A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications.
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ABSTRACT:
SUBMITTER: Wright GW
PROVIDER: S-EPMC8459709 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
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A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications.
Wright George W GW Huang Da Wei DW Phelan James D JD Coulibaly Zana A ZA Roulland Sandrine S Young Ryan M RM Wang James Q JQ Schmitz Roland R Morin Ryan D RD Tang Jeffrey J Jiang Aixiang A Bagaev Aleksander A Plotnikova Olga O Kotlov Nikita N Johnson Calvin A CA Wilson Wyndham H WH Scott David W DW Staudt Louis M LM
Cancer cell 20200401 4
The development of precision medicine approaches for diffuse large B cell lymphoma (DLBCL) is confounded by its pronounced genetic, phenotypic, and clinical heterogeneity. Recent multiplatform genomic studies revealed the existence of genetic subtypes of DLBCL using clustering methodologies. Here, we describe an algorithm that determines the probability that a patient's lymphoma belongs to one of seven genetic subtypes based on its genetic features. This classification reveals genetic similariti ...[more]
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Project description:Diffuse large B-cell lymphoma (DLBCL) comprises molecularly distinct subgroups such as activated B-cell-like (ABC) and germinal center B-cell-like (GCB) DLBCLs. We previously reported that CD5(+) and CD5(-)CD10(+) DLBCL constitute clinically relevant subgroups. To determine whether these 2 subgroups are related to ABC and GCB DLBCLs, we analyzed the genomic imbalance of 99 cases (36 CD5(+), 19 CD5(-)CD10(+), and 44 CD5(-)CD10(-)) using array-based comparative genomic hybridization (CGH). Forty-six of these cases (22 CD5(+), 9 CD5(-)CD10(+), 1 CD5 (-), and 14 CD5(-)CD10(-)) were subsequently subjected to gene-expression profiling, resulting in their division into 28 ABC (19 CD5(+) and 9 CD5(-)CD10(-)) and 18 GCB (3 CD5(+), 7 CD5(-)CD10(+), and 8 CD5(-)CD10(-)) types. A comparison of genome profiles of distinct subgroups of DLBCL demonstrated that (1) ABC DLBCL is characterized by gain of 3q, 18q, and 19q and loss of 6q and 9p21, and GCB DLBCL is characterized by gain of 1q, 2p, 7q, and 12q; (2) the genomic imbalances characteristic of the CD5(+) and CD5(-)CD10(+) groups were similar to those of the ABC and GCB types, respectively. These findings suggest that CD5(+) and CD5(-)CD10(+) subgroups are included, respectively, in the ABC and GCB types. Finally, when searching for genomic imbalances that affect patients' prognosis, we found that 9p21 loss (p16(INK4a) locus) marks the most aggressive type of DLBCL.
2009-07-10 | GSE16920 | GEO
Project description:Diffuse large B-cell lymphoma (DLBCL) comprises molecularly distinct subgroups such as activated B-cell-like (ABC) and germinal center B-cell-like (GCB) DLBCLs. We previously reported that CD5(+) and CD5(-)CD10(+) DLBCL constitute clinically relevant subgroups. To determine whether these 2 subgroups are related to ABC and GCB DLBCLs, we analyzed the genomic imbalance of 99 cases (36 CD5(+), 19 CD5(-)CD10(+), and 44 CD5(-)CD10(-)) using array-based comparative genomic hybridization (CGH). Forty-six of these cases (22 CD5(+), 9 CD5(-)CD10(+), 1 CD5 (-), and 14 CD5(-)CD10(-)) were subsequently subjected to gene-expression profiling, resulting in their division into 28 ABC (19 CD5(+) and 9 CD5(-)CD10(-)) and 18 GCB (3 CD5(+), 7 CD5(-)CD10(+), and 8 CD5(-)CD10(-)) types. A comparison of genome profiles of distinct subgroups of DLBCL demonstrated that (1) ABC DLBCL is characterized by gain of 3q, 18q, and 19q and loss of 6q and 9p21, and GCB DLBCL is characterized by gain of 1q, 2p, 7q, and 12q; (2) the genomic imbalances characteristic of the CD5(+) and CD5(-)CD10(+) groups were similar to those of the ABC and GCB types, respectively. These findings suggest that CD5(+) and CD5(-)CD10(+) subgroups are included, respectively, in the ABC and GCB types. Finally, when searching for genomic imbalances that affect patients' prognosis, we found that 9p21 loss (p16(INK4a) locus) marks the most aggressive type of DLBCL. Expression profiles in 46 patients with DLBCL and 8 for normal lymph node biopsy samples.
