Daratumumab in Combination with Lenalidomide Plus Dexamethasone Results in Persistent Natural Killer (NK) Cells with a Distinct Phenotype and Expansion of Effector Memory T-Cells in Pollux, a Phase 3 Randomized Study
Notice bibliographique
Résumé
Abstract Introduction: Daratumumab (DARA) is a human monoclonal IgG1κ CD38-targeted antibody that has several mechanisms of action, including complement-dependent cytotoxicity, antibody-dependent cellular cytotoxicity, antibody-dependent cellular phagocytosis, modulation of CD38 enzymatic activity, and induction of apoptosis. DARA (16 mg/kg) single-agent, phase 1/2 translational studies (MMY2002 and GEN501) revealed an additional, novel immunomodulatory mechanism of action that increased the adaptive immune response (Krejcik J et al, Blood 2016;128[3]:384-94). NK cells were reduced in these studies, with no effect on DARA efficacy or safety (Casneuf et al, Presented at EHA. June 9-12, 2016, Copenhagen, Denmark, Abstract P286; Adams et al, Presented at ASH. Dec 3-6, 2016, San Diego, CA. Abstract 4521). To explore the ability of DARA to promote adaptive T-cell responses and immune changes, and to investigate the immunophenotype of NK cells that persist, we have incorporated cytometry by time-of-flight (CyTOF®) technology and profiled patient blood samples at baseline and upon treatment with lenalidomide and dexamethasone (Rd) or DARA plus Rd (DRd). Methods: Relapsed/refractory multiple myeloma patient whole blood samples from both treatment arms of POLLUX were analyzed at baseline (DRd, n=40; Rd, n=45) and after two months of therapy (DRd, n=31; Rd, n=33). Samples were stained with a metal-conjugated antibody panel and evaluated on the CyTOF platform. Similar cellular events were clustered into nodes using the spanning tree progression of density normalized events (SPADE; Qui P, et al. Nature Biotechnology . 2011;29(10):886-891) algorithm and annotated into immune population bubbles via Cytobank® software. P-values derived from t-tests and single cell level bootstrap adjusted p-values corrected for multiple dependent hypothesis testing defined differences in marker intensity and cell populations within subgroups of this study. Results were visualized by SPADE blend trees, coloring each cluster using a combination of p-values related to marker intensity and cell population size changes, and Radviz projections. Results: Consistent with previous DARA monotherapy and combination therapy studies, a reduction in circulating NK cells was observed with DRd in POLLUX. Interestingly, the NK cells that persisted had a distinct phenotype: decreased expression of PD-1 and increased expression of HLA-DR, CD69, CD127 and CD27. These effects were not observed with Rd and may affect the adaptive immune response. The proportion of T-cells increased preferentially in deep responders (≥complete response) receiving DRd and correlated with a higher proportion of CD8+ vs. CD4+ T-cells. Regardless of the treatment received, the phenotype of all cells shifted toward CD45RO+. However, the DRd arm induced greater increases in HLA-DR expression, particularly for effector memory CD8+ T-cells. Furthermore, DRd led to a higher proportion of effector memory T-cells vs Rd. Consistent with observations from DARA monotherapy studies, CD38+ regulatory T-cells (Tregs), a cell population which we have demonstrated potently suppresses T-cell proliferation (Krejcik J et al), were exclusively decreased by DRd. Conclusion: DARA in combination with Rd specifically induced unique phenotypic changes in residual NK cells, suggesting that these cells are able to contribute to immune homeostasis. DARA also induced T-cell profile changes, including expansion of effector memory T-cells and increased expression of activation markers. This study supports the immunomodulatory mechanism of action of DARA and provides additional insight into changes in NK cells, T-cell subtypes and activation status following DARA-based therapy. Disclosures Van De Donk: Janssen, Celgene, Bristol-Myers Squibb, Amgen: Research Funding. Adams: Janssen: Employment. Vanhoof: Janssen: Employment. Van der Borght: Janssen: Employment. Casneuf: Janssen: Employment. Smets: Janssen: Employment. Axel: Janssen: Employment. Abraham: Janssen: Employment. Ceulmans: Janssen: Employment. Stevenaert: Janssen: Employment. Usmani: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Research Funding; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Array BioPharma: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Speakers Bureau. Plesner: Janssen, Genmab: Membership on an entity's Board of Directors or advisory committees; Janssen, Takeda: Consultancy; Janssen: Research Funding. Lokhorst: OncoImmune: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genmab: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Mutis: Celgene: Research Funding; Novartis: Research Funding; OncoImmune: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genmab: Research Funding; Gilead: Research Funding. Bahlis: Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Schecter: Janssen: Employment. Chiu: Janssen: Employment. Avet-Loiseau: Celgene, Janssen, Amgen, Bristol-Myers Squibb, Sanofi: Honoraria, Speakers Bureau; Celgene, Janssen: Research Funding; Janssen, Sanofi, Celgene, Amgen: 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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».