Tumor tissues exhibit cell-specific genetic variations and gene expression profiles critical to disease diagnosis, progression, and treatment outcomes. Single cell sequencing offers detailed insights into the heterogeneity of cancer cells which is pivotal for developing personalized treatment strategies. However, current high-throughput methods primarily detect gene expression levels, lacking information on genetic variants such as mutations or gene fusions. Understanding tumor responses to therapy requires linking cell-type specific mutations with gene expression profiles to understand cancer development and progression. We developed FocuSCOPE, a high-throughput single cell sequencing solution that simultaneously measures gene expression and detects genetic variantion from individual cells. Single cell partitioning is performed using SCOPE-chip®, a portable microwell chip the size of a microscopy slide. The technology utilizes barcoding microbeads conjugated with oligos to efficiently capture mRNA and target sequences. The optimized chemistry facilitates amplification and library construction of both mRNA and target regions, including point mutations, fusion genes, and non-polyadenylated RNAs such as viral RNAs. NB4, CCRF, and K562 cell lines were mixed in equal proportions. Libraries were prepared using the FocuSCOPE Blood Cancer Panel. Experiments demonstrated the specific detection of KRAS and TP53 mutations in mixed cell lines (NB4, CCRF, and K562) using the FocuSCOPE Blood Cancer Panel. The NB4 cell line contained KRAS (A18D) and TP53 (R248Q) mutations, CCRF contained KRAS (G12D) and TP53 (R248Q, R175H), and K562 contained only the BCR-ABL1 fusion gene. Additionally, FocuSCOPE effectively captured PML-RARA and BCR-ABL1 fusion genes with high sensitivity. FocuSCOPE enables comprehensive analysis of gene expression profiles and targeted genetic sequences, capturing driver mutations, fusion genes, and non-polyA sequences such as viral sequences with high sensitivity, thus advancing our understanding of tumor biology and therapeutic responses. This makes it an invaluable tool for personalized medicine, guiding therapeutic decisions and improving patient outcomes. Ankhita Nair, Rachael Lee, Sujatha Narayanankutty, Yingting Wang, Jonathan Scolnick. FocuSCOPE: A Multi-omics Solution to Simultaneously Analyse Genetic Variants and Transcriptome in Single Cells in Leukemia [abstract]. In: Proceedings of Frontiers in Cancer Science 2024; 2024 Nov 13-15; Singapore. Philadelphia (PA): AACR; Cancer Res 2025;85(15_Suppl):Abstract nr P17.

Chimeric antigen receptor (CAR) T cell therapies targeting B cell maturation antigen (BCMA) are transforming treatment for relapsed or refractory multiple myeloma (RRMM). We analyze 61 RRMM patients receiving idecabtagene vicleucel (Ide-cel; n = 34) or ciltacabtagene autoleucel (Cilta-cel; n = 27) and find that Cilta-cel achieves higher complete response (CR) rates (78% vs. 38%) and longer progression-free survival. Using a longitudinal single-cell multi-omics atlas of 135 blood samples, we show that Cilta-cel induces expansion of CD4+ cytotoxic T cells associated with CR and immune-related toxicities, whereas non-CR CD8+ T cells display impaired effector programs. Among non-B cells, plasmacytoid dendritic cells (pDCs) show the highest BCMA expression and BCMA-targeted agents eradicate a blastic plasmacytoid dendritic cell neoplasm line, suggesting a novel therapeutic avenue for this disease. Greater reductions in soluble BCMA correlate with enhanced CAR T expansion and systemic inflammation. These findings reveal cellular mechanisms driving differential efficacy and toxicity of BCMA-directed immunotherapy.

