ArticleFrontiers in immunology2023
Immune profiles according to EGFR mutant subtypes and correlation with PD-1/PD-L1 inhibitor therapies in lung adenocarcinoma.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed, 5 citations in OpenAlex.
- ATAD2 drives immunotherapy resistance by promoting lactic acid-mediated CD8Frontiers in immunology · 2026Article
- Dacomitinib in Combination with chemotherapy is effective in lung adenocarcinoma with rareFrontiers in oncology · 2026Article
- Shaping the battlefield: EGFR and KRAS tumor mutations' role on the immune microenvironment and immunotherapy responses in lung cancer.Cancer metastasis reviews · 2025Review
- Machine Learning and Weighted Gene Coexpression Network-Based Identification of Biomarkers Predicting Immune Profiling and Drug Resistance in Lung Adenocarcinoma.International journal of genomics · 2025Article
- Immunological features of EGFR-mutant non-small cell lung cancer and clinical practice: a narrative review.Journal of the National Cancer Center · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: We examined the distributions of 22 immune cell types and the responses to PD-1/PD-L1 inhibitors according to EGFR mutation profile, in three independent datasets of lung adenocarcinoma (LUAD). Methods: We used CIBERSORTx to analyze the distributions of immune cells, and tumor immune dysfunction and exclusion (TIDE) or tumor mutation burden (TMB) to analyze responses to anti-PD-1/PD-L1 therapy, in two public LUAD datasets. The results were verified with a validation set that included patients treated with PD-1/PD-L1 inhibitors. Results: Compared to EGFR mutants, EGFR wild-type carcinomas had higher numbers of CD8+ T cells, CD4 memory activated T cells and neutrophils, and lower numbers of resting dendritic cells and resting mast cells, in two of the datasets. In our subgroup analyses, CD8+ T cells and CD4 memory activated T cells were more numerous in EGFR rare variants than in wild-types, L858R mutants, and exon 19 deletion mutants. In our TIDE or TMB analyses, EGFR rare variants were predicted to respond better to PD-1/PD-L1 inhibitors than wild-types, L858R mutants, and exon 19 deletion mutants. In the validation set verified by immunohistochemical staining, levels of CD8+ T cells in the EGFR rare variant or wild-type groups were significantly higher than in the EGFR L858R and exon 19 deletion groups. In patients treated with PD-1/PD-L1 inhibitors, the survival rates of patients with EGFR wild-type and rare mutant carcinomas were higher than those with L858R and exon 19 deletion carcinomas. Conclusion: The EGFR rare mutation form of LUAD shows a higher immune activation state compared to wild-type, L858R, and exon 19 deletion variants, indicating it as a potential target for PD-1/PD-L1 inhibitor therapy.
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.