Accrual Of Patients With Relapsed and Refractory DLBCL Onto Clinical Trials
Notice bibliographique
Résumé
Abstract Background New drug development in diffuse large B cell lymphoma (DLBCL) has become a very active area of research in the last several years with the advent of monoclonal antibodies and targeted therapies. We sought to determine the rate of accrual of patients with relapsed/refractory (R/R) diffuse large B cell lymphoma (DLBCL) onto early phase clinical trials, and hurdles to their enrolment by performing a retrospective analysis of all DLBCL cases diagnosed at our institution from 01/2006 to 03/2012. This time span represents an active time period for clinical trials in lymphoma at our institution. Methods DLBCL cases were identified through the hospital tumor registry. Patients were included in the analysis if they had any diagnosis ofDLBCL relapsed or refractory to standard therapy. Baseline demographics and disease characteristics, details of treatment, responses, relapse, evaluation for clinical trials and participation in clinical trials were determined by review of hospital charts. Only Phase I and II clinical trials were considered for this analysis. Results Of a total of 284 patients, 76 had relapsed/refractory disease, 10 of 25 had a successful autologous stem cell transplant (ASCT), and there is insufficient data on 1 patient. Of the remaining 65, 11 (17%) made it to trial. The median age was 65, 34 were male, median number of prior therapies was 2, 74% had at least one comorbidity and 46% had at least 2. Sixty-two percent of patients had de novo DLBCL, 18% transformed and 20% had a composite lymphoma including DLBCL. Reasons for failing to enroll on trial included prohibitive comorbidity (21%), rapid progression (15%), decision for palliation (15%), prior second malignancy (9%), thrombocytopenia (13%), CNS disease (9%), proximity to ASCT (2%), no protocol available(6%), palliative radiation (6%). Patients on trial tended to be younger (58.4 vs. 65.8 years), and to have a lower IPI (mean 3.1 vs 3.4). There was no difference in the number of prior of therapies (2.31 vs. 2.26) or number of comorbidities (2.18 vs 2.21). However, out of the 11 who made it to trial, 7 patients had failed ASCT (46.7% of patients who failed ASCT) vs. 4 (10%) who never had a transplant (p=0.005, Fisher’s exact test). Among the relapsed and refractory cases, 81% of cases were discussed at tumor board. Conclusions Although our study is small, we demonstrate that in an active research center, where a large number of R/R DLBCL patients are discussed at tumor board during which information about clinical trials is disseminated, a minority of patients with DLBCL not responding to standard therapy make it to trial.Patients having failed an ASCT are more successfully enrolled onto clinical trials. Interestingly, a similar accrual rate was seen in relapsed non-small cell lung cancer (Baggstrom,J Thor Oncol 2011) and similar barriers to enrolment were found for solid tumor patients (Lara, JCO 2001). Accrual of R/R DLBCL patients onto clinical trials is possible but challenging. A multi-institution analysis is underway. Disclosures: No relevant conflicts of interest to declare.
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 | 0,018 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».