Background High risk (HR) Multiple Myeloma (MM) remains an unmet clinical need. Translocation 4;14 is a HR cytogenetic abnormality occurring in 15% of MM, and is associated with an adverse prognosis. We sought to identify factors that correlated with progression free survival (PFS) in a cohort of t(4;14) patients through high dimensional profiling of the malignant plasma cells (PCs) and tumor micro-environment (TME) through single cell multi-omics. Methods We analysed diagnostic bone marrow (BM) samples from 13 patients with t(4;14) MM using the ESCAPE™ platform from Singleron Biotechnologies which simultaneously measures gene and cell surface protein expression of 64 proteins in single cells. Cryopreserved BM samples were stained with antibodies and subsequently sorted on CD138 expression. The CD138 positive and negative fractions were recombined at a 1:1 ratio for analysis using the 10x Genomics 3‘ RNAseq kit. Resulting data were analyzed with Singleron Biotechnology's MapSuite™ single cell analytics platform. Subsequent validation of our findings was performed using the 5‘ chemistry version of ESCAPE™ with the 10x Genomics RNAseq platform. Results We analysed 107,382 cells from 13 patients and 2 controls. All patients received novel agent-based induction and had a median age of 63 years. Median PFS and overall survival(OS) were 22 and 34 months respectively. No gene or protein expression patterns within the PCs were identified that correlated with PFS or OS. We detected a population of proliferative, effector CD8 T-cells (cluster 26) whose identity was confirmed by independent bioinformatics tools “MapCell” and “DISCO”. For each patient, we derived the proportion of Cluster 26 cells among TME cells and defined the patients in the top 50% as “Clus26 high” and the bottom 50% as “Clus26 low” samples. Survival analysis revealed that Clus26 high patients had significantly shorter PFS (P=0.04, log rank test).This result was independent of R-ISS stage and treatment characteristics. Notably, Cluster 26 expressed LAG3 and TGIT mRNA, suggesting an exhausted phenotype, akin to that reported in dysfunctional CD8 T-cells from solid tumors. We next derived a gene signature from Cluster 26 cells, comprising CD8A+ and MKI67+ which we applied to an independent cohort of 15 MM patients. Using the gene signature to stratify patients into “Gene-sig high” and “Gene-sig low” groups similarly to what we did for cluster 26 cells, the gene signature also correlated significantly with adverse PFS in this cohort (P=0.03). We subsequently validated the presence of Cluster 26 cells using multiplexed immunofluorescence in a tissue microarray from a separate cohort of 86 MM patients including those without t(4;14). We derived the proportion of cells co-expressing CD8 and Ki67(double-positive cells), among the CD138-negative cells and selected a cut-off of 3.0% double positive (DP) cells to split the patients into “DP high”, (n=49), and “DP low” (n=37) groups. “DP high” patients had an inferior PFS (P=0.04) in keeping with the results from the ESCAPEseq cohorts. Lastly, we performed multiplexed immunohistochemistry on whole slide BM trephine biopsies, preliminary analyses show that proliferative CD8 T-cells are spatially related to PCs, suggesting intercellular interactions. Conclusions We present the first application of single cell multi-omic immune profiling in a genomically defined subset of MM and have identified a proliferative T-cell subset of prognostic significance. This population also retains its negative correlation with PFS in non t(4;14) patients, suggesting a broader significance across cytogenetic subtypes of MM. Further work is ongoing to delineate the functional characteristics and therapeutic implications of cluster 26.

Introduction: The two commercially available BCMA-directed CAR T cell therapies idecabtagene vicleucel (ide-cel) and ciltacabtagene autoleucel (cilta-cel) have revolutionized the treatment of relapsed/refractory multiple myeloma (RRMM). Results from clinical trials suggest superior outcome with cilta-cel compared to ide-cel. We conducted a comprehensive longitudinal single-cell multi-omics study in a large real-world cohort of RRMM patients to identify markers associated with response, resistance or side effects after ide-cel or cilta-cel. Methods: Peripheral blood mononuclear cells (PBMCs) were isolated on the day of leukapheresis (LP), and on days 30 and 100 post infusion. PBMCs were subjected to single cell RNA, TCR, BCR and surface protein analysis. Additionally, peripheral blood samples collected at the day of LP, after lymphodepletion and on days 7, 14, 30 and 100 following CAR T cell therapy were analyzed by flow cytometry to monitor CAR T cell expansion, immune checkpoint and cytotoxicity marker expression and T cell differentiation. Response was evaluated according to IMWG criteria on day 30 after CAR T cell infusion. Progression-free survival (PFS) was analyzed using the Kaplan-Meier method and the log-rank test. Results: We retrospectively included 62 RRMM patients treated with cilta-cel (n=28) or ide-cel (n=34). We observed a significantly higher overall response rate (93% vs 70%, p<0.05) and complete response rate (75% vs. 35%, p<0.01) in patients treated with cilta-cel vs. ide-cel. At a median follow-up of 10 [95%CI: 8-12] months, this led to a significant improvement in median PFS (not reached (n.r.) in cilta-cel vs. 7 [3-n.r.] months in ide-cel patients, p<0.01). We observed a lower rate of CRS in cilta-cel patients (grade 1 or higher: 52% vs. 79% in ide-cel patients, p<0.05). Four patients who received cilta-cel (15%) and one patient who received ide-cel (3%) experienced ICANS (p=0.16). CAR T cell expansion was initially slower in patients treated with cilta-cel, but reached significantly higher levels on day 14 compared to ide-cel (p<0.01). There was a significantly higher proportion of CD4+ CAR T cells in cilta-cel compared to ide-cel patients (day 14: 35% vs 10%, p<0.01). More than 500,000 cells from 144 samples collected longitudinally before and after CAR T cell infusion passed quality assessment after single cell sequencing. Cellular composition of peripheral blood between patients with or without CRS was distinct at LP and on days 30 and 100. More CD4+ cells were detected in patients with CRS grade 2 at the time of LP and CD8+ cells on day 30 and 100 after infusion. Fewer classical monocytes were observed at all time points in patients without CRS. We observed a significantly (p<0.05) higher proportion of CD4+ cells with CRS at the time of LP. TCR repertoires showed a higher cytotoxic enrichment score in patients with CRS for the largest CD4+ clones compared to CD8+ clones on day 30. We also observed a significantly (p<0.05) higher proportion of polyfunctional T-cells with increasing CRS on day 30 following infusion. Increased clonality in TCR repertoires was revealed between non-PD and PD patients at time of leukapheresis and on day 30. TCR diversity analysis of CD4+ T cells showed increased diversity (p<0.05) over day 30 to day 100 and for CD8+ T cells over day 30 in patients with non-PD. In addition, T cell subtypes revealed an increased diversity of cytotoxic CD4+ cells in non-PD compared to PD across all time points. Conclusion: Patients treated with cilta-cel had significantly improved PFS than patients treated with ide-cel, which was driven by significant expansion of CD4+ CAR T cells. CRS was associated with increased polyfunctional heterogeneity of T cells and occurred less frequently after cilta-cel therapy. ICANS was more common with cilta-cel. Finally, long-term response to CAR T cell therapy was associated with a diversification of the TCR repertoire.

