ArticleBMC bioinformatics2021
neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival.
Article in BMC bioinformatics, 2021. 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
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Characterization of Tumor Antigens from Multi-omics Data: Computational Approaches and Resources.Genomics, proteomics & bioinformatics · 2025Review
- Cellular and molecular features related to exceptional therapy response and extreme long-term survival in glioblastoma.Cancer medicine · 2023Review
- GNIFdb: a neoantigen intrinsic feature database for glioma.Database : the journal of biological databases and curation · 2022Article
- Multiomics Analysis Reveals the Prognostic Non-tumor Cell Landscape in Glioblastoma Niches.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
16 