ReviewCancers2020
From Tumor Mutational Burden to Blood T Cell Receptor: Looking for the Best Predictive Biomarker in Lung Cancer Treated with Immunotherapy.
Review in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Pooled it
- Recent advances in lipid nanoparticles for cancer vaccine delivery: Challenges and future perspectives.International journal of pharmaceutics: X · 2026Review
- Unveiling non-small cell lung cancer treatment effect heterogeneity: a comparative analysis of statistical methods.Journal of the National Cancer Institute · 2025Article
- FLT3LG modulates the infiltration of immune cells and enhances the efficacy of anti-PD-1 therapy in lung adenocarcinoma.BMC cancer · 2025Article
- Article
- Efficacy of Pembrolizumab vs. Nivolumab Plus Ipilimumab in Metastatic NSCLC in Relation to PD-L1 and TMB Status.Cancers · 2024Article
- Modern cancer therapy: cryoablation meets immune checkpoint blockade.Frontiers in oncology · 2024Review
- Review
- Can we predict T cell specificity with digital biology and machine learning?Nature reviews. Immunology · 2023Review
- Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors.Nature genetics · 2023Article
- Identifying Candidates for Immunotherapy among Patients with Non-Melanoma Skin Cancer: A Review of the Potential Predictors of Response.Journal of clinical medicine · 2022Review
- Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types.Genes · 2022Article
- Immune Checkpoint Inhibitors in Triple Negative Breast Cancer: The Search for the Optimal Biomarker.Biomarker insights · 2022Review
- Progress on neoadjuvant immunotherapy in resectable non-small cell lung cancer and potential biomarkers.Frontiers in oncology · 2022Review
- Article
- Plasma Proteomic Analysis in Non-Small Cell Lung Cancer Patients Treated with PD-1/PD-L1 Blockade.Cancers · 2021Article
- Article
- Diagnostic and Predictive Biomarkers in Lung Cancer.Cancers · 2021Article
- Review
- Current Status of Immune Checkpoint Inhibitor Immunotherapy for Lung Cancer.Frontiers in oncology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite therapeutic advances, lung cancer (LC) is one of the leading causes of cancer morbidity and mortality worldwide. Recently, the treatment of advanced LC has experienced important changes in survival benefit due to immune checkpoint inhibitors (ICIs). However, overall response rates (ORR) remain low in unselected patients and a large proportion of patients undergo disease progression in the first weeks of treatment. Therefore, there is a need of biomarkers to identify patients who will benefit from ICIs. The programmed cell death ligand 1 (PD-L1) expression has been the first biomarker developed. However, its use as a robust predictive biomarker has been limited due to the variability of techniques used, with different antibodies and thresholds. In this context, tumor mutational burden (TMB) has emerged as an additional powerful biomarker based on the observation of successful response to ICIs in solid tumors with high TMB. TMB can be defined as the total number of nonsynonymous mutations per DNA megabases being a mechanism generating neoantigens conditioning the tumor immunogenicity and response to ICIs. However, the latest data provide conflicting results regarding its role as a biomarker. Moreover, considering the results of the recent data, the use of peripheral blood T cell receptor (TCR) repertoire could be a new predictive biomarker. This review summarises recent findings describing the clinical utility of TMB and TCRβ (TCRB) and concludes that immune, neontigen, and checkpoint targeted variables are required in combination for accurately identifying patients who most likely will benefit of ICIs.
Indexed as
Identifiers
What Socratic 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 Socratic graph.