2009-07-10 | E-GEOD-16920 | biostudies-arrayexpress
Project description:Gene-expression profiling has been used to define 3 molecular subtypes of diffuse large B-cell lymphoma (DLBCL), termed germinal center B-cell-like (GCB) DLBCL, activated B-cell-like (ABC) DLBCL, and primary mediastinal B-cell lymphoma (PMBL). To investigate whether these DLBCL subtypes arise by distinct pathogenetic mechanisms, we analyzed 203 DLBCL biopsy samples by high-resolution, genome-wide copy number analysis coupled with gene-expression profiling. Of 272 recurrent chromosomal aberrations that were associated with gene-expression alterations, 30 were used differentially by the DLBCL subtypes (P < 0.006). An amplicon on chromosome 19 was detected in 26% of ABC DLBCLs but in only 3% of GCB DLBCLs and PMBLs. A highly up-regulated gene in this amplicon was SPIB, which encodes an ETS family transcription factor. Knockdown of SPIB by RNA interference was toxic to ABC DLBCL cell lines but not to GCB DLBCL, PMBL, or myeloma cell lines, strongly implicating SPIB as an oncogene involved in the pathogenesis of ABC DLBCL. Deletion of the INK4a/ARF tumor suppressor locus and trisomy 3 also occurred almost exclusively in ABC DLBCLs and was associated with inferior outcome within this subtype. FOXP1 emerged as a potential oncogene in ABC DLBCL that was up-regulated by trisomy 3 and by more focal high-level amplifications. In GCB DLBCL, amplification of the oncogenic mir-17-92 microRNA cluster and deletion of the tumor suppressor PTEN were recurrent, but these events did not occur in ABC DLBCL. Together, these data provide genetic evidence that the DLBCL subtypes are distinct diseases that use different oncogenic pathways.
| S-EPMC2533222 | biostudies-literature
Project description:Correct classification of cancer patients into subtypes is a prerequisite for acute diagnosis and effective treatment. Currently this classification relies mainly on histological assessment, but gene expression analysis bymicroarrays has shown great promise. Here we show that high accuracy, quantitative proteomics can robustly segregate cancer subtypes directly at the level of expressed proteins. We investigated two histologically indistinguishable subtypes of diffuse large B-cell lymphoma (DLBCL): activated Bcell- like (ABC) and germinal-center B-cell-like (GCB) subtypes, by first developing a general lymphoma stable isotope labeling with amino acids in cell culture (SILAC) mix from heavy stable isotope-labeled cell lines. This super- SILAC mix was combined with cell lysates from five ABCDLBCL and five GCB-DLBCL cell lines. Shotgun proteomic analysis on a linear ion trap Orbitrap mass spectrometer with high mass accuracy at the MS and MS/MS levels yielded a proteome of more than 7,500 identified proteins. High accuracy of quantification allowed robust separation of subtypes by principal component analysis. The main contributors to the classification included proteins known to be differentially expressed between the subtypes such as the transcription factors IRF4 and SPI1/ PU.1, cell surface markers CD44 and CD27, as well as novel candidates. We extracted a signature of 55 proteins that segregated subtypes and contained proteins connected to functional differences between the ABC and GCB-DLBCL subtypes, including many NF-kappa B-regulated genes. Shortening the analysis time to single-shot analysis combined with use of the new linear quadrupole Orbitrap analyzer (Q Exactive) also clearly differentiated between the subtypes. These results show that high resolution shotgun proteomics combined with super-SILAC-based quantification is a promising new technology for tumor characterization and classification.
2015-04-24 | PXD002098 | Pride
Project description:BackgroundDiffuse large B-cell lymphoma (DLBCL) is a spectrum of disease comprising more than 30% of non-Hodgkin lymphomas. Although studies have identified several molecular subgroups, the heterogeneous genetic background of DLBCL remains ambiguous. In this study we aimed to develop a novel approach and to provide a distinctive classification system to unravel its molecular features.MethodA cohort of 342 patient samples diagnosed with DLBCL in our hospital were retrospectively enrolled in this study. A total of 46 genes were included in next-generation sequencing panel. Non-mutually exclusive genetic signatures for the factorization of complex genomic patterns were generated by random forest algorithm.ResultsA total of four non-mutually exclusive signatures were generated, including those with MYC-translocation (MYC-trans) (n = 62), with BCL2-translocation (BCL2-trans) (n = 69), with BCL6-translocation (BCL6-trans) (n = 108), and those with MYD88 and/or CD79B mutations (MC) signatures (n = 115). Comparison analysis between our model and traditional mutually exclusive Schmitz's model demonstrated consistent classification pattern. And prognostic heterogeneity existed within EZB subgroup of de novo DLBCL patients. As for prognostic impact, MYC-trans signature was an independent unfavorable prognostic factor. Furthermore, tumors carrying three different signature markers exhibited significantly inferior prognoses compared with their counterparts with no genetic signature.ConclusionCompared with traditional mutually exclusive molecular sub-classification, non-mutually exclusive genetic fingerprint model generated from our study provided novel insight into not only the complex genetic features, but also the prognostic heterogeneity of DLBCL patients.
