolerated and Efficacious in Older Relapsed AML Patients.Updates from the Ongoing Phase II Trial (NCT02488408) and Preliminary Translational Results Indicating Bemcentinib Elicits Anti-AML Immune Responses

Background: Relapsed (REL) & refractory (REF) r/r AML pts unsuitable for intensive therapy (IT) due to age or co-morbidities, have limited treatment options. Low-dose cytarabine (LDAC) demonstrated limited survival benefit (mOS ≤6 months), highlighting the significant unmet need for new treatments in this patient population. Bemcentinib (BEM) is an orally bioavailable, highly selective AXL-inhibitor. AXL is a receptor tyrosine kinase conferring poor prognosis, resistance to chemotherapy and decreased antitumor immune response. AXL is overexpressed on leukemic cells, especially in the stem cell compartment, thus representing an important novel target in AML.Aims: The ongoing BGBC003 PhII trial cohorts receiving BEM+LDAC (B+L) include newly diagnosed (ND) and r/r AML pts unfit for IT. Based on the initial efficacy signal observed, the r/r AML patient sub-group was selected for an expansion cohort, to further explore safety and efficacy and to pursue translational biomarker analysis. Here, we present preliminary efficacy data in r/r pts, with a safety overview for all pts treated with B+L. We additionally include translational plasma biomarker and multiomics data from bone marrow mononuclear cells (BMMNC).Methods: Pts received the combination of BEM at the RP2D (200mg PO/d) and LDAC SoC schedule. Efficacy endpoints were objective response (OR) and clinical benefit (OR+unchanged [UC]+stable disease [SD=UC for at least 3 BEM cycles]). Secondary objectives looked at overall survival (OS) and exploratory biomarker analyses.Longitudinal BMMNC samples (n=36) from 15 patients were subjected to scRNA-seq and CiteSeq (Chromium 10x genomics; TotalSeq, Biolegend). For scRNA-seq data analyses, Cell Ranger (v3.1.0) and the Seurat (v.4.0.1) in R (v.4.0) were used. Pts were stratified by best response: CR, CRi, PR for Responders; SD, UC, PD for Non-Responders. Cell type annotations were based on the identified clusters and inferred from the expression of known markers at both RNA and protein level.Results: As of 15 July 2021, the B+L cohorts comprised 27 r/r (20 REL, 7 REF) AML pts. Median prior lines of therapy: 1 [1-8] in REL, 3 [1-4] in REF. Median age: 75.5yrs [66-86] for REL, 75yrs [71-81] for REF. Adverse cytogenetic risk profile: 6/18 (33%) in REL; 2/7 (29%) in REF. 17/20 REL pts were evaluable for efficacy (BM assessment post-baseline). 4/17 (24%) achieved remission (4 CR/CRi) between wk13(C5)-wk19(C7); 4 pts had SD, 6 pts were UC; observed clinical benefit rate was 82%. Late onset responses may reflect immunological mechanism of action targeting AXL + innate immune cells in REL pts and may also contribute to a longer time-on-treatment (ToT) and survival. Median ToT was 36.9 wks for CR/CRi pts; mDOR 33wks [12.0-69.9]; 4 pts remain on treatment. Median OS currently 13.3 months (historical controls suggest 4.5 months mOS in this population) continues to mature. In contrast, REF pts showed no response (0/7), with 4/7 (57%) demonstrating clinical benefit; mToT 12.0wks for benefitting patients and mOS 5.3 months; no pts ongoing on treatment.Overall, the safety of B+L (compared with previously published BEM monotherapy) is comparable with the known safety profile of LDAC. TRAEs of ≥G3 observed in ≥10% of pts were anaemia (21% B+L; 4% BEM), and ECG QT prolonged (12% B+L; 7% BEM). No G5 TRAEs reported.scRNA and multiomic analysis of longitudinal samples reflect differences in the immune compartment, underscoring the clinical impression of an immune-mediated MOA associated with response to BEM. CD8+ effector T-cells of responding patients demonstrated enhanced pro-inflammatory signatures involving TNF-alpha and IFN-gamma as compared with non-responders. Furthermore, increased activation of B plasma cells was observed in correlation with response to BEM confirming that BEM mediates an anti-AML immune response through activation of the two major adaptive immune cell populations.Conclusion: B+L is well tolerated and offers promising survival benefit to older unfit REL AML patients. Translational research including scRNA and multiomics, identified specific activation of CD8+ T cells and B plasma cells associated with response to treatment, indicating that BEM elicits activation of the two major adaptive immune cell populations responsible for anti-AML immune responses. B+L warrants evaluation in a randomized pivotal trial in this population. DisclosuresLoges: BerGenBio ASA: Honoraria, Research Funding, Speakers Bureau; BMS: Research Funding; Eli Lilly: Research Funding; Roche Pharma: Research Funding; ADC Therapeutics: Research Funding; BMS: Honoraria, Speakers Bureau; Boehringer Ingelheim: Honoraria, Speakers Bureau; Eli Lilly: Honoraria, Speakers Bureau; Roche Pharma: Honoraria, Speakers Bureau; Medac GmbH and Sanofi Aventis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; AstraZeneca: Honoraria, Speakers Bureau; Pfizer: Honoraria, Speakers Bureau; Takeda: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Bayer: Honoraria, Speakers Bureau. Heuser: Astellas: Research Funding; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Research Funding; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer Pharma AG: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; BergenBio: Research Funding; BMS/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolremo: Membership on an entity's Board of Directors or advisory committees. Kapp-Schwoerer: BerGenBio ASA: Research Funding. Lemoli: Celgene: Other: Support for attending meetings and/or travel; Jazz, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie, Daiichi Sankyo, Servier: Honoraria, Speakers Bureau. Ben Batalla: BerGenBio ASA: Research Funding. Hellesøy: BerGenBio ASA: Research Funding. Rayford: BerGenBio ASA: Current Employment. Nautiyal: BerGenBio ASA: Current Employment. Berkman-Gottlieb: BerGenBio ASA: Consultancy, Ended employment in the past 24 months. Micklem: BerGenBio ASA: Current Employment, Current equity holder in publicly-traded company. Gabra: BerGenBio ASA: Current Employment, Current equity holder in publicly-traded company. Gorcea-Carson: BerGenBio ASA: Current Employment. Lorens: BerGenBio ASA: Current equity holder in publicly-traded company, Ended employment in the past 24 months, Patents & Royalties. Fiedler: Servier: Consultancy, Other: support for meeting attendance; Amgen: Consultancy, Other: support for meeting attendance, Patents & Royalties, Research Funding; ARIAD/Incyte: Consultancy; Daiichi Sankyo: Consultancy, Other: support for meeting attendance; Stemline: Consultancy; Abbvie: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Other: support for meeting attendance; Novartis: Consultancy; Pfizer: Consultancy, Research Funding; Celgene: Consultancy; Morphosys: Consultancy. Alvarado: Jazz Pharmaceuticals: Research Funding; BerGenBio: Research Funding; Daiichi-Sankyo: Research Funding; FibroGen: Research Funding; CytomX Therapeutics: Consultancy; Astex Pharmaceuticals: Research Funding; MEI Pharma: Research Funding; Sun Pharma: Consultancy, Research Funding. Gjertsen: BerGenBio: Consultancy; Pfizer Inc.: Consultancy; Alden Cancer Therapy: Current holder of stock options in a privately-held company; KinN Therapeutics: Current holder of stock options in a privately-held company; Novartis: Consultancy.

