Obstacles for T-lymphocytes in the tumour microenvironment: Therapeutic challenges, advances and opportunities beyond immune checkpoint
Bibliographic record
Abstract
The tumour microenvironment (TME) imposes a major obstacle to infiltrating T-lymphocytes and suppresses their function. Several immune checkpoint proteins that interfere with ligand/receptor interactions and impede T-cell anti-tumour responses have been identified. Immunotherapies that block immune checkpoints have revolutionized the treatment paradigm for many patients with advanced-stage tumours. However, metabolic constraints and soluble factors that exist within the TME exacerbate the functional exhaustion of tumour-infiltrating T-cells. Here we review these multifactorial constraints and mechanisms - elevated immunosuppressive metabolites and enzymes, nutrient insufficiency, hypoxia, increased acidity, immense amounts of extracellular ATP and adenosine, dysregulated bioenergetic and purinergic signalling, and ionic imbalance - that operate in the TME and collectively suppress T-cell function. We discuss how scientific advances could help overcome the complex TME obstacles for tumour-infiltrating T-lymphocytes, aiming to stimulate further research for developing new therapeutic strategies by harnessing the full potential of the immune system in combating cancer.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".