IFN-γ is required for cytotoxic T cell-dependent cancer genome immunoediting
Bibliographic record
Abstract
Genetic evolution that occurs during cancer progression enables tumour heterogeneity, thereby fostering tumour adaptation, therapeutic resistance and metastatic potential. Immune responses are known to select (immunoedit) tumour cells displaying immunoevasive properties. Here we address the role of IFN-γ in mediating the immunoediting process. We observe that, in several mouse tumour models such as HA-expressing 4T1 mammary carcinoma cells, OVA-expressing EG7 lymphoma cells and CMS5 MCA-induced fibrosarcoma cells naturally expressing mutated extracellular signal-regulated kinase (ERK) antigen, the action of antigen-specific cytotoxic T cell (CTL) in vivo results in the emergence of resistant cancer cell clones only in the presence of IFN-γ within the tumour microenvironment. Moreover, we show that exposure of tumours to IFN-γ-producing antigen-specific CTLs in vivo results in copy-number alterations (CNAs) associated with DNA damage response and modulation of DNA editing/repair gene expression. These results suggest that enhanced genetic instability might be one of the mechanisms by which CTLs and IFN-γ immunoedits tumours, altering their immune resistance as a result of genetic evolution.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".