scholarly journals Correction: A brain-specific PGC1a fusion transcript affects gene expression and behavioral outcomes in mice

2021 ◽  
Vol 5 (2) ◽  
pp. e202101295
Author(s):  
Oswaldo A Lozoya ◽  
Fuhua Xu ◽  
Dagoberto Grenet ◽  
Tianyuan Wang ◽  
Korey D Stevanovic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Behavioral Outcomes ◽  
Fusion Transcript

scholarly journals NUTM1 Is a Rare but Recurrent Fusion Gene Partner in B Cell Precursor Acute Lymphoblastic Leukemia Associated with Increased Expression of Genes on Cytoband 10p12.31

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2823-2823
Author(s):  
Femke M. Hormann ◽  
Alex Q. Hoogkamer ◽  
H. Berna Beverloo ◽  
Aurélie Boeree ◽  
Ronald W. Stam ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Rna Sequencing ◽  
Histone Acetyltransferase ◽  
Lymphoblastic Leukemia ◽  
Fusion Transcript ◽  
Population Based ◽  
Fusion Genes ◽  
Cell Precursor ◽  
Cytogenetic Aberration

Abstract INTRODUCTION In 20-25% of the pediatric B cell precursor acute lymphoblastic leukemia (BCP-ALL) patients, the driving cytogenetic aberration is unknown. It is important to identify more primary lesions in this remaining B-other group to provide better risk stratification and identify possible treatment options. In this study, we aimed to identify novel recurrent fusion genes in BCP-ALL through RNA sequencing. METHODS We used paired-end total RNA Illumina sequencing to detect fusion genes with STAR-fusion and FusionCatcher in a population-based ALL cohort (n=71). We used Affymetrix U133 Plus2 expression arrays in a larger population-based ALL cohort (n=661) and an infant ALL cohort (n=70) to compare gene expression levels. Fluorescent in situ hybridization (FISH) was performed using Cytocell NUTM1 break-apart probe set MPH4800. RESULTS We identified an in-frame SLC12A6-NUTM1 fusion transcript composed of exons 1-2 of SLC12A6 fused to exons 3 to 8 of NUTM1 by RNA sequencing. Both genes are located on 15q14 within 5.3 Kb distance on opposite strands, and the fusion could result from an inversion. The fusion transcript is predicted to encode almost the total NUTM1 protein including the acidic binding domain for the histone acetyltransferase EP300. The SLC12A6-NUTM1 fusion case showed high NUTM1 expression, while NUTM1 expression was absent in the remaining cases. Using gene expression profiling, we identified four additional pediatric and two non-KMT2A-rearranged infant BCP-ALL cases with high NUTM1 expression. In the population-based cohort reflecting all different cytogenetic subtypes, these cases were restricted to the B-other group without known sentinel cytogenetic abnormalities. FISH showed a NUTM1 break apart pattern in all four tested NUTM1-positive cases indicative of a balanced translocation. RNA sequencing confirmed an ACIN1-NUTM1 fusion in one of the infant cases. We conclude that NUTM1 is normally not expressed in leukemic lymphoblasts, and that its expression can be induced by a gene fusion. The karyotypes of the predicted NUTM1 fusion cases combined with RNA sequencing data suggest that different chromosomal rearrangements are involved, likely resulting in different NUTM1 fusion partners. In literature, BRD9-NUTM1, IKZF1-NUTM1, and CUX1-NUTM1 fusions were reported in pediatric B-other cases, and BRD9-NUTM1 and ACIN1-NUTM1 fusions were reported in non-KMT2A-rearranged infants. Our combined aberrant gene expression and FISH results indicate that NUTM1 fusions occur in 2.4% (5/210) of pediatric and in 28% (2/7) of infant BCP-ALL cases without a sentinel cytogenetic aberration. The recurrence of NUTM1 aberrations in BCP-ALL cases without a known driver and the resulting expression of NUTM1 suggests that this fusion could be a new oncogenic driver in leukemia. All seven patients with a NUTM1 fusion achieved continuous complete remission with a median follow-up time of 8.3 years (range 4.8-13.8 years), suggesting that NUTM1 fusions in BCP-ALL have a favorable prognosis. To get an insight in the underlying biology, we compared gene expression between NUTM1-positive and NUTM1-negative pediatric B-other cases. We identified 130 differentially expressed probe sets (FDR ≤0.01) with a peculiar enrichment of those located on chromosome band 10p12.31 (Bonferroni adjusted p=4.05E-04). The genes in cytoband 10p12.31, including BMI1, were variably upregulated in 6/7 NUTM1-positive cases and positively correlated to NUTM1 expression levels. The NUTM1 protein is capable of binding and hereby stimulating the histone acetyltransferase activity of the EP300 protein. The EP300 protein preferentially binds a risk allele of BMI1 associated with increased risk for BCP-ALL. The BMI1 protein has been shown to convert BCR-ABL1-positive progenitor cells into BCR-ABL1-positive BCP-ALL cells. Hence, we postulate that NUTM1 fusion proteins contribute to leukemogenesis by stimulating EP300, leading to upregulation of BMI1 and other 10p12.31 genes in BCP-ALL. CONCLUSION NUTM1 fusions are a rare but recurrent event in BCP-ALL that seems to have a good prognosis. The NUTM1 fusions result in expression of the normally silent NUTM1 gene and are associated with upregulation of a cluster of genes on 10p12.31 including the leukemogenic BMI1 gene. Disclosures No relevant conflicts of interest to declare.

