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Record W4379981685 · doi:10.1002/hon.3164_210

Biological and clinical relevance of CD79 protein and gene expression in diffuse large B‐cell lymphoma

2023· article· en· W4379981685 on OpenAlexaff
Yasuto Naoi, Ryota Chijimatsu, Takahiro Urata, Kazutaka Sunami, Tamaki Imai, Yukifumi Nawa, Yasushi Hiramatsu, Kazuhiko Yamamoto, Soichiro Fujii, Isao Yoshida, Tomonori Yano, Kazuhiro Ikeuchi, Hiroki Kobayashi, Kazutoshi Tani, Yasuharu Sato, M. Boyle, Aixiang Jiang, Yoshinobu Maeda, David W. Scott, Daisuke Ennishi

Bibliographic record

VenueHematological Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsSpinal Cord Injury BCBC Cancer Agency
Fundersnot available
KeywordsDiffuse large B-cell lymphomaLymphomaMedicineInternal medicineRituximabImmunohistochemistryOncologyGerminal centerHematologyPathologyAntibodyB cellImmunology

Abstract

fetched live from OpenAlex

Introduction: CD79B is a target of polatuzumab vedotin, an antibody–drug conjugate, which may improve 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 in DLBCL is largely unknown. 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 according to a previous study (Sehn L et al. JCO 2020). We also performed gene expression profile-based cell-of-origin (COO) classification, including double-hit signature (DHITsig) which has been renamed to dark zone signature (DZsig) (Waleed A et al. Blood 2022), using the NanoString DLBCL90 assay. Results: CD79B IHC expression was evaluable in 602 cases. We idefined two groups according to median H-score of CD79B expression: CD79Bhigh and CD79Blow. The COO subtypes were assigned as follows: 308 patients (51%) with activated B-cell-like (ABC)-DLBCL, 196 (33%) with germinal center B-cell-like (GCB)-DLBCL, 32 (5%) with DZsig-pos DLBCL and 66 (11%) with unclassified (UNC). H-score of CD79B was the lowest in patients with ABC-DLBCL followed by GCB-DLBCL and DZsig-pos DLBCL in ascending order (Kruskal–Wallis test P < .00001; Figure A). Indeed, CD79Blow tumors were significantly enriched in ABC-DLBCL (57%) compared to GCB-DLBCL (40%) and DZsig-pos DLBCL (22%), respectively (both, P < .001). Consistently, using publicly available DLBCL datasets (Schmitz et al. NEJM 2018 and Ennishi et al. JCO 2019), we revealed that CD79B gene expression was the lowest in ABC-DLBCL compared to GCB- and DHITsig-DLBCL (Kruskal–Wallis test P = .01; Figure B and C). The association of CD79B expression with COO prompted us to evaluate CD79B expression in normal germinal center B cells. Notably single-cell transcriptomic analyses of six reactive lymphoid tissues from publicly available datasets revealed that the lowest expression of CD79B was found in plasmablasts followed by light zone B cells and dark zone B cells in ascending order (Kruskal–Wallis test P < .0001), supporting the differential expression of CD79Baccording to COO subtype. CD79Blow group had significantly shorter overall survival (OS) in the total DLBCL cohort (log-rank, P < .001) and within ABC-DLBCL (P = .001, Figure D and E). Moreover, CD79B protein expression was significantly associated with OS after adjusting for International Prognostic Index in the total cohort (Cox regression model; P< .001). Keywords: Aggressive B-cell non-Hodgkin lymphoma, Diagnostic and Prognostic Biomarkers Conflicts of interests pertinent to the abstract. K. Sunami Honoraria: Celgene, Sanofi, BMS, Ono, Janssen Research funding: Takeda, AbbVie, GSK, Chugai, Otsuka, MSD, Novartis, Astellas Amgen, Pfizer, Parexel, Kyowa Kirin, Symbio, Agios Y. Hiramatsu Honoraria: Chugai Pharmaceutical Co, Nippon Shinyaku Co, Bristol Myers Squibb, Sanofi K.K. I. Yoshida Honoraria: Kyowa Kirin, Chugai, Eisai, Jannsen, Nippon-shinyaku, Otsuka, Symbio, Takeda, Sumitomo Pharma, Meiji Research funding: Kyowa Kirin, Chugai Y. Maeda Research funding: Chugai, Nippon-shinyaku Other remuneration: Chugai, Eisai, Otsuka, Kyowa Kirin, Takeda D. W. Scott Consultant or advisory role: Abbvie, AstraZeneca, Janssen, Incyte Honoraria: AstraZeneca Research funding: Janssen, Roche/Genentech D. Ennishi Honoraria: Chugai, Eisai, Kyowa Kirin Research funding: Nipponshinyaku, Chugai

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.030
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.358
Teacher spread0.305 · 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 teacher head, 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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Citations0
Published2023
Admission routes1
Has abstractyes

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