Background: Chimeric antigen receptor (CAR) T cell therapy has revolutionized treatment of relapsed/refractory multiple myeloma (RRMM). Robust variables that predict long-term response are currently missing. Limited data are especially available on the impact of bridging therapies on manufacturing and outcome. We conducted a longitudinal single-cell multi-omics study to identify factors that predict response to BCMA-directed CAR T cells. Changes in the immune microenvironment associated with response were analyzed as well as the impact of prior bridging therapy with bispecific antibodies on subsequent CAR T cell manufacturing and outcome. Methods: Peripheral blood mononuclear cells (PBMCs) were isolated from 29 consecutive MM patients treated with commercially available anti-BCMA CAR T cells on the day of leukapheresis as well as days 30 and 100 after CAR T cell infusion. PBMCs were subjected to single cell RNA, T-cell receptor (TCR) and B-cell receptor (BCR) sequencing. A custom panel of 57 oligonucleotide-coupled antibodies was used to study surface proteomics. Downstream analyses were performed with Seurat. Differences in cellular compositions at all three time points were analyzed with scCODA. To analyze CAR T cell functionality, CAR T cells from peripheral blood were isolated 7 days after infusion and subjected to an in vitro cytotoxicity assay after expansion and stimulation. Patients were grouped based on their best response following CAR T cell infusion (CR: n=12, non CR: n=17). Results: In total, 375,338 cells were sequenced (median 7246 cells/sample, range 1,569-10,972 cells) and 354,878 cells (94.5%) passed quality assessment. Quantitative and qualitative differences in the cellular composition of peripheral blood between CR and non CR patients were detected at the time of leukapheresis as well as on days 30 and 100 following infusion. CR patients harbored significantly more CD8+ effector memory T cells (TEM) at leukapheresis and less NK cells on day 30 after therapy compared to non CR patients. Regulatory T cells isolated at the time of leukapheresis from non CR patients exhibited significantly higher surface protein levels of CXCR3, CD40, CD95 and KLRG1 (p<0.015, respectively) that have been associated with T cell senescence and impaired tumor immunity. No significant differences in cell numbers between CR and non CR were detected on day 100 after CAR T cell infusion. However, single cell TCR analysis revealed an increasing diversity in the TCR repertoire over time in patients with CR, while Shannon diversity decreased from leukapheresis over day 30 to day 100 in non CR patients (p=0.004). The prior administration of the bispecific antibody teclistamab had no significant impact on the quantitative cellular composition at the time of leukapheresis. However, termination of manufacturing in the first attempt occurred in all patients with a close proximity of teclistimab administration and apheresis. Differential gene expression analysis showed that the application of teclistamab was associated with impaired T cell activation and exhaustion indicated by upregulation of e.g. CTLA4, TIGIT, LAG3 and GZMK. After discontinuation of teclistamab (median 4 weeks) and successful manufacturing of CAR T cells, we found no significant differences for in vitro cytotoxicity and in vivo expansion of CAR T cells. CAR T cells isolated at day 7 post-infusion from patients in CR, non CR or with prior teclistamab exposure, effectively eliminated MM cells (U-266). Tracking of single CAR T cells over time showed that the majority of CAR+ cells were CD8+ TEMs regardless of remission achievement or prior teclistamab exposure. Conclusion: We demonstrate that differences between MM patients achieving a CR and patients with suboptimal response upon anti-BCMA CAR T cell therapy can already be identified at the time of leukapheresis. Long-term changes associated with CR include a diversification of the TCR repertoire. Successful CAR T cell manufacturing is hampered by exposure to bispecific antibodies but can be successfully achieved by allowing for a wash-out phase of ca. 4 weeks.