Identification of Myeloma-Initiating Cells By Single-Cell RNA Sequencing

Background: The outcomes of patients with multiple myeloma (MM) have improved due to treatment advances. However, some patients still experience rapid progression, multiple drug resistance or recurrent relapse. Tumor-initiating cells, also known as cancer stem cells (CSC) in some cancer types, have been speculated to induce recurrence of the disease. The aim of this study is to infer the identity of myeloma-initiating cells (MICs) utilizing single-cell transcriptome analysis and explore the unique biological characteristics relating with high-risk and drug-resistance.Method: We applied single cell RNA sequencing to fresh bone marrow mononuclear cell samples collecting from 7 healthy donors and 12 newly diagnosed MM patients utilizing 10x Chromium platform.Results: Firstly, we segregated the patients by tumor cell infiltration at single cell resolution and found that Myc pathway was significantly enriched in patients with high tumor burden (HTB). Next, we performed clustering analysis to tumor cells and identified 13 tumor subpopulations in total. Surprisingly, the distribution pattern of tumor subpopulations presented similarity among HTB patients, whereas we did not find the uniform subpopulation composition among the patients with low (LTB) or medium tumor burden (MTB). Via the tumor subpopulation analysis, we clarified the divergence in biological characteristics of these 13 malignant subpopulations. We identified plasmablasts as displaying high expression of B-cell gene signatures (CD19, CD27, MS4A1 and CD79B) and relatively low expression of plasma cell gene signatures (SDC1 and BCMA). Additionally, we also noted that they showed high level of CD24, which has been validated to be the marker gene for MICs. We next examined proliferative capability and utilized the 70 high-risk gene model and 56 drug resistance-related gene model to further distinguish subpopulations with the most malignant gene expression features. Notably, we found that plasmablasts possessed characteristics of high proliferation, drug-resistance and high-risk gene profiling, indicating their role as the root of myeloma, namely MIC subpopulation. Gene enrichment analysis also implicated that Wnt pathway, Notch pathway, stem cell differentiation pathway and Hedgehog pathway were enriched in MIC subpopulation which were associated with the proliferation, migration and drug resistance of MM. Differentially expressed gene (DEG) analysis showed that common driver genes in myeloma, such as CCND2, ITGB7 and CD74, were upregulated in MIC subpopulation comparing with other subpopulations.Conclusion: Our work presents an integral profiling for tumor cells in myeloma at single cell resolution. We uncovered divergence in the distribution of malignant subclusters across patients and distinct heterogeneity in gene expression across malignant subclusters as well. Plasmablasts expressing high level of CD24, CD27 and dim CD138 presented as the MICs with characteristics of higher proliferation, drug-resistance and high-risk gene profiling. DisclosuresNo relevant conflicts of interest to declare.

