Whole-exome sequencing and transcriptome analysis for IgM multiple myeloma

2015 ◽  
Vol 15 ◽  
pp. e106
Author(s):  
D. Ryu ◽  
H.J. Kim ◽  
J.-G. Joung ◽  
H.-O. Lee ◽  
J. Bae ◽  
...  
2021 ◽  
Vol 21 ◽  
pp. S64
Author(s):  
Ritu Gupta ◽  
Gurvinder Kaur ◽  
Akanksha Farswan ◽  
Lingaraja Jena ◽  
Anubha Gupta ◽  
...  

2017 ◽  
Vol 17 (1) ◽  
pp. e108
Author(s):  
Surinder Sahota ◽  
Dean Bryant ◽  
Nicola Weston-Bell ◽  
Will Tapper ◽  
Arnold Bolomsky ◽  
...  

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
S. Manier ◽  
J. Park ◽  
M. Capelletti ◽  
M. Bustoros ◽  
S. S. Freeman ◽  
...  

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 5-5
Author(s):  
Ehsan Malek ◽  
E. Ricky Chan ◽  
Daniel Qu ◽  
Jane Reese ◽  
Robert Fox ◽  
...  

Introduction: Multiple myeloma (MM) is a plasma cell neoplasm associated with heterogeneous somatic alterations. Despite the development of novel anti-myeloma agents that have significantly prolonged patient survival, disease relapse remains a daunting problem. Our goal was to employ whole-exome sequencing (WES) to better describe the mutational landscape in MM beyond the tumor cell and identify genomic factors that might predict relapse. WES was performed using autograft samples obtained from MM patients that were then treated with high dose melphalan and autologous hematopoietic cell transplant (HCT). We identified a panel of genes that were most frequently mutated in all patients and then identified those genes mutated with greater frequency in patients that relapsed. A relapse burden signature was generated based upon the genes that were most frequently mutated genes in relapsed patients. Finally, the relapse burden signature was correlated with patient progression-free survival (PFS) and overall survival (OS) following autologous HCT. Methods. DNA was extracted from one ml of cryopreserved, mobilized hematopoietic cell product obtained from patients (N=93) that underwent HCT and was provided by the Case Comprehensive Cancer Center Hematopoietic Biorepository Core. Targeted sequencing was performed using the Tempus xE whole exome platform (Tempus, Chicago, IL). Variants were identified using a variant allele frequency (VAF) ≥0.1 for each sample. Variants were tabulated for each gene in each patient. Patients were grouped according to their relapse status; "No Relapse" (N=39) and "Relapse" (N=54) which corresponded to their post-HCT outcome. Relapse time was defined as time from transplant to event. Variants identified in each gene and patient group were counted and ranked. A relapse burden signature was defined and included twenty-two genes over-represented in the relapse group compared to the non-relapse group by > 10%. Genes in the relapse burden signature were subjected to gene set enrichment analysis (GSEA) and cross referenced against Gene Ontology (GO) categories. PFS and OS were defined as the time from transplant until the event of interest, with censoring at time of last follow up. Patients were regrouped according to their mutation burden in the relapse signature genes ("High burden" defined as >=six signature genes with variants) and their OS and PFS were analyzed with an R package (survival) to generate Kaplan-Meier curves and statistical significance based on a Chi-square test between low and high burden patients. Results: In total, 3,523 genes were identified as containing variants. Table 1 lists the top thirty genes that were identified and ranked based upon total number of mutations (mutational count) and most frequently mutated in relapsed and non-relapsed patients (sample count). We then identified those genes that were more frequently mutated by at least 10% in relapsed patients compared to non-relapsed patients (Fig. 1A). GSEA revealed that the relapse burden gene signature was associated with protein O-linked glycosylation, glycan processing, Golgi lumen and innate immune response activating cell surface receptor signaling pathways (Table 2). Interestingly, multiple mucin family members (Muc2, Muc3A, Muc12 and Muc19) were represented in the relapse burden signature. GO analysis indicated that the individual mucin genes were associated with the same signaling pathways that had been associated with the relapse burden signature by GSEA (Table 3). Importantly, a high relapse burden signature was correlated with a statistically significant reduction in both PFS and OS (Fig. 1B, C). Conclusion: Taken together, our results support the feasibility of WES to generate a relapse burden signature that predicts the risk of MM patients for relapse following HCT. Moreover, the mutational landscape associated with relapse, i.e. the specific genes mutated, has provided insights on the mechanisms of relapse. It is noteworthy that the relapse burden signature genes identified here were mutated at a much greater frequency than genes associated with clonal hematopoiesis of indeterminate potential (CHIP). The identification of patient subgroups at heightened risk of relapse can better guide treatment decisions. Future studies will be conducted to evaluate the effect of pathways identified here on myeloma cell survival and to validate actionable therapeutic targets. Disclosures Malek: Bluespark: Research Funding; Takeda: Other: Advisory board , Speakers Bureau; Medpacto: Research Funding; Janssen: Other: Advisory board, Speakers Bureau; Sanofi: Other: Advisory board; Clegene: Other: Advisory board , Speakers Bureau; Amgen: Honoraria; Cumberland: Research Funding. Caimi:Amgen: Other: Advisory Board; Bayer: Other: Advisory Board; Verastem: Other: Advisory Board; Kite pharmaceuticals: Other: Advisory Board; Celgene: Speakers Bureau; ADC therapeutics: Other: Advisory Board, Research Funding. de Lima:Celgene: Research Funding; BMS: Other: Personal Fees, advisory board; Incyte: Other: Personal Fees, advisory board; Kadmon: Other: Personal Fees, Advisory board; Pfizer: Other: Personal fees, advisory board, Research Funding.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4449-4449
Author(s):  
James W Murray ◽  
Christopher Fegan ◽  
Chris Pepper

