Assessing Gender and Racial Inclusion in CAR-T Cell Therapy Clinical Studies: A Scoping Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".