scholarly journals Expression of varicella-zoster virus VLT-ORF63 fusion transcript induces broad viral gene expression during reactivation from neuronal latency

2020 ◽  
Author(s):  
Werner J. D. Ouwendijk ◽  
Daniel P. Depledge ◽  
Labchan Rajbhandari ◽  
Tihana Lenac Rovis ◽  
Stipan Jonjic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Varicella Zoster Virus ◽  
Viral Gene Expression ◽  
Fusion Transcript ◽  
Viral Gene ◽  
Reading Frame ◽  
Varicella Zoster ◽  
Rapid Amplification

SummaryVaricella-zoster virus (VZV) establishes lifelong neuronal latency in most humans world-wide, reactivating in one-third to cause herpes zoster and occasionally chronic pain. How VZV establishes, maintains and reactivates from latency is largely unknown. Latent VZV gene expression is restricted to VZV latency-associated transcript (VLT) and open reading frame 63 (ORF63) in naturally VZV-infected human trigeminal ganglia (TG). Notably, these transcript levels positively correlated suggesting co-regulated transcription during latency. Here, we used direct RNA-sequencing to identify fusion transcripts that combine VLT and ORF63 loci (VLT-ORF63) and are expressed during both lytic and latent VZV infections. Furthermore, real-time PCR, RNA in situ hybridization and 5’ rapid amplification of cDNA ends (RACE) all confirmed VLT-ORF63, but not canonical ORF63, expression in human TG. During lytic infection, one of the two major VLT-ORF63 isoforms encodes a novel fusion protein combining VLT and ORF63 proteins (pVLT-ORF63). In vitro, VLT is transcribed in latently VZV-infected human sensory neurons, whereas VLT-ORF63 expression is induced by reactivation stimuli. Moreover, the pVLT-ORF63-encoding VLT-ORF63 isoform induced transcription of lytic VZV genes. Collectively, our findings show that VZV expresses a unique set of VLT-ORF63 transcripts, potentially involved in the transition from latency to lytic VZV infection.

