Evolution of the tumor immune landscape during treatment with tebentafusp, a T cell receptor-CD3 bispecific
Bibliographic record
Abstract
Metastatic uveal melanoma is an aggressive disease with poor outcome, which is refractory to immune checkpoint inhibitors. A T cell receptor (TCR)-based CD3 bispecific, tebentafusp, delivers clinical benefit in patients with metastatic uveal melanoma. Understanding the molecular basis for the anti-tumor activity of tebentafusp in an indication where checkpoint inhibitors are ineffective could aid in identification of other solid tumor indications where CD3 bispecifics may serve an unmet need. By analyzing tumor biopsies taken prior to treatment, early on-treatment, and at progression (NCT02570308), using RNA sequencing (RNA-seq) and immunohistochemistry (IHC), we show that expression of interferon-related genes in the tumor prior to treatment is associated with improved overall survival and tumor reduction on tebentafusp, that T cell recruitment occurs even in tumors with a low baseline level of T cell infiltration, and that durability of changes induced in the tumor microenvironment is key for survival duration.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".