ReviewHuman vaccines & immunotherapeutics2024
Predictive biomarkers for immune checkpoint inhibitors therapy in lung cancer.
Review in Human vaccines & immunotherapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive value of serum cytokines in patients with non-small-cell lung cancer receiving anti-PD-1 blockade therapy: a meta-analysis.Clinical and experimental medicine · 2025Pooled it
- Automatic framework for PD-L1 expression evaluation in Latino patients with non-small cell lung cancer.Journal of pathology informatics · 2026Article
- Role of the Wnt5A pathway in resistance to immune checkpoint inhibitors in advanced Non-Small Cell Lung Cancer.Cancer immunology, immunotherapy : CII · 2026Article
- Psychological distress as a putative host-state determinant of cancer immunotherapy response.Frontiers in immunology · 2026Review
- The application and challenges of immune checkpoint inhibitors in lung cancer therapy.Frontiers in immunology · 2026Review
- Immunotherapy for Elderly Patients with Advanced Non-Small Cell Lung Cancer: Challenges and Perspectives.International journal of molecular sciences · 2025Review
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
Immune checkpoint inhibitors (ICIs) have changed the treatment mode of lung cancer, extending the survival time of patients unprecedentedly. Once patients respond to ICIs, the median duration of response is usually longer than that achieved with cytotoxic or targeted drugs. Unfortunately, there is still a large proportion of lung cancer patients do not respond to ICI. Effective biomarkers are crucial for identifying lung cancer patients who can benefit from them. The first predictive biomarker is programmed death-ligand 1 (PD-L1), but its predictive value is limited to specific populations. With the development of single-cell sequencing and spatial imaging technologies, as well as the use of deep learning and artificial intelligence, the identification of predictive biomarkers has been greatly expanded. In this review, we will dissect the biomarkers used to predict ICIs efficacy in lung cancer from the tumor-immune microenvironment and host perspectives, and describe cutting-edge technologies to further identify biomarkers.
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.