Molecular Profiling of Extramedullary and Medullary Plasmacytomas.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1806-1806 ◽  
Author(s):  
Anuj Mahindra ◽  
Samir B Amin ◽  
Gabriela Motyckova ◽  
Aliyah R. Sohani ◽  
Kishan Patel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Plasma Cell ◽  
Research Funding ◽  
Plasma Cells ◽  
Expression Patterns ◽  
Tissue Microarrays ◽  
Molecular Characteristics ◽  
Fish Analysis ◽  
New Therapeutic Approaches ◽  
Risk Of Progression

Abstract Abstract 1806 Poster Board I-832 Plasmacytomas are rare clonal proliferations of plasma cells that though cytologically identical to plasma cell myeloma, present with osseous or extraosseous growth pattern. Understanding their molecular characteristics can provide crucial insights into their pathogenesis and risk of progression to multiple myeloma (MM). To investigate the differences between extramedullary (EMP) and medullary plasmacytomas (MP) and MM without plasmacytomas, we sought to molecularly profile these tumors by tissue microarrays, gene expression, microRNA, and FISH. We identified 85 patients from our data base with a pathological diagnosis of plasmacytoma. Of the 85 patients, 13 patients presented with EMP, and 72 had MP. Among the patients with EMP (n=13), 2 patients presented with multiple lesions. Three of 13 (23%) patients progressed to develop MM at a median of 12 months. 72 patients presented with MP, of which 21 had solitary lesions and 27 (37%) progressed to MM at a median of 20.5months. There was a male preponderance (67% vs 33%) and the median age at diagnosis was 60.5 years (range 27.7-87.6). The mean overall survival for patients with EMP was 121 months (95% confidence interval[CI] 97-144 months) and for patients with MP was 102 months (95% CI 93-128 months) { p=0.025} MicroRNA (miRNAs) profiling was performed on MP (n=19) and MM samples (n=66). Data was normalized using U6 endogenous control. Three hundred and one miRNAs out of a total 665 were significantly differentially expressed between MP vs MM samples. Gene expression profiling performed on MP will be correlated with the miRNA data to identify genes and transcripts of interest which will be functionally validated. Tissue microarrays were performed on 52 patients (8: EMP, 44: MP,) in whom paraffin-embedded tissue was available. Of samples analyzed, CD56 positivity was observed in 55% MP and 71% EMP samples (p=0.67). Additional staining for cyclin D1, Bcl 2 and FISH analysis will be reported. Differential expression patterns of factors involved in proliferation, survival, adhesion, and stroma-tumor cell interactions may help explain plasmacytoma biology and identify factors responsible for progression to MM. These insights may help identify new therapeutic approaches and targets in the treatment of these plasma cell disorders. Disclosures Hochberg: Enzon: Consultancy, Speakers Bureau; Biogen-Idec: Speakers Bureau; Genentech: Speakers Bureau; Amgen: Speakers Bureau. Anderson:Millennium: Research Funding. Raje:Celgene, Norvartis, Astrazeneca: Research Funding.

Molecular Profiling of Extramedullary and Medullary Plasmacytomas Compared to Multiple Myeloma

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 4042-4042
Author(s):  
Anuj Mahindra ◽  
Samir B. Amin ◽  
Aliyah R. Sohani ◽  
Gabriela Motyckova ◽  
Kishan Patel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Plasma Cell ◽  
Plasma Cells ◽  
Conflicts Of Interest ◽  
Expression Patterns ◽  
Metastatic Potential ◽  
Tissue Microarrays ◽  
Molecular Characteristics ◽  
New Therapeutic Approaches