Single Cell Analysis of Adult Human Hematopoietic Stem and Progenitor Cells Identifies a Novel Lymphoid Primed Multipotent Progenitor That Expands in Relapsed Acute Myeloid Leukemia

Background: Hematopoietic stem and progenitor cells (HSPCs) are capable of generating all human blood cells. Although these cells have been extensively evaluated using both sorted (Corces et al. Nat. Genet. 2016) and single cell assays (Pellin et al. Nat. Commun. 2019), there remains significant uncertainty as to the degree of heterogeneity within HSPC subpopulations and their relevance to disease. The phenotypic diversity within HSPCs needs to be better characterized in order to understand the pathogenesis of numerous blood disorders including hematologic malignancies. To address this need, we extensively characterized healthy bone marrow mononuclear cells (BMMCs) with both whole transcriptome analysis (WTA) and surface marker evaluation. We hypothesized that by utilizing concurrent RNA and multiplexed (n=35) surface marker analysis, we could not only improve HSPC clustering but also characterize specific phenotypic states along unique hematopoietic differentiation trajectories. This analysis allowed us to identify new subpopulations within currently defined hematopoietic stem cells (HSCs), multipotent progenitors (MPPs) and lymphoid primed multipotent progenitors (LMPPs).Methods: Three healthy adult BMMC samples were stained with antibody conjugated oligonucleotides (BD Abseq) and analyzed using a nanowell cell capture system (BD Rhapsody). We filtered 8,070 high quality cells for 2,508 HSPCs, myeloid cells, and lymphocyte precursors. These cells were first clustered using matrix factorization in order to identify unique HSPC states. Novel clusters within the HSC, MPP, and LMPP subpopulations were purified using flow cytometry, and functionally evaluated with both in vitro and in vivo assays.Results: The antibody-derived tags (ADTs) obtained from BD Abseq comprised 33 of the most informative features (n=2000) and resulted in more stable clustering as determined by within sum of squares (WSS = 898 versus 934 for mRNA alone for 20 clusters). Additionally, we were able to design a targeted HSPC panel (n=500 genes) with Abseq which identified similar cell clusters compared to the WTA alone and WTA plus ADT data (Rand index = 0.88). HSPC clustering identified the putative hematopoietic stem cell (HSC), a multipotent progenitor (MPP), and a new lymphoid primed multipotent progenitor (LMPP). A new computational sorting strategy was derived to purify these primitive HSPCs, and subsequently validated with flow cytometry. The functional evaluation of these sorted populations revealed that HSC and MPP cells were capable of increased serial replating ability in vitro suggesting enhanced self-renewal capabilities compared to LMPP cells. All three HSPC subpopulations produced gradients of erythroid and myeloid colonies in methylcellulose, and T-cells in T-cell expansion assays in vitro. The HSCs were the only cells that were able to produce long term engraftment in immunodeficient mice after serial transplantations. Additionally, common myeloid progenitor (CMP), granulocyte-monocyte progenitor (GMP), and megakaryocyte-erythroid progenitor (MEP) clusters that expressed canonical surface markers were identified. The primitive HSPC clusters were converted into a signature matrix using CIBERSORTx (Newman et al. Nat. Biotechnol. 2019), and bulk acute myeloid leukemia (AML) and healthy samples were deconvolved into respective healthy cell clusters. Using multivariate Cox proportional hazard analysis, we found that high levels of MPPs at diagnosis was the strongest feature associated with worse overall survival (HR = 78.46, 95% confidence interval 7.32-828). Interestingly, after performing paired analysis of diagnostic and relapse cases, the HSC and LMPP expanded significantly in relapsed AML after chemotherapy (p < 0.05) whereas the MPP expanded considerably after stem cell transplant but did not reach significance (p = 0.09).Conclusions: In summary, we identified new cell type clusters within previously defined HSC, MPP, and LMPP subpopulations, and unique surface marker associations using combined single cell WTA and surface marker analysis. We were able to purify these primitive HSPCs using these new markers, in addition to classical markers like CD34, CD90, and CD45RA. Importantly, deconvolution analysis provided preliminary insights into their clinical relevance in AML. DisclosuresEdiriwickrema: Nanosive SAS: Patents & Royalties. Majeti: BeyondSpring Inc: Membership on an entity's Board of Directors or advisory committees; Circbio inc: Membership on an entity's Board of Directors or advisory committees; Kodikaz Therapeutics Solutions Inc: Membership on an entity's Board of Directors or advisory committees; Coherus Biosciences: Membership on an entity's Board of Directors or advisory committees; Acuta Capital Partners: Consultancy; Gilead Sciences, Inc: Patents & Royalties.

A Comprehensive Characterization of Monoallelic Expression During Hematopoiesis and Leukemogenesis via Single-Cell RNA-Sequencing.