Secreted proteins play critical roles in cellular communication. Methods enabling concurrent measurement of cellular protein secretion, phenotypes and transcriptomes are still unavailable. Here we describe time-resolved assessment of protein secretion from single cells by sequencing (TRAPS-seq). Released proteins are trapped onto the cell surface and probed by oligonucleotide-barcoded antibodies before being simultaneously sequenced with transcriptomes in single cells. We demonstrate that TRAPS-seq helps unravel the phenotypic and transcriptional determinants of the secretion of pleiotropic T H 1 cytokines (IFNγ, IL-2 and TNF) in activated T cells. In addition, we show that TRAPS-seq can be used to track the secretion of multiple cytokines over time, uncovering unique molecular signatures that govern the dynamics of single-cell cytokine secretions. Our results revealed that early central memory T cells with CD45RA expression (T CMRA ) are important in both the production and maintenance of polyfunctional cytokines. TRAPS-seq presents a unique tool for seamless integration of secretomics measurements with multi-omics profiling in single cells.

Alzheimer’s disease (AD) patients show sustained levels of inflammation in the brain and the peripheral immune system. It is not known how various peripheral blood mononuclear cells (PBMCs) differ in AD patients and whether those differences can act as biomarkers of AD. Here we performed a multi-omic profiling of PBMCs from AD patients and compared the composition of their cell type and cell state as well as their gene and protein expression to normal controls. Single-cell proteogenomics analysis was performed on PBMCs from 20 AD patients and 15 controls using Singleron Biotechnologies’ ESCAPE platform. Seven of the AD and four control samples were additionally analyzed for bulk protein expression using Sciomics’ scioDiscover platform. Bulk proteomics identified 100 proteins with a significant differential abundance between AD patients and controls. Data point to a higher platelet activation and degranulation, as well as changes in the EGFR / MAPK3 and VEGF signaling in AD. As an individual marker CD163 was identified at a higher abundance in AD PBMCs pointing to an increase in monocyte / macrophage activity. From the single-cell analysis we found that AD patients had significantly more CD14+ monocytes. We further found that the CD14+ monocytes could be split into seven clusters based on their gene expression with only two clusters having a significantly higher number of cells in AD patients. One of the overrepresented clusters showed high expression of Alarmin genes, suggesting an increased inflammatory environment, while the other cluster showed a higher level of HLA expression suggesting a state primed for activation. We found significant changes in both gene and protein expression in PBCMs from AD patients that indicate an increased inflammatory state . While there was a good overlap of findings between gene and protein expression, some of the changes are only seen at the protein level, while others are only observed at the level of gene expression. The combination of the two measurement techniques provides us with additional insights into inflammatory nature of the peripheral immune system in AD patients and provide hints at the mechanisms different cells use to generate those inflammatory signals.

Background: Multiple Myeloma (MM) is the second most common hematologic malignancy and remains incurable. Daratumumab (dara) is a potent anti-CD38 monoclonal antibody used for MM treatment, but responses are heterogeneous, and resistance is inevitable. There is hence an urgent need for biomarkers to predict the response to dara. We hypothesized that gene expression profiles (GEP) of tumor subpopulations could be used to predict the response to dara-based therapy. Methodology: We used the Enhanced Single-Cell Analysis with Protein Expression (ESCAPE) RNA-Seq platform (Singleron Biotechnologies) to simultaneously profile the gene and protein expression of plasma cells from the bone marrow of 15 MM patients prior to treatment with dara-based regimens. These data were combined with publicly available single-cell data from Cohen et al. 2021, resulting in a total of 32 samples. Response was defined based on the international myeloma working group (IMWG) criteria, considering patients achieving a very good partial response (VGPR) or better as “responders,” while those achieving a partial response (PR) or worse were deemed “non-responders”. The data were split into 23 training samples (15 responders and 8 non-responders) and 9 validation samples (3 responders and 6 non-responders), each set containing a combination of cases from the two sources of single-cell data. An interpretable machine-learning model was then built to predict the response of each individual cell. The median response score for each sample was used to predict the patient-level response. The Shapley Additive Explanations (SHAP) values from the dara response prediction model were then used as a starting point for creating a general prognostic model. The 200 most influential genes from the dara response prediction model were used to create a linear response model based on the TCGA (MMRF-Compass) bulk RNAseq dataset. The bulk RNAseq dataset was split 80:20 into training and test datasets. Results from the test dataset are presented here. Results: We analyzed single-cell data from a total of 13,094 plasma cells from 32 BM samples. These included 17 cases of relapsed MM described by Cohen et al., who were treated with dara, carfilzomib, lenalidomide, and dexamethasone. The patients treated at our centre comprised 7 cases of relapsed MM treated with dara, thalidomide, and dexamethasone, and 8 samples from patients with newly diagnosed MM treated with dara, bortezomib, and dexamethasone on clinical trials. All 9 patients in the test dataset had their responses correctly predicted (Fig 1). A separate cohort of non-dara-treated patient samples was also tested using the same model, and the response prediction was not accurate (AUC=0.67), suggesting that this model has specificity for predicting response to dara and is not a general response classifier (data not shown). Based on the SHAP values from the dara response prediction model, we developed a prognostic model from bulk RNAseq data consisting of four genes. A combination of this four-gene signature plus the international staging system (ISS) score resulted in a superior prediction of overall survival compared to the ISS alone with a hazard ratio of 13.5 compared to 2.5 for the ISS alone in the same dataset (Fig 2). Conclusions: Combining single-cell RNA sequencing with machine-learning methods may have value in predicting response to immunotherapy in MM. We propose that gene signatures derived from single-cell data may augment clinical decision-making for determining treatment allocation. We also show that these signatures may be combined with existing prognostic scores, resulting in improved delineation of risk groups. Furthermore, the applicability of our gene signature to a bulk gene expression dataset enhances its clinical relevance. Our study demonstrates the power of single-cell omics to identify novel predictors of response to therapy and prognosis in MM, which may be translated into clinical use.