Abstract Abstract 4042 Plasmacytomas are rare clonal proliferations of plasma cells that though cytologically identical to plasma cell myeloma, present with osseous or extraosseous growth pattern. Understanding their molecular characteristics can provide crucial insights into their pathogenesis and risk of progression to multiple myeloma (MM). To investigate the differences between extramedullary (EMP) and medullary plasmacytomas (MP) and MM without plasmacytomas, we sought to molecularly profile these tumors by tissue microarrays, gene expression, microRNA, and FISH. We identified 85 patients from our data base with a pathological diagnosis of plasmacytoma. Of the 85 patients, 13 patients presented with EMP, and 72 had MP. Among the patients with EMP (n=13), 2 patients presented with multiple lesions. Three of 13 (23%) patients progressed to develop MM at a median of 12 months. 72 patients presented with MP, of which 21 had solitary lesions and 27 (37%) progressed to MM at a median of 20.5months. There was a male preponderance (67% vs 33%) and the median age at diagnosis was 60.5 years (range 27.7–87.6). The mean overall survival for patients with EMP was 121 months (95% confidence interval[CI] 97–144 months) and for patients with MP was 102 months (95% CI 93–128 months) {p=0.025}. MicroRNA (miRNAs) profiling was performed on MP (n=19), EMP (n=7) and MM samples (n=66). Data was normalized using U6 endogenous control. Gene expression profiling was performed and correlated with the miRNA data to identify genes and transcripts of interest. miRNA 127, which regulates SET D8, was upregulated four fold in both MP and EMP compared to MM. miRNA 493, which regulates cadherin 11 and PTCH 1, both of which have been associated with metastatic potential in solid tumors, was similarly downregulated four fold in both MP and EMP compared to MM. A tissue microarray was created on 52 patients (8: EMP, 44: MP,) in whom paraffin-embedded tissue was available. Additional evaluation using SET 8, cadherin 11 antibodies and validation of additional functional targets is ongoing and will be reported. Differential expression patterns of factors involved in proliferation, survival, adhesion, and stroma-tumor cell interactions may help explain plasmacytoma biology and identify factors responsible for progression to MM. These insights may help identify new therapeutic approaches and targets in the treatment of these plasma cell disorders. Disclosures: No relevant conflicts of interest to declare.

scholarly journals AL Amyloidosis — Pathogenesis and Prognosis Are Determined By the Amyloidogenic Potential of the Light Chain and the Molecular Characteristics of Malignant Plasma Cells

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 187-187
Author(s):  
Anja Seckinger ◽  
Ute Hegenbart ◽  
Susanne Beck ◽  
Martina Emde ◽  
Tilmann Bochtler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Plasma Cell ◽  
Light Chain ◽  
Research Funding ◽  
Plasma Cells ◽  
Memory B Cells ◽  
Light Chains ◽  
Molecular Characteristics ◽  
Al Amyloidosis ◽  
Malignant Plasma Cell