Single-cell RNA-sequencing (scRNA-seq) is becoming a powerful tool to investigate monoallelic expression (MAE) in various developmental and pathological processes. However, our knowledge of MAE during hematopoiesis and leukemogenesis is limited. In this study, we conducted a systematic interrogation of MAEs in bone marrow mononuclear cells (BMMCs) at single-cell resolution to construct a MAE atlas of BMMCs. We identified 1,020 constitutive MAEs in BMMCs, which included imprinted genes such as MEG8, NAP1L5, and IRAIN. We classified the BMMCs into six cell types and identified 74 cell type specific MAEs including MTSS1, MOB1A, and TCF12. We further identified 114 random MAEs (rMAEs) at single-cell level, with 78.1% single-allele rMAE and 21.9% biallelic mosaic rMAE. Many MAEs identified in BMMCs have not been reported and are potentially hematopoietic specific, supporting MAEs are functional relevance. Comparison of BMMC samples from a leukemia patient with multiple clinical stages showed the fractions of constitutive MAE were correlated with fractions of leukemia cells in BMMCs. Further separation of the BMMCs into leukemia cells and normal cells showed that leukemia cells have much higher constitutive MAE and rMAEs than normal cells. We identified the leukemia cell-specific MAEs and relapsed leukemia cell-specific MAEs, which were enriched in immune-related functions. These results indicate MAE is prevalent and is an important gene regulation mechanism during hematopoiesis and leukemogenesis. As the first systematical interrogation of constitutive MAEs, cell type specific MAEs, and rMAEs during hematopoiesis and leukemogenesis, the study significantly increased our knowledge about the features and functions of MAEs.

Targeted Next Generation Sequencing of Circulating Tumor DNA in Multiple Myeloma

Introduction: Immunophenotypic and chromosomal analysis among patients with multiple myeloma (MM) has traditionally been performed on plasma cells enriched from bone marrow aspirates. However, due to the patchy nature of MM, a bone marrow aspirate may not accurately represent the total body burden of disease nor does it reflect the true heterogeneity of the cancer cells. Additionally, since it is an invasive procedure, frequent resampling to monitor clonal evolution is infeasible. Next generation sequencing of circulating tumor DNA is a promising new approach that may address these shortcomings. Circulating tumor DNA, either from cell-free DNA or circulating tumor cells, originates from multiple different sites of disease and may be obtained from a single blood sample. This non-invasive approach may enable clinicians to detect early signs of resistance, monitor minimal residual disease, identify therapeutic targets, and provide prognostic information to help guide treatment decisions.Methods: Bone marrow mononuclear cells (BMMC), peripheral blood mononuclear cells (PBMC), and plasma samples were obtained from 45 patients treated at our center at the time of MM diagnosis, remission, or disease relapse. All samples were obtained through an Institutional Review Board approved protocol. DNA was extracted and library preparation for next generation sequencing was performed using unique molecular identifiers (UMI). The incorporation of UMIs allows for the identification and removal of errant base calls introduced during PCR amplification and Illumina sequencing thereby increasing the sensitivity of the assay to detect rare mutations. Target enrichment was performed using a panel of primers specific to exons of genes known to be significantly, recurrently mutated in MM as well as genes with predictive or prognostic value. Illumina sequencing was performed to a depth of 10,000X. Comparison of single nucleotide variants (SNV) and indels between matched BMMC, PBMC and plasma samples was determined. Correlation of clinical outcomes with mutation status and variant allele frequency was performed.Results: While good concordance was found between BMMC, PBMC, and plasma samples among the SNVs and indels detected, distinct variants unique to each tissue compartment were also identified. Indicating the assay may have utility for disease monitoring, variants detected in the blood pre-treatment were no longer detectable in post-treatment remission samples. Additionally, new variants in blood samples not present prior to initiation of therapy were detected at later time points providing evidence for clonal evolution. Finally, driver mutations were found in genes for which known targeted therapy is available or in genes that may theoretically confer drug resistance such as CUL4A that binds to Cereblon, a target of the immunomodulatory drug lenalidomide.Conclusions: While bone marrow biopsy represents the standard of care for diagnosis and disease monitoring in patients with multiple myeloma, the procedure is subject to sampling bias and frequent serial biopsies are impractical. A noninvasive approach using targeted next generation sequencing of circulating tumor cells is an appealing alternative. We demonstrate the feasibility of this technique in MM PBMC and plasma samples and find evidence that the assay may support clinical decision making. DisclosuresNo relevant conflicts of interest to declare.