Recent advances in multimodal approaches toward single-cell analyses present valuable data points that can complement standard flow cytometry data. In particular, the overlay of cell-surface proteome data with gene expression analysis presents a necessary advancement, particularly in the field of immunology. Here we describe a copper-free click chemistry method for the generation of antibody-oligonucleotide complexes and present the steps for its employment in the context of the 10× genomics droplet-based single-cell RNA-seq workflow, providing a method for coupling proteomic and transcriptomic analyses in an efficient and cost-effect manner.

Abstract Background Multiple Myeloma (MM) is an incurable plasma cell (PC) malignancy and high risk MM remains an unmet clinical need. Translocation 4;14 occurs in 15% of MM and is associated with an adverse prognosis. A deeper understanding of the biology and immune micro-environment of t(4;14) MM is necessary for the development of effective targeted therapies. Single Cell multi-omics provides a new tool for phenotypic characterization of MM. Here we used Proteona's ESCAPE™ single cell multi-omics platform to study a cohort of patients with t(4;14) MM. Methods Diagnostic bone marrow (BM) samples from 13 patients with t(4;14) MM (one of whom had samples at diagnosis and relapse) were analysed using the ESCAPE™ platform from Proteona which simultaneously measures gene and cell surface protein expression of 65 proteins in single cells. Cryopreserved BM samples were stained with antibodies and subsequently sorted on CD138 expression. The CD138 positive and negative fractions were recombined at a 1:1 ratio for analysis using the 10x Genomics 3' RNAseq kit. Resulting data were analyzed with Proteona's MapSuite™ single cell analytics platform. In particular, Mapcell was used to annotate the cells and MapBatch was used for batch normalization in order to preserve rare cell populations. Results Patients had a median age of 63 years and received novel agent-based induction. Median progression free and overall survival (PFS and OS) were 22 and 34 months respectively. We first analyzed serial BM samples from an individual patient that were taken at diagnosis and relapse following bortezomib based treatment. The PCs in this patient showed variations in gene expression between diagnosis and relapse (Fig 1A), including the reduction of HIST1H2BG expression, which has previously been correlated with resistance to bortezomib. Subsequent analysis of the immune cells identified a shift in the ratio of T cells to CD14 monocytes from 5.7 at diagnosis to 0.6 at relapse suggesting a major change in the BM immune micro-environment in response to therapy. Next, we analyzed the malignant PCs of the diagnostic samples. As expected, MMSET (NSD2) was overexpressed in all PCs compared to normal PCs, while FGFR3 expression could be categorized into no expression of FGFR3, low expression (<10% of cells expressing FGFR3) or high expression (>80% of cells expressing FGFR3) (Fig 1B). No gene or protein expression patterns within the PCs were identified that correlated with PFS or OS in this cohort. Finally, we analyzed the immune micro-environment in the diagnostic samples (Fig 1C). While there was no overall discernable pattern of cell types present, one cluster of cells, annotated as 'unknown' cell type, suggested a small population of cells that had not been previously annotated in published single cell RNA-seq data. The cells were CD45+ and CD138 - both at the protein and RNA level, suggesting they are not plasma cells. We tested if the number of the 'unknown' cells in each sample correlated with PFS, but there was no significant correlation. We then used these cells to derive a gene signature profile which was expressed in most of the cells in the 'unknown' cluster as well as a minor fraction of cells in other clusters including some PCs. The number of cells expressing the gene signature negatively correlated with PFS, with samples containing more cells expressing the signature having a lower PFS than samples with fewer signature positive cells (Fig 2). The correlation remained significant whether we included PCs in the analysis or not, but was not significant amongst only the PC population, suggesting that the cells responsible for the correlation are from the immune micro-environment. Conclusions We present the first application of single cell multi-omic immune profiling in high-risk MM and demonstrate that t(4;14) is a phenotypically heterogenous disease. While no consistent gene or protein expression patterns were identified within the malignant cell population, we did identify gene expression changes in a relapsed patient sample that may reflect key alterations in the PCs responsible for therapy resistance. In addition, we identified a gene signature expressed in a rare population of non-plasma cells that significantly correlated with PFS in this patient cohort. These data highlight the potential of single cell multi-omic analysis to identify immune micro-environmental signatures that correlate with response to therapy in t(4;14) MM. Figure 1 Figure 1. Disclosures Scolnick: Proteona Pte Ltd: Current holder of individual stocks in a privately-held company. Huo: Proteona Pte Ltd: Ended employment in the past 24 months. Xu: Proteona Pte Ltd: Current Employment. Chng: Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria; Abbvie: Honoraria.

