CCL5 production in lung cancer cells leads to an altered immune microenvironment and promotes tumor development
Bibliographic record
Abstract
Current immunotherapies for lung cancer are only effective in a subset of patients. Identifying tumor-derived factors that facilitate immunosuppression offers the opportunity to develop novel strategies to supplement and improve current therapeutics. We sought to determine whether expression of driver oncogenes in lung cancer cells affects cytokine secretion, alters the local immune environment, and influences lung tumor progression. We demonstrate that oncogenic EGFR and KRAS mutations, which are early events in lung tumourigenesis, can drive cytokine and chemokine production by cancer cells. One of the most prominent changes was in CCL5, which was rapidly induced by KRASG12V or EGFRL858R expression, through MAPK activation. Immunocompetent mice implanted with syngeneic KRAS-mutant lung cancer cells deficient in CCL5 have decreased regulatory T cells (Tregs), evidence of T cell exhaustion, and reduced lung tumor burden, indicating tumor-cell CCL5 production contributes to an immune suppressive environment in the lungs. Furthermore, high CCL5 expression correlates with poor prognosis, immunosuppressive regulatory T cells, and alteration to CD8 effector function in lung adenocarcinoma patients. Our data support targeting CCL5 or CCL5 receptors on immune suppressive cells to prevent formation of an immune suppressive tumor microenvironment that promotes lung cancer progression and immunotherapy insensitivity.
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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) | 0.001 | 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".