scholarly journals Abstract 2198: An integrated workflow for liquid biopsy of circulating multiple myeloma cells (CMMCs) with single cell resolution reveals tumor heterogeneity

Author(s):  
Mario Terracciano ◽  
Claudio Forcato ◽  
Edoardo Petrini ◽  
Alberto Ferrarini ◽  
Valentina del Monaco ◽  
...  
Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3385-3385 ◽  
Author(s):  
Amit Kumar Mitra ◽  
Holly Stessman ◽  
Michael A. Linden ◽  
Brian Van Ness

Abstract Multiple myeloma (MM) is a plasma cell neoplasm with significant complexity and heterogeneity. Proteasome inhibitors (PI) including bortezomib (Velcade/Bz), carfilzomib (Kyprolis/Cz) and Ixazomib are effective chemotherapeutic agents in the treatment of MM, used alone or in combination with other anti-cancer agents. However, in spite of the recent improvements in treatment strategies, MM still remains a difficult disease to cure with median survival rate of around 7 years. In a recently published study, we have shown that the heterogeneity in response to proteasome inhibitor (PI)-based treatment in MM is governed by underlying molecular characteristics of the subclones within tumor population (Stessman et al. 2013). We confirmed the presence of residual resistant sub-population comprising up to 15% of the bulk Bz-sensitive cell population in drug-naïve MM tumors. We hypothesize that this pre-existing resistant sub-population may give rise to emerging resistance in course of treatment with PIs. In the current study, we used single cell transcriptomics analysis to identify tumor subclones within Human Myeloma Cell Lines (HMCLs) based on a 48-gene model of predictive genetic signature for baseline PI response. Automated single-cell capture and cDNA synthesis from cellular RNA were performed using Fluidigm’s C1TM Single-Cell Auto Prep System. The cDNA was then harvested and transferred to BioMark HD System for single-cell targeted high-throughput qPCR-based gene expression analysis of a 48 gene-panel using Fluidigm DELTAgene assays. Our 48-gene model combines our previously published 23 gene expression profiling (GEP) signature that could discriminate between sensitive and resistant responsiveness to Bz, and the Shaughnessy et al prognostic 17-gene GEP model along with control genes, including cell cycle genes, anti-apoptotic genes, proteasome subunit genes, house-keeping genes and internal negative controls. Based on the differential expression of these 48 genes used in the modeling, distinct subclonal populations were then identified using a combination of Fluidigm’s analysis software and the R Statistical analysis package. Further, a principal component analysis (PCA) score plot was generated as a two-dimensional grid to visualize the separate populations associated with resistant profiles. Finally, hierarchical clustering (HC) analysis was used to generate heat maps that group expression patterns associated with response. Our results demonstrated the presence of pre-existing subclones of cells within untreated myeloma cells with a characteristic genetic signature profile distinct from the pre-treatment overall (bulk) profile of myeloma cells. As an additional validation of subclonal architecture, we demonstrated the presence of subclones within HMCLs using multi-color flow cytometry. The results presented will help identify the presence and extent of intra-tumor heterogeneity in MM by single cell transcriptomics and may define residual pre-existing subclones resistant to PI therapies. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3091-3091
Author(s):  
Julia Frede ◽  
Praveen Anand ◽  
Andrew J. Yee ◽  
Tushara Vijaykumar ◽  
Monica S. Nair ◽  
...  

