Assessing Gender and Racial Inclusion in CAR-T Cell Therapy Clinical Studies: A Scoping Review
Notice bibliographique
Résumé
Background: Race and gender significantly impact the phenotypic and genetic diversity in hematological diseases, thereby affecting disease prognosis and CAR-T therapy outcomes. To develop effective CAR-T therapy strategies for patients from diverse backgrounds, it is essential to provide equal opportunities for all gender and racial groups to participate in CAR-T clinical studies. Methods: A scoping review was conducted to analyze gender and racial inclusion in CAR-T studies. PubMed and EMBASE were searched for all prospective and retrospective CAR-T studies involving adults from 2015 to 2024. A total of 349 titles and abstracts were screened for eligibility criteria of including at least 10 adults. The review process, conducted through Covidence software, involved dual screening by reviewers, with conflicts resolved by a third reviewer, resulting in the exclusion of 222 studies. A total of 127 studies advanced to full-text review, leaving 99 studies for data extraction. Data on the racial distribution of enrolled participants, sex distribution, cancer type, randomization status, study type and phase, location, and year of study reporting were manually abstracted. Additionally, we evaluated trends in gender and racial disparity within the studies over the years. Results: A total of 99 studies, including 7,186 patients, were analyzed, showing a steady year-wise increase over time and peaking in 2021 with 23 studies. The highest number of participants was recorded in 2022 (1,857). The majority of studies were conducted in the United States (46) and China (32), followed by multinational collaborations (14), with fewer studies from France, Australia, Switzerland, and Canada. A total of 91 out of 99 studies were not randomized, with study types including retrospective studies (33), Phase 1 clinical trials (26), Phase 2 (16), Phase 1/2 (15), Phase 2/3 (1), Phase 3 (6), and observational (2). Gender data was available for 6,854 out of 7,186 patients, comprising 4,199 males (61.2%) and 2,655 females (38.8%). The gender distribution showed slight fluctuations over the years, with male predominance ranging from 47.1% to 84.4% and female representation from 15.6% to 52.9%. Only 28 out of 99 studies reported race data. Out of 2,974 participants in these studies, race data was available for 2,558 participants, which comprised 35.6% of the total patients included in this review. Of participants for whom racial data was available, 2,007 (78.5%) were White, 147 (5.7%) Black, 113 (4.4%) Asian, and 291 (11.4%) from other races. In 2017, there was minimal racial diversity, with only 12 White (92.3%), 1 Black (7.7%), and no Asian participants. The peak was in 2022, showing 729 White (74.3%), 39 Black (4.0%), 73 Asian (7.4%), and 43 participants from other races (4.4%). In 2024, studies reported 482 White (78.7%), 46 Black (7.5%), 0 Asian (0.0%), and 105 participants from other races (17.1%). In the United States, out of 1,883 participants, 82.42% are White, 3.72% are Black, 3.24% are Asian, and 10.62% belong to other races. Canada has a total of 30 participants, with 80.0% White, 16.67% Asian, and 3.33% from other races. Multinational studies, comprising 596 participants, present a diverse cohort with 67.28% White, 12.58% Black, 5.87% Asian, and 14.26% from other races. Conclusion: Our review reveals notable gender and racial disparities in CAR-T cell therapy clinical trials. Although there has been a gradual increase in the inclusion of diverse racial groups, non-White participants remain underrepresented. Only 28% of the studies reported race data, with a predominance of White participants (78.5%). To ensure equity of CAR-T therapies, future clinical trials must prioritize more inclusive and representative participant demographics.
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 | 0,075 | 0,257 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,010 |
| Bibliométrie | 0,029 | 0,023 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».