Understanding how immune maturity shapes responses to cancer therapy
Bibliographic record
Abstract
Abstract Clinical translation of cancer therapies from mice to human remains a constant challenge. Cancer immunotherapy aims develop a sustained long-term adaptive immune response to tumors. This raises the question of whether the typical pathogen-free animal models reflect the human immunological context and cancer pathology. Recent research suggests that exposure of pathogen-free mice to pet shop mice leads to their immunological maturation. We hypothesize that such a model may be more reflective of a human adult context. Specific-pathogen-free (SPF) Balb/c mice were co-housed with pet shop mice for 40 days to generate the “dirty mouse” model. Dirty mice were challenged with Listeria m. infection to test immune maturity. In parallel, CT26LacZ, CT26WT, EMT6 and K7M2 tumor cell lines were used to measure tumor growth in dirty mice. In particular, CT26LacZ tumor model was treated with oncolytic virus to assess the impact of immune maturity over efficacy of therapy. Complementary comparative experiments were performed in SPF mice. Dirty mouse immune response to Listeria challenge was more robust than in clean mice. Immune phenotyping revealed a different distribution of immune populations. A matured immune system impacted the growth of tumors to varying extents, with some models experiencing complete tumor rejection. Finally, oncolytic virus in dirty mice implanted with CT26-LacZ showed an improved efficacy on survival and tumor burden. Immune response to infections and tumor models between SPF mice and dirty mice reveals the potential impact of a mature immune system on immune-therapeutic responses. Therefore, establishment of model that better recreates the state of immune maturity may result in higher chances of translational impact.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".