| S-EPMC7393908 | biostudies-literature
Project description:Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease whose prognosis is associated with clinical features, cell-of-origin and genetic aberrations. Recent integrative, multi-omic analyses had led to identifying overlapping genetic DLBCL subtypes. We used targeted massive sequencing to analyze 84 diagnostic samples from a multicenter cohort of patients with DLBCL treated with rituximab-containing therapies and a median follow-up of 6 years. The most frequently mutated genes were IGLL5 (43%), KMT2D (33.3%), CREBBP (28.6%), PIM1 (26.2%), and CARD11 (22.6%). Mutations in CD79B were associated with a higher risk of relapse after treatment, whereas patients with mutations in CD79B, ETS1, and CD58 had a significantly shorter survival. Based on the new genetic DLBCL classifications, we tested and validated a simplified method to classify samples in five genetic subtypes analyzing the mutational status of 26 genes and BCL2 and BCL6 translocations. We propose a two-step genetic DLBCL classifier (2-S), integrating the most significant features from previous algorithms, to classify the samples as N12-S, EZB2-S, MCD2-S, BN22-S, and ST22-S groups. We determined its sensitivity and specificity, compared with the other established algorithms, and evaluated its clinical impact. The results showed that ST22-S is the group with the best clinical outcome and N12-S, the more aggressive one. EZB2-S identified a subgroup with a worse prognosis among GCB-DLBLC cases.
| S-EPMC7820010 | biostudies-literature
Project description:Next Generation Sequencing-based subtyping and interim- and end of treatment positron emission tomography (i/eot-PET) monitoring have high potential for upfront and on-treatment risk assessment of diffuse large B-cell lymphoma patients. We performed Dana Farber Cancer Institute (DFCI) and LymphGen genetic subtyping for the HOVON84 (n = 208, EudraCT-2006-005174-42) and PETAL (n = 204, EudraCT-2006-001641-33) trials retrospectively combined with DFCI genetic data (n = 304). For all R-CHOP treated patients (n = 592), C5/MCD- and C2/A53-subtypes show significantly worse outcome independent of the international prognostic index. For all subtypes, adverse prognostic value of i/eot-PET-positive status is confirmed. Consistent with frequent primary refractory disease, only 67% C2 patients become eot-PET-negative versus 81-88% for other subtypes. Indicative of high relapse rates, outcome of C5 i/eot-PET-negative patients remains significantly worse in HOVON-84, which trend validates in the PETAL and SAKK38-07 trials (NCT00544219). These results show the added value of integrated genetic subtyping and PET monitoring for prognostic stratification and subtype-specific trial design.
| S-EPMC11696268 | biostudies-literature
Project description:Correct classification of cancer patients into subtypes is a prerequisite for acute diagnosis and effective treatment. Currently this classification relies mainly on histological assessment, but gene expression analysis bymicroarrays has shown great promise. Here we show that high accuracy, quantitative proteomics can robustly segregate cancer subtypes directly at the level of expressed proteins. We investigated two histologically indistinguishable subtypes of diffuse large B-cell lymphoma (DLBCL): activated Bcell- like (ABC) and germinal-center B-cell-like (GCB) subtypes, by first developing a general lymphoma stable isotope labeling with amino acids in cell culture (SILAC) mix from heavy stable isotope-labeled cell lines. This super- SILAC mix was combined with cell lysates from five ABCDLBCL and five GCB-DLBCL cell lines. Shotgun proteomic analysis on a linear ion trap Orbitrap mass spectrometer with high mass accuracy at the MS and MS/MS levels yielded a proteome of more than 7,500 identified proteins. High accuracy of quantification allowed robust separation of subtypes by principal component analysis. The main contributors to the classification included proteins known to be differentially expressed between the subtypes such as the transcription factors IRF4 and SPI1/ PU.1, cell surface markers CD44 and CD27, as well as novel candidates. We extracted a signature of 55 proteins that segregated subtypes and contained proteins connected to functional differences between the ABC and GCB-DLBCL subtypes, including many NF-kappa B-regulated genes. Shortening the analysis time to single-shot analysis combined with use of the new linear quadrupole Orbitrap analyzer (Q Exactive) also clearly differentiated between the subtypes. These results show that high resolution shotgun proteomics combined with super-SILAC-based quantification is a promising new technology for tumor characterization and classification.
