Impact of EGFR-TKI Treatment on the Tumor Immune Microenvironment in <i>EGFR</i> Mutation–Positive Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Abstract Purpose: The impact of EGFR tyrosine kinase inhibitors (TKI) on the tumor immune microenvironment (TME) in non–small cell lung cancer (NSCLC) is unclear. Experimental Design: We retrospectively identified 138 patients with EGFR-mutated NSCLC who underwent rebiopsy after progression during EGFR-TKI treatment. PD-L1 and CD73 expression in tumor cells and tumor-infiltrating lymphocyte (TIL) density at baseline and after progression were determined by IHC. Tumor mutation burden (TMB) was determined by next-generation sequencing. Results: The proportion of patients with a PD-L1 expression level of ≥50% (high) increased from 14% before to 28% after EGFR-TKI (P = 0.0010). Whereas CD8+ and FOXP3+ TIL densities were significantly lower after EGFR-TKI treatment than before, CD8+ TIL density was maintained in tumors with a high PD-L1 expression level. Expression of CD73 in tumor cells after EGFR-TKI treatment was higher than that before in patients with a high PD-L1 expression level. TMB tended to be higher after EGFR-TKI treatment than before (3.3→4.1 mutations/Mbp, P = 0.0508). Median progression-free survival for subsequent treatment with antibodies to PD-1 was longer for patients with a high than for those with a low PD-L1 expression after EGFR-TKI (7.1 vs. 1.7 months, P = 0.0033), and two of five patients whose PD-L1 expression level changed from low to high after EGFR-TKI treatment achieved a PFS of >6 months. Conclusions: EGFR-TKI treatment was associated with changes in the TME of EGFR-mutated NSCLC, and such changes may provide clues for optimization of subsequent PD-1 inhibitor treatment.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".