Longitudinal Molecular Profiling in Patients with IDH1-Mutant Newly Diagnosed Acute Myeloid Leukemia Treated with Ivosidenib

Background: Somatic mutations in isocitrate dehydrogenase 1 (IDH1) are reported in 6-10% of patients (pts) with acute myeloid leukemia (AML), resulting in production of the oncometabolite D-2-hydroxyglutarate. Ivosidenib (IVO) is an oral, potent, targeted inhibitor of mutant IDH1 (mIDH1) and is FDA-approved for the treatment of mIDH1 relapsed or refractory (R/R) AML and mIDH1 newly diagnosed (ND) AML in adults ≥ 75 years of age or with comorbidities precluding intensive induction chemotherapy. In a phase 1 study (NCT02074839), durable remissions in pts with mIDH1 ND AML (n = 33) were achieved with IVO, with a complete remission (CR) plus CR with partial hematologic recovery (CRh) rate of 42.4%, and median overall survival of 12.6 months (mo), as of 02Nov2018. The most frequent co-occurring mutations at baseline were ASXL1, DNMT3A, RUNX1, SRSF2, TET2, and NRAS. Aim: To characterize the longitudinal evolution of gene mutations in pts with mIDH1 ND AML treated with IVO 500 mg once daily, including relapse mechanisms and depth of molecular response for mIDH1 and co-occurring mutations. Methods: The mIDH1 variant allele frequency (VAF) was assessed in bone marrow mononuclear cells (BMMCs), peripheral blood mononuclear cells (PBMCs), and neutrophils using BEAMing digital polymerase chain reaction (PCR) technology (Sysmex Inostics, Inc.), which has a lower limit of detection for mIDH1 of 0.02-0.04%. Deep IDH1 mutation clearance (MC) was defined as reduction in mIDH1 VAF to below the limit of detection for ≥ 1 on-treatment timepoint. Baseline and longitudinal co-occurring mutation profiling was conducted on BMMC or PBMC samples by next-generation sequencing (NGS; detection sensitivity of 1-5%). Single-cell targeted DNA sequencing (DNA-seq) was performed on PBMCs using a microfluidic platform (Tapestri®). The clinical data cut-off for this analysis was 02Nov2018. Results: In pts who achieved a best response of CR or CRh, the IDH1-MC rate in BMMCs was 64.3% (9/14), and 72.7% (8/11) in both PBMCs and neutrophils, by sensitive digital PCR (Table). Median time to IDH1-MC was 7.4 mo (BMMCs), 6.9 mo (PBMCs), and 5.1 mo (neutrophils) in pts achieving CR or CRh. IDH1-MC was significantly associated with a best response of CR or CRh (p &amp;lt; 0.001, Table). Overall survival at 12 mo was 88.9% (95% CI 43.3, 98.4) for pts with IDH1-MC in BMMCs (n = 9), as compared with 38.5% (95% CI 17.7, 59.0) for pts who did not achieve IDH1-MC (n = 21). The longitudinal evolution of mIDH1 and co-occurring mutations during IVO treatment was profiled by NGS in 27 pts. In 13 pts who achieved a best response of CR/CRh and with available data, non-DTA (DNMT3A, TET2, ASXL1) gene mutation clearance was observed for IDH1 (11/13), RUNX1 (2/4), SRSF2 (2/3), and NPM1 (2/2). One pt had all co-occurring mutations (IDH1, FLT3, and NPM1) cleared by IVO monotherapy, and maintained CR for 30.2 mo as of the data cut-off. The most frequent mutations acquired at relapse or disease progression following IVO treatment were mutations in receptor tyrosine kinase (RTK) pathway genes (38.5%; 5/13), followed by mutations in chromatin remodeling (15.4%; 2/13) and IDH2 (7.7%; 1/13; Table). No IDH1 second-site mutations were observed in this cohort by NGS; however, emergence of an IDH1 R119P second-site mutation was observed in 1 pt using a single-cell DNA-seq assay. Conclusions: IDH1-MC across examined cell types (BMMCs, PBMC, and neutrophils) suggests that IVO can alter the biology of mIDH1 ND AML via reduction of the primitive mIDH1 cells. Similar to previous findings in R/R AML, the observed trend of improved overall survival in pts with deep molecular remission (ie, IDH1-MC) warrants further investigation in a larger pt cohort. Relapse is mediated by diverse emergent mutations, most frequently in RTK pathway genes, chromatin remodeling genes, and IDH2. Clonal architecture and evolution in ~ 20 pts revealed by single-cell DNA-seq analysis will be presented. Disclosures Choe: Agios Pharmaceuticals: Current Employment, Current equity holder in private company. Wang:Agios Pharmaceuticals: Current Employment, Current equity holder in private company. Roboz:Array BioPharma: Consultancy; MEI Pharma: Consultancy; Helsinn: Consultancy; Epizyme: Consultancy; Jasper Therapeutics: Consultancy; Cellectis: Research Funding; Trovagene: Consultancy; Takeda: Consultancy; Otsuka: Consultancy; Bayer: Consultancy; Celltrion: Consultancy; Eisai: Consultancy; Jazz: Consultancy; Roche/Genentech: Consultancy; Sandoz: Consultancy; Abbvie: Consultancy; Pfizer: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Astex: Consultancy; Amphivena: Consultancy; Agios: Consultancy; Orsenix: Consultancy; AstraZeneca: Consultancy; Daiichi Sankyo: Consultancy; Astellas: Consultancy; Argenx: Consultancy; Actinium: Consultancy. DiNardo:Agios: Consultancy, Honoraria, Research Funding; Jazz: Honoraria; AbbVie: Consultancy, Honoraria, Research Funding; ImmuneOnc: Honoraria; Syros: Honoraria; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; Novartis: Consultancy; Notable Labs: Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria; Calithera: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; MedImmune: Honoraria. Stein:Syros: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy; Syndax: Consultancy, Research Funding; Amgen: Consultancy; Abbvie: Consultancy; PTC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Agios Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Biotheryx: Consultancy; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bayer: Research Funding; Daiichi-Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees. Mims:Jazz Pharmaceuticals: Other: Data Safety Monitoring Board; Syndax Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Kura Oncology: Membership on an entity's Board of Directors or advisory committees; Leukemia and Lymphoma Society: Other: Senior Medical Director for Beat AML Study; Novartis: Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy. Watts:Pfizer: Consultancy; Celgene: Consultancy; Jazz: Consultancy, Speakers Bureau; Takeda: Research Funding. Fan:Agios Pharmaceuticals: Current Employment, Current equity holder in private company. Nejad:Agios Pharmaceuticals: Current Employment, Current equity holder in private company. Zhang:Agios Pharmaceuticals: Current Employment, Current equity holder in private company. Liu:Agios Pharmaceuticals: Current Employment, Current equity holder in private company. Attar:Agios Pharmaceuticals: Current Employment, Current equity holder in private company. Wu:Agios Pharmaceuticals: Current Employment, Current equity holder in private company. Stone:Takeda: Consultancy; Arog: Research Funding; Argenx: Consultancy, Other: Data and safety monitoring board; Agios: Consultancy, Research Funding; Actinium: Consultancy; Novartis: Consultancy, Research Funding; Jazz: Consultancy; AbbVie: Consultancy, Research Funding; Gemoab: Consultancy; Elevate: Consultancy; Daiichi-Sankyo: Consultancy; Stemline: Consultancy; Syndax: Consultancy; Syntrix: Consultancy; Hoffman LaRoche: Consultancy; Macrogenics: Consultancy; Janssen: Consultancy; Syros: Consultancy; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy; Biolinerx: Consultancy; Celgene: Consultancy, Other: Data and safety monitoring board; Trovagene: Consultancy; Pfizer: Consultancy; Otsuka: Consultancy.

