Abstract A19: Integrated single cell analysis reveals co-evolution of malignant B cells and the tumor microenvironment in transformed follicular lymphoma
Notice bibliographique
Résumé
Abstract Introduction: Histological transformation from follicular lymphoma (FL) to aggressive B-cell lymphoma (tFL) is a disease course altering event linked to poor prognosis for affected patients. From a biological point of view, it is paradigmatic of disease dynamics with distinct clinical stages that project onto genetically and phenotypically divergent states. Aim: By applying a series of high-dimensional single cell (sc)RNA and DNA profiling techniques, we aimed to characterize the clonal and phenotypic evolution of tumor B cells and to reveal dynamic interactions with components of the tumor microenvironment (TME) during transformation. Methods: We included 11 tFL patients with paired FL (tFL-FL) and DLBCL (tFL-DLBCL) timepoint biopsies, and 11 indolent FL controls (with > 6y of follow-up without evidence of progression or transformation). Single cell whole transcriptome (scWTS) and BCR sequencing was performed for all samples and single cell whole genome sequencing (scWGS) for transformation pairs. Results: In each transformation pair, BCR sequencing confirmed the clonal relationship between FL and DLBCL timepoints. Clustering of scRNA data from each pair showed an inverse correlation between transcriptional similarity and time between the two biopsies. Some tFL-FL cells could always be found within the tFL-DLBCL clusters. Therefore, we labeled these cells as presumed “early-DLBCL cells”. Phylogenetic analysis using scWGS data showed distinct FL and DLBCL clones, and “mixed-clones” composed of cells from both timepoints in most pairs. FL cells in DLBCL clones were favored to represent precursor cells of transformation. DLBCL cells in FL clones likely represent residual FL cells after transformation, and could be found in the majority of the pairs. Divergent evolution from FL to DLBCL with specific copy number abnormalities unique to each timepoint was the most common mode of evolution during transformation, and only one pair showed linear evolution. Integrative analysis of scRNA and scDNA data highlighted that samples with the fewest genomic changes showed the least transcriptomic changes and vice versa. Differential expression and gene set enrichment analysis of malignant cells identified “MYC targets V1“ as the main pathway enriched in tFL-DLBCL cells in comparison to tFL-FL cells. Cells from the indolent control FL cases had a significantly lower MYC score than cells from pre-transformed FL. In parallel to the insights into tumor cell evolution, scRNAseq analysis also revealed significant shifts in TME composition, from T cells with a TFH and central memory phenotype in tFL-FL samples, to cells with an exhausted cytotoxic phenotype in tFL-DLBCL samples. Conclusion: Applying high-dimensional scRNA and DNA profiling techniques we identified precursor cell populations of transformation at the genomic and phenotypic level and linked genomic and phenotypic evolution with shifting TME composition in a comprehensive disease evolution model of transformation. Citation Format: Clementine Sarkozy, Shaocheng Wu, Katsuyoshi Takata, Tomohiro Aoki, Susana B Neriah, Katy Milne, Brad Nelson, Andrew Weng, David Scott, Jeffrey W Craig, Christian Steidl, Andrew Roth. Integrated single cell analysis reveals co-evolution of malignant B cells and the tumor microenvironment in transformed follicular lymphoma [abstract]. In: Proceedings of the Third AACR International Meeting: Advances in Malignant Lymphoma: Maximizing the Basic-Translational Interface for Clinical Application; 2022 Jun 23-26; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2022;3(5_Suppl):Abstract nr A19.
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 ».