Abstract Background: Understanding the pathology of Multiple Myeloma and the testing of therapeutic options has relied heavily on isogeneic cell lines due to the inability to sustain myeloma plasma cells in long-term in vitro culture. The cell lines MM.1S and MM.1R are well recognised in the field of myeloma research, providing a model of glucocorticoid drug resistance, primarily believed to be through variable expression of the glucocorticoid receptor NR3C1 but here we found no evidence of a genetic basis for this. Here we set out to examine the phenotype, function and genotype of the MM.1S and MM.1R cell lines in order to explore the origins of glucocorticoid drug resistance manifested by MM.1R cells and establish whether exome analysis could identify sub-clones with preferential sensitivity to molecular targeted inhibitors. Methods: MM.1S and MM.1R cell lines were purchased from ATCC. A 10-colour flow cytometry panel (CD38, CD138, CD19, CD45, CD56, CD49d, CXCR4, MMP-9, Ki-67, IL-6) was analysed on a BD LSR Fortessa flow cytometer and MM.1S subsets were sorted using a FACS Aria III. Telomere length was assessed using Single Telomere Length Analysis (STELA) and drug toxicity assays using Annexin V-FITC/PI staining. Bioinformatics of whole exome sequencing was carried out on the GATK platform and gene list analysis using Enrichr. PI3K isoforms were analysed by quantitative PCR and immunoblotting. Results: The MM.1S cell line demonstrated bimodal CD38 expression, with a 1.5 log difference in CD38 expression (p<0.0001) between the two populations (termed MM.1Sdim and MM.1Sbright). In contrast the MM.1R cell line was uniformly CD38bright, with expression a further 0.5 log higher than MM.1Sbright. When cell sorted subsets of MM.1S cells were subjected to increasing concentrations of Dexamethasone, the MM.1Sbright cells had a significantly higher LC50 than the MM.1Sdim cells (62nM v 29nM respectively; p<0.0001). In contrast, these subsets showed no significant difference in sensitivity to bortezomib (p=0.84. Furthermore, MM.1Sbright cells had a shorter doubling time than both MM.1Sdim (p=0.0001) and MM.1R (p=0.048). This was underscored by an increased proportion of MM.1Sbright cells in S-phase coupled with shorter mean telomere length when compared with MM.1Sdim and MM.1R (2.58 Kb v 3.29Kb v 3.2Kb respectively). We next subjected purified MM.1Sbright, MM.1Sdim and MM.1R cells to whole exome sequencing. The common clonal origin of the three cell lines was evident from the analysis but each line possessed unique genetic lesions. For example, MM.1Sbright had a FIP1L1-PDGFRA fusion mutation that was not present in the MM.1Sdim cells. This was associated with increased expression of the p110d isoform in MM.1Sdim cells. We therefore analysed the effects of the PI3Kd inhibitor, Idelalisib, on the two cell lines and showed that MM.1Sdim cells were more sensitive (p=0.003) to the effects of this agent. The specific nature of this response was confirmed by the fact that the pan p13K inhibitor PKI-402, was equipotent in both MM.1Sbright and MM.1Sdim cells (p=0.89). Conclusion: Analysis of two phenotypically distinct subsets within the MM.1S cell line revealed differences in function and genetics thereby confirming the sub-clonal architecture within this cell line. Intriguingly, our data point to the pre-existence of a dexamethasone resistant sub-clone the MM.1Sbright (CD38+) population. The subsequent production of the dexamethasone resistant cell line (MM.1R) allowed us to perform comparative genomics thereby identifying the genetic origins of dexamethasone resistance (selection) in MM.1Sbright cells and to track the subsequent clonal evolution (induction) in the MM.1R cells. Furthermore, we showed the potential for developing bespoke treatment plans based on the identification of cell signalling pathway mutations via genomic sequencing. By selective targeting of of these genetic lesions it may be possible to remove multiple sub-clones thereby diminishing the potential for clonal tiding and the development of drug resistance. In theory this could result in longer time to relapse and ultimately improved overall survival. Disclosures Fegan: Roche: Honoraria; Gilead Sciences: Honoraria; AbbVie: Honoraria.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 5651-5651
Author(s):  
Dean Bryant ◽  
Will Tapper ◽  
Nicola J Weston-Bell ◽  
Arnold Bolomsky ◽  
Li Song ◽  
...  