Introduction: Despite recent advances in the treatment of multiple myeloma, responses may be short-lived and therapeutic resistance develops almost invariably. Non-genetic cellular plasticity and dedifferentiation have recently emerged as a basis for therapeutic resistance in cancer as cells acquire transcriptional states which no longer depend on the drug target. Therefore, a better understanding of plasticity and adaptive state changes in myeloma cells is critical to develop effective therapeutic approaches that can overcome drug resistance. Here we show that cellular plasticity, though frequently invoked as a basis for therapeutic resistance in cancer, can also lead to new therapeutic opportunities. Methods: To define transcriptional states in myeloma at a single cell level, we performed fluorescence activated cell sorting and full-length single-cell RNA sequencing. We assayed a total 6000 CD38+CD138+ plasma cells and CD45+ immune cells from the bone marrow of 8 patients with relapsed and refractory multiple myeloma (RRMM) before and after immuno-modulatory treatment on a clinical trial with elotuzumab, pomalidomide, bortezomib and dexamethasone (Elo-PVD; NCT02718833) and 2 healthy donors. Surface expression of selected markers was validated by flow cytometry. Results: Assessing pre-treatment samples, we discovered that the transcriptional states of single myeloma cells are highly distinct between individual patients, despite the presence of the same established genomic classifiers, such as t(11;14). Furthermore, distinct transcriptional states co-exist within individual patients, indicating there is substantial inter- and intra-individual heterogeneity. Transcriptional states diverge from normal plasma cells towards more immature cells, of the B lymphoid lineage, suggesting a substantial cellular plasticity. Notably, we detected co-expression of myeloid and lymphoid developmental programs in the same single cells. Interestingly, these altered differentiation states were associated with up-regulation of potential immunotherapeutic targets, such as CD20, CD19, and CD33, indicating that this plasticity may result in novel therapeutic vulnerabilities. To define gene-regulatory relationships, we identified a shared core regulatory network present in malignant and normal plasma cells with the active transcription factors XBP1, ATF4, and CREB3, suggesting that myeloma cells retain lineage-specific regulons. However, we further identified patient-specific regulons not detected in any of the mature immune cell populations assayed, such as TEAD4, ELF3 and SNAI1, illustrating an aberrant and promiscuous activation of transcriptional regulators in myeloma cells. Consistent with this finding, we observed an increased number of expressed genes in myeloma cells compared to normal plasma cells as well as an increase in single cell transcriptional entropy, measures that have been linked to cell potency in normal development and cancer. Comparison of pre- and post-treatment samples interestingly revealed a further increase in transcriptional diversity and signatures associated with stemness and developmental potential following treatment. Conclusions: In conclusion, we find that higher transcriptional diversity and activation of alternate gene regulatory programs facilitate the emergence of altered transcriptional states. Interestingly, these altered states are associated with up-regulation of putative immune-therapeutic targets in myeloma cells, thus providing novel therapeutic vulnerabilities. Disclosures Lipe: amgen: Research Funding; Celgene: Consultancy; amgen: Consultancy. O'Donnell:Celgene: Consultancy; Takeda: Consultancy; BMS: Consultancy; Sanofi: Consultancy; Amgen: Consultancy. Munshi:Celgene: Consultancy; Amgen: Consultancy; Oncopep: Consultancy; Janssen: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Adaptive: Consultancy; Oncopep: Consultancy; Takeda: Consultancy. Richardson:Karyopharm: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees. Anderson:Gilead Sciences: Other: Advisory Board; Janssen: Other: Advisory Board; Sanofi-Aventis: Other: Advisory Board; OncoPep: Other: Scientific founder ; C4 Therapeutics: Other: Scientific founder . Lohr:T2 Biosystems: Honoraria; Celgene: Research Funding. OffLabel Disclosure: Samples for ancillary research were obtained in the context of a phase II clinical trial evaluating Elotuzumab, pomalidomide, bortezomib, dexamethasone The combination of elo-PVD is off label.


2015 ◽  
Vol 407 (18) ◽  
pp. 5273-5280 ◽  
Author(s):  
Md Amir Hossen ◽  
Yasuyuki Nagata ◽  
Michihiko Waki ◽  
Yoshimi Ide ◽  
Shiro Takei ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Duojiao Chen ◽  
Mohammad I. Abu Zaid ◽  
Jill L. Reiter ◽  
Magdalena Czader ◽  
Lin Wang ◽  
...  

Single-cell RNA sequencing reveals gene expression differences between individual cells and also identifies different cell populations that are present in the bulk starting material. To obtain an accurate assessment of patient samples, single-cell suspensions need to be generated as soon as possible once the tissue or sample has been collected. However, this requirement poses logistical challenges for experimental designs involving multiple samples from the same subject since these samples would ideally be processed at the same time to minimize technical variation in data analysis. Although cryopreservation has been shown to largely preserve the transcriptome, it is unclear whether the freeze-thaw process might alter gene expression profiles in a cell-type specific manner or whether changes in cell-type proportions might also occur. To address these questions in the context of multiple myeloma clinical studies, we performed single-cell RNA sequencing (scRNA-seq) to compare fresh and frozen cells isolated from bone marrow aspirates of six multiple myeloma patients, analyzing both myeloma cells (CD138+) and cells constituting the microenvironment (CD138−). We found that cryopreservation using 90% fetal calf serum and 10% dimethyl sulfoxide resulted in highly consistent gene expression profiles when comparing fresh and frozen samples from the same patient for both CD138+ myeloma cells (R ≥ 0.96) and for CD138– cells (R ≥ 0.9). We also demonstrate that CD138– cell-type proportions showed minimal alterations, which were mainly related to small differences in immune cell subtype sensitivity to the freeze-thaw procedures. Therefore, when processing fresh multiple myeloma samples is not feasible, cryopreservation is a useful option in single-cell profiling studies.


2016 ◽  
Vol 8 (363) ◽  
pp. 363ra147-363ra147 ◽  
Author(s):  
J. G. Lohr ◽  
S. Kim ◽  
J. Gould ◽  
B. Knoechel ◽  
Y. Drier ◽  
...  

2020 ◽  
Vol 18 (3) ◽  
pp. 241-246
Author(s):  
Yu Dan ◽  
Wan Sheng ◽  
Hu Lili

This study aimed to investigate the mechanism of betulinic acid on multiple myeloma cell resistance to bortezomib. To this end, the bortezomib-resistant RPMI-8226-R cells were generated by prolonged treatment of RPMI-8226 cells with increasing concentrations of bortezomib. Based on the measurements of cell viability and colony number, RPMI-8226-R cells exhibited enhanced resistance to bortezomib than RPMI-8226 cells. Treatment with betulinic acid resulted in increased sensitivity of RPMI-8226-R to bortezomib. When RPMI-8226-R cells were co-treated with bortezomib and betulinic acid, there was an increase in apoptosis rate, cleaved caspase-3, cleaved caspase-9 expression and the decrease in p-AKT/AKT and p-mTOR/mTOR levels. These results suggest that betulinic acid enhances the sensitivity of RPMI-8226-R cells to bortezomib by inhibiting the activation of the AKT/mTOR pathway in bortezomib-resistant multiple myeloma cells.


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