ArticleFrontiers in oncology2026
Genetic landscape features associated with PD-L1 expression status in advanced lung adenocarcinoma: a targeted sequencing analysis.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The tumor immune microenvironment influences non-small cell lung cancer (NSCLC) progression and therapy response. Programmed death-ligand 1 (PD-L1) is a key biomarker for immune checkpoint blockade, yet the genomic correlates of its expression status require elucidation. Methods: We analyzed 97 advanced lung adenocarcinoma patients, (13 cases-all from the PD-L1-unknown subgroup-excluded due to insufficient sequencing quality, leaving 84 cases for downstream analyses) categorized by PD-L1 expression (positive: n=32; negative: n=45; unknown: n=20). Genomic profiling was performed using a 95-gene targeted NGS panel. Analyses included mutational profiling, pathway enrichment (GO/KEGG), gene interaction patterns, and genomic complexity. Results: The cohort was predominantly metastatic (99.0%). Dominant mutations were in EGFR(43.80%), TP53(38.02%), and KRAS(11.57%). PD-L1-positive tumors more frequently harbored EGFRE746_A750del, while TP53 mutations were enriched in PD-L1-negative cases. Somatic interaction analysis revealed significant EGFR/TP53 co-occurrence in PD-L1-positive tumors and TP53/RB1 co-occurrence in negative tumors. GO analysis showed PD-L1-positive tumors were enriched for kinase-related functions, reflecting a distinct EGFR-driven biology characterized by a striking 59.26% prevalence of EGFR mutations. KEGG analysis indicated immune pathway enrichment in this subgroup, supporting an oncogene-induced immune phenotype frequently synergized by TP53 co-alterations. In contrast, PD-L1-negative tumors correlated with co-mutation-prone, metabolism-oriented profiles and immune suppression. Mutual exclusivity patterns differed: in PD-L1-positive tumors, EGFRmutations were mutually exclusive with KRAS/NRAS/HRASmutations, suggesting a single dominant driver. PD-L1-negative tumors displayed more co-occurring alterations (e.g., TP53 with STK11 or PTEN). Furthermore, 24% of PD-L1-positive tumors had multiple concurrent alterations, a proportion significantly higher than in PD-L1-negative tumors (8%; P<0.05). Conclusion: This study delineates distinct genomic landscapes associated with PD-L1 expression. PD-L1-positive status is linked to an immune-active, kinase-signaling-driven phenotype with mutually exclusive drivers. PD-L1-negative status correlates with a more immunosuppressive, co-mutation-prone, and metabolism-oriented profile. These findings provide molecular insights into immune heterogeneity and may inform tailored combination therapies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.