Single Cell Whole Exome Sequencing in an Index Case of Amp1q21 Multiple Myeloma to Define Intraclonal Variation

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.

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

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 237-237
Author(s):  
Karma Salem ◽  
Jihye Park ◽  
Claudia Freymond ◽  
Marzia Capelletti ◽  
Daisy Huynh ◽  
...  

Abstract Introduction: Recent data shows that multiple myeloma (MM) almost always arises from precursor states called Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), but not all patients with MGUS or SMM develop MM. Risk factors of progression for SMM patients are largely based on tumor load as represented by an M-protein ≥ 3 g/dL, a free light chain (FLC) ratio outside the range of 0.125 to 8, and ≥ 10% plasma cells in the BM. However, the genetic lesions that underlie progression, the molecular factors that cause rapid versus slow progression, and the factors that distinguish the relatively indolent MGUS from SMM are not well known. Further, the genomic landscape of SMM is not well characterized. One potential factor is MYC overexpression. Bergsagel et al. have found that MYC levels increase when comparing MGUS, SMM and overt MM. Other frequently altered pathways in MM are NF-kB, MAPK and DNA damage. In addition, limited studies of paired SMM and MM samples show that in many cases, the aggressive subclones can already be detected, in small cell fractions, before overt MM develops. However, the cause of progression to MM is unclear, in large part because sequential genomic studies of MGUS/SMM progression have yet to be undertaken. To address these questions, in this study we examine clinically-annotated samples from patients with SMM. Methods: We performed whole exome sequencing (WES) (mean target coverage 50X/100X) on 49 germline-tumor matched samples from patients with SMM (DNA from bone marrow CD138+ plasma cells matched with germline DNA from peripheral blood mononuclear cells). Libraries were constructed using Agilent SureSelect XT2 library prep kit, and hybridized to Agilent's whole exome V5+UTR capture probes and then sequenced on HiSeq 2500 (Illumina). We also performed targeted deep sequencing using a custom enrichment bait set on 25 samples of progressor (n=12) and non-progressor (n=13) SMM samples. Libraries were also constructed with Agilent SureSelect XT2 library prep kit and enriched by hybridizing to an in-house designed customized target bait, then sequenced on HiSeq 2500. Sequencing data were analyzed using previously established analytic pipelines including MuTect, RecapSeg, GISTIC, MutSig, and ABSOLUTE. Results: The number of Somatic Single Nucleotide Variants (SSNVs) seen in SMM ranged from 1 to 98 nonsilent mutations with an average of 1.14 mutations/Mb, which is slightly lower than MM (1.6 mutations/Mb) from previous studies (p-value=0.05). This large and varying range of mutational load among samples suggests that SMM is likely a heterogenous entity where some patients are closer to MGUS and others closer to MM. We identified likely drivers in SMM in about ~32% of the samples, including mutations in MM candidate driver genes such as NRAS, KRAS and PTPN11(overall 36 events were present in COSMIC). SMM also had somatic CNAs in about ~50% of SMM samples, such as hyperdiploidy, gain of chromosome 1q, deletion of 13p and 17p, which match the hallmark chromosome changes seen in MM. Comparing deep targeted sequencing of 100 genes (mean target coverage 361X) in samples from 12 SMM patients who progressed to myeloma vs. 13 SMM patients who did not, we found non-synonymous mutations exclusive to progressors, suggesting that with more samples we may find genetic alterations that predict progression in SMM. Conclusion: This study demonstrates that WES and targeted sequencing of SMM patients can detect MM candidate driver genes as well as hallmark CNAs seen in MM patients. Further, there may be potential different mutational features between progressors and non-progressors. Thus, this approach can be used to identify genetic drivers of clonal progression from MGUS/SMM to MM that may present opportunities for early therapeutic intervention and prevention of disease progression. Disclosures Roccaro: Takeda Pharmaceutical Company Limited: Honoraria. Ghobrial:Takeda: Honoraria; Noxxon: Honoraria; Amgen: Honoraria; Novartis: Honoraria; BMS: Honoraria, Research Funding; Celgene: Honoraria, Research Funding.


2021 ◽  
Vol 21 ◽  
pp. S64
Author(s):  
Ritu Gupta ◽  
Gurvinder Kaur ◽  
Akanksha Farswan ◽  
Lingaraja Jena ◽  
Anubha Gupta ◽  
...  

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 ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 726-726 ◽  
Author(s):  
Eileen M Boyle ◽  
Brian A Walker ◽  
Dorota Rowczienio ◽  
Christopher P Wardell ◽  
Alexander Murison ◽  
...  

