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Enregistrement W2986516670 · doi:10.1182/blood-2019-130647

Evaluation of T-Cell Compartment By Complex Multiparameter Flow Cytometry Reveals Distinct Patterns of T-Cell Exhaustion in DLBCL, FL and HL Patients

2019· article· en· W2986516670 sur OpenAlexaffabout
Alexis Vallée, Mitra Shourian, Nathalie A. Johnson, Hélène Decaluwe

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueCancer Immunotherapy and Biomarkers
Établissements canadiensMcGill UniversityUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Organismes subventionnairesnon disponible
Mots-clésLymphomaImmunophenotypingCD8Cancer researchT cellFollicular lymphomaDiffuse large B-cell lymphomaMedicineFlow cytometryImmunotherapyImmunologyOncologyInternal medicineImmune system

Résumé

récupéré en direct d'OpenAlex

Background: Immune checkpoint (IC) blockade has revolutionized the treatment of chemo-refractory solid tumors and has demonstrated promising results for the treatment of resistant blood cancers, including lymphoma. However, clinical response to PD1 blockade depends on the subtype of lymphoma, with durable responses observed in 70 % of Hodgkin lymphoma (HL) patients versus only 36 to 40% of Diffuse Large B-Cell Lymphoma (DLBCL) and Follicular Lymphoma (FL) patients(1-3). The biological aspects that hinder the efficacy of immunotherapy in DLBCL and FL patient are not well-known. Primary tumor resistance to PD1 blockade could be driven by inherent genomic alterations or by ineffective anti-tumor T cell responses. ICs expressed at the surface of tumor-infiltrating-lymphocytes (TILs) use distinct suppressive mechanisms to enforce T-cell dysfunction. The collective expression of ICs defines multiple subsets of exhausted T cells during cancer(4). Since multiple of these ICs collaborate to repress anti-tumor T cell functions, we hypothesized that TILs from DLBCL and FL patients express high levels of ICs and present a more advanced exhausted phenotype compared to TILs from HL patients. Methods: We studied lymphoma samples collected at diagnosis and frozen as a viable cell suspension from 57 patients with HL, FL or DLBCL. We performed an extended immunophenotype of TILs through flow cytometry using maturation markers, exhaustion markers, homing and chemokine receptors. The results were analyzed with FlowJo. Boolean gating was used to assess IC co-expression. Statistical analyses were performed using Prism. Results: Our data indicated that CD8 TILs from DLBCL patients express higher levels of classical ICs such as PD1 (1059 vs 1015 and 648), Tim3 (1747 vs 868 and 918,) and Lag3 (2603 vs 1013 and 1031) compared to FL and HL patients (p-value < 0.01), while the expression of 2B4, BTLA and CD160 were similar. Boolean analysis revealed that DLBCL and FL had a higher frequency of CD8T cells that co-expressed 6-7 ICs, compared to HL, suggesting a more advanced state of exhaustion (19% and 26% vs 14%, p-value < 0.05). This correlated with an increased frequency of PD-1highCD39high T cells in DLBCL and FL patients compared to HL (32% and 24% vs 14%, p-value < 0.005), which correlated with increased frequency of EOMEShigh cells in both DLBCL and FL (84% and 76% vs 55%, p-value < 0.005). These markers identify populations of cells with phenotypic and transcriptomic profiles of terminal exhaustion with reduced cytokine production, proliferative capacity and persistence (5, 6). In contrast, stem-like exhausted CD8 T cells are defined by intermediate expression of PD-1 and expression of the chemokine receptor CXCR5, and are driven by the transcription factor TCF-1(7, 8). We observed that DLBCL exhibited significantly less progenitor Tim3-/CXCR5+ cells compared to FL and HL (27% vs 52% and 40%, p-value < 0.01). Further, the frequency and mean fluorescent intensity of TCF-1 was significantly reduced in DLBCL compared to FL and HL (43% / 495 ± 64 vs 70% / 752 ± 42 and 61% / 670 ± 74, p-value < 0.01). This data correlated with lower frequency of CD27+ CD8 T cells in DLBCL vs FL and HL (82% vs 96% and 96%, p-value < 0.01). Conclusion: Collectively, our data indicate that TILs from DLBCL and FL patients express a whole array of ICs that have non-redundant suppressive functions. Further, these patients present subsets of cells with increased functional exhaustion, subsets demonstrated to be unresponsive to PD-1 blockade(7). Further, DLBCL patients lack protective stem-like T-cell subsets which were recently demonstrated to correlate with clinical outcome(9). These results enlighten our knowledge of the cellular defects precluding potent anti-tumor T cell functions in DLBCL and FL and propose novel targets for immunotherapies in PD-1-unresponsive lymphomas. Research funding: This work is supported by the Canadian Institutes of Health Research (PJT-155996), Canadian Cancer Society and Cole Foundation (grant #705478). References: 1. S. M. Ansell et al., in N Engl J Med 372, 311 (2015). 2. R. Chen et al., J Clin Oncol35, 2125 (2017). 3. A. M. Lesokhin et al., J Clin Oncol34, 2698 (2016). 4. B. Bengsch et al., Immunity48, 1029 (2018). 5. M. A. Paley et al., Science338, 1220 (2012). 6. P. K. Gupta et al., PLoS pathogens11, e1005177 (2015). 7. S. J. Im et al., Nature537, 417 (2016). 8. D. T. Utzschneider et al., Immunity45, 415 (2016). 9. M. Sade-Feldman et al., Cell176, 404 (2019). Disclosures Johnson: Roche: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Abbvie: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BD Biosciences: Other: Provided a significant proportion of the antibodies used in this project free of cost.; Seattle Genetics: Honoraria; Lundbeck: Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
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 enseignants0,001
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,028
Tête enseignante GPT0,285
Écart entre enseignants0,257 · 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

Citations1
Publié2019
Routes d'admission2
Résumé présentoui

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