Comprehensive Characterization and Validation of the Tumor Microenvironment in Patients with Relapsed/Refractory Large B-Cell Lymphoma Identifies Subgroups with Greatest Benefit from CD19 CAR T-Cell Therapy
Notice bibliographique
Résumé
Multiple immune therapies such as CD19 CAR T-cells and bispecific antibodies are now approved for patients with relapsed/refractory large B-cell lymphoma (rrLBCL). The efficacy of these therapies is likely influenced by lymphoma microenvironment (LME) characteristics, but these have not been comprehensively characterized in rrLBCL. We performed single-nucleus multiome (RNA + ATAC), bulk RNA sequencing, and whole exome sequencing (WES) of 120 biopsies from 115 patients with rrLBCL to capture both hematopoietic and non-hematopoietic cell (NHC) types. After stringent quality control, 970,239 cells were analyzed. Non-B-cell lineages were classified into 76 transcriptionally-distinct cell types using unsupervised clustering (21 T/NK subsets; 25 myeloid subsets; 30 NHC subsets), including many subpopulations that have not been previously characterized in lymphoma. LME archetypes were defined by non-negative matrix factorization (NMF) of non-B cell types, yielding 5 cellular modules: lymph-node 1 [LN1] and lymph-node 2 [LN2] characterized by lymph-node structural cell types, antigen presenting cells, and naïve and memory T cells; T-effector/exhausted [TEX] characterized by high frequencies of effector and exhausted CD8 T-cells; and fibroblast/macrophage 1 [FMAC1] and 2 [FMAC2] characterized by high frequencies of macrophage and fibroblast subsets including cancer associated fibroblasts (CAFs). The LN1 and LN2 modules and the FMAC1 and FMAC2 modules were each correlated and therefore considered collectively in tumor archetype construction, resulting in three major archetypes: LN (33% of tumors), TEX (25% of tumors) and FMAC (42% of tumors). The TEX archetype was significantly enriched for the activated B-cell (ABC) cell of origin subtype (P = 0.01) and germinal center B-cell (GCB) subtype occurred more frequently within the FMAC archetype (P = 0.08). The FMAC archetype was significantly enriched for “Dark-zone” signature (DZsig)-positive (P < 0.001), and the LN archetype significantly enriched for DZsig-negative (P=0.002) tumors. There was no significant association between archetype and LymphGen subtype. Cell-cell communication analysis revealed significant differences in predicted ligand-receptor pair interactions between archetypes, with the FMAC archetype being characterized by TGFB1 and PDGF signaling; the TEX archetype by PD1, CTLA4 and TIM3 signaling; and the LN archetype by CXCL12, IL7, CCL19 and CCL21 signaling. Among these biopsies, 17 were pre- and 13 post-CAR T cell therapy. Analysis of these cases generated the hypothesis that the LN archetype was associated with greater benefit from CAR T cell therapy. To test this, we leveraged our bulk RNA-sequencing data plus published Nanostring data from the ZUMA7 study of axicabtagene ciloleucel (axi-cel) in second line rrLBCL to develop a Naïve Bayes classifier for these archetypes. Evaluation of response data from ZUMA7 showed that the greatest benefit for axi-cel compared to chemotherapy was observed within the LN subtype (HR=0.2; P<0.0001), compared to the FMAC (HR=0.34; P<0.0001) and TEX (HR=0.65; P=0.12). As such, LN subtype patients had significantly longer PFS compared to FMAC and TEX subtype patients in the axi-cel arm (HR=0.5, P=0.01), with 1 year PFS of 70%, 46% and 37%, respectively. There was no significant difference in PFS between archetypes in the chemotherapy arm (HR=1.1; P=0.74). In conclusion, accurate construction of the rrLBCL LME using direct cell measurements of both hematopoietic and non-hematopoietic cells with single-nucleus genomics permits the identification of cell types, cell modules, pathways of cell-cell interaction, and LME archetypes with important implications for LBCL biology, and may present an opportunity for LME-guided selection of patients most likely to benefit from cellular therapy.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».