Gene Expression Profiling Of Multiple Myeloma Samples Using RNA Sequencing Identifies Frequent Rearrangements Of FCHSD2

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 534-534
Author(s):  
Venkata D Yellapantula ◽  
Christopher Murray ◽  
Winnie Liang ◽  
Daniel Auclair ◽  
Joan Levy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Genome Sequencing ◽  
Expression Profiling ◽  
Fusion Transcript ◽  
Rna Seq ◽  
Gene Index

Abstract The Multiple Myeloma Research Consortium (MMRC) has characterized over 300 hundred patient samples using a variety of platforms as part of the Multiple Myeloma Genomics Initiative (MMGI). Part of this large study includes a subset of 84 patients that were screened for somatic mutations using whole genome sequencing (WGS) or whole exome sequencing (WES) in combination with mRNA sequencing. This represents one of the first cohorts of myeloma patients with matched genome and transcriptome sequencing results. Given the historic value of microarray based gene expression profiling (GEP), this cohort provides the unique opportunity to compare gene expression measurements from the two platforms as Affymetrix U133Plus2.0 based GEP was performed on 42 of these samples. As part of the MMGI study, the Broad Institute has completed the genome sequencing, using WGS and WES, for 213 patients. A frequently mutated list of 9 genes including NRAS, KRAS, TP53, PNRC1, MAGED1, FAM46C, DIS3, CCND1 and ALOX12B were identified initially. Given the potential for RNAseq data to be used to define gene expression levels and to identify mutations in expressed genes we tested the feasibility of mutation calling on RNAseq alone. We independently called mutations on the entire transcriptome of the 84 patients and used a filtering method to eliminate likely germline variants in the absence of a matched normal control. We looked for point mutation concordance between, the calls identified by RNA-Seq alone and the previously reported variants through exome sequencing in the 9 frequently mutated genes. Out of the 66 SNV’s identified by these criteria using WGS or WES sequencing, 55(84%) were detected using RNA-Seq. Of the remaining 11 loci, 7(10%) were not detectably expressed and in 4(6%) cases the mutation was not detectable even though there was ample coverage. It is unclear if the last 6% represent false positives in the genome calls or the preferential expression of the wild-type allele. To interrogate the utility of RNAseq based GEP in myeloma we independently recapitulated many of common GEP measurements. First we independently used the 84 samples to define cutoffs for the implementation of the TC classification method. We compared our independent assignment of the 42 samples with matched gene expression array data, to their existing microarray assignments. This resulted in 40/42 (95%) samples being classified 40(95%) into identical TC classes. The two discordant samples MMRC0312 and MMRC0387 classified as TC class “none” by expression arrays were classified as other classes by RNAseq. MMRC0312 exhibited high CCND3 expression using RNA-Seq and was assigned to ‘6p21’ class. MMRC0387 exhibited elevated CCND1 expression and was classified as ‘D1’ using RNAseq. For the indexes we showed a strong correlation for the proliferation index (R2=0.971) and the NFKB index (R2=0.961) but only a moderate correlation for the 70-gene index (R2=0.761). The decreased correlation in the 70-gene index is clearly due to the large number of probesets used, which are associated with genes that are clearly not expressed by RNA-seq. One additional advantage of RNAseq over microarray based gene expression measurements is the potential to detect fusion transcripts. We have applied fusion transcript detection to this cohort of patients and 69 human myeloma cell lines, which were also screened by RNAseq and WES as part of the MMGI study. The most common fusion transcript detected is the @IGH-MMSET fusion characteristic of t(4;14). The next most common fusion we identified appears to be a promoter replacement event were the highly expressed gene, FCHSD2, is fused to multiple partners including known myeloma related genes, MMSET and MYC, and previously unreported genes in myeloma, CARNS1 and NCF2. Additional structural rearrangements involving FCHSD2 are also predicted based on the high frequency of copy number abnormalities encompassing the 5′ region of this gene as detected by comparative genomic hybridization in the MMGI study. This study should provide the basis for the migration of myeloma based gene expression profiling from microarrays to RNA sequencing based approaches. In the future RNA sequencing has the potential to provide novel classification schemes that leverage the multitude of measurements that can be made from this single assay. Disclosures: Levy: MMRC: Employment.

scholarly journals Gene Expression, Single Nucleotide Variant and Fusion Transcript Discovery in Archival Material from Breast Tumors

