MER4 endogenous retrovirus correlated with better efficacy of anti-PD1/PD-L1 therapy in non-small cell lung cancer
Bibliographic record
Abstract
BACKGROUND: Endogenous retroviruses (ERVs) are highly expressed in various cancer types and are associated with increased innate immune response and better efficacy of antiprogrammed death-1/ligand-1 (anti-PD1/PD-L1)-directed immune checkpoint inhibitors (ICI) in preclinical models. However, their role in human non-small cell lung cancer (NSCLC) remains unknown. METHODS: We conducted a retrospective study of patients receiving ICI for advanced NSCLC in two independent cohorts. ERV expression was determined by RNA sequencing. The primary endpoint was progression-free survival (PFS) under ICI. The secondary endpoint was overall survival (OS) from ICI initiation. We studied expression of 6205 ERVs. Multivariate Cox regression model with lasso penalty was estimated on the training set to select ERVs significantly associated with survival. The predictive power of these ERVs was compared with that of previously described transcriptomic signatures. RESULTS: We studied two independent cohorts of 89 and 70 patients, used as training and validation sets. Clinicopathological characteristics included 75% of patients with non-squamous NSCLC. We selected four ERVs significantly associated with PFS. Only high MER4 ERV was associated with better PFS and OS in both cohorts. From a biological point of view, high MER4 expression is associated with higher infiltration of eosinophils and inflammatory gene signatures, while low MER4 expression is associated with enrichment in metabolism and proliferation signatures. Adding MER4 to previously described transcriptomic signatures of response to ICI improved their predictive power. CONCLUSIONS: MER4 ERV expression is useful to stratify risk and predict PFS and OS in patients treated with ICI for NSCLC. It also improves the predictive power of other known transcriptomic signatures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".