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The Percentage of Cytotoxic T-Cells in Mantle Cell Lymphoma (MCL) Biopsies Predicts Response to Rituximab.

2009· article· en· W2555574679 on OpenAlexaffabout
Stephen Opat, Pedro Farinha, Merrill Boyle, Nathalie A. Johnson, Hilary M O'Leary, James R. Cook, Raymond R. Tubbs, Lin Wang, Ryan Woods, Joseph M. Connors, Randy D. Gascoyne

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMantle cell lymphomaFollicular lymphomaMedicineLymphomaRituximabBiopsyPathologyBone marrowCyclin D1SurvivinCD8Gene signatureImmunophenotypingCancer researchBiologyInternal medicineImmunologyFlow cytometryCancerCell cycleGene expressionImmune system

Abstract

fetched live from OpenAlex

Abstract Abstract 2923 Poster Board II-899 Background: MCL is characterized by the presence of the t(11;14) translocation that juxtaposes the cyclin D1 gene downstream of the immunoglobulin heavy-chain gene promoter resulting in enhanced G1μS phase transition leading to cellular proliferation. Despite the common genetic lesion, patients exhibit considerable heterogeneity in their clinical behavior and response to therapy. The clinical significance of the microenvironment in follicular lymphoma has been highlighted by gene expression studies demonstrating that a T-cell response signature correlates with a superior outcome compared to that seen with a macrophage signature. In contrast, the significance of the non-malignant cellular component in MCL biopsies remains unknown. We report the results of a retrospective study examining flow cytometry (FCM) of MCL diagnostic tissue biopsies, focusing on the non-malignant lymphoid cells. Method: Patients were included in the study if they had MCL diagnosed according to the 2008 WHO criteria, had FCM performed on their diagnostic nodal or tissue biopsy and had adequate clinical information that included baseline clinical characteristics, treatment regimens and clinical outcome. Patients were excluded if they were too frail to receive chemotherapy or if FCM was only performed on peripheral blood or bone marrow. 122 patients met the criteria for inclusion in the study; of which 56 were also assessable by tissue microarray (TMA). FCM data were re-analyzed for expression of CD3, CD4, CD8, CD19, CD20 and kappa and lambda light chains on cells gating on lymphoid populations. Estimates of the non-neoplastic B-cells were derived from total CD19/CD20 positive B cells excluding the light chain restricted population. ‘High' and ‘low' expressers for each marker were determined by examining frequency histograms for a trough. TMAs were prepared from diagnostic paraffin-embedded blocks according to established protocols. Sections were immunostained for CD3, CD4, CD8, CD68, CD34, TIA-1, CD163, FOXP3, PD-1, CD57, CD21, P53 and Ki67. Survival correlates were assessed by Cox regression using SPSS. Results: The median age was 67 y (range 22-94) with 69% being male. 88% had advanced-stage disease with 16% having a high IPI (4/5). 50 (41%) patients received rituximab as part of initial or subsequent therapy. Primary and secondary treatment regimens included: observation (19); single agent alkylators (33); CHOP-like with rituximab (39); CHOP-like without rituximab (32); CVP-like (11); higher intensity regimens (9); fludarabine-based (19); gemcitabine-based (9); bortezimib (4); flavopiridol (2); autologous stem cell transplant (12); allogeneic stem cell transplant (2); radiation (45); and therapeutic splenectomy (6). The median follow up of the living patients was 30 months. The 5-y OS for the group was 21%. FCM median tumor content was high at 84% (range 21-100%) with median CD3, CD4, CD8 and non-tumor CD20 populations of 12%, 7%, 4% and 1%, respectively. Univariate analysis revealed CD8<8% (p=0.009), IPI (p<0.001) and rituximab therapy (p<0.001) as predictive of favorable OS while Cox regression analysis identified only IPI (p<0.001) and rituximab therapy (p<0.001) to be independent predictors of OS. Restricting analysis to those who received rituximab revealed a survival advantage for patients with <8% CD8+ T cells in their biopsies which was independent of the IPI (5-y OS with CD8<8% (37) 49%; CD8≥8% (13) 0%, p=0.023). The number of CD3, CD4 and the non-neoplastic B cells did not appear to significantly predict survival. TMA analysis confirmed the adverse impact of elevated CD8+ lymphocytes (<1% versus ≥1%) in patients who received rituximab (p=0.004) and suggested that many CD8 cells were cytotoxic with increased TIA-1+ expression predicting an inferior outcome (p=0.009). Ki-67 expression >35% was also adversely associated with OS (p=0.028). Conclusion: While the non-neoplastic cellular infiltrate often constitutes only a minor fraction of the tumor mass in MCL, it appears to significantly influence response to rituximab therapy. Further studies are required to validate this observation prospectively and define the mechanism by which the cytotoxic T-cells exert their influence. Disclosures: Connors: Roche Canada: Research Funding. Gascoyne:Roche Canada, Genentech, Lilly, Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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)0.002
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.001

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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".

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Citations2
Published2009
Admission routes2
Has abstractyes

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