Integrated Single Cell Analysis Reveals Co-Evolution of Malignant B Cells and the Tumor Microenvironment in Transformed Follicular Lymphoma
Notice bibliographique
Résumé
Introduction: The transformation of follicular lymphoma (FL) is believed to follow a divergent evolutionary model. However, the relationship between clonal (genetic) evolution and phenotype has been largely unexplored. Similarly, it is unknown if transformation arises from the expansion of a pre-existing clone with a transformed genotype, or if instead it requires the acquisition of genetic and phenotypic traits absent at diagnosis. In addition, previous bulk sequencing studies have provided only limited information about tumor microenvironment (TME) components. To address these shortcomings, we used single cell whole transcriptome (scWTS) and whole genome sequencing (scWGS) to characterize the clonal and phenotypic evolution of malignant B cells during transformation and elucidate interactions within TME. Methods: Our study included 11 transformed FL (tFL) patients with paired FL and DLBCL time point biopsies, and 11 indolent FL controls (>6y follow-up without progression/transformation). Combined scWTS and BCR sequencing was performed for all samples, along with scWGS for transformation pairs. We included two independent validation cohorts of pre-treatment FLs (N=125 treated with Rituximab-Bendamustine and N=154 with Rituximab-CVP (N=154)). Results: Phylogenetic analysis using scWGS showed a non-uniform pattern of evolution across the tFL pairs. In 3 pairs, all genetically-defined clones were composed of cells from both FL and DLBCL time points (called mixed-clones). Conversely, in 5 pairs fully divergent evolution was observed, featuring unique genetic clones at the DLBCL timepoint. Finally, 3 pairs had both mixed and unique DLBCL clones. Clustering of malignant B cells from tFL pairs using scWTS data indicated that the transcriptional similarity between the FL and DLBCL samples was inversely correlated with the time between sampling. Importantly, in the 6 pairs with well-separated FL and DLBCL clusters, some FL cells could always be found within the DLBCL clusters (so-called "DLBCL-like cells") and vice-versa. Integrative analysis of scWGS and scWTS data highlighted that samples with the fewest genomic changes showed the least transcriptional change. However, this correlation did not hold true for all tFL pairs. In particular, 2 pairs showed minimal genomic change but a high degree of phenotypic change, suggesting other determinants of cellular phenotype. Furthermore, in the 5 pairs with an extreme genomic clonal divergence, the strict co-evolution of malignant B-cell genotypes and phenotypes could not account for the presence of cells with a "DLBCL-like" phenotype in FL time point biopsies (Figure A). Differential expression and GSEA of malignant cells identified "MYC targets V1'', "mTORC1", and "OXPHOS" as pathways enriched in malignant B cells at the transformed time point compared to the earlier FL. Within these pathways, control non-tFL cells had significantly lower gene expression scores than pre-transformed FL, independent of cell-cycle state. At the TME level, a significant composition shift was observed from T cells with a TFH and central memory phenotype in FL samples, to cells with an exhausted phenotype at the time of transformation. The largest shift was an increase in the CD8+/LAG3+/PD1+ population (more than doubled, Figure B), confirmed by multicolour-IHC analysis. The magnitude of the TME shift was correlated with the magnitude of the malignant B-cell phenotypic evolution. A cell-cell interaction analysis highlighted MIF/CD44 and CD27/CD70 pathways as enriched at the time of transformation, while IL15/IL15RA signaling was more prominent in FLs. Finally, in two independent cohorts of pre-treatment FL, the proportion of CD8+/LAG3+ cells within the CD20-negative TME compartment was associated with time to transformation, time to progression and disease specific survival, independently of FLIPI score and FL histologic grade. Conclusion: By applying high-dimensional scWTS and scWGS techniques, we describe the range of possible relationships between cellular genotype and phenotype during FL transformation. We observed different modes of clonal evolution, from the expansion of a precursor cell population present at the time of diagnosis, to the emergence of novel clones present only at transformation. Shifts in the TME composition allowed us to identify a CD8+/LAG3+ population that could be used as a predictive marker of transformation risk. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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 | 0,000 | 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 ».