Vaccine Therapy in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Immunotherapy using immune checkpoint modulators has revolutionized the oncology field, emerging as a new standard of care for multiple indications, including non-small cell lung cancer (NSCLC). However, prognosis for patients with lung cancer is still poor. Although immunotherapy is highly effective in some cases, not all patients experience significant or durable responses, and further strategies are needed to improve outcomes. Therapeutic cancer vaccines are designed to exploit the body's immune system to activate long-lasting memory against tumor cells that ensure tumor regression, with minimal toxicity. A unique feature of cancer vaccines lies in their complementary approach to boost antitumor immunity that could potentially act synergistically with immune checkpoint inhibitors (ICIs). However, single-line immunization against tumor epitopes with vaccine-based therapeutics has been disappointingly unsuccessful, to date, in lung cancer. The high level of success of several recent vaccines against SARS-CoV-2 has highlighted the evolving advances in science and technology in the vaccines field, raising hope that this strategy can be successfully applied to cancer treatments. In this review, we describe the biology behind the cancer vaccines, and discuss current evidence for the different types of therapeutic cancer vaccines in NSCLC, including their mechanisms of action, current clinical development, and future strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".