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e81925 ◽  
Author(s):  
Nadine Norton ◽  
Zhifu Sun ◽  
Yan W. Asmann ◽  
Daniel J. Serie ◽  
Brian M. Necela ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fusion Transcript ◽  
Breast Tumors ◽  
Single Nucleotide Variant ◽  
Single Nucleotide ◽  
Archival Material ◽  
Transcript Discovery

scholarly journals Varicella-zoster virus VLT-ORF63 fusion transcript induces broad viral gene expression during reactivation from neuronal latency

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Werner J. D. Ouwendijk ◽  
Daniel P. Depledge ◽  
Labchan Rajbhandari ◽  
Tihana Lenac Rovis ◽  
Stipan Jonjic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Varicella Zoster Virus ◽  
World Wide ◽  
Viral Gene Expression ◽  
Fusion Transcript ◽  
Viral Gene ◽  
Trigeminal Ganglia ◽  
Varicella Zoster ◽  
Viral Gene Transcription

AbstractVaricella-zoster virus (VZV) establishes lifelong neuronal latency in most humans world-wide, reactivating in one-third to cause herpes zoster and occasionally chronic pain. How VZV establishes, maintains and reactivates from latency is largely unknown. VZV transcription during latency is restricted to the latency-associated transcript (VLT) and RNA 63 (encoding ORF63) in naturally VZV-infected human trigeminal ganglia (TG). While significantly more abundant, VLT levels positively correlated with RNA 63 suggesting co-regulated transcription during latency. Here, we identify VLT-ORF63 fusion transcripts and confirm VLT-ORF63, but not RNA 63, expression in human TG neurons. During in vitro latency, VLT is transcribed, whereas VLT-ORF63 expression is induced by reactivation stimuli. One isoform of VLT-ORF63, encoding a fusion protein combining VLT and ORF63 proteins, induces broad viral gene transcription. Collectively, our findings show that VZV expresses a unique set of VLT-ORF63 transcripts, potentially involved in the transition from latency to lytic VZV infection.

scholarly journals Multiplexed Gene Expression and Fusion Transcript Analysis to Detect ALK Fusions in Lung Cancer

2013 ◽  
Vol 15 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Maruja E. Lira ◽  
Tae Min Kim ◽  
Donghui Huang ◽  
Shibing Deng ◽  
Youngil Koh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Fusion Transcript ◽  
Transcript Analysis

scholarly journals Identification of Novel Fusion Genes and Differentially Expressed Genes in Acute Leukemia through Transcriptome Analysis

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1220-1220
Author(s):  
Dong-Hyun Lee ◽  
Young-Uk Cho ◽  
Seongsoo Jang ◽  
Chan-Jeoung Park ◽  
Mi Hyun Bae ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Leukemia ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Fusion Transcript ◽  
Differentially Expressed ◽  
Fusion Genes ◽  
Next Generation ◽  
Fusion Transcripts ◽  
Novel Transcript

