Immune-enrichment of non-small cell lung cancer baseline biopsies for multiplex profiling define prognostic immune checkpoint combinations for patient stratification
Bibliographic record
Abstract
BACKGROUND: Permanence of front-line management of lung cancer by immunotherapies requires predictive companion diagnostics identifying immune-checkpoints at baseline, challenged by the size and heterogeneity of biopsy specimens. METHODS: An innovative, tumor heterogeneity reducing, immune-enriched tissue microarray was constructed from baseline biopsies, and multiplex immunofluorescence was used to profile 25 immune-checkpoints and immune-antigens. RESULTS: Multiple immune-checkpoints were ranked, correlated with antigen presenting and cytotoxic effector lymphocyte activity, and were reduced with advancing disease. Immune-checkpoint combinations on TILs were associated with a marked survival advantage. Conserved combinations validated on more than 11,000 lung, breast, gastric and ovarian cancer patients demonstrate the feasibility of pan-cancer companion diagnostics. CONCLUSIONS: In this hypothesis-generating study, deepening our understanding of immune-checkpoint biology, comprehensive protein-protein interaction and pathway mapping revealed that redundant immune-checkpoint interactors associate with positive outcomes, providing new avenues for the deciphering of molecular mechanisms behind effects of immunotherapeutic agents targeting immune-checkpoints analyzed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".