Abstract INTRODUCTION. Systemic light chain amyloidosis (AL) is caused by accumulation of plasma cells producing misfolded monoclonal light chains depositing as amyloid fibrils in different organs, most frequently heart and kidney. AIM of our study is first assessing the molecular characteristics of malignant plasma cells from AL-patients in relation to those from MGUS, asymptomatic, and symptomatic myeloma: Are these plasma cells different, does this difference explain amyloidogenicity? Does AL correspond to a certain developmental stage during evolution of symptomatic myeloma? Secondly, to what extent is prognosis determined by amyloid-deposition (organotropism, amount, amyloidogenicity) vs. number and molecular characteristics of malignant plasma cells? PATIENTS & METHODS . Consecutive patients (n=3023) with AL (n=582), MGUS (n=306), asymptomatic (n=444, AMM), or previously untreated, therapy-requiring multiple myeloma (n=1691, MM) were included. CD138-purified plasma cell samples were subjected to iFISH (n=582/306/444/1691), 1297 to gene expression profiling using Affymetrix U133 2.0 plus arrays (n=196/64/272/765), 712 to RNA- (n=124/52/38/489), and 258 to whole exome sequencing (n=115/53/39/51). Samples of normal bone marrow plasma cells, memory B-cells, and polyclonal plasmablasts were used as comparators. The CoMMpass-cohort (n=647) was used as comparator for the mutational spectrum of myeloma. RESULTS . Prognosis. By AL-factors. Expectedly, organ involvement, i.e. heart only vs. kidney only vs. heart+kidney vs. other (overall survival (OS), P=.001), the amount of free light chains (dFLC ≥18 mg/dL, HR=2.56, P=.01), and the cardiac European Mayo IIIB score (I/II/IIIA/IIIB, median OS 110/55/16/3 months, HR=1/1.94/3.73/7.90, P<.001) strongly determine prognosis (Fig. 1A). By malignant plasma cell factors. High proliferation rate (HR=3.58, P=.001) and expression-based risk factors for MM (GEP70 high, HR=2.38, P=.005; Rs-score high HR=4.63, P<.001) identify patients with very adverse prognosis (Fig. 1A). Tumor load, e.g. plasma cell infiltration >10%/>30% (HR=1.31/1.81, P=.01, P=.002) and M-protein ≥ 30g/l (HR=3.01, P=.005), are likewise prognostic (Fig. 1A). In multivariate analysis, all tested AL-specific (European Mayo IIIB score) and malignant plasma cell factors (proliferation or GEP70 and plasma cell infiltration) are independent. Molecular characteristics.iFISH. As MM (96.2%) and AMM (92.8%) AL-patients (93.1%) carry at least one recurrent myeloma typical aberration. The mean number of progression-associated aberrations in AL (n=0.98) fits between MGUS (n=0.85) and AMM (n=1.45) with significant difference compared to AMM (P<.001) unlike to MGUS. Main differences in frequency are found for t(11;14) and hyperdiploidy with a comparable pattern of non-etiologic aberrations. Gene expression (GEP and RNA-seq). Aberrant plasma cells in AL amyloidosis show the least difference with AMM, followed by MGUS and MM. In principal component analysis, AL overlaps with AMM and MGUS, independent of presence or absence of heart involvement (Fig. 1B). Pairwise assessment of similarity using a multivariate generalization of the squared Pearson correlation coefficient shows closest similarity to AMM and MM followed by MGUS, with comparable differences to normal plasma cells, polyclonal plasmablasts, and memory B-cells. Significantly more AL-patients present with higher proliferation rate vs MGUS (P<.001) and AMM (P<.02). AL and MM differ significantly regarding distinct molecular entities as determined by GEP (e.g. TC-classification; Fig. 1C). Mutation spectrum in AL amyloidosis vs. MM. From the 20 most frequently synonymously mutated non-Ig transcripts (CoMMpass-cohort), 16 could likewise be detected in AL amyloidosis, i.e. KRAS, NRAS, IGLL5, DIS3, FAM46C, MUC16, BRAF, TRAF3, PCLO, RYR2, FATA4, CSMD3, TP53, DNAH5, RYR2A, and FLG. CCND1 mutations were significantly more frequent in AL and AMM compared to MM (P=.02). DISCUSSION & CONCLUSION. Pathogenesis and prognosis of AL amyloidosis are explained both by AL-specific and malignant plasma cell characteristics. Aberrant plasma cells in AL amyloidosis show the same aberration- and expression pattern and a "molecular age" between MGUS and AMM, most closely resembling the latter. AL amyloidosis is thus mostly a rather early plasma cell dyscrasia with an unstable and toxic immunoglobulin light chain. Disclosures Seckinger: Celgene: Research Funding; EngMab: Research Funding; Sanofi: Research Funding. Hose:Celgene: Honoraria, Research Funding; Sanofi: Research Funding; EngMab: Research Funding.

Gene Expression Analysis Of Plasmablastic Lymphoma Identifies Down Regulation Of B Cell Receptor Signaling and Additional Unique Transcriptional Programs

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3779-3779 ◽  
Author(s):  
Jennifer R. Chapman-Fredricks ◽  
Andrew J. Gentles ◽  
Daxing Zhu ◽  
Victoria Sujoy ◽  
Izidore S Lossos
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Plasma Cell ◽  
Plasma Cells ◽  
Expression Patterns ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Receptor Signaling ◽  
B Cell Receptor Signaling ◽  
Cell Receptor Signaling

