IGNYTE-ESO: A master protocol to assess safety and activity of letetresgene autoleucel (lete-cel; GSK3377794) in HLA-A*02+ patients with synovial sarcoma or myxoid/round cell liposarcoma (Substudies 1 and 2).
Bibliographic record
Abstract
TPS11582 Background: Letetresgene autoleucel (lete-cel; GSK3377794) is an autologous T-cell product using a genetically modified T-cell receptor to target cancer cells expressing the cancer testis antigen New-York esophageal squamous cell carcinoma 1 (NY-ESO-1). Lete-cel is currently being investigated alone and in combination in multiple tumor types [1,2]. NY-ESO-1 is expressed in 70‒80% of synovial sarcoma (SS) and 80‒90% of myxoid/round cell liposarcoma (MRCLS) tumors [3,4], suggesting these tumors may be prime lete-cel targets. This master protocol design (IGNYTE-ESO; NCT03967223) enables evaluation of multiple cell therapies in multiple tumor types and treatment stages in separate substudies, beginning with lete-cel in Substudies 1 and 2 for SS and MRCLS. Methods: Substudy 1 is a single-arm study assessing lete-cel in treatment-naïve patients (pts; ie, anthracycline therapy-naïve for metastatic disease) with advanced (metastatic/unresectable) NY-ESO-1+ SS or MRCLS as a first line of therapy (n=10 planned). Substudy 2 is a pivotal, single-arm study assessing lete-cel in pts with NY-ESO-1+ SS or MRCLS who progressed after anthracycline therapy (n=70 planned). Key eligibility criteria are age ≥10 y and NY-ESO-1 and HLA-A*02 positivity. Exclusion criteria include prior NY-ESO-1–specific/gene therapy, allogeneic stem cell transplant, and central nervous system metastases. Screened pts undergo leukapheresis for lete-cel manufacture, lymphodepletion, lete-cel infusion, and follow-up (FU). Long-term FU (15 y) may be done under a separate protocol. The Substudy 2 primary endpoint is overall response rate (ORR) per RECIST v1.1 assessed by central independent review. Substudy 1 is not testing any formal hypotheses; statistical analysis will be descriptive. Substudy 2 is comparing ORR with the historical control assuming at least 90% power with 0.025 one-sided type I error. Secondary endpoints include efficacy (time to/duration of response, disease control rate, progression-free survival), safety (adverse event [AE] frequency/severity, serious AEs, AEs of special interest), and pharmacokinetic (maximum transgene expansion [Cmax], time to Cmax, area under the time curve from zero to time t as data permit). Enrollment began in December 2019. References: 1. Reckamp KL, et al. Ann Oncol 2019;30(Suppl_5):v602–v660. 2. Rapoport A, et al. J Clin Oncol 2020 38:15_suppl, TPS8555. 3. D’Angelo SP, et al. Cancer Discov 2018;8(8):944–957. 4. D’Angelo SP, et al. J Clin Oncol 2018 36:15_suppl, 3005. Funding: GSK. Editorial support was provided by Eithne Maguire, PhD, of Fishawack Indicia, part of Fishawack Health, and funded by GSK. Previously presented at BSG 2021 (P914542). Clinical trial information: NCT03967223.
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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".