HLA‐E/β2 microglobulin overexpression in colorectal cancer is associated with recruitment of inhibitory immune cells and tumor progression
Bibliographic record
Abstract
The host immune response plays a major role in colorectal carcinoma (CRC) progression. A mechanism of tumor immune escape might involve expression of the human leucocyte antigen (HLA)-E/β2m on tumor cells. The inhibitory effect of HLA-E/β2m on CD8+ cytotoxic T lymphocytes and natural killer (NK) cells is mediated by the main HLA-E receptor CD94/NKG2A. As the pathophysiological relevance of this mechanism in CRC remains unknown, this prompted us to examine, in situ, in a series of 80 CRC (i) the HLA-E and β2m coexpression by tumor cells, (ii) the density of CD8+, cytotoxic, CD244+ and NKP46+ intraepithelial tumor-infiltrating lymphocyte (IEL-TIL) and (iii) the expression of CD94/NKG2 receptor on IEL-TIL. These data were then correlated to patient survival. We provided (i) the in situ demonstration of HLA-E/β2m overexpression by tumor cells in 21% of CRC characterized by an overrepresentation of signet ring cell carcinomas, mucinous carcinomas and medullary carcinomas, (ii) the significant association between HLA-E/β2m overexpression by tumor cells and increased density of CD8+ cytotoxic, CD244+ and CD94+ IEL-TIL and (iii) finally, the unfavorable prognosis associated with HLA-E/β2m overexpression by tumor cells. Our findings show that HLA-E/β2m overexpression is a surrogate marker of poor prognosis and point to a novel mechanism of tumor immune escape in CRC in restraining inhibitory IEL-TIL.
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".