Abstract Plasmablastic lymphoma (PBL) is currently recognized as a distinct sub-type of diffuse large B-cell lymphoma (DLBCL), but remains a poorly characterized B-cell malignancy. We conducted gene expression profiling of 15 PBL, 10 DLBCL, and 5 EOP (extraosseous isolated plasmacytoma). 864 genes were significantly over- or under-expressed (at<1% false discovery rate) uniquely in one of these diseases relative to the other two. Of these, 102 were highly expressed in PBL relative to DLBCL and EOP, while 166 showed low expression in PBL. This set of 268 genes defined a distinct transcriptional program operating in PBL. Among these were surface markers such as CD320, CD300A, and IL6 receptor, as well as the cytokine Oncostatin M, which was highly expressed in PBL but almost never expressed in DLBCL or EOP. CD320 plays a role in generation and proliferation of plasma cells in the germinal center in response to IL-10 stimulation, while the immunoglobulin superfamily member CD300 is variably expressed across the hematopoietic hierarchy. The apoptosis-inducer BAX (Bcl-2 associated X protein) was highly expressed in PBL, similar to reports in plasma cell neoplasms. In addition the CpG methyltransferase gene Dnmt3b showed high expression levels in PBL. By comparing malignancy-specific gene expression patterns to known biological pathways, we found that expression of components of the B-cell receptor signaling pathway (Cd79a, Cd79b, Blk, Lyn, Syk, Ptprc, Csk, Pik3cd, Swap70, and Rel) were repressed by 2-fold or more on average in PBL relative to DLBCL. We observed a similar pattern in EOP relative to DLBCL. In contrast, mitochondrial genes were more highly expressed in PBL than in DLBCL. Analysis against a large compendium of sets of transcription factor targets from motif and ChIP analyses identified that targets of MYB, a major transcriptional regulator of hematopoietic differentiation, were up-regulated in PBL; whereas targets of NFKB1 were repressed relative to DLBCL. Both PBL and EOP highly expressed genes that have previously been described as up-regulated in plasmacytomas. To further investigate the potential cell of origin of these malignancies, we compared genes expressed in PBL, DLBCL, and EOP to genes that are highly expressed in specific sub-types of B-cells. Genes highly expressed in plasma cells relative to other types of B-cells were highly expressed in PBL and EOP compared to DLBCL. Notably, this included the transcription factor XBP1 (X-box binding protein 1), which is a critical regulator of plasma cell differentiation. The plasma cell marker CD138 (syndecan-1, encoded by the Sdc1 gene) was also over-expressed in the PBL and EOP samples. We have validated the array data by reanalyzing expression of four candidate genes (Lyn, Syk, SPIB, and Swap70) by real time PCR. These four genes were highly expressed in DLBCL, but their expression was low in both in PBL and EOP. The observed overexpression of Swap70 in DLBCL as compared to PBL and EOP was subsequently validated by immunohistochemistry (IHC). Immunostaining for Swap70 was performed in all 30 cases used for array analysis and was negative in all cases of PBL (0/15 positive) and EOP (0/5 positive) but was diffusely positive in all but one of the DLBCLs (9/10 positive). Swap70 analysis by IHC was subsequently performed in 7 additional cases of DLBCL and was diffusely positive in all of these cases (7/7), thus suggesting that immunohistochemical analysis for Swap70 may be useful in differentiating PBL and EOP from DLBCL. Overall our results provide insight into the unique transcriptional programs distinguishing PBL from morphologic and clinical mimics DLBCL and EOP, as well as identify similarities between them. Most notably, we observed that B-cell receptor signaling pathway genes are significantly down-regulated in PBL and EOP compared to DLBCL. These findings corroborate the downregulation of surface immunoglobulin expression as seen in PBL and EOP and suggest a biologic similarity between these two neoplasms. Among normal B-cell sub-populations, PBL and EOP were most similar in their expression patterns to plasma cells and plasmablasts in that they expressed several well-known plasma cell markers. These findings additionally identify novel candidate genes that provide opportunities for further phenotypic and functional characterization of these neoplasms. Disclosures: No relevant conflicts of interest to declare.

A Novel Role For Histone Deacetylase 11 (HDAC11) In Plasma Cell Differentation and Survival

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1907-1907
Author(s):  
Eva Sahakian ◽  
Jason B. Brayer ◽  
John Powers ◽  
Mark Meads ◽  
Allison Distler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
B Cells ◽  
Plasma Cell ◽  
Research Funding ◽  
Plasma Cells ◽  
Myeloma Cells ◽  
Peripheral Blood Plasma

