New Strategies and Combinations to Improve Outcomes in Immunotherapy in Metastatic Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Immune checkpoint inhibitors have transformed the treatment of metastatic non-small-cell lung cancer, yielding marked improvements in survival and the potential for durable clinical responses. Primary and acquired resistance to current immune checkpoint inhibitors constitute a key challenge despite the remarkable responses observed in a subset of patients. Multiple novel combination immunotherapy and adoptive cell therapy strategies are presently being developed to address treatment resistance. The success of these strategies hinges upon rational clinical trial design as well as careful consideration of the immunologic mechanisms within the variable tumor immune microenvironment (TIME) which underpin resistance to immunotherapy. Further research is needed to facilitate a deeper understanding of these complex mechanisms within the TIME, which may ultimately provide the key to restoring and enhancing an effective anti-tumor immune response. This review aims to provide an introduction to some of the recent and notable combination immunotherapy and cell therapy strategies used in advanced non-small-cell lung cancer (NSCLC), and the rationale for their use based on current understanding of the anti-tumor immune response and mechanisms of resistance within the TIME.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| 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".