Abstract 297: Effect of hypoxia on the expression of the T cell co-inhibitory ligands B7-H3 and B7-H1 in cancer
Bibliographic record
Abstract
Abstract Escape from adaptive immunity is important for malignant progression. B7-H3 and B7-H1 ligands provide co-inhibitory signals to T cells resulting in T cell anergy or apoptosis. Their expression has been shown to increase in cancer cells and to correlate with disease progression. Tumour hypoxia is a major contributor to the spread of cancer and resistance to radiation and chemotherapy. We proposed that hypoxia results in the up-regulation of the B7 molecules B7-H3 and B7-H1, thus contributing to immune escape. Using breast and prostate cancer cell lines, we investigated whether hypoxia increases the expression of these ligands and, if so, whether the transcription factor hypoxia-inducible factor-1 (HIF-1) is required for this hypoxic effect. We incubated MDA-MB-231 breast cancer cells and DU145 prostate cancer cells in standard culture conditions (20% O2, 37°C, 5% CO2), hypoxic conditions (0.5% O2, 37°C, 5% CO2), or with 100μM CoCl2 (known to stabilize HIF-1α) for 24 hours. Our findings revealed that B7-H3 expression is not increased in hypoxia. However, hypoxia and exposure to CoCl2 increased B7-H1 expression, which correlated with the levels of HIF-1α accumulation. Furthermore, HIF-1α knockdown attenuated B7-H1 mRNA levels. These results indicate a role for hypoxia in the up-regulation of B7-H1 on cancer cells, thus potentially contributing to immune escape of cancer cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 297. doi:1538-7445.AM2012-297
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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) | 0.003 | 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".