Abstract The role of HDACs in cellular biology, initially limited to their effects upon histones, is now appreciated to encompass more complex regulatory functions that are dependent on their tissue expression, cellular compartment distribution, and the stage of cellular differentiation. Recently, our group has demonstrated that the newest member of the HDAC family of enzymes, HDAC11, is an important regulator of IL-10 gene expression in myeloid cells (Villagra A Nat Immunol. 2009). The role of this specific HDAC in B-cell development and differentiation is however unknown. To answer this question, we have utilized a HDAC11 promoter-driven eGFP reporter transgenic mice (TgHDAC11-eGFP) which allows the monitoring of the dynamic changes in HDAC11 gene expression/promoter activity in B-cells at different maturation stages (Heinz, N Nat. Rev. Neuroscience 2001). First, common lymphoid progenitors are devoid of HDAC11 transcriptional activation as indicated by eGFP expression. In the bone marrow, expression of eGFP moderately increases in Pro-B-cells and transitions to the Pre- and Immature B-cells respectively. Expression of eGFP doubles in the B-1 stage of differentiation in the periphery. Of note, examination of both the bone marrow and peripheral blood plasma cell compartment demonstrated increased expression of eGFP/HDAC11 mRNA at the steady-state. These results were confirmed in plasma cells isolated from normal human subjects in which HDAC11 mRNA expression was demonstrated. Strikingly, analysis of primary human multiple myeloma cells demonstrated a significantly higher HDAC11 mRNA expression in malignant cells as compared to normal plasma cells. Similar results were observed in 4/5 myeloma cell lines suggesting that perhaps HDAC11 expression might provide survival advantage to malignant plasma cells. Support to this hypothesis was further provided by studies in HDAC11KO mice in which we observed a 50% decrease in plasma cells in both the bone marrow and peripheral blood plasma cell compartments relative to wild-type mice. Taken together, we have unveiled a previously unknown role for HDAC11 in plasma cell differentiation and survival. The additional demonstration that HDAC11 is overexpressed in primary human myeloma cells provide the framework for specifically targeting this HDAC in multiple myeloma. Disclosures: Alsina: Millennium: Membership on an entity’s Board of Directors or advisory committees, Research Funding. Baz:Celgene Corporation: Research Funding; Millenium: Research Funding; Bristol Myers Squibb: Research Funding; Novartis: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding.

scholarly journals Single Cell Multi-Omic Profiling of Multiple Myeloma with t(4;14) Finds an Immune Microenvironment Gene Signature That Correlates with Clinical Outcomes

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2653-2653
Author(s):  
Sanjay De Mel ◽  
Jonathan Adam Scolnick ◽  
Chern Han Yong ◽  
Xiaojing Huo ◽  
Stacy Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Protein Expression ◽  
Single Cell ◽  
Research Funding ◽  
Plasma Cells ◽  
Expression Patterns ◽  
Gene Signature ◽  
Response To Therapy ◽  
Diagnostic Samples

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 (&lt;10% of cells expressing FGFR3) or high expression (&gt;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.

Molecular Profiling of Extramedullary and Medullary Plasmacytomas

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 5111-5111
Author(s):  
Gabriela Motyckova ◽  
Aliyah Rahemtullah ◽  
Nileshwari Vaghela ◽  
Sonia Vallet ◽  
Adam Ackerman ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Plasma Cells ◽  
Radiation Treatment ◽  
Expression Patterns ◽  
Tissue Microarrays ◽  
Ki 67 ◽  
Growth And Survival ◽  
New Therapeutic Approaches ◽  
Fresh Frozen ◽  
Extramedullary Plasmacytomas

Abstract Plasmacytomas are rare plasma cells disorders and are potentially cured with local approaches such as radiation treatment. Extramedullary plasmacytomas usually progress slowly, can recur locally, and develop into multiple myeloma (MM) less frequently (8–36%) compared to solitary medullary plasmacytomas (60%). Biologically these cells are independent of bone marrow microenvironment for their growth and survival and the understanding of the differences in their underlying molecular characteristic can provide crucial insights into the pathogenesis of progression to MM. To investigate the differences between extramedullary and medullary plasmacytomas, we sought to molecularly profile these tumors by tissue microarrays, gene expression, microRNA, and FISH. We identified 78 patients from our data base with a pathological diagnosis of plasmacytoma. Of the 78 patients, 11 patients presented with extramedullary plasmacytomas, and 50 had medullary plasmacytomas. Seventeen patients who had been previously identified as having plasmacytomas were later confirmed to have MM. Among the patients with extramedullary plasmacytomas (n=11), 1 patient presented with multiple lesions. Three of 11 (27%) patients progressed to develop MM at a median of 12 months. Fifty patients presented with medullary plasmacytomas, of which 23 had solitary lesions and 27 (54%) progressed to MM at a median of 20.5 months. There was a male preponderance (76% vs 34%) and the median age at diagnosis was 59.6 years (range 30.4–84.3). Tissue microarrays were performed on 52 patients (7: extramedullary, 29: medullary, 16: MM) in whom paraffin-embedded tissue was available. Samples were stained for CD138, CD27, CD56, cyclin D1, Bcl-2, Ki-67 and light chain restriction by immunohistochemistry and/or in situ hybridization. Both paraffin and fresh frozen tissue was available for 12 medullary, 3 extramedullary, and 2 MM samples at diagnosis for comparison. FISH for del 13q, t(4;14), t(11;14), and t(14;16) was performed on these samples. Gene and microRNA profiling was also performed. The differential profile of gene and microRNA expression between the two subtypes of plasmacytomas and MM will be presented. Differential expression patterns of factors involved in proliferation, survival, adhesion, and stroma-cancer cell interactions may help explain plasmacytoma biology and identify factors responsible for progression to multiple myeloma. These insights may help identify new therapeutic approaches and targets in the treatment of plasma cell disorders.

scholarly journals Molecular Characteristics of Streptococcus pyogenes Isolated From Chinese Children With Different Diseases

2021 ◽  
Vol 12 ◽  
Author(s):  
Dingle Yu ◽  
Yunmei Liang ◽  
Qinghua Lu ◽  
Qing Meng ◽  
Wenjian Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Antibiotic Resistance ◽  
Streptococcus Pyogenes ◽  
Wide Spectrum ◽  
Expression Patterns ◽  
Virulence Gene ◽  
High Rate ◽  
Molecular Characteristics ◽  
Virulence Gene Expression ◽  
Obstructive Sleep

Streptococcus pyogenes is a bacterial pathogen that causes a wide spectrum of clinical diseases exclusively in humans. The distribution of emm type, antibiotic resistance and virulence gene expression for S. pyogenes varies temporally and geographically, resulting in distinct disease spectra. In this study, we analyzed antibiotic resistance and resistance gene expression patterns among S. pyogenes isolates from pediatric patients in China and investigated the relationship between virulence gene expression, emm type, and disease categories. Forty-two representative emm1.0 and emm12.0 strains (n = 20 and n = 22, respectively) isolated from patients with scarlet fever or obstructive sleep apnea-hypopnea syndrome were subjected to whole-genome sequencing and phylogenetic analysis. These strains were further analyzed for susceptibility to vancomycin. We found a high rate and degree of resistance to macrolides and tetracycline in these strains, which mainly expressed ermB and tetM. The disease category correlated with emm type but not superantigens. The distribution of vanuG and virulence genes were associated with emm type. Previously reported important prophages, such as φHKU16.vir, φHKU488.vir, Φ5005.1, Φ5005.2, and Φ5005.3 encoding streptococcal toxin, and integrative conjugative elements (ICEs) such as ICE-emm12 and ICE-HKU397 encoding macrolide and tetracycline resistance were found present amongst emm1 or emm12 clones from Shenzhen, China.

The Human Plasma Cell Gene Expression Program.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 3242-3242
Author(s):  
John De Vos ◽  
Dirk Hose ◽  
Thierry Reme ◽  
Hartmut Goldschmidt ◽  
Jean-Francois Rossi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Differentiation ◽  
Plasma Cell ◽  
Expression Profile ◽  
Plasma Cells ◽  
Plasma Cell Differentiation ◽  
Unfolded Protein ◽  
The Mean ◽  
Protein Response

Abstract Seven purified peripheral blood memory B-cells (BM), seven in-vitro-generated polyclonal plasmablastic cells (PPC) and seven purified bone marrow mature plasma cells (BMPC) were studied by oligonucleotide microarrays. All samples were obtained from healthy volunteers. The gene expression profiling of these samples was determined with Affymetrix pan genomic U133A + B arrays (44 928 oligonucleotide probesets). We determined that 2313 genes were differentially expressed between these three B cell categories (P 〈 0.01 by a Kruskal-Wallis test and a ratio between two categories 〉 3). These 2313 genes were classified into six categories, according to the expression profile: early plasma cell genes (EPC), late plasma cell genes (LPC), genes lost early during plasma cell differentiation (LEPC), genes lost late during plasma cell differentiation (LLPC), genes upregulated only in plasmablasts (PBO) and genes lost only in plasmablasts (LPBO). As expected, Ig transcripts where essentially classified as EPC. As a corollary, genes involved in protein synthesis or degradation, transmembrane transporters and metabolism genes were overrepresented in EPC genes. Interestingly, genes involved in intercellular communication and extracellular matrix were enriched in LPC, highlighting the fact that mature plasma cells develop tight interactions with the bone marrow environment. Of note, genes involved in cell cycle are upregulated mainly in plasmablasts, whereas antiapoptotic genes are lost in plasmablasts only. Mains genes known to be involved in plasma cell differentiation display an expression profile in agreement with published data, as illustrated for transcription factors in Figure 1, validating this DNA microarray dataset. However most of these 2313 genes have either never been described yet or have no yet been linked to plasma cell differentiation. The description of those genes among our genome whose expression vary most during plasma cell differentiation will be an essential step in understanding the biology of a cell type essential to immune defenses and involved in deadly diseases. Figure 1: Transcription factors involved in plasma cell differentiation. Color indicates the expression profile category. For each gene is given the ratio of the mean expression value in plasma cell samples (PPC and BMPC) to the mean expression value in BM. UPR: Unfolded Protein Response. Figure 1:. Transcription factors involved in plasma cell differentiation. Color indicates the expression profile category. For each gene is given the ratio of the mean expression value in plasma cell samples (PPC and BMPC) to the mean expression value in BM. UPR: Unfolded Protein Response.

Gene Expression Distinguishes Burkitt Lymphoma from Other Aggressive Lymphomas and Identifies Patients Who Are Highly Curable with Intensive Chemotherapeutic Regimens.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 415-415 ◽  
Author(s):  
Sandeep S. Dave ◽  
K. Fu ◽  
G. Wright ◽  
Lloyd Lam ◽  
T. C. Greiner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Burkitt Lymphoma ◽  
Target Genes ◽  
Molecular Subtype ◽  
Performance Status ◽  
B Cell Lymphoma ◽  
Aggressive Lymphoma ◽  
Molecular Characteristics ◽  
Fish Analysis ◽  
Stem Cell Rescue

Abstract Background Burkitt lymphoma(BL) is a potentially curable, aggressive lymphoma. The distinction between BL and diffuse large B-cell lymphoma (DLBCL) is important because they differ significantly in clinical management. The distinction can be difficult because DLBCL can resemble BL in morphology, immunophenotype and cytogenetics. We investigated whether gene expression profiling (GEP) could create a molecular definition of BL that can reliably distinguish it from DLBCL. Methods Biopsy samples were collected from 312 patients with a diagnosis of sporadic BL or Burkitt-like lymphoma, or DLBCL. All cases were reviewed by a panel of expert hematopathologists. GEP of all the samples was carried out using a specialized oligonucleotide microarray. We constructed a predictor using 197 genes to distinguish BL from each molecular subtype of DLBCL. Leave-one-out cross-validation was used to evaluate the predictor’s performance. Chemotherapy treatments were grouped into either CHOP-like(CHOP, CNOP) or intensive(BFM, CODOX-M IVAC, regimens requiring stem cell rescue). Results After pathology review, the samples were reclassified as:classic BL(25 cases), atypical BL(19), DLBCL(261), and unclassifiable lymphoma(7). All classic BL and 18/19 cases of atypical BL shared a profile that was strikingly different from that of all the molecular subtypes of DLBCL, including those DLBCL cases that have a c-myc translocation. C-myc and its target genes, and genes related to germinal center differentiation were expressed at high levels in BL. NF-kB and its target genes and MHC class-I genes were expressed at very low levels in BL. Interestingly, 10 cases that were DLBCL by pathology were classified as BL by the predictor. The diagnosis of BL was supported by FISH analysis indicating a c-myc translocation. Among adults identified as having BL by the predictor (with full clinical data in N=15), overall survival was markedly superior for those receiving intensive regimens compared to CHOP-like regimens(Fig 1). The groups were similar with regard to age, stage, performance status and sites of involvement. Conclusion This study demonstrates that the molecular characteristics of BL can be used to accurately distinguish it from DLBCL. Importantly, a subgroup of BL was identified by the predictor that could not be diagnosed as BL by conventional criteria. The ability of the predictor to identify patients who benefit from aggressive therapies suggests that it will be useful in the diagnosis and management of patients with Burkitt lymphoma. Figure Figure

Evaluation of NCAM and DKK1 Expression in Multiple Myeloma and MGUS by Gene and Tissue Microarrays: Expression Accompanies Progression.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3399-3399
Author(s):  
Sushil Gupta ◽  
Yongsheng Huang ◽  
James Stewart ◽  
Fenghuang Zhan ◽  
Bart Barlogie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Plasma Cells ◽  
Molecular Classification ◽  
Tissue Microarrays ◽  
Bone Lesions ◽  
Bone Marrow Biopsies ◽  
Dkk1 Expression ◽  
Significant Difference ◽  
Expression Microarrays

Abstract Introduction: Expression NCAM1, a cell adhesion molecule involved in neuron-neuron adhesion, is also expressed by multiple myeloma (MM PC). Osteolytic bone lesions are a hallmark of MM and elevated expression of NCAM in MM PC has been correlated with this process (Ely and Knowles, 2003). We have previously reported that MM PC express DKK1 and MM blocks osteoblast differentiation in a DKK1-specific manner, suggesting that secretion of the Wnt signaling inhibitor plays a role in MM bone disease. Herein we used gene expression microarrays and tissue microarrays (TMAs) to investigate the simultaneous expression of DKK1 and NCAM in MM and MGUS. Methods: The study population consisted of 198 newly diagnosed MM and 44 MGUS. RNA from CD138-selected plasma cells was hybridized to Affymetrix U133Plus microarrays and data processed with Affymetrix Microarray Suite GCOS1.1 software. TMAs were constructed from formalin-fixed, paraffin embedded bone marrow biopsies. Serial sections of TMA were immunostained for CD138, NCAM and DKK1. TMAs were scanned using ScanScope using 20x lens, assessed using TMA lab software (Aperio Technologies) and scored as an average number of cells in the context of CD138 staining. Results: When put in context of a recently described molecular classification (Zhan et al., 2006), NCAM and DKK1 were found significantly co-over-expressed (DKK1+/NCAM+) in HYPERDIPLOID MM (P < 0.01); NCAM+/DKK1− was typical for MMSET-spike MM (P<0.001); NCAM−/DKK1+ DKK1 was characteristic of MM in MAF-spike disease (P < 0.001), CCND1 spikes with co-expression of CD20 (P<0.01), and the so-called MYELOID subgroup (P<0.001). In contrast, virtually all cases of MGUS were DKK1−/NCAM-. On TMAs, DKK1 expression varied in CD138-positive cells in MM, and in osteocytes and megakaryocytes in MM and MGUS, but was clearly negative in osteoblasts/lining cells. NCAM expression, was also variably expressed in PC of MM cases and in virtually 100% of osteoblasts/lining cells in both MM and MGUS. Osteocytes were distinctly negative for NCAM in both diseases. Of 195 analyzable MM biopsies, PC were DKK1+ in 1% to > 90% of PC in 191 (98%); of these, 94 were also NCAM+ in 5% to > 90% of PC; 1 case was positive for NCAM+/DKK− and 3 were NCAM−/DKK−. There was no significant difference in clinical parameters and survival in a comparison of NCAM+/DKK+ and NCAM−/DKK+ groups. DKK1 gene expression was higher in DKK1+/NCAM+ than in DKK1+/NCAM− MM (P=0.006); NCAM gene expression was higher in DKK1+/NCAM+ than in DKK+/NCAM− MM (P<0.0001) and NCAM was the number one SAM-defined gene over-expressed in DKK1+/NCAM+ relative to DKK1+/NCAM− disease. Consistent with GEP data, DKK1+/NCAM+ MM was over-represented in HYPERDIPLOID MM (32% v. 10%; P<0.001), while DKK1+/NCAM− disease was overrepresented in MAF-spike (15% v. 2%; P=0.003) and MYELOID subgroups (32% v. 18%; P=0.03). DKK1+/NCAM+ MM was completely absent in CCND1-spike /CD20-negative disease, with only 4% of DKK1+/NCAM− cases in this subgroup. No difference was observed for the other subtypes. Conclusion: DKK1 is expressed in all cases of MM with half also expressing NCAM. Neither gene is expressed in MGUS PC. These data suggests that expression of these genes in plasma cells accompanies disease progression.

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