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Record W4411335183 · doi:10.1002/hon.70093_5

5 | LARGE B‐CELL LYMPHOMA MICROENVIRONMENT ARCHETYPE PROFILES (LYMPHOMAPS) IDENTIFY SUBGROUPS WITH GREATEST BENEFIT FROM CD19 CAR T‐CELL THERAPY

2025· article· en· W4411335183 on OpenAlexaff
David A. Russler‐Germain, Xin Li, Kiran Singhal, Qing Deng, Dai Chihara, Usama Khamis Hussein, Jennifer A. Foltz, J. Henderson, Ashley Wilson, Joshua W.D. Tobin, Maher K. Gandhi, E. Schmidt, Imran Nizamuddin, Ryan Sun, Akhil Kesaraju, Lisette Hilton, David W. Scott, Francisco Vega, Chris R. Flowers, Jason R. Westin, Obi L. Griffith, Todd A. Fehniger, Malachi Griffith, M. Green

Bibliographic record

VenueHematological Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsSpinal Cord Injury BC
FundersMorphoSysGenentechIncyteGilead SciencesAstraZeneca
KeywordsCD19LymphomaMedicineB cellOncologyInternal medicineCancer researchImmunology

Abstract

fetched live from OpenAlex

X. Li and K. Singhal equally contributing author. Introduction: Immunotherapies such as chimeric antigen receptor (CAR) T-cells are approved for patients with relapsed/refractory large B-cell lymphoma (LBCL) and are being assessed in earlier lines of therapy. Efficacy of these therapies is likely influenced by the lymphoma microenvironment (LME), but comprehensive LME characterization in LBCL is lacking. Methods: We performed single-nucleus multiome (RNA+ATAC), bulk RNA sequencing, and whole exome sequencing on 232 biopsies (217 from patients with LBCL [114 newly-diagnosed; 103 relapsed/refractory] and 15 benign controls) to assess hematopoietic and non-hematopoietic cell (NHC) types. After stringent quality control, 1,886,312 cells were analyzed. Non-B-cell lineages were classified into 71 transcriptionally-distinct cell subsets by unsupervised clustering (21 T/NK, 25 myeloid, and 25 NHC subsets), including subpopulations not previously characterized in lymphoma. Results: We defined LME archetypes by non-negative matrix factorization of non-B cell types, yielding five cell modules condensing into three dominant archetypes (LymphoMAPs): lymph-node (LN; 33% of tumors) characterized by lymph-node structural cells, antigen presenting cells, and naïve and memory T cells; T-effector/exhausted (TEX; 30% of tumors) enriched for effector and exhausted CD8 T cells; and fibroblast/macrophage (FMAC; 37% of tumors) with abundant macrophage and fibroblast subsets including cancer associated fibroblasts (CAFs). The “dark zone” signature was significantly enriched in the FMAC archetype (p < 0.001) and ABC subtype was enriched in the TEX archetype (p = 0.046). LymphoMAPs and LymphGen subtypes were not significantly associated. Cell-cell communication analysis revealed significant differences in ligand-receptor interactions among archetypes. FMAC was characterized by TGFB1 and PDGF signaling; TEX by PD1, CTLA4, and TIM3 signaling; LN by CXCL12, IL7, CCL19, and CCL21 signaling. Examining the biopsies from our cohort pre- versus post-CAR T therapy, the LN archetype was associated with greater benefit from CAR T therapy. To validate this observation, we integrated our bulk RNAseq data with published Nanostring PanCancer IO360 data from ZUMA7 (axicabtagene ciloleucel [axi-cel] in second line rrLBCL) to develop a Naïve Bayes classifier for our LymphoMAPs. In ZUMA7, the greatest benefit for axi-cel over chemotherapy was observed in the LN subtype (HR = 0.21; p < 0.0001), compared to FMAC (HR = 0.38; p < 0.0001) and TEX (HR = 0.7; p = 0.21). As such, LN subtype patients had significantly longer progression-free survival (PFS) compared to FMAC and TEX patients in the axi-cel arm (HR = 0.49, p = 0.0035), with 1-year PFS of 67%, 43%, and 35%, respectively. LymphoMAPs did not significantly impact PFS in the chemotherapy arm (p = 0.24). Conclusions: LymphoMAPs describe major patterns of LBCL LME biology that influence patient outcome, identify patients most likely to benefit from cellular therapy, and identify opportunities for LME-targeted therapies. Keywords: aggressive B-cell non-Hodgkin lymphoma; microenvironment; tumor biology and heterogeneity Potential sources of conflict of interest: D. A Russler-Germain Consultant or advisory role: Regeneron, Ipsen, Tempus D. Chihara Honoraria: SymBio, BeiGene D. W Scott Consultant or advisory role: Roche, Genmab, Abbvie, AstraZenenca, Veracyte Other remuneration: Patents related to Nanostring C. R. Flowers Consultant or advisory role: Abbvie, Bayer, BeiGene, Celgene, Denovo Biopharma, Foresight Diagnostics, Genentech/Roche, Genmab, Gilead, Karyopharm, N-Power Medicine, Pharmacyclics/Janssen, SeaGen, Spectrum Stock ownership: Foresight Diagnostics, N-Power Medicine Other remuneration: Research funding from 4D, Abbvie, Acerta, Adaptimmune, Allogene, Amgen, Bayer, Celgene, Cellectis EMD, Gilead, Genentech/Roche, Guardant, Iovance, Janssen Pharmaceutical, Kite, Morphosys, Nektar, Novartis, Pfizer, Pharmacyclics, Sanofi, Takeda, TG Therapeutics, Xencor, Ziopharm J. R. Westin Other remuneration: Research funding/advisory board for Abbvie, ADC therapeutics, Allogene, AstraZeneca, BMS, Genentech, Janssen, Kite/Gilead, Morphosys/Incyte, Novartis, Nurix, Pfizer, Regeneron T. A. Fehniger Consultant or advisory role: Affimed, AI Proteins Stock ownership: Wugen, Orca Bio, Indapta Therapeutics Other remuneration: Inventor on patent/patent applications (15/983275, 62/963971, PCT/US2019/060005) held by Washington University; research funding from HCW Biologics, Wugen, Affimed, AI Proteins M. R. Green Consultant or advisory role: Abbvie, Allogene, Bristol Myers Squibb, Arvinas, Johnson & Johnson Stock ownership: KDAc Therapeutics Honoraria: BMS, Daiichi Sankyo, DAVA Oncology Other remuneration: Research funding from Sanofi, Kite/Gilead, Abbvie, Allogene

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.312
Teacher spread0.286 · 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.

Study designBench or experimental
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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Citations1
Published2025
Admission routes1
Has abstractyes

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