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Distinct Genetic Aberrations in Molecular Subtypes of Diffuse Large B Cell Lymphoma Detected by Array CGH.

2007· article· en· W2993344323 on OpenAlexaff
Georg Lenz, George W. Wright, Sandeep S. Davé, Wenming Xiao, John Powell, Andreas Rosenwald, Hans–Konrad Müller–Hermelink, Randy D. Gascoyne, Joseph M. Connors, Elı́as Campo, Elaine S. Jaffe, Jan Delabie, Erlend B. Smeland, Lisa M. Rimsza, Richard I. Fisher, Wing C. Chan, Louis M. Staudt

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsComparative genomic hybridizationDiffuse large B-cell lymphomaBiologyLymphomaGerminal centerGene expression profilingCancer researchLocus (genetics)Copy-number variationB cellGeneticsGeneMolecular biologyGene expressionGenomeImmunologyAntibody

Abstract

fetched live from OpenAlex

Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous diagnostic category with at least three different molecular subtypes distinguishable by gene expression profiling, termed germinal center B cell-like (GCB) DLBCL, activated B cell-like (ABC) DLBCL, and primary mediastinal B cell lymphoma (PMBL). We performed array comparative genomic hybridization (aCGH) in patient samples and cell lines representing different DLBCL subtypes to determine if they utilize distinct pathogenetic mechanisms. Using an array consisting of 386, 165 oligonucleotides (NimbleGen), we performed aCGH on 203 untreated de novo DLBCL samples and 30 DLBCL cell lines, and the same samples were profiled for gene expression using Affymetrix U133 plus arrays. Patient samples included 72 GCB DLBCLs, 74 ABC DLBCLs, 31 PMBLs, and 26 unclassified DLBCLs. Following segmentation of the aCGH data into intervals with a uniform copy number, segments were combined into minimal common regions (MCRs) that were recurrently altered in more than one sample. Statistical differences in MCR frequency between DLBCL subtypes were corrected for multiple hypothesis testing using a false discovery rate (FDR) calculation. The DLBCL subtypes differed in the frequency of MCRs residing at many chromosomal loci, and we used gene expression data to define potential target genes in these MCRs. The INK4a/ARF tumor suppressor locus on 9p21 was selectively lost in ABC DLBCL: homozygous deletions of INK4a/ARF was observed in 20% of ABC DLBCLs but in only 3% of GCB DLBCLs and never in PMBLs (FDR=4.5 E-3). Among ABC DLBCLs, loss of INK4a/ARF was associated with increased proliferation rate, as measured by a proliferation gene expression signature, and adverse survival (p=0.007, log rank test). 16% of ABC DLBCL cases had gain/amplification and overexpression of SPIB, a gene on 19q13 encoding an ETS family transcription factor that is characteristically expressed in ABC DLBCL. This copy number alteration was observed much less frequently in GCB DLBCL (3%) and never in PMBL (FDR=2.6 E-2). GCB DLBCLs had recurrent amplification and overexpression of C13orf25, which encodes the mir-17-92 polycistronic cluster of microRNAs that is transcriptionally activated by c-myc and cooperates with c-myc to accelerate tumor development. C13orf25 amplification was detected in 16% of GCB DLBCLs but in only 3% of PMBLs and never in ABC DLBCL (FDR=3.8 E-3). Recurrent amplification and overexpression of JAK2 on 9p24 was observed in 35% of PMBL cases, but only in 5% of GCB DLBCLs and 4% of ABC DLBCLs respectively (FDR=6.2 E-4). In summary, aCGH revealed copy number abnormalities in DLBCL that had strikingly different frequencies in the three DLBCL subtypes, supporting the hypothesis that these subtypes represent distinct diseases that utilize different oncogenic mechanisms. Our analysis specifically implicated the INK4a/ARF locus as a tumor suppressor and SPIB as an oncogene in ABC DLBCL, the mir-17-92 microRNA cluster as an oncogene in GCB DLBCL, and JAK2 as an oncogene in PMBL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.221
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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