Abstract Background Chromosomal translocations in acute leukemia frequently result in gene fusions that are associated with leukemogenesis. Next-generation sequencing technology has opened up a systematic characterization of transcriptomes including gene expression, novel transcript, and fusion transcripts. We used next-generation RNA sequencing to identify fusion genes responsible for novel chromosomal translocations in acute leukemia and to find their differentially expressed genes. Methods We selected 10 acute leukemia (AML, 6; B-ALL, 3; and T-ALL, 1) patients with novel translocations by G-banding. Total RNA was extracted from leukemia cells and cDNA libraries were constructed with TruSeq RNA kit. Paired-end sequencing was performed on HiSeq2500. Reads were aligned with TopHat/BowTie, and deFuse was used to detect fusion transcripts. Transcript assembly and abundance estimation were done using Cufflinks, and expression levels were quantified by fragments per kilobase of transcript per million mapped reads (FPKM). The candidate fusion transcripts were validated with fluorescence in situ hybridization (FISH), and reverse-transcription PCR followed by Sanger-sequencing. Results We found 5 in-frame fusion genes exactly matched on translocation breakpoints from 3 AML patients and 1 B-ALL patient: USP34-ASAP3/t(1;2)(p36.1;p11.2), MAZ-MKL1/t(16;22)(p11.2;q13), MLL-SEPT6 and SEPT6-CDCA5/t(X;11)(q24;q13), and RCSD1-ABL1/t(1;9)(q24;q34). The USP34-ASAP3 fusion produced a novel transcript between USP34 exon 2 and ASAP3 exon 18. The protein encoded by the ASAP3 gene promotes cell differentiation and migration and has been implicated in cancer cell invasion. Comparing gene expression in this sample to nine other samples, we found six overexpressed genes; CLEC3B, SNAR-A14, H19, HOTS, SNORD35A, and S100A1. CLEC3B is associated with human disorders affecting bone and connective tissue. H19 is located in an imprinted region of chromosome 11 and is associated with Wilms tumorigenesis. S100A1 is involved in the regulation of a number of cellular processes such as cell cycle progression and differentiation. The MAZ-MKL1 fusion transcript was composed of MAZ exon 4 and MKL1 exon 4. MAZ was a novel partner gene of MKL1 which had been reported in acute megakaryoblastic leukemia carrying RBM15-MKL1/t(1;22)(p13;q13). MS4A2, RPLP0, and ARP5J2 genes were overexpressed in this rearrangement. MS4A2 is related PI3K cascade pathway and immune response pathway. RPLP0 is responsible for RNA binding and structural constituent of ribosome. AML patient with t(X;11)(q24;q13) had two fusion transcripts, MLL-SEPT6 and SEPT6-CDCA5 resulting from complex MLL rearrangement. While the MLL-SEPT6 fusion has been known in AML cases, the SEPT6-CDCA5 was a novel fusion. SNORD88B, MYL6, PTMA, MKX, NDUFAF3, and CNTN1 gene were more highly expressed than other samples. Among them, MKX and CNTN1 genes are related with cell adhesion function. The RCSD1-ABL1/t(1;9)(q24;q34) in B-ALL was previously reported to encode an aberrant tyrosine kinase. This translocation had also reciprocal ABL1-RCSD1 fusion transcript which could result in an alteration of cellular function. Six genes were specifically overexpressed in this sample RCBTB2, SERHL2, MIR941-2, FAM150B, GPR110, and SNORA27. RCBTB2 encodes a protein that is related to regulator of chromosome condensation. We also investigated leukemia subtype-specific expression profiles. The five significant genes were higher expressed in AML as compared with ALL (MIR4461, SET, RNU6ATAC, NINJ2, and ATP6V0C). Especially, MIR4461 was over 6000 FPKM in 5 of 6 AML samples, but was never expressed in ALL samples. B-ALL specific overexpressed genes were C17orf62, and MIR941-1, whereas T-ALL specific overexpressed gene was SNORD33. Conclusions Using next-generation RNA sequencing, we have discovered 5 candidate fusion genes in 10 acute leukemia patients with novel translocations, and identified 3 novel fusion genes to be predicted as oncogenic potential. Through the comparison of expression profiling, we were able to define differentially expressed genes in acute leukemia with novel fusion genes and leukemia subtype-specific gene expression. RNA-sequencing is a powerful tool for the discovery of leukemia-associated fusion genes and their related genes as well as molecular pathways. Disclosures No relevant conflicts of interest to declare.

scholarly journals Location of the t(4;14) Translocation Breakpoint Identifies a Subset of Newly-Diagnosed Multiple Myeloma Patients with Poor Prognosis

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2681-2681
Author(s):  
Nicholas Stong ◽  
Maria Ortiz ◽  
Fadi Towfic ◽  
William Pierceall ◽  
Erin Flynt ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Copy Number ◽  
Fusion Transcript ◽  
Newly Diagnosed ◽  
Rna Seq ◽  
Publicly Traded ◽  
Genomic Dataset ◽  
Fusion Transcripts ◽  
Breakpoint Location