2016-03-10 | MSV000079572 | MassIVE
Project description:ObjectiveDiffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma, with significant clinical heterogeneity. Recent studies suggest that the intratumoral microbiome may influence the tumor microenvironment, affecting patient prognosis and therapeutic responses. This study aims to identify microbiome-related subtypes in DLBCL and assess their impact on prognosis, immune infiltration, and therapeutic sensitivity.MethodsTranscriptomic and microbiome data from 48 DLBCL patients were obtained from public databases. Consensus clustering was used to classify patients into distinct microbiome-related subtypes. Functional enrichment analysis, immune infiltration assessments, and single-cell RNA sequencing were performed to explore the biological characteristics of these subtypes. Drug sensitivity predictions were made using the OncoPredict tool. Hub genes' expression and biological function were validated and inferred in cell lines and independent cohorts of DLBCL.ResultsTwo distinct microbiome-related subtypes were identified. Patients in Cluster 1 exhibited significantly better overall survival (P < 0.05), with higher immune infiltration of regulatory T cells and M0 macrophages compared to Cluster 2, which was associated with poorer outcomes. Functional enrichment analysis revealed that genes in Cluster 1 were involved in immune regulatory pathways, including cytokine-cytokine receptor interactions and chemokine signaling, suggesting enhanced anti-tumor immune responses. In contrast, genes in Cluster 2 were enriched in immunosuppressive pathways, contributing to a less favorable prognosis. Single-cell RNA sequencing analysis revealed significant heterogeneity in immune cell populations within the tumor microenvironment. B cells exhibited the most notable heterogeneity, as indicated by stemness and differentiation potential scoring. Intercellular communication analysis demonstrated that B cells played a key role in immune cell interactions, with significant differences observed in MIF signaling between B-cell subgroups. Pseudo-time analysis further revealed distinct differentiation trajectories of B cells, highlighting their potential heterogeneity across different immune environments. Metabolic pathway analysis showed significant differences in the average expression levels of metabolic pathways among B-cell subgroups, suggesting functional specialization. Furthermore, interaction analysis between core genes involved in B-cell differentiation and microbiome-driven differentially expressed genes identified nine common genes (GSTM5, LURAP1, LINC02802, MAB21L3, C2CD4D, MMEL1, TSPAN2, and CITED4), which were found to play critical roles in B-cell differentiation and were influenced by the intratumoral microbiome. DLBCL cell lines and clinical cohorts validated that MMEL1 and CITED4 with important biologically function in DLBCL cell survival and subtype classification.ConclusionsThis study demonstrates the prognostic significance of the intratumoral microbiome in DLBCL, identifying distinct microbiome-related subtypes that impact immune infiltration, metabolic activity, and therapeutic responses. The findings provide insights into the immune heterogeneity within the tumor microenvironment, focusing on B cells and their differentiation dynamics. These results lay the foundation for microbiome-based prognostic biomarkers and personalized treatment approaches, ultimately aiming to enhance patient outcomes in DLBCL.
| S-EPMC11876501 | biostudies-literature
Project description:Diffuse large B-cell lymphoma (DLBCL) is the most common form of lymphoma in adults. The disease exhibits a striking heterogeneity in gene expression profiles and clinical outcomes, but its genetic causes remain to be fully defined. Through whole genome and exome sequencing, we characterized the genetic diversity of DLBCL. In all, we sequenced 73 DLBCL primary tumors (34 with matched normal DNA). Separately, we sequenced the exomes of 21 DLBCL cell lines. We identified 322 DLBCL cancer genes that were recurrently mutated in primary DLBCLs. We identified recurrent mutations implicating a number of known and not previously identified genes and pathways in DLBCL including those related to chromatin modification (ARID1A and MEF2B), NF-?B (CARD11 and TNFAIP3), PI3 kinase (PIK3CD, PIK3R1, and MTOR), B-cell lineage (IRF8, POU2F2, and GNA13), and WNT signaling (WIF1). We also experimentally validated a mutation in PIK3CD, a gene not previously implicated in lymphomas. The patterns of mutation demonstrated a classic long tail distribution with substantial variation of mutated genes from patient to patient and also between published studies. Thus, our study reveals the tremendous genetic heterogeneity that underlies lymphomas and highlights the need for personalized medicine approaches to treating these patients.
| S-EPMC3557051 | biostudies-literature