Combining surgery and immunotherapy: turning an immunosuppressive effect into a therapeutic opportunity
Bibliographic record
Abstract
BACKGROUND: Cancer surgery is necessary and life-saving. However, the majority of patients develop postoperative recurrence and metastasis, which are the main causes of cancer-related deaths. The postoperative stress response encompasses a broad set of physiological changes that have evolved to safeguard the host following major tissue trauma. These stress responses, however, intersect with cellular mediators and signaling pathways that contribute to cancer proliferation. MAIN: Previous descriptive and emerging mechanistic studies suggest that the surgery-induced prometastatic effect is linked to impairment of both innate and adaptive immunity. Existing studies that combine surgery and immunotherapies have revealed that this combination strategy is not straightforward and patients have experienced both therapeutic benefit and drawbacks. This review will specifically assess the immunological pathways that are disrupted by oncologic surgical stress and provide suggestions for rationally combining cancer surgery with immunotherapies to improve immune and treatment outcomes. SHORT CONCLUSION: Given the prevalence of surgery as frontline therapy for solid cancers, the emerging data on postoperative immunosuppression and the rapid development of immunotherapy for oncologic treatment, we believe that future targeted studies of perioperative immunotherapy are warranted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".