Background: Treatment with anti-CD38 antibodies is a standard of care in newly diagnosed and relapsed/refractory multiple myeloma (MM). Clonal tiding and evolution as well as genomic heterogeneity are major drivers towards drug-resistant disease. However, modes of resistance to anti-CD38 directed therapies are currently unknown and might include loss of antigen expression and/or exhaustion of the immune system. We performed longitudinal single-cell multi-omic analyses of circulating tumor cells (CTC) and non-malignant cells to decipher clonal heterogeneity and tiding upon response and resistance to daratumumab (Dara)-based therapies in MM and plasma cell leukemia (PCL). Methods: Peripheral blood mononuclear cells (MNCs) were isolated from 7 patients (PCL n=5, MM n=2) before and after exposure to Dara. Samples were collected at four different time points: 1. before administration of Dara (CD38-naïve). 2. Four days after first application of Dara (CD38-exposed). 3. After relapse from Dara (CD38-refractory). 4. After an additional line of non-CD38 targeted therapy following relapse from Dara. In total, we analyzed 17 PB samples. To demonstrate that results from PB are comparable to bone marrow (BM), four paired PB and BM samples were included (21 samples in total). MNCs were subjected to single cell RNA (scRNAseq), B-cell (scBCRseq), and T-cell receptor sequencing (scTCRseq). Surface protein expression was measured using a custom panel of 57 DNA-barcoded antibodies. Sequencing files were processed with CellRanger (10x Genomics) and data were analyzed with the Seurat toolkit for single cell sequencing. Cell types were annotated with SingleR based on the Human Primary Cell Atlas built-in references. ScRepertoire was used for longitudinal scTCRseq and scBCRseq data. Copy number variations (CNVs) in CTCs were detected with inferCNV, and cellular interactions were investigated with CellPhoneDB. Results: In total, we sequenced 130.038 MNCs (PB: 108.236, range 414-8700/patient; BM: 21802, range 3375-8516/patient, Figure 1A). Malignant plasma cells were identified using scBCRseq and restricted light chain expression from scRNAseq. Even in MM patients without morphological evidence for CTC, we could identify and characterize single malignant plasma cells in PB. By inferring genome-wide CNVs from scRNAseq, we were able to characterize intra-patient genomic heterogeneity. Longitudinal tracking of CTC revealed tiding of clones characterized by distinct CNV profiles and transcriptomes upon Dara-exposure and resistance. Early transcriptional changes in CTCs upon Dara-exposure included inflammatory signaling (e.g. upregulation of IFI6, IFI35, IFIT1, IFITM1). Analysis of CD38 protein surface expression using DNA-barcoded antibodies showed significantly decreased levels after Dara exposure. To rule out that this might have resulted from competitive binding of the diagnostic antibody and Dara, we repeated analyses after discontinuing Dara and after application of a further line of therapy. In contrast to CD38 RNA expression, surface protein expression decreased significantly upon exposure and relapse, but recovered after an additional line of therapy. Expression of other druggable antigens (BCMA, SLAMF7, GPRCD5, FcRL5, Figure 1B) was not affected by treatment with Dara. Next, we investigated co-evolution of non-malignant cells. Longitudinal scTCRseq showed concomitant expansion and stagnation of TCRs. Receptor-ligand interaction analyses demonstrated that exposure to Dara resulted in significant differences in CD38-PECAM1-mediated interactions between CTCs with non-malignant B-cells and CD8+ T-cells. We detected recurrently increased immunosuppressive receptor-ligand interactions between malignant plasma cells and CD4+, CD8+, and regulatory T-cells via CD86-CTLA4 and NECTIN3-TIGIT axis upon Dara exposure. Conclusion: We demonstrate that single-cell multi-omic analyses of PB provide a minimal invasive approach to study genomic heterogeneity and evolution of malignant plasma cells and non-malignant cells of the immune system. Longitudinal analyses reveal different patterns of response and resistance to targeted myeloma therapies and might affect future therapeutic decision-making. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The promise of precision medicine in oncology is to identify the ideal treatment for each patient, eliminating failed treatment cycles and reducing treatment burdens on patients and payors. While gene sequencing can predict effective treatment options in some cases, studies have estimated that only 10-15% of cancer patients are treated with genotype matched drugs. Additional treatment selection tools for predicting patient response thus remains a critical clinical need. While gene sequencing has clear utility in predicting therapeutic response, gene expression is less commonly used in clinical tests to guide treatment selection. Gene expression is theoretically attractive to predicting therapeutic response because, unlike mutational analysis, it provides a direct readout of the dysregulated biochemical activities in the cells which may be targeted by particular therapies. One of the major problems with conventional approaches to using gene expression to predict therapeutic response is tumor heterogeneity; that is, a biopsied tumor contains multiple cells types and even different tumor clones that can confound a conventional bulk gene expression analysis. More recently, single-cell RNA expression analysis technologies have grown in popularity. These technologies enable the fine characterization of tumors and their cellular makeup and hold the promise of increasing the power of gene expression measurements for identifying therapy response prediction tools. Here we show the application of single cell gene expression analysis with Proteona’s MapResponse™ machine-learning algorithms to developing a predictive classifier for patient response to Daratumumab treatment in multiple myeloma. Starting with publicly available data (Cohen et al. Nat. Medicine 2021), we developed a classifier that accurately predicted the response of 94% of subjects in the published study. This classifier was able to accurately predict response in an independent cohort of Daratumumab-treated patients. In contrast, the classifier performed poorly in a cohort of patients who did not receive Daratumumab. Together these data suggest the classifier has specificity to predicting response to Daratumumab and is not a general prognostic signature. These early findings point to the potential value of combining single-cell RNA sequencing with machine-learning methods such as MapResponse™ to bring precision medicine to more patients. Citation Format: Jonathan Scolnick, Stacy Xu, Sanjay de Mel, Cinnie Yentia Soekojo, Wee Joo Chng. Using single cell gene expression to develop treatment response predictions in multiple myeloma [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 5141.

