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Record W3017091259 · doi:10.1182/blood.v112.11.477.477

Deletion in Chromosome 17p12 and Gains in Chromosome 9q33.3 by Array Comparative Hybridization Are Associated with R-CHOP Treatment Failure in Patients with Diffuse Large B Cell Lymphoma

2008· article· en· W3017091259 on OpenAlexaff
Nathalie Johnson, Ronald J. de Leeuw, Julia Chae, Sohrab P. Shah, Wan L. Lam, Thomas Relander, Joseph M. Connors, Douglas E. Horsman, Randy D. Gascoyne

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsComparative genomic hybridizationDiffuse large B-cell lymphomaCHOPOncologyPMS2Internal medicineBiologyInternational Prognostic IndexLymphomaChromosomeMedicineBioinformaticsCancer researchGeneticsGeneMutationGermline mutation

Abstract

fetched live from OpenAlex

Abstract Background: The genetic alterations associated with survival in patients with diffuse large B cell lymphomas (DLBCL) treated with combined rituximab-CHOP (R-CHOP) chemo-immunotherapy are not well understood. Methods: 92 patients with DLBCL treated with R-CHOP who had a biopsy available at the time of diagnosis were included in the study. 31 patients were classified as treatment failures defined as progression < 6 months of completing R-CHOP and 61 patients were classified as treatment successes defined as a maintained remission >2 years after diagnosis. We used genome-wide BAC array comparative genomic hybridization (aCGH) to determine the genomic copy number imbalances. The presence of genetic gains and losses were determined using the intersection between visual annotations and a Hidden Markov model algorithm. DLBCL cell of origin (COO) subtype distinctions (GCB vs ABC) were determined using a Bayesian predictor model on gene expression derived from custom Affymetrix arrays (Dave, N Engl J Med, 2006;354:2431). A permutation test was used to identify genetic regions that were significantly different between treatment failures and treatment successes. Functional pathway analysis was performed using Ingenuity software. A novel model based clustering algorithm was applied to the normalized data to determine if any association with outcome correlated with the observed genetic alterations. Results: Lymphoma progressed in 31/92 (34%) patients < 6 months after R-CHOP (median follow-up = 4 y). All 92 patients had successful aCGH and 81 had COO available for this analysis. The International Prognostic Index (IPI) and COO were predictive of outcome (p=0.04, p=0.02, respectively). 451 regions containing 338 genes were associated with treatment failure with a p-value of <10−6. Gains in 9q33.3 were found in 13 patients (14%) and were significantly associated with treatment failure p<10−8. This region contains genes such as HSPA5, a negative regulator of apoptosis and PPP6C, a positive regulator of the cell cycle by targeting IKBe thereby removing inhibition of the NFkB pathway. Deletions in 17p12 were detected in 24 (26%) and were the most statistically significantly associated with treatment failure p<10−9. This region contains tumor necrosis factor (TNF) receptor superfamily member (TNFRSF13B or TACI) which, when deleted or mutated, has been previously shown to lead to activation of the noncanonical NFkB pathway and B cell proliferation. 21 of these 24 patients also had deletion of 17p13 at the TP53 locus (p<10−6). Neither 9p33.3 nor 17p12 deletion was associated with COO distinctions. Using Ingenuity, pathways involving apoptosis and cellular proliferation, specifically those involving P53, MYC and HSPA5 genes, were over-represented in treatment failures (p=2.04 × 10−4). Unsupervised clustering of the aCGH data demonstrated that 60% of cases could be stratified into 4 genetic sub-groups based on the presence of 1q+, 6q−, +7 and the concurrent presence of +3 and +18. Supervised analysis demonstrated that the +3/+18 group and the 6q− group were associated with ABC subtype of DLBCL whereas the +7 and 1q+ groups were associated with the GCB subtype. However, these genetic groups did not correlate with treatment outcome. Conclusions: Some genetic alterations cluster together and can distinguish COO subtypes of DLBCL. Gains on 9q33.3 and deletions of 17p12 are common alterations detected by high resolution aCGH in DLBCL. Most importantly, these alterations involve genes known to be critical in B cell proliferation and apoptosis and alterations at these sites are strongly associated with treatment failure (p values <10−8) in patients treated with R-CHOP.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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