Abstract Introduction: Systemic light chain amyloidosis (AL) is characterized by the deposition of immunoglobulin light chains as amyloid fibrils in different organs, where they form toxic protein aggregates. The underlying disease is a plasma cell disorder, likely a monoclonal gammopathy, but limited data are available on the biology of the plasma cell clone underlying AL and existing studies have concentrated on chromosomal abnormalities. We report the final findings of the first exome sequencing to define the plasma cell signature in AL and compared this to other mature lymphoid malignancies. Methods: Whole exome sequencing was performed on 27 newly diagnosed, histologically proven amyloidosis patients. DNA was extracted from peripheral blood and CD138+ plasma cells and whole exome sequencing was performed using SureSelect (Agilent). In addition to capturing the exome, extra baits were added covering the IGH, IGK, IGL and MYC loci in order to determine the breakpoints associated with translocations in these genes. Tumour and germline DNA were sequenced and data processed to generate copy number, acquired variants and translocation breakpoints in the tumour. Patient demographics: The median age at diagnosis was 69 (range: 41-81) years old. All cases were histologically proven, newly diagnosed AL amyloid. 74% were lambda restricted and 26% kappa with median respective median involved sFLC were 180 mg/L (range: 58.9-986 mg/L) and 730 mg/L (609-3190 mg/L) respectively. The median plasmocytosis was 17.5% (range: 2-90%). 78% of them had evidence of heart involvement, 70% had renal involvement and 33% had liver involvement. Mutation load: The median number of acquired non-synonymous variants per sample was 65 (range 7-285) with 40 (4-251) potentially disease causing variants per sample. Mutational landscape: Although no genes were significantly mutated, the genes closest to significance were NRAS, PIM1, and HIST1H3F. We identified 2 cases with NRAS mutations in the codon 61 (Q61R and Q61H) but no KRAS mutations were seen. Interestingly, there were mutations in some of the significantly mutated genes in myeloma such as EGR1 (Q95R), DIS3 (M505L and D317E) and TRAF3 (splice site). One patient bore a CARD11 (R1077W) mutation, more commonly seen in non-Hodgkin’s lymphoma. Although 22% of our samples had a t(11;14) translocations we did not observe any mutations in CCND1. We identified a t(1;14) (p36;q32) previously described in non-hodgkin lymphoma in one patient. We also identified a Myc translocation in a patient who met the criteria for smouldering myeloma. As previously described in myeloma, both DIS3 mutants occurred in patients with a del(13q). Finally, there was no APOBEC signature in our small samples cohort butwe identified an unspecific mutational signature that was related to age. When comparing the spectrum of mutated genes in both amyloidosis (n=27) and previously sequenced myeloma samples (n=463), we identified 948 genes in common between myeloma and amyloidosis. Four hundred and forty two genes were only mutated in amyloidosis most of them being in housekeeping genes. The clustering of the most frequent and significantly mutated genes in each B-cell malignancy, suggests amyloidosis resembles myeloma and MGUS more than other B-cell malignancies. Discussion: The mutational landscape of amyloidosis resembles myeloma with no disease defining mutations but a variety of mutations occurring in different pathways such as RAS and NF-kB. Two samples had an NRAS mutation, which is a known driver mutation also found in MM. We identified a non-canonical IgH translocation that is a rare event in myeloma. There was little overlap in mutated genes indicating a diverse spectrum of mutations, which is in common with MM. Given the diverse mutational spectrum it will be necessary to study a large cohort to fully understand the genetic complexity of the disease. Conclusion: We conclude that exome sequencing identifies a genetic signature of AL amyloidosis which is similar to other plasma cell disorders in terms of translocations and non-synonymous mutations. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.


2019 ◽  
Vol 125 ◽  
pp. e424-e428 ◽  
Author(s):  
Junlong Sun ◽  
Wenwu Zhou ◽  
Kangcheng Mao ◽  
Yunfeng He ◽  
Junzhong Yao ◽  
...  

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16518-e16518
Author(s):  
Jin Huang ◽  
Guofeng Zhao ◽  
Qiu Peng ◽  
Jian Ma ◽  
Pansong Li ◽  
...  