Abstract 3779: Multiomic single cell analysis of normal human bone marrow identifies a unique stem and progenitor population that expands in AML

Abstract Hematopoietic stem and progenitor cells (HSPCs) can generate a diversity of blood cells throughout the human lifespan. Although these cells have been evaluated using both sorted (Corces et al. Nat. Genet. 2016) and single cell assays (Pellin et al Nat. Commun. 2019), there remains uncertainty in the degree of heterogeneity within HSPC subpopulations and their associated differentiation trajectories. The phenotypic diversity within HSPCs needs to be better characterized in order to understand the pathogenesis of blood disorders including hematologic malignancies. To address this need, we characterized healthy bone marrow mononuclear cells (BMMCs) with whole transcriptome analysis (WTA) and surface marker evaluation. We hypothesized that by utilizing concurrent RNA and surface marker analysis (n=35), we can improve HSPC clustering and characterize specific phenotypic states along unique hematopoietic differentiation trajectories. Three healthy BMMC samples were stained with antibody conjugated oligonucleotides (BD® Abseq) and analyzed using BD Rhapsody™. We filtered 8,070 high quality cells for 2,508 HSPCs, myeloid cells, and lymphocyte precursors. The antibody-derived tags (ADTs) obtained from BD® Abseq comprised 33 of the most informative features (n=2000) and resulted in more stable clustering as determined by within sum of squares (WSS = 898 verses 934 for WTA alone for 20 clusters). Additionally, we designed a targeted HSPC panel (n=500 genes) with BD® Abseq which identified similar cell clusters compared to the WTA alone and WTA plus ADT data (rand index = 0.88). HSPC clustering identified putative hematopoietic stem cell (HSC), common myeloid progenitor (CMP), and megakaryocyte-erythroid progenitor (MEP) clusters that expressed canonical surface markers. Of interest, we identified candidate novel myeloid, T-cell, and B-cell primed precursors and new surface marker expression gradients that align with specific differentiation trajectories. The results of this analysis will be presented. The HSPC clusters were converted into a signature matrix using Cibersortx (Newman et al. Nat. Biotechnol. 2019), and bulk acute myeloid leukemia (AML) and healthy samples were deconvolved into respective healthy cell clusters. We subsequently performed multivariate Cox proportional hazard analysis, and observed that high levels of healthy cluster 8 (H8; HR 3.40, 95% confidence interval 1.24-9.34), the candidate lymphoid-primed multipotent progenitor (LMPP), and low levels of healthy cluster 6 (H6; HR 0.26, 95% confidence interval 0.12-0.58), a candidate erythrocyte precursor, at diagnosis were associated with worse overall survival. Deconvolution using sorted healthy sub-populations (Corces et al. Nat. Genet. 2016) only identified erythrocyte precursors as statistically relevant (HR 0.27, 95% confidence interval 0.12-0.61). Of note, H8 had a distinct gene expression profile compared to that identified for the sorted LMPP sub-population using differential gene expression analysis. In summary, we identified novel cell type clusters and surface marker associations using combined single cell WTA and surface marker analysis (BD® Abseq). We were able to correlate cell types with both canonical and novel surface markers, and deconvolution analysis provided preliminary insights into their clinical relevance in AML. Citation Format: Asiri Ediriwickrema, Sreejith Ramakrishnan, Margaret Nakamoto, Smita Ghanekar, Bogdan Luca, Aaron Newman, Andrew Gentles, Ravindra Majeti. Multiomic single cell analysis of normal human bone marrow identifies a unique stem and progenitor population that expands in AML [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3779.

