A Mouse Tumor Model of Surgical Stress to Explore the Mechanisms of Postoperative Immunosuppression and Evaluate Novel Perioperative Immunotherapies
Bibliographic record
Abstract
Surgical resection is an essential treatment for most cancer patients, but surgery induces dysfunction in the immune system and this has been linked to the development of metastatic disease in animal models and in cancer patients. Preclinical work from our group and others has demonstrated a profound suppression of innate immune function, specifically NK cells in the postoperative period and this plays a major role in the enhanced development of metastases following surgery. Relatively few animal studies and clinical trials have focused on characterizing and reversing the detrimental effects of cancer surgery. Using a rigorous animal model of spontaneously metastasizing tumors and surgical stress, the enhancement of cancer surgery on the development of lung metastases was demonstrated. In this model, 4T1 breast cancer cells are implanted in the mouse mammary fat pad. At day 14 post tumor implantation, a complete resection of the primary mammary tumor is performed in all animals. A subset of animals receives additional surgical stress in the form of an abdominal nephrectomy. At day 28, lung tumor nodules are quantified. When immunotherapy was given immediately preoperatively, a profound activation of immune cells which prevented the development of metastases following surgery was detected. While the 4T1 breast tumor surgery model allows for the simulation of the effects of abdominal surgical stress on tumor metastases, its applicability to other tumor types needs to be tested. The current challenge is to identify safe and promising immunotherapies in preclinical mouse models and to translate them into viable perioperative therapies to be given to cancer surgery patients to prevent the recurrence of metastatic disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".