ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Efficient Predictor for Immunotherapy Efficacy: Detecting Pan-Clones Effector Tumor Antigen-Specific T Cells in Blood by Nanoparticles Loading Whole Tumor Antigens.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- First-line PD-1/PD-L1 inhibitors plus chemotherapy vs. chemotherapy alone in stage IIIB-IV non-squamous NSCLC: an updated meta-analysis of phase 3 RCTs.BMC cancer · 2025Pooled it
- Redefining Dendritic Cell Vaccines: Synergistically Co-priming DC and B Cells With Nanoparticles Loading Whole Cell Antigens Maximizes the Efficacy of DC Vaccines.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Elevating MHC I expression on tumor cells by nanovesicles loading tyrosine kinase inhibitors can improve the efficacy of cancer vaccines.Frontiers in immunology · 2025Article
- Microbial infection and treatment strategies in cancer patients.Frontiers in microbiology · 2025Review
- Efficient Predictor for Immunotherapy Efficacy: Detecting Pan-Clones Effector Tumor Antigen-Specific T Cells in Blood by Nanoparticles Loading Whole Tumor Antigens.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Cancer involves tumor cells and tumor-specific immunity. The ability to accurately quantify tumor-specific immunity is limited. Most immunotherapies function by activating new effector tumor antigen-specific T cells (ETASTs) or reactivating the pre-existing ETASTs repertoire. Therefore, the amount of ETASTs can be used to characterize immunotherapy efficacy. Tumor antigens are highly heterogeneous and detecting most ETASTs is challenging. Therefore, nanoparticles loading whole-cell tumor antigens are used to activate and detect pan-clones ETASTs in the blood. The differences between ETASTs and other T cells are transformed into activated and non-activated states. By measuring markers of the activated status and cytotoxic function of ETASTs, it can distinguish ETASTs from other T cells. ETASTs in patients with lung cancer are higher than those in healthy individuals and those with benign pulmonary nodules. Therapeutic efficacy positively correlated with the number of ETASTs in the blood. ETATS levels increase only in the blood of patients who respond to immunotherapy. Single-cell sequencing studies validated these findings. This study provides a highly accurate, specific, non-invasive, and efficient biomarker for predicting immunotherapy efficacy in lung and other cancers. This method can also be applied to evaluate the efficacy of other treatments, such as radiotherapy, oncolytic viruses, and nanomedicine-based therapies.
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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.