Kim Steve Bergkvist

Diffuse Large B-Cell Lymphoma Classification System That Associates Normal B-Cell Subset Phenotypes With Prognosis

PURPOSE Current diagnostic tests for diffuse large B-cell lymphoma use the updated WHO criteria based on biologic, morphologic, and clinical heterogeneity. We propose a refined classification system based on subset-specific B-cell-associated gene signatures (BAGS) in the normal B-cell hierarchy, hypothesizing that it can provide new biologic insight and diagnostic and prognostic value. PATIENTS AND METHODS We combined fluorescence-activated cell sorting, gene expression profiling, and statistical modeling to generate BAGS for naive, centrocyte, centroblast, memory, and plasmablast B cells from normal human tonsils. The impact of BAGS-assigned subtyping was analyzed using five clinical cohorts (treated with cyclophosphamide, doxorubicin, vincristine, and prednisone [CHOP], n = 270; treated with rituximab plus CHOP [R-CHOP], n = 869) gathered across geographic regions, time eras, and sampling methods. The analysis estimated subtype frequencies and drug-specific resistance and included a prognostic meta-analysis of patients treated with first-line R-CHOP therapy. RESULTS Similar BAGS subtype frequencies were assigned across 1,139 samples from five different cohorts. Among R-CHOP-treated patients, BAGS assignment was significantly associated with overall survival and progression-free survival within the germinal center B-cell-like subclass; the centrocyte subtype had a superior prognosis compared with the centroblast subtype. In agreement with the observed therapeutic outcome, centrocyte subtypes were estimated as being less resistant than the centroblast subtype to doxorubicin and vincristine. The centroblast subtype had a complex genotype, whereas the centrocyte subtype had high TP53 mutation and insertion/deletion frequencies and expressed LMO2, CD58, and stromal-1-signature and major histocompatibility complex class II-signature genes, which are known to have a positive impact on prognosis. CONCLUSION Further development of a diagnostic platform using BAGS-assigned subtypes may allow pathogenetic studies to improve disease management.

Stable Phenotype Of B‐Cell Subsets Following Cryopreservation and Thawing of Normal Human Lymphocytes Stored in a Tissue Biobank

Cryopreservation is an acknowledged procedure to store vital cells for future biomarker analyses. Few studies, however, have analyzed the impact of the cryopreservation on phenotyping.

Cell of origin associated classification of B-cell malignancies by gene signatures of the normal B-cell hierarchy

Abstract Recent findings have suggested biological classification of B-cell malignancies as exemplified by the “activated B-cell-like” (ABC), the “germinal-center B-cell-like” (GCB) and primary mediastinal B-cell lymphoma (PMBL) subtypes of diffuse large B-cell lymphoma and “recurrent translocation and cyclin D” (TC) classification of multiple myeloma. Biological classification of B-cell derived cancers may be refined by a direct and systematic strategy where identification and characterization of normal B-cell differentiation subsets are used to define the cancer cell of origin phenotype. Here we propose a strategy combining multiparametric flow cytometry, global gene expression profiling and biostatistical modeling to generate B-cell subset specific gene signatures from sorted normal human immature, naive, germinal centrocytes and centroblasts, post-germinal memory B-cells, plasmablasts and plasma cells from available lymphoid tissues including lymph nodes, tonsils, thymus, peripheral blood and bone marrow. This strategy will provide an accurate image of the stage of differentiation, which prospectively can be used to classify any B-cell malignancy and eventually purify tumor cells. This report briefly describes the current models of the normal B-cell subset differentiation in multiple tissues and the pathogenesis of malignancies originating from the normal germinal B-cell hierarchy.

Inherited Inflammatory Response Genes Are Associated with B-Cell Non-Hodgkin’s Lymphoma Risk and Survival

Background Malignant B-cell clones are affected by both acquired genetic alterations and by inherited genetic variations changing the inflammatory tumour microenvironment. Methods We investigated 50 inflammatory response gene polymorphisms in 355 B-cell non-Hodgkin’s lymphoma (B-NHL) samples encompassing 216 diffuse large B cell lymphoma (DLBCL) and 139 follicular lymphoma (FL) and 307 controls. The effect of single genes and haplotypes were investigated and gene-expression analysis was applied for selected genes. Since interaction between risk genes can have a large impact on phenotype, two-way gene-gene interaction analysis was included. Results We found inherited SNPs in genes critical for inflammatory pathways; TLR9, IL4, TAP2, IL2RA, FCGR2A, TNFA, IL10RB, GALNT12, IL12A and IL1B were significantly associated with disease risk and SELE, IL1RN, TNFA, TAP2, MBL2, IL5, CX3CR1, CHI3L1 and IL12A were, associated with overall survival (OS) in specific diagnostic entities of B-NHL. We discovered noteworthy interactions between DLBCL risk alleles on IL10 and IL4RA and FL risk alleles on IL4RA and IL4. In relation to OS, a highly significant interaction was observed in DLBCL for IL4RA (rs1805010) * IL10 (rs1800890) (HR = 0.11 (0.02–0.50)). Finally, we explored the expression of risk genes from the gene-gene interaction analysis in normal B-cell subtypes showing a different expression of IL4RA, IL10, IL10RB genes supporting a pathogenetic effect of these interactions in the germinal center. Conclusions The present findings support the importance of inflammatory genes in B-cell lymphomas. We found association between polymorphic sites in inflammatory response genes and risk as well as outcome in B-NHL and suggest an effect of gene-gene interactions during the stepwise oncogenesis.

