MHC II lung cancer vaccines prime and boost tumor‐specific CD4 <sup>+</sup> T cells that cross‐react with multiple histologic subtypes of nonsmall cell lung cancer cells
Bibliographic record
Abstract
Nonsmall cell lung cancer (NSCLC) is the major cause of lung cancer-related deaths in the United States. We are developing cell-based vaccines as a new approach for the treatment of NSCLC. NSCLC is broadly divided into 3 histologic subtypes: adenocarcinoma, squamous cell carcinoma and large cell carcinoma. Since these subtypes are derived from the same progenitor cells, we hypothesized that they share common tumor antigens, and vaccines that induce immune reactivity against 1 subtype may also induce immunity against other subtypes. Our vaccine strategy has focused on activating tumor-specific CD4(+) T cells, a population of lymphocytes that facilitates the optimal activation of effector and memory cytotoxic CD8(+) T cells. We now report that our NSCLC MHC II vaccines prepared from adeno, squamous or large cell carcinomas each activate CD4(+) T cells that cross-react with the other NSCLC subtypes and do not react with HLA-DR-matched normal lung fibroblasts or other HLA-DR-matched nonlung tumor cells. Using MHC II NSCLC vaccines expressing the DR1, DR4, DR7 or DR15 alleles, we also demonstrate that antigens shared among the different subtypes are presented by multiple HLA-DR alleles. Therefore, MHC II NSCLC vaccines expressing a single HLA-DR allele activate NSCLC-specific CD4(+) T cells that react with the 3 major classes of NSCLC, and the antigens recognized by the activated T cells are presented by several common HLA-DR alleles, suggesting that the MHC II NSCLC vaccines are potential immunotherapeutics for a range of NSCLC 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) | 0.001 | 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) | 0.002 | 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".