Personalized Ubiquitination Signatures for Predicting Response to Proteasome Inhibitor-Based Therapy of MM Patients

Introduction Proteasome inhibition has emerged as an important therapeutic strategy in multiple myeloma (MM) and its first-in-class agent, bortezomib, has become a major player in MM treatment. Bortezomib reversibly inhibits the proteasome, inducing an accumulation of ubiquitinated proteins in MM cells, which results in endoplasmic reticulum stress-related apoptosis. However, up to 30% of patients (pts) have been shown to fail bortezomib-based induction and the underlying mechanisms remain poorly understood. As protein degradation by the proteasome, is reliant on protein ubiquitination, uncovering the ubiquitination profile of bone marrow mononuclear cells (BM-MNC) of MM pts, may offer putative biomarkers for predicting the response to treatment and in the future shed new light on disease pathophysiology. In collaboration with PTM Biosciences, we investigated ubiquitination patterns in BM-MNC samples obtained from MM pts treated with bortezomib-based induction. We hypothesized that ubiquitination patterns at diagnosis would differ between responders vs non-responders and would serve as a platform for identifying potential new therapeutic targets involved in induction of primary resistance. Methods BM-MNC samples, collected prior to therapy from 31 NDMM pts, treated with Velcade (bortezomib)-Cyclophosphamide-Dexamethasone (VCD) or VD, were analyzed. Response to treatment was determined after 4-6 cycles of treatment (employing the IMWG criteria), unless progressed earlier. Functional total cells extract that preserve the cellular enzymatic activity were prepared from each sample and applied to protein microarrays containing ~9000 known proteins (ProtoArray®, Thermo Fisher). Following the exposure of the protein array surface to the sample, enzymes from the sample modified the thousands of proteins spotted on the array, to generate a patient-specific signatures of ubiquitination (Experimental Biology and Medicine 2016; 0: 1-8). The ubiquitination level of every protein was quantified, data was normalized using the quantile normalization method and sorted by descending order for each sample. The ranking position of a protein at each sample within one group was compared to its ranking positions on the other group samples. The proteins differing in their ranking level (p-value ≤ 0.005) between groups were selected. Results Thirty-one BM-MNC samples obtained from MM pts prior VCD (n=27) or VD (n=4) induction, were tested. The cohort included 16 responding patient that achieved at least PR and 15 non-responding pts that obtained SD or less (Table 1). PTM analysis initially revealed 241 proteins that were considered as differentially modified between the 2 groups, mostly in the non-responders (153 vs 88). Ubiquitination of a specific protein was determined as "highly discriminating", if shown to be discriminating in at least 50% of the rounds. We identified 44 classifier proteins; 11 that were highly ubiquitinated in the responders and 33 in the non-responders. These 44 proteins were defined as "highly discriminating", providing a unique PTM signature for responders vs non-responders (Figure 1). Among the 44 proteins, 14 (31.8%) were previously suggested to serve as biomarkers in other cancers and 6 other proteins (13.6 %) were found to be related to the ubiquitin/ubiquitin-like or proteasome machinery. At least 3 of these proteins (USP28, SQSTM1 &amp; PSME1) are known to be related to MM and responsiveness to bortezomib treatment. Further validation of 6 of these targets, confirmed their expression in BM-MNC samples in an independent assay. Prospective clinical validation is required to examine the specificity and sensitivity of the identified biomarkers. Conclusions Here we provide a proof-of-concept for the ability of ubiquitination profiling of BM-MNC samples to identify putative biomarkers for predicting response to bortezomib-based therapy. Among the proteins identified some were associated with response to bortezomib treatment and other novel candidates require further prospective validation. Algorithm training, validation cohort and investigation of the predictive value of this approach in MM pts treated with other therapeutic regimens are underway. Our study provides the first example in clinical samples, highlighting the potential significance of this approach in the clinic and offers a novel strategy for proteomics-based personalized medicine. Disclosures Tsubery: PTM Biosciences: Employment. Gali:PTM Biosciences: Employment. Gali:PTM Biosciences: Employment. Sela:PTM Biosciences: Consultancy. Merbl:PTM Biosciences: Consultancy, Equity Ownership. Cohen:VBL Therapeutics Â: Employment; Madison Â: Consultancy; Takeda Â: Consultancy; Neopharm Â: Consultancy; Amgen Â: Consultancy, Research Funding; Janssen Â: Consultancy.

Comprehensive long non-coding RNA expression profiling by RNA sequencing reveals potential biomarkers for acute myeloid leukemia risk.

