Temporal Profiles of Lymphocyte Subsets and the Correlation with Infectious Events in Idelalisib-Treated Patients
Notice bibliographique
Résumé
Abstract Introduction: Idelalisib (IDELA) is a targeted PI3Kd inhibitor approved as monotherapy in relapsed follicular lymphoma and in combination with rituximab in relapsed chronic lymphocytic leukemia (CLL). Increased rates of adverse events (AEs) were recently observed in the IDELA vs placebo arms of randomized controlled trials (RCT) evaluating IDELA added to standard therapies in front-line CLL and early-line indolent non-Hodgkin lymphoma (iNHL). AEs leading to death were mainly infectious and included pneumocystis jirovecii pneumonia (PJP) and cytomegalovirus (CMV). This analysis across trials in the relapsed population evaluated whether quantitative changes in lymphocyte subsets may have contributed to these AEs. Methods: Peripheral blood immunophenotypic data available for analysis from patients (pts) (n = 1,480) treated in 5 IDELA RCTs were analyzed. Three studies (n = 787) included pts with relapsed CLL (NCT01569295: IDELA + bendamustine-rituximab [BR] vs placebo + BR; NCT01539512: IDELA + R vs placebo + R and NCT0165902: IDELA + ofatumumab [O] vs placebo + O) and 2 studies (n = 693) included R/R iNHL pts (NCT01732913: IDELA + R vs placebo + R and NCT01732926: IDELA +BR vs placebo + BR). Absolute numbers of T (CD4+ and CD8+), B (CD19+) and NK (CD16+/CD56+) cells were analyzed longitudinally in both IDELA and placebo pts across the 5 studies. Lymphocyte subsets were analyzed separately in those who died and then correlated with specific grade ≥3 AEs including infections, febrile neutropenia, and respiratory (acute respiratory failure, pneumonitis) events. Analysis was conducted within individual study and for all studies combined. Of note, samples were collected more frequently during the first 6 months (during combination therapy) and collection times varied among the 5 studies. Results: There was no specific trend noted with the CD8+ T-cells between treatment groups across the studies. Generally, NK-cells were decreased to a similar degree in both IDELA and placebo pts at weeks 10 to 12 with recovery starting around week 24. There were no differences in median NK- and CD8+ T-cell counts between pts with grade ≥3 AEs and no AEs within either group. In both BR trials, CD4+ T-cells nadir to <200 cells/µl occurred at week 22 in both groups. Recovery of CD4 to ≥200 cells/µL occurred at week 30 in CLL pts and at week 72 in iNHL pts. Median CD4+ T-cells in pts on the BR trials were lower in groups both with and without AEs, compared with non-BR trials (Table 1). There were a total of 31 cases of PJP (13 in the BR trials) and 32 cases of CMV infection (28 in the BR trials). Analysis of PJP and CMV pts with available immunophenotypic data (n = 46) revealed that 33 pts had CD4 <200 cells/µL; 31 of these were treated with IDELA plus combination therapy (Figure 1). Finally, while there were more grade ≥3 AEs within IDELA arms, these did not occur at any specific CD4 level and, in fact, grade ≥3 AEs were noted even in pts with CD4 >900 cells/µL. Conclusion: Within 5 RCTs evaluating IDELA vs placebo in combination with an anti-CD20 mAb or BR in R/R CLL or iNHL, there was no correlation between grade ≥3 AEs and NK- or CD8+ T-cell counts. Median CD4+ T-cells in pts on the BR trials were lower in both groups in those with and without AEs, compared with non-BR trials. In addition, pts with PJP and CMV infections were noted to have CD4+ T-cells <200 cells/ µL, and this was more common in IDELA-treated patients, especially when combined with BR, suggesting that the lymphosuppressive effect of IDELA may augment the myelosuppressive effect of bendamustine. While this current study involves the quantitative analysis of various immune cell subsets, it may be the qualitative function of these cells that contributed to infections. Assays evaluating the qualitative function of these cells are being investigated. All IDELA trials have been amended to include PJP prophylaxis and CMV monitoring. Figure 1 Figure 1. Incidence of PJP and CMV Infections and Correlation with CD4 Count. Disclosures Sharman: Gilead Sciences, Inc.: Honoraria, Research Funding. Salles:Mundipharma: Honoraria; Amgen: Consultancy, Honoraria; Gilead: Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Roche/Genentech: Consultancy, Honoraria, Research Funding. Jurczak:Celltrion, Inc: Research Funding; Janssen: Research Funding; Gilead Sciences: Research Funding; Acerta: Research Funding; Bayer: Research Funding. Jones:AbbVie: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Gilead Sciences: Consultancy, Research Funding; PCYC: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Owen:Janssen: Honoraria; Gilead: Honoraria, Research Funding; Pharmacyclics: Research Funding; Celgene: Honoraria, Research Funding; Abbvie: Honoraria; Lundbeck: Honoraria, Research Funding; Novartis: Honoraria; Roche: Honoraria, Research Funding. Munugalavadla:Gilead Sciences: Employment, Equity Ownership. Dreiling:Gilead Sciences: Employment, Equity Ownership. Xiao:Gilead Sciences: Employment, Equity Ownership. Rao:Gilead Sciences: Employment, Equity Ownership. Flinn:Janssen: Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Gilead Sciences: Research Funding; ARIAD: Research Funding; RainTree Oncology Services: Equity Ownership. O'Brien:Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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) | 0,000 | 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 tête enseignante, 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 ».