Neoadjuvant Immunotherapy for High-Risk, Resectable Malignancies: Scientific Rationale and Clinical Challenges
Bibliographic record
Abstract
Neoadjuvant immunotherapy involves administering immune checkpoint inhibitors before surgical resection in high-risk resectable disease. This strategy was shown to have a high pathological response rate and prolonged relapse-free survival in randomized trials in melanoma, glioblastoma, and colon cancer with small numbers of patients. In resectable cancers, immune checkpoint inhibitors such as anti-programmed cell death-1 (PD1) and anti-cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) can enhance antitumor immunity by activating antigen-specific T cells found in the primary tumor. These tumor-reactive T cells continue to exert antitumor effects on remaining neoplastic cells after the resection of the primary tumor, potentially preventing relapses from occurring. Based on the scientific rationale and early clinical observations with surrogate survival endpoints, neoadjuvant immunotherapy may provide an effective alternative to other therapeutic strategies such as adjuvant treatment. However, this can be determined only by conducting randomized controlled trials comparing neoadjuvant immunotherapy with the current standard of care for each tumor site. This review discusses the cellular mechanisms that occur during successful neoadjuvant immunotherapy and highlights the clinical data from the available human studies that support the preclinical mechanistic data. Here we also discuss strategies required for successful neoadjuvant immunotherapy, including combination treatment strategies and resistance mechanisms to neoadjuvant treatment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".