Autologous T cell therapy for MAGE-A4+ solid cancers in HLA-A*02+ patients: a phase 1 trial
Bibliographic record
Abstract
Affinity-optimized T cell receptors can enhance the potency of adoptive T cell therapy. Afamitresgene autoleucel (afami-cel) is a human leukocyte antigen-restricted autologous T cell therapy targeting melanoma-associated antigen A4 (MAGE-A4), a cancer/testis antigen expressed at varying levels in multiple solid tumors. We conducted a multicenter, dose-escalation, phase 1 trial in patients with relapsed/refractory metastatic solid tumors expressing MAGE-A4, including synovial sarcoma (SS), ovarian cancer and head and neck cancer ( NCT03132922 ). The primary endpoint was safety, and the secondary efficacy endpoints included overall response rate (ORR) and duration of response. All patients (N = 38, nine tumor types) experienced Grade ≥3 hematologic toxicities; 55% of patients (90% Grade ≤2) experienced cytokine release syndrome. ORR (all partial response) was 24% (9/38), 7/16 (44%) for SS and 2/22 (9%) for all other cancers. Median duration of response was 25.6 weeks (95% confidence interval (CI): 12.286, not reached) and 28.1 weeks (95% CI: 12.286, not reached) overall and for SS, respectively. Exploratory analyses showed that afami-cel infiltrates tumors, has an interferon-γ-driven mechanism of action and triggers adaptive immune responses. In addition, afami-cel has an acceptable benefit-risk profile, with early and durable responses, especially in patients with metastatic SS. Although the small trial size limits conclusions that can be drawn, the results warrant further testing in larger studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".