The aim of this work was to extensively explore the long non-coding RNA (lncRNA) expression profiles in acute myeloid leukemia (AML) and to propose candidate lncRNAs with predictive value for AML risk. The bone marrow mononuclear cell samples from 10 AML patients and 10 age and gender matched controls were obtained and subjected to next-generation RNA sequencing. Then, the top 5 upregulated and top 5 downregulated lncRNAs in AML patients compared with controls were selected for further quantitative polymerase chain reaction (qPCR) validation in 40 AML patients and 40 age and gender matched controls. The effect of candidate lncRNA RP11-342M1.7 on proliferation and apoptosis in AML cells was further investigated. RNA sequencing observed 216 upregulated and 412 downregulated lncRNAs in AML patients compared with controls. Enrichment analyses exhibited that these differentially expressed lncRNAs were involved in neoplastic signaling pathways such as cAMP and MAPK signaling pathways. In further qPCR validation, lncRNA RP11-342M1.7 and lncRNA CDCA4P3 were upregulated, while lncRNA CES1P1, lncRNA AC008753.6 and lncRNA RP11-573G6.10 were downregulated in AML patients compared with controls. Multivariate logistic regression analysis disclosed that lncRNA RP11-342M1.7, lncRNA CES1P1 and lncRNA AC008753.6 were independent predictive factors for AML risk, and most importantly, the combination of these three lncRNAs was of remarkably good predictive value for AML risk (AUC: 0.901; 95% CI: 0.835-0.966). Besides, lncRNA RP11-342M1.7 was correlated with higher CR while lncRNA AC008753.6 and lncRNA CTD-2562J15.6 were correlated with lower CR. LncRNA RP11-342M1.7 knockdown suppressed cell proliferation by promoting cell apoptosis in AML cells. This study reveals the comprehensive lncRNAs expression profiles in AML, and proposes candidate lncRNAs that are potential biomarkers for AML risk.

Identification of predictive genetic signatures of Cytarabine responsiveness using a 3D acute myeloid leukaemia model.

This study reports the establishment of a bone marrow mononuclear cell (BMMC) 3D culture model and the application of this model to define sensitivity and resistance biomarkers of acute myeloid leukaemia (AML) patient bone marrow samples in response to Cytarabine (Ara‐C) treatment. By mimicking physiological bone marrow microenvironment, the growth conditions were optimized by using frozen BMMCs derived from healthy donors. Healthy BMMCs are capable of differentiating into major hematopoietic lineages and various types of stromal cells in this platform. Cryopreserved BMMC samples from 49 AML patients were characterized for ex vivo growth and sensitivity to Ara‐C. RNA sequencing was performed for 3D and 2D cultures to determine differential gene expression patterns. Specific genetic mutations and/or gene expression signatures associated with the ability of the ex vivo expansion and response to Ara‐C were elucidated by whole‐exome and RNA sequencing. Data analysis identified unique gene expression signatures and novel genetic mutations associated with sensitivity to Ara‐C treatment of proliferating AML specimens and can be used as predictive therapeutic biomarkers to determine the optimal treatment regimens. Furthermore, these data demonstrate the translational value of this ex vivo platform which should be widely applicable to evaluate other therapies in AML.

Abstract LB-029: Identification of predictive genetic signatures of Cytarabine responsiveness in acute myeloid leukemia using a novel 3D translational culture model of primary bone marrow

Abstract Acute myeloid leukemia (AML) is a malignancy with complex genetics, poor survival rates and high rates of chemotherapy resistance. This study reports the establishment of a novel bone marrow mononuclear cell 3D-culture model and the application of this model to define sensitivity and resistance biomarkers as evident by DNA and RNA expression signatures in response to standard Cytarabine (Ara-C) chemotherapy. By simulating the bone marrow microenvironment, we optimized the growth conditions using frozen bone marrow mononuclear cells (BMMC) derived from healthy donors. We validated our platform by demonstrating that healthy BMMCs are capable of differentiating into all hematopoietic lineages and various types of stromal cells. Using this platform, we then characterized BMMC samples from 47 AML patients. Sixty percent of AML samples were capable of proliferating and 23 samples demonstrated robust growth. RNA sequencing was performed for 3D and 2D cultures to determine which conditions are superior at maintaining original gene expression signatures as compared to the uncultured bone marrow, To elucidate specific genetic mutations and/or gene expression signatures associated with the ability of ex vivo growth of these AML donors, whole exosome sequencing and RNA Sequencing were performed. The model identified unique gene expression signatures that correlated with sensitive as well as resistant donors in response to Ara-C treatment. These data demonstrate the translational value of our platform which should be widely applicable to evaluate other therapies in AML. Citation Format: Haiyan Xu, Eric S. Muise, Sarah Javaid, Lan Chen, Razvan Cristescu, My Sam Mansueto, Nicole Follmer, Jennifer Cho, Kimberley Kerr, Rachel Altura, Michelle Machacek, Benjamin Nicholson, George Addona, Ilona Kariv, Hongmin Chen. Identification of predictive genetic signatures of Cytarabine responsiveness in acute myeloid leukemia using a novel 3D translational culture model of primary bone marrow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-029.