Abstract Introduction: The recombination of chromosomes 4 and 14 (t(4;14)) is a primary, predominantly clonal event in newly diagnosed multiple myeloma (ndMM) that is present in ~15% of patients. The translocation results in enhancer regions from the immunoglobulin heavy chain locus upregulating the expression of NSD2 and FGFR3 genes implicated in the disease biology of this subset of MM patients (Chesi et al. Blood. 1998, Keats et al, Leuk Lymph. 2006). The presence of t(4;14) translocation is a considered a biomarker of aggressive disease and is part of the Revised International Staging System (R-ISS) for clinical risk stratification. However, historically only ~40% of t(4;14) patients are high-risk based on the GEP70 gene expression signature. (Weinhold et al. Leukemia. 2016) Our previous analysis of a large cohort of ndMM patients described the genomic features of t(4;14) vs ndMM overall population demonstrating that only ~25% of t(4;14) patients died within 24 months of diagnosis and described biomarkers in this high-risk subset. This analysis identified both known and novel aberrations in ndMM, including some that were associated with high-risk t(4;14) (Ortiz et al Blood. 2019; 134 (Suppl_1):366). In this updated analysis, we provide a more robust analysis of the t(4;14) dataset and demonstrate the prognostic value of the NSD2 breakpoint location. Methods: We generated a large genomic dataset from t(4;14) ndMM patients with whole genome sequencing (WGS) and RNA-seq from a TOUL dataset (t(4;14) N=114) patients treated in routine practice), the IFM2009 trial (N=19), and the Myeloma Genome Project (MGP) (N=34) for discovery and validation. Gene expression, copy number aberration, single nucleotide variant and translocations were derived from RNAseq and WGS profiling of biopsies from patients aged less than 75 years who received transplant, and integrated with clinical information (including age, OS). Cytogenetic assessments from WGS were made by MANTA and used to identify translocation DNA breakpoint location. Results: In all datasets, three DNA breakpoint locations were identified, and based on their position with respect to the NSD2 gene named "no-disruption" (upstream of NSD2 gene), "early-disruption" (in the 5' UTR of NSD2 gene) and "late-disruption" (in the coding region of NSD2 gene). Using paired RNA-seq data, we identified IGH-NSD2 RNA fusion transcripts relative to the breakpoints that corresponded with previously described NSD2 isoforms. "No-disruption" and "early-disruption" breakpoints predominantly produced a fusion transcript (MB4-1) that retained the full coding sequence of the gene, while the "late-disruption" produced truncated fusion transcripts (MB4-2/3). We conducted survival analysis in our datasets based on both DNA breakpoint location and RNA fusion transcripts. This analysis demonstrated a significant difference in outcome between the patient samples with "no-disruption" and the "late-disruption" breakpoints that associated with good and poor OS, respectively (OS pval < 3e-4) in the discovery TOUL dataset. Patients with "late-disruption" had a median OS of 28.64 mo vs 59.18 mo for "early disruption" and 82.26 mo for those with "no disruption" (Figure). This association was replicated in an independent dataset (MGP N=33, replication pval<4.3e-5). The mOS difference of patients based on which fusion transcript they express is less than the difference based on breakpoint (mOS MB4-1 = 47.38 mo. vs. MB4-2/3 = 60.89 mo.). These analyses demonstrate that the breakpoint location has a stronger association with outcome than fusion transcript expression. Conclusion: From a large genomic dataset, we were able to discover and validate a clear association between the translocation breakpoints and survival outcome in t(4:14) ndMM patients. While prospective validation is needed before clinical application of our finding, molecular identification of high-risk t(4;14) patients using DNA breakpoint location may enable proper risk classification for this patient group at diagnosis, and would provide improved opportunities for risk-adjusted therapy and identification of a therapeutic target for this high-risk subpopulation. Ongoing work on mutations, copy number, and differential gene expression analyses between translocation breakpoint sub-groups and will be presented. Figure 1 Figure 1. Disclosures Stong: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Ortiz: Bristol Myers Squibb: Current Employment. Towfic: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Pierceall: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Flynt: Bristol Myers Squibb: Current Employment. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties.

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