Abstract Introduction Multiple myeloma (MM) is a largely incurable plasma cell malignancy characterised by marked genomic heterogeneity, in which chromosome 1q21 amplification (amp1q21) associates with poor prognosis. Genomic analysis using next generation sequencing has identified recurrent mutations, but no universal acquired somatic mutation(s) have emerged in MM, suggesting that understanding pathways of survival will require analysis of individual tumours in distinct disease subsets. To compound complexity of the problem, intraclonal variation (ICV), known as a major driver mechanism in cancer plasticity, in which clonal competitor cells undergo selection during disease evolution and progression by Darwinian principles, will need to be fully mapped at the genome level. Identifying the true level of ICV in a tumour will thus require analysis at the level of whole exome sequencing (WES) in single cells (SCs). In this study, we sought to establish WES methodology able to identify ICV in SCs in an index case of amp1q21 MM. Methods Cell selection and sequencing CD138+ tumour cells and CD3+ T-cells were isolated from a presentation case of amp1q21 MM as bulk populations to high purity (>97%). Single MM cells and normal T cells were individually isolated and used for single cell (SC) whole exome sequencing (WES). Whole genome amplification (WGA) was performed by multiple displacement amplification (Qiagen REPLI-g Mini kit), and exome capture was performed using Agilent SureSelect. Libraries were then 90 bp paired end sequenced on an Illumina HiSeq2000 (BGI, China). Data analysis Data was produced for bulk (1000 cells) MM and bulk germline T cells, twenty MM SCs and five T cell SCs. Raw data was aligned to hg19 reference sequence using NovoAlignMPI (v3.02.03). Variant calling was performed using SAMtools (v1.2.1) and VarScan (v2.3.6) and variants were annotated using ANNOVAR. High confidence variants were called in the bulk tumour WES by pairwise comparison with bulk germline WES. Variant lists were also cross-searched against various variant databases (CG46, 1000 genomes, dbSNP, esp650 and in-house database) in order to exclude variants that occur in the general population. Multiple quality control measures were employed to reduce the number of false positive calls. Results and Discussion Data and bioinformatics pipelines are of a high quality SC WES generated raw data reads that were similar to bulk WES of 1000 cells, with comparable mapping to Agilent SureSelect target exome (69-76% SC vs. 70% bulk) and mean fold coverage (56.8-59.1x vs. 59.7x bulk). On average, 82% of the exome was covered sufficiently for somatic variant (SV) calling (often considered as ≥ 5x), which was higher than seminal published SC WES studies (70-80%) (Hou et al., Cell, 2012; Xu et al., Cell, 2012). We identified 33 potentially deleterious SVs in the bulk tumour exome with high confidence bioinformatics, 21 of which were also identified in one or more SC exomes. The variants identified include suspected deleterious mutations in genes involved in MAPK pathway, plasma cell differentiation, and those with known roles in B cell malignancies. To confirm SV calls, randomly selected variants were validated by conventional Sanger sequencing, and of 15/15 variants in the bulk WES and of 55/55 variants in SCs, to obtain 100% concordance. Intraclonal variation in MM Significantly, ICV was apparent from the SC exome variant data. Total variant counts varied considerably among SCs and most variant positions had at least several cells where no evidence of the variant existed. Bulk WES lacks crucial information We identified an additional 23 variants that were present in 2+ SC exomes, but absent in the bulk MM tumour exomes. Of these, 30% (7 variants) were examined for validation, and were amplifiable in at least one cell to deliver 100% concordance with variant calls. These variants are of significant interest as they reveal a marked occurrence of subclonal mutations in the MM tumour population that are not identified by bulk exome sequencing. They indicate that the mutational status of the MM genome may be substantially underestimated by analysis at the bulk tumour population level. Conclusion In this work we establish the feasibility of SC WES as a method for defining intraclonal genetic variation in MM. Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Vol 36 (1) ◽  
pp. 13
Author(s):  
Malek Abbaspourkharyeki ◽  
Naveen Jayaram Anvekar ◽  
Nallur B Ramachandra

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