5 | LARGE B‐CELL LYMPHOMA MICROENVIRONMENT ARCHETYPE PROFILES (LYMPHOMAPS) IDENTIFY SUBGROUPS WITH GREATEST BENEFIT FROM CD19 CAR T‐CELL THERAPY
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».