e16518 Background: Gastric linitis plastica (LP) is a rare and aggressive type of gastric cancer (GC) for which the genomic landscape and architecture have gone largely undescribed. Methods: 4 LP patients were enrolled. 10 region tumor samples of each LP patient and matched peripheral blood were collected. Matched blood cells of each patient were also collected for removing germline background Whole-exome sequencing(WES), TCR sequencing, TCGA gastric cancer and several WES articles data were used to investigate intra and inter patient genomic and immune heterogeneity. Results: All 4 LP patients were female and were in stage III. LP biopsies were sequenced with median 290.6x effective depth. A total of 11,504 somatic mutations including 6,339 non-silent mutations were identified. The median non-silent tumor mutation burden (TMB) of biopsy samples was 3.23 mutations/Mb (range from 1.36 to 4.88), which was comparable to gastric adenocarcinoma(p = 0.3). Phylogenetic trees of 4 LP patients demonstrated clear evidence of branched evolution, and the phylogenetic trees varied extensively across the four cases. The percentages of trunk mutations of 4 LP were 12.8%, 5.4%, 5.4% and 30.7%, respectively, while the proportions of trunk neoantigens were 6.2%, 2.2%, 12% and 12.4, respectivelyWhen comparing LP to other multiregion WES studies, e.g., lung adenocarcinoma, renal cell carcinoma, and esophageal squamous cell carcinoma, LP was one of the most heterogeneous tumor types. The top mutational signatures in this cohort associated with spontaneous deamination, DNA mismatch repair (MMR), and small indels at repeats etc. Furthermore, profound TCR ITH was observed in all 4 LP patients. None of the T cell clones were shared among all tumor regions and 94.23-94.41% T cells were restricted to individual tumor regions. To quantify the TCR ITH, we utilized the Morisita overlap index (MOI), which ranged from 0.34 to 0.56 across different regions within the same tumors suggesting marked inter-individual TCR repertoire heterogeneity and profound intratumor TCR heterogeneity. Conclusions: Based on whole-exome sequencing and TCR sequencing, we demonstrate that LP is highly heterogeneous for mutations, neoantigens and T cells, which contributes to its poor prognosis.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 258-258
Author(s):  
Johann Greil ◽  
Tobias Rausch ◽  
Thomas Giese ◽  
Obul Reddy Bandapalli ◽  
Volker Daniel ◽  
...  

Abstract Abstract 258 Primary immunodeficiencies represent model diseases for the mechanistic understanding of the human innate and the adaptive immune response and are per se clinically highly relevant, because in SCID patients infections by opportunistic pathogens are typically life-threatening early in life. We identified an infant of consanguineous parents suffering from a novel form of SCID, who presented with a life-threatening Pneumocystis jirovecii pneumonia. This entity was characterized by agammaglobulinemia and profoundly deficient T-cell function despite quantitatively normal T- and B-lymphocytes. Lymphocyte proliferation was strongly inhibited after stimulation of PBMCs with T-cell mitogens such as PHA, Con A, or anti-CD3 monoclonal antibody. The expression of several T-cell response associated cytokines upon stimulation with PMA/ionomycin was dramatically reduced in comparison to normal controls. By contrast, proliferation induced by the classical B-cell mitogen PWM was almost comparable to healthy controls. Immunophenotyping revealed a predominantly naïve phenotype (CD45RA+ CCR7+) in CD4+ and CD8+ T-lymphocytes, whereas central memory T-lymphocytes (CD45RA− CCR7+) were nearly absent. B-lymphocytes from peripheral blood were mainly naïve B-cells (CD27−) with a uniformly immature transitional B-lymphocyte phenotype (CD24++, CD38++). Patient B-lymphocytes retained the ability to proliferate and differentiate in response to BCR-independent stimuli, while their response to BCR activation was defective. Our findings thus revealed a combined defect of TCR-mediated T-lymphocyte functions and BCR-mediated B-lymphocyte functions but did not enable us to link the immunological phenotype with one of the known molecularly defined categories of SCID. Diagnostic whole-exome sequencing and systematic variant categorization revealed a single pathogenic homozygous nonsense mutation of the caspase recruitment domain 11 (CARD11) gene. CARD11 is a scaffold protein that is known to be required for the assembly and activation of the NF-kB complex. In reconstitution assays we demonstrated that the patient derived truncated CARD11 protein is defective in antigen receptor signaling and NF-kB activation. Several lines of evidence substantiate the involvement of the identified CARD11 mutation in the new form of SCID that we report here. First, PCR and Sanger re-sequencing validated the truncating CARD11 mutation to be homozygous in the patient and heterozygous in the parents, in agreement with the recessive transmission of the mutation through the healthy consanguineous parents. Second, CARD11 is a scaffold protein required for TCR- and BCR-induced NF-kB activation as well as lymphocyte activation and proliferation, which is specifically expressed in hematopoietic cells, consistent with a causative role of CARD11 mutations in the context of an immune disorder. Third, the GUK domain of CARD11, which is missing in the mutated form of CARD11 due to truncation, was previously reported to be necessary for NF-kB activation by PMA/ionomycin treatment, further supporting the presumed damaging nature of the homozygous CARD11 mutation observed in the female patient reported here. Finally, the immunological findings in this patient are compatible with the phenotype of a previously described Card11 −/− k.o. mouse, which shows a selective defect in NF-κB activation leading to diminished antigen receptor or PKC mediated proliferation and defective cytokine production in T-cells and B-cells. Thus, we have identified an inactivating CARD11 mutation linking defective NF-kB signaling with a novel cause of autosomal recessive SCID, which must be considered in the diagnostic assessment of patients with suspected SCID but with quantitatively normal T-cells. Disclosures: No relevant conflicts of interest to declare.


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.


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