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Record W4389247375 · doi:10.1182/blood-2023-172972

CD79 Expression Is Associated with Cell-of-Origin and Outcome in Diffuse Large B-Cell Lymphoma

2023· article· en· W4389247375 on OpenAlexaff
Yusuke Naoi, Ryota Chijimatsu, Tomohiro Urata, Kazutaka Sunami, Toshi Imai, Yuichiro Nawa, Yasushi Hiramatsu, Kazuhiko Yamamoto, Soichiro Fujii, Isao Yoshida, Tomofumi Yano, Kazuhiro Ikeuchi, Hiroki Kobayashi, Katsuma Tani, Hiroyuki Murakami, Hideki Ujiie, Yasuharu Sato, Katsuyoshi Takata, Merrill Boyle, Aixiang Jiang, Yoshinobu Maeda, David W. Scott, Daisuke Ennishi

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer AgencySpinal Cord Injury BC
Fundersnot available
KeywordsDiffuse large B-cell lymphomaMedicineInternal medicineRituximabOncologyCohortLymphomaImmunohistochemistryPathology

Abstract

fetched live from OpenAlex

Introduction: CD79B is a target of polatuzumab vedotin, an antibody-drug conjugate, which significantly improved the prognosis of both previously untreated and relapsed/refractory patients with diffuse large B-cell lymphoma (DLBCL). However, the biological and clinical significance of CD79B protein and gene expression have not been fully explored in DLBCL, thus we aimed at examining these relationships. Methods: We retrospectively analyzed de novo DLBCL patients, who were diagnosed and received rituximab-based immunochemotherapy from 2008 through 2018 in the Okayama Hematology Study Group from Japan. Immunohistochemistry (IHC) staining was performed using a CD79B antibody (AT107-2), and protein expression was assessed based on H-score as described in a previous study (Sehn LH et al. JCO 2020), integrating with publicly available representative bulk RNA sequencing DLBCL datasets (BCC cohort from Ennishi D et al. JCO 2019 and NCI cohort from Schmitz R et al. NEJM 2018). We also performed CD8 and MHC class-I IHC to evaluate the tumor microenvironment. Gene expression profile-based cell-of-origin (COO) classification was performed including double-hit signature (DHITsig), recently renamed the dark zone signature (DZsig), using the NanoString DLBCL90 assay. In addition, simultaneous epitope and transcriptome measurement in single cells from lymphoid tissues was conducted. Results: CD79B IHC was evaluable in 576 cases. DLBCL90 assay classified the entire cohort into 293 ABC (50.9 %), 189 GCB (32.8 %), 31 DZsig-positive (5.4 %) and 63 unclassified (10.9 %). Furthermore, we dichotomized the cohort into 288 CD79B high cases and 288 CD79B low cases according to the median CD79B H-score. A dynamic range of CD79B protein expression was observed across COO, where ABC-DLBCL showed the lowest values followed by GCB-DLBCL and DZsig-positive-DLBCL, in ascending order (Kruskal-Wallis test, P < .00001; Figure A). Indeed, CD79B low cases were significantly enriched in ABC-DLBCL (58 %) compared to GCB-DLBCL (26 %) and DZsig-positive-DLBCL (2 %), respectively (Chi-squared test, P < .001). Consistently, we revealed that CD79B expression was the lowest in ABC-DLBCL compared to GCB-DLBCL and DZsig-positive-DLBCL at the transcriptomic level (Kruskal-Wallis test, P = .011 for BCC cohort and P = .022 for NCI cohort). In addition, we identified different CD79B staining patterns, composed of 433 with cytoplasmic pattern (75 %), 86 with membranous pattern (14.9 %), and 52 cases being IHC negative. These patterns significantly varied across COO (Fisher's exact test, P = .015) and CD79B H-score was the highest in the membranous pattern followed by the cytoplasmic pattern (Kruskal-Wallis test, P < .0001). Of note, the composition of CD8 positive T-cells in CD79B low tumors was significantly higher than that of CD79B high tumors (Wilcoxon rank sum test, P < .0001). However, MHC class-I expression was decreased in CD79B low cases compared to CD79B high (Wilcoxon rank sum test, P = .0002), suggesting an immune escape mechanism with downregulation of MHC class-I in the presence of cytotoxic T-cells, which is often seen in solid cancers. Significant association of CD79B expression with COO further prompted us to evaluate CD79B expression in normal germinal center B cells. Notably, the single-cell simultaneous epitope and transcriptome analysis (CITEseq, n = 2) and single-cell RNAseq analysis (n = 6) of reactive lymph nodes revealed that both CD79B gene and protein expression were the lowest in cells exhibiting plasmablastic signatures, followed by light zone and dark zone B cells (Kruskal-Wallis test, P < .0001; Figure B), supporting the relation of CD79B expression to COO subtype in DLBCL. Regarding prognostic impact, CD79B low group had significantly poorer prognosis in the entire DLBCL cohort (Log-rank test, P = .0005 for overall survival (OS) and P = .008 for progression-free survival (PFS)) and in ABC-DLBCL (Log-rank test, P = .003 for OS and P = .031 for PFS). Moreover, CD79B protein expression was significantly associated with OS after adjusting for International Prognostic Index in the entire DLBCL cohort (Cox regression model; P = .035). Conclusion: Our study identifies distinct CD79B expression patterns across COO subtypes, with CD79B low cases enriched in ABC-DLBCL and demonstrating poorer prognosis, suggesting its potential as a prognostic marker and for targeted therapies.

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 score0.004

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 communication0.0010.000
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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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
Published2023
Admission routes1
Has abstractyes

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