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Enregistrement W2609208895 · doi:10.1182/blood.v128.22.2939.2939

Revealing the Tumor Ecosystem in Follicular Lymphoma By Mass Cytometry

2016· article· en· W2609208895 sur OpenAlexaff
Xuehai Wang, Manabu Kusakabe, Deanne Gracias, G Simkin, Ainsleigh Hill, Robert Kridel, Justin Meskas, David W. Scott, Christian Steidl, Randy D. Gascoyne, Ryan R. Brinkman, Andrew P. Weng

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensUniversity of British ColumbiaBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésMass cytometryBiologyImmune systemFollicular lymphomaPopulationSomatic evolution in cancerImmunologyLymphomaCancer researchPhenotypeCancerGeneticsMedicineGene

Résumé

récupéré en direct d'OpenAlex

Abstract In follicular lymphoma (FL), clinical progression/histologic transformation occurs as a result of emergence of biologically aggressive malignant clones that escape the immune response. Whether such clones are preexisting or arise as a consequence of spontaneous or therapy-induced DNA mutation/genomic instability remains an open question; however, it has become increasingly clear that it will be important to understand the multi-clonal structure of tumors in order to treat them more effectively. As well, tumor-infiltrating immune cells represent a complex and heterogeneous population and are hypothesized to either help or hinder tumor progression. Several studies have searched for prognostic value in various infiltrating immune cell subsets, but results have been inconsistent, likely in part because most studies focus only on a few, incompletely characterized subsets at a time. Furthermore, the potential for co-variance between malignant subclones and the complement of infiltrating immune cells has not yet been explored. We would propose that understanding the entire tumor "ecosystem" in FL is needed to make further strides in predicting biological behavior and ultimately in guiding selection of the most appropriate of available rational therapies. In this study, we sought to use mass cytometry (CyTOF) to characterize both the clonal substructure of malignant populations and the infiltrating immune repertoire with unparalleled breadth, yet at single-cell resolution. We designed and optimized a 2-tube, 40-parameter CyTOF panel in which one tube is dedicated to characterizing population substructure among malignant B-cells and the other to profiling the composition of the infiltrating T-cell repertoire. We accessed viably frozen single cell suspensions from excess lymph node biopsy material remaining after diagnostic flow cytometry have been prospectively banked for the past 2 decades. We stained 12 FL, 23 diffuse large B-cell lymphoma (DLBCL), and 5 reactive lymph node (rLN) samples using the B-cell marker tube, acquired CyTOF data, and performed analyses using tSNE and Complicity mapping algorithms, which led us to make the following observations: First, over half of FL samples contain at least two phenotypically distinct tumor subclones. In contrast, very few of the DLBCL samples that we have examined thus far exhibit definitive phenotypic subclones. Lower intra-tumoral heterogeneity in DLBCL may imply that these tumors represent outgrowth of a highly evolved, dominant clone as compared to a less evolved collection of "untested" clones in FL. Second, tSNE mapping of FL tumors revealed two distinct subtypes, one in which the individual tumors showed highly similar and partially overlapping phenotypes that localized in proximity to normal germinal center B cells ("GC" subtype), and the other which was composed of more phenotypically heterogeneous tumors that were localized more distantly from normal germinal center B cells ("non-GC" subtype). We also stained 6 FL and 5 rLN samples using the T-cell marker tube, acquired CyTOF data, and performed analysis using tSNE and Scaffold mapping algorithms. We found the relative abundance of T-cells in FL samples was comparable to rLN (36.1% vs. 37.4% of total cells, p=0.90); however, most of the FL samples showed elevated CTL fractions (20.1% vs. 13.0% of total T-cells, p=0.02). Using Scaffold mapping, we were able to resolve a dramatic diversity of T and T/NK cell subsets, each of whose abundance varied substantially from one sample to the next. For example, the majority of CTL cells in rLN samples exhibited a relatively homogeneous, naïve-like phenotype, while CTL cells in FL typically contained a heterogeneous mixture of activated and exhausted subsets. The T-helper compartment exhibited a similar picture with relative population homogeneity within rLN samples, contrasting with heterogeneity within FL samples. Composite analysis correlating B- and T-cell features within the same FL specimen revealed that loss of HLA-DR and CD124 on malignant cells was correlated with CTL exhaustion, suggesting a potential immune escape mechanism. Our findings illustrate that novel information is revealed by 40-dimensional CyTOF analysis and provide insight into important, but understudied aspects of intra-tumoral heterogeneity and variable host immune response in lymphoma. Disclosures Scott: Janssen: Consultancy; Celgene: Consultancy; BC Cancer Agency: Patents & Royalties: Inventor on a patent licensed to NanoString Technologies; Roche: Honoraria.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,226
Écart entre enseignants0,218 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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