LAG-3 and PD-1+LAG-3 inhibition promote anti-tumor immune responses in human autologous melanoma/T cell co-cultures
Bibliographic record
Abstract
Despite the success of immunotherapy using checkpoint blockade, many patients with solid tumors remain refractory to these treatments. In human cancer, the experimental options to investigate the specific effects of antibodies blocking inhibitory receptors are limited and it is still unclear which cell types are involved. We addressed the question whether the direct interaction between T cells and tumor cells can be enforced through blocking a set of inhibitory receptors including PD-1, TIM-3, BTLA and LAG-3, blocked either individually or in dual combinations with the anti-PD-1 antibody, and to determine the condition that induces maximal T cell function preventing tumor cell proliferation. Using short-term Melan-A-specific or autologous re-stimulations, checkpoint blockade did not consistently increase cytokine production by tumor-derived expanded T cells. We next set up a 5-day co-culture assay with autologous melanoma cell lines and expanded tumor infiltrating T cells, originating from tumor specimens obtained from 6 different patients. Amongst all combos tested, we observed that blockade of LAG-3 alone, and more strongly when combined with PD-1 blockade, enforced T cell responses and tumor cell growth control. The combination of anti-LAG-3 plus anti-PD-1 acted through CD8 T cells and led to increased IFNγ production and cytotoxic capacity. Our results show that LAG-3 and PD-1 are regulating the direct interaction between tumor cells and autologous T cells, suggesting that therapy effects may be promoted by enhanced access of the corresponding blocking reagents to the tumor microenvironment.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".