Single-Cell Profiling Reveals Distinct Tumor Subtypes and Their Associated T-Cell Environments in Follicular Lymphoma
Notice bibliographique
Résumé
Abstract Follicular lymphoma (FL) is an indolent, but incurable malignancy as most patients eventually experience progressive disease. We hypothesized that clonal heterogeneity and patient-specific immune responses would contribute to variable clinical outcomes and that understanding the complexity of the entire tumor "ecosystem" would allow us to better match patients with specific types of tumor- and immune-targeted therapies. In this study, we performed 38-dimensional single-cell phenotyping by mass cytometry (CyTOF) to simultaneously characterize both the substructure of malignant B cell populations as well as the T cell microenvironment in a cohort of 77 diagnostic patient FL biopsies and 35 benign reactive LN (rLN) biopsies. We first applied the t-distributed Stochastic Neighbour Embedding (t-SNE) algorithm to explore intra- and inter- tumoral heterogeneity among malignant B cell populations. t-SNE mapping of individual samples showed that more than a third of FL samples contain at least two phenotypically distinct tumor subpopulations, supporting the notion of multi-clonal tumor architectures presumably due to ongoing clonal evolution. Batched analysis combining all 77 FL cases together with 35 rLN samples revealed two distinct tumor subtypes comprising about 25% (type "A") and 10% (type "B") of total FL samples, respectively, with individual tumors within each subtype showing highly similar and partially overlapping phenotypes. Mapping the same data using Uniform Manifold Approximation and Projection (UMAP), a dimensional reduction algorithm similar to t-SNE but preserves global structure more accurately, revealed that type A tumors localized in close proximity to normal germinal center (GC) B cells, thus fulfilling conventional expectations as to the histogenesis of FL. In contrast, type B tumors localized more closely to pre-GC B cells, implying the existence of an alternate histogenic path in FL. Importantly, we also performed single-cell RNA-Seq on a subset of FL cases which independently confirmed the type A vs type B distinction in whole transcriptomic space. We next analyzed matching T cell data using a modified Statistical Scaffold algorithm in order to place distinct subsets in context with conventionally defined normal T cell populations. Clustering analysis using multi-layer phenograph performed on T cells from all FL and rLN samples combined yielded hundreds of small, but phenotypically distinct populations that were then annotated according to the nearest conventionally defined T cell subset. These imputed designations were used as features to perform hierarchical clustering of samples which revealed 3 major clusters. Cluster1 was characterized by mostly naive T cell populations and contained the majority of rLN samples. Cluster2 was characterized by more differentiated effector T cell populations and was dominated by FL samples. Samples within Cluster2 could be further divided into Tfh, Treg and Th1-rich subgroups. Cluster3 was characterized by a diverse T cell environment including naive, memory and differentiated effector subsets and contained a mixture of rLN and FL samples. Integrative analysis correlating B- and T- cell features revealed type B FL tumors were associated with a Tfh-rich immune landscape. Taken together, these data reveal pervasive phenotypic heterogeneity in both malignant and immune cell compartments of patient FL samples and suggest that defining tumoral subtypes as well as the status of the local immune response within individual samples will support more refined diagnostic classification and highlight functional interactions most amenable to therapeutic targeting. Disclosures Gascoyne: NanoString: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies. Scott:Celgene: Consultancy, Honoraria; Roche: Research Funding; NanoString: Patents & Royalties: Named Inventor on a patent licensed to NanoString Technologies, Research Funding; Janssen: Research Funding. Steidl:Juno Therapeutics: Consultancy; Seattle Genetics: Consultancy; Nanostring: Patents & Royalties: patent holding; Bristol-Myers Squibb: Research Funding; Tioma: Research Funding; Roche: Consultancy.
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) | 0,000 | 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 ».