The promise of precision medicine is to identify the ideal treatment for each patient, eliminating failed treatment cycles and reducing treatment burdens on patients and payors. While gene sequencing can predict effective treatment options in some cases, studies have estimated that only 10-15% of cancer patients are treated with genotype matched drugs. Gene expression is theoretically an attractive alternative to predicting therapeutic response because, it provides a direct readout of dysregulated cellular activities including druggable pathways. However, the use of bulk gene expression to predict therapeutic response is hampered by tumor heterogeneity; that is, a biopsied tumor contains multiple cells types and different tumor clones and a bulk analysis only generates an average gene expression signal across all of the cells, leading to a decreased sensitivity for identifying biomarkers found only in specific cell subgroups. More recently, single-cell gene expression analysis has shown promise in improving precision medicine through a focused look into the biology of the tumor cells themselves. For example, we previously showed (Scolnick, et al ASCO 2022) that a model trained on single-cell gene expression data collected from 17 Multiple Myeloma (MM) patient's plasma cells (PCs) could be used to predict the therapeutic response of seven independent MM patient samples at 100% accuracy. Here we combine single-cell gene expression analysis with Singleron Biotechnology's MapResponse™ machine learning algorithms to extend our predictions to data from peripheral blood mononuclear cells (PBMCs) of plasma cell leukemia patients who were treated with daratumumab. Our model accurately predicted the response to daratumumab treatment for both patients based on the PCs collected from blood samples. These early findings point to the potential value of combining single-cell RNA sequencing with machine learning methods such as MapResponse™ to bring precision medicine to more patients based on minimal invasive assessment of PBMCs.

Background: Multiple Myeloma (MM) is the second most common hematological malignancy in the world, characterized by diverse genomic landscape with clonal heterogeneity and complex interaction between malignant plasma cells and the immune microenvironment. Treatment advancement has improved MM survival remarkably over the past decade. However, despite being managed similarly, there are still patients who had early death while some having long survival. We aim to use single-cell sequencing to build a machine learning-based classifier that could predict patients’ clinical outcomes. Methods: Single-cell study evaluating the transcriptomic profile and protein expression, T-cell receptor and B-cell receptor sequencing were performed using droplet-based 10X Genomics 5’ version 2 platform with 60 DNA-barcoded antibodies. We evaluated a total of 26 frozen bone marrow samples from functional high-risk (FHR) group who had early death within 3 years of MM diagnosis and suboptimal response to induction therapy or early relapse within 12 months and long survival (LS) group who are alive for more than 7 years from MM diagnosis and still alive at the time or recruitment. This cohort also included longitudinal samples at presentation, post-treatment, and/or at relapse from 4 FHR and 3 LS patients. All patients were deemed fit to receive intensive treatment at diagnosis and received proteasome-inhibitor based induction therapy. Results: This is a preliminary result of a classifier from 11 patients. We trained the model on all cells from 3 newly diagnosed FHR samples and 5 newly diagnosed LS samples. We then tested our classifier on 3 samples that were unseen during training, which consisted of 2 relapsed FHR samples and 1 refractory LS samples. We showed in a histogram that the number of cells binned by the response predictor score correlated well with patients’ outcomes, in which samples with large numbers of poor scoring cells would respond poorly to treatment. We also evaluated the top 30 most important genes found by our classifier across all cell types in a heat map. For the next steps, we plan to include more samples to build our model. We also plan to run our classifier on selected cell types and identify key genes from these selected cell types, including plasma cells, T-cells, and monocytes. Longitudinal analysis of the samples from the same patient will also be performed to track clonal evolution with treatment and disease progression. Conclusion: We developed a machine learning-based classifier that allows us to identify MM patients with early death and long survival based on single-cell sequencing. Citation Format: Cinnie Y. Soekojo, Jonathan Adam Scolnick, Stacy Xu, Fangfang Song, Melissa Ooi, Sanjay de Mel, Wee Joo Chng. Single-cell sequencing of multiple myeloma patients with early death and long survival [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 4078.

