Attacking Postoperative Metastases using Perioperative Oncolytic Viruses and Viral Vaccines
Bibliographic record
Abstract
Surgical resection of solid primary malignancies is a mainstay of therapy for cancer patients. Despite being the most effective treatment for these tumors, cancer surgery has been associated with impaired metastatic clearance due to immunosuppression. In preclinical surgery models and human cancer patients, we and others have demonstrated a profound suppression of both natural killer (NK) and T cell function in the postoperative period and this plays a major role in the enhanced development of metastases following surgery. Oncolytic viruses (OV) were originally designed to selectively infect and replicate in tumors, with the primary objective of directly lysing cancer cells. It is becoming increasingly clear, however, that OV infection results in a profound inflammatory reaction within the tumor, initiating innate and adaptive immune responses against it that is critical for its therapeutic benefit. This anti-tumor immunity appears to be mediated predominantly by NK and cytotoxic T cells. In preclinical models, we found that preoperative OV prevents postoperative NK cell dysfunction and attenuates tumor dissemination. Due to theoretical safety concerns of administering live virus prior to surgery in cancer patients, we characterized safe, attenuated versions of OV, and viral vaccines that could stimulate NK cells and reduce metastases when administered in the perioperative period. In cancer patients, we observed that in vivo infusion with oncolytic vaccinia virus and ex vivo stimulation with viral vaccines promote NK cell activation. These preclinical studies provide a novel and clinically relevant setting for OV therapy. Our challenge is to identify safe and promising OV therapies that will activate NK and T cells in the perioperative period preventing the establishment of micrometastatic disease in cancer patients.
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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) | 0.000 | 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".