Abstract LB001: Identifying MAGE-A4-positive tumors for SPEAR T-cell therapies in HLA-A*02-eligible patients
Bibliographic record
Abstract
Abstract Autologous T-cells engineered with T-cell receptors (TCRs) targeting tumor antigens are promising therapies for metastatic solid cancers.1 Specific peptide enhanced affinity receptor (SPEAR) T-cell therapies are T-cells with engineered HLA-restricted TCRs that precisely target tumor cells with specific antigens, such as MAGE-A4 (a cancer testis antigen), presented on the surface by HLA molecules. In SPEAR T-cell clinical trials targeting MAGE-A4, a 2-step prescreen is done before enrolment. 1) Patients undergo HLA typing via a high-resolution (allelic, 4-digit) sequence-based assay, and those who are positive for the inclusion alleles (HLA-A*02:01P, 02:02P, 02:03P, 02:06P) and not positive for the exclusion allele A*02:05P are eligible. 2) Tumor MAGE-A4 testing is done via an immunohistochemical clinical trial assay (MAGE-A4+ cutoff: ≥30% tumor cell staining at ≥2+ intensity) in HLA-eligible patients. A screening protocol (NCT02636855) has been used in Phase 1 trials of first- and next-generation SPEAR T-cells targeting MAGE-A4 (NCT03132922, NCT04044859) with responses in multiple MAGE-A4+ tumors. As of November 19, 2021, 6,168 patients with 9 solid tumor types were screened at 32 sites across North America and Europe in this screening protocol; among which, 2,744 were HLA-eligible (eligibility rate: 45%, range per tumor type: 42%-55%). HLA-A*02:01 was the most frequent HLA-A*02 allele. Of these HLA-eligible patients, 1,549 had tumor tissues evaluable for MAGE-A4 expression; among which, 313 were MAGE-A4+ (MAGE-A4+ rate: 20%, range: 8%-54%) (Table). MAGE-A4 showed highest prevalence in synovial sarcoma and myxoid/round cell liposarcoma but was seen across all tumor types investigated. Our results will be discussed in the context of tumor histopathology, disease status, and demography. HLA and MAGE-A4 biomarker data will inform the therapeutic opportunities for SPEAR T-cells targeting MAGE-A4 in metastatic solid cancers. 1. D’Angelo et al. Cancer Discov. 2018;8:944. HLA eligibility and MAGE-A4 prevalence in a screening protocol (NCT02636855) Indication Esophageal cancer Esophagogastric junction cancer Gastric cancer Head and neck squamous cell carcinoma Non-small cell lung cancer Melanoma Ovarian cancer Urothelial cancer Synovial sarcoma and myxoid/round cell liposarcoma HLA screened (N) 284 228 271 601 3189 668 539 270 118 HLA eligible (%) 46 45 43 42 43 50 49 43 55 MAGE-A4 evaluable (N) 104 91 73 200 457 245 225 93 61 MAGE-A4 positive (%) 22 25 8 22 14 16 24 32 54 Citation Format: Tianjiao Wang, Jean-Marc Navenot, Stavros Rafail, Mark Carroll, Ruoxi Wang, Cheryl McAlpine, Swethajit Biswas, Francine Brophy, Erica Elefant, Paige Bayer, Sandra McGuigan, Dennis Williams, George Blumenschein, Marcus Butler, Jeffrey M. Clarke, Justin F. Gainor, Ramaswamy Govindan, Victor Moreno, Janet Tu, David S. Hong. Identifying MAGE-A4-positive tumors for SPEAR T-cell therapies in HLA-A*02-eligible patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB001.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".