Multiple myeloma (MM) is a heterogenous disease in which clonal evolution or preexisting reservoirs of phenotypically divergent cells may be harbingers of therapeutic resistance. Prior research has suggested the presence of myeloma cells with immunophenotypic and expression profiles dedifferentiated from that of terminally differentiated plasma cells that may be found in circulation, marrow, or potentially other lymphoid compartments; however, this has not been conclusively demonstrated in patients. The identification of such clonotypic myeloma cells could lead to a better understanding of myeloma biology and resistance, and inform therapies targeted at these populations, and single-cell technologies provide the granularity to characterize them. Next-generation sequencing (NGS)-based measurable residual disease (MRD) clinical-grade assays (eg. clonoSEQ®, Adaptive Biosciences) identify individuals’ unique heavy and light chain sequences. Theoretically this sequencing data could be overlaid with research-grade single-cell multiomics to identify clusters of clonotypic cells with variable differentiation. In this pilot study using the Enhanced Single Cell Analysis with Protein Expression (ESCAPE) platform (Singleron Biotechnologies) we aimed to identify peripherally circulating clonotypic MM cells by their NGS-MRD sequence and simultaneously determine whether their multiomic profile reflected possible dedifferentiated clusters found in paired marrow. Using scRNA-Seq (10x Genomics) data previously generated at our site from bone marrow mononuclear cells we identified MM cell clusters enriched for differentially expressed genes known to be expressed in B-cells and plasmablasts. These genes included CD19, PAX5, BCL6, EZH2, STMN1, HMGB2, MKI67 and CD27. We next sought to assay the peripheral blood mononuclear cells (PBMC) of patients (n=2) with an overrepresentation of cells in these clusters. To evaluate both the transcriptomic and immunophenotypic profiles of the PBMCs at single-cell resolution we utilized Singleron's Immune Profiling panel on the ESCAPE platform. Using Singleron's MapSuite™ analysis pipeline, we annotated cell-type classifications, and highlighted cells within the mature B-cell/plasma cell differentiation spectrum (Figure A). Within this subset, we attempted to identify gene expression reflective of the previously identified "dedifferentiated” marrow clusters with specific genes of interest, but found that there was not significant overlap. Heatmaps comparing the 5 peripheral subclusters with marrow plasma cells demonstrate some concordance with the marrow subclusters, with examination of these overlaps ongoing, but these were not the "dedifferentiated” marrow clusters. The included patients also had clonoSEQ® identification sequences of heavy chain and/or light chain available. By cross-referencing the NGS-MRD sequence data (clonoSEQ®, Adaptive Biosciences), we were able to use ESCAPE-Seq to identify definitive clonotypic circulating MM cells within the PBMC samples at single cell granularity. Among PBMCs from sample 2 we were able to identify circulating clonotypic myeloma cells with 100% match to the IgH and IgK MRD sequences, which also represented the dominant V(D)J clonotype in peripheral circulating. Concomitant single-cell surface protein analysis of this clonotypic cell population demonstrated significant expression of CD38, without significant differential expression of CD138 nor surface markers of B-cells (Figure B). While these data are preliminary and studies are ongoing, we successfully leveraged clinical-level MRD sequencing data to inform single-cell multiomics, a potentially powerful translational research pathway for any hematologic malignancy in which NGS-MRD is clinically available. We definitively identified a small subset of circulating MM cells and used the ESCAPE platform to efficiently generate a multiomics profile. This profile suggested elevated CD38 expression without elevated CD138 expression, which previously has been associated with dedifferentiated plasma cells and plasmablasts, although further analyses are ongoing. In future studies, we will enrich for B-lymphoid spectrum cells prior to single-cell analytics. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Background: Approximately 20% of AML patients do not respond to induction chemotherapy (primary resistance) and 40-60% of patients develop secondary resistance, eventually leading to relapse followed by refractory disease (RR-AML). Diversified molecular mechanisms have been proposed for drug resistance and RR phenotype. However, we still cannot predict when relapse will occur, nor which patients will become resistant to therapy.

Biologists have long desired to understand multi-cellular processes at the resolution of the single cell. Tremendous efforts have been made over more than a century to decipher biology at the single cell level from the advent of immunohistochemistry to high-plex multi-parametric cytometry. More recently, technological developments in extracting and labelling nucleic acids from single cells have boosted single-cell information acquisition to include the genome, transcriptome, epigenome, proteome, and more, even simultaneously collecting data from multiple modalities. Here we will review some of the original motivations that have driven the development of new single cell tools, providing perspective on why these new tools were created and which tools we hope to see developed in the future.