Interactions between SNPs affecting inflammatory response genes are associated with multiple myeloma disease risk and survival

Abstract The origin of multiple myeloma depends on interactions with stromal cells in the course of normal B-cell differentiation and evolution of immunity. The concept of the present study is that genes involved in MM pathogenesis, such as immune response genes, can be identified by screening for single-nucleotide polymorphisms (SNPs) involved in the immune response and a subsequent statistical analysis that focusses on the association of SNPs, certain haplotypes or SNP–SNP interactions with MM risk and prognosis. We genotyped 348 Danish patients and 355 controls for 13 SNPs located in the TNFA, IL-4, IL-6, IL-10 and CHI3L1 gene promoters. The occurrence of single polymorphisms, haplotypes and SNP–SNP interactions were statistically analyzed for association with disease risk and outcome following high-dose therapy. Identified genes that carried SNPs or haplotypes that were identified as risk or prognostic factors were studied for expression in normal B-cell subsets and myeloma plasma cells. We observed a significantly reduced risk when harboring the TNFA-238A allele (OR = 0.51 (0.29–0.86)) and interactions between the TNFA-1031T/C * and IL-10 -3575T/A (p = .007) as well as the TNFA-308G/A * and IL-10-1082G/A (p = .008) allels. By statistical approaches, we observed association between prognosis and the TNFA-857CC genotype (HR = 2.80 (1.29–6.10)) and IL-10-1082GG + GA genotypes (HR = 1.93 (1.07–3.49)) and interactions between IL-6-174G/C and IL-10-3575T/A (p = .001) and between TNFA-308G/A and IL-4-1098T/G (p= .005). The ‘risk genes’ were analyzed for expression in normal B-cell subsets (N = 6) from seven healthy donors and we found TNFA and IL-6 expressed both in naïve and in memory B cells when compared to preBI, II, immature and plasma cells. The ‘prognosis genes’ CHI3L1, IL-6 and IL-10 were differential expressed in malignant plasma cells when comparing poor and good prognosis groups based on to the TC classification. In summary, these findings suggest that TNFA, IL-4, IL-6, IL-10 and CHI3L1 might be important players in MM pathogenesis during disease initiation and drug resistance in multiple myeloma.

A model system for assessing and comparing the ability of exon microarray and tag sequencing to detect genes specific for malignant B-cells.

BackgroundMalignant cells in tumours of B-cell origin account for 0.1% to 98% of the total cell content, depending on disease entity. Recently, gene expression profiles (GEPs) of B-cell lymphomas based on microarray technologies have contributed significantly to improved sub-classification and diagnostics. However, the varying degrees of malignant B-cell frequencies in analysed samples influence the interpretation of the GEPs. Based on emerging next-generation sequencing technologies (NGS) like tag sequencing (tag-seq) for GEP, it is expected that the detection of mRNA transcripts from malignant B-cells can be supplemented. This study provides a quantitative assessment and comparison of the ability of microarrays and tag-seq to detect mRNA transcripts from malignant B-cells. A model system was established by eight serial dilutions of the malignant B-cell lymphoma cell line, OCI-Ly8, into the embryonic kidney cell line, HEK293, prior to parallel analysis by exon microarrays and tag-seq.ResultsWe identified 123 and 117 differentially expressed genes between pure OCI-Ly8 and HEK293 cells by exon microarray and tag-seq, respectively. There were thirty genes in common, and of those, most were B-cell specific. Hierarchical clustering from all dilutions based on the differentially expressed genes showed that neither technology could distinguish between samples with less than 1% malignant B-cells from non-B-cells. A novel statistical concept was developed to assess the ability to detect single genes for both technologies, and used to demonstrate an inverse proportional relationship with the sample purity. Of the 30 common genes, the detection capability of a representative set of three B-cell specific genes - CD74, HLA-DRA, and BCL6 - was analysed. It was noticed that at least 5%, 13% and 22% sample purity respectively was required for detection of the three genes by exon microarray whereas at least 2%, 4% and 51% percent sample purity of malignant B-cells were required for tag-seq detection.ConclusionA sample purity-dependent loss of the ability to detect genes for both technologies was demonstrated. Taq-seq, in comparison to exon microarray, required slightly less malignant B-cells in the samples analysed in order to detect the two most abundantly expressed of the selected genes. The results show that malignant cell frequency is an important variable, with fundamental impact when interpreting GEPs from both technologies.