Immunotherapy for Lung Malignancies
Bibliographic record
Abstract
Harnessing the immune system to fight cancer is an exciting advancement in lung cancer therapy. Antitumor immunity can be augmented by checkpoint blockade therapy, which removes the inhibition/brakes imposed on the immune system by the tumor. Checkpoint blockade therapy with anti-programmed cell death protein 1 (anti-PD-1)/anti-programmed death ligand 1 (anti-PDL-1) antibodies causes tumor regression in about 25% of patients with lung cancer. In another approach, the immune system is forced or accelerated to attack the tumor through augmentation of the antitumor response against mutations carried by each lung tumor. This latter approach has become feasible since the advent of next-generation sequencing technology, which allows identification of the specific mutations that each individual lung tumor bears. Indeed lung cancers are now known to have high mutation rates, making them logical targets for mutation-directed immune therapies. We review how sequencing of lung cancer mutations leads to better understanding of how the immune system recognizes tumors, providing improved opportunities to track antitumor immunity and ultimately leading to the development of personalized vaccine strategies aimed at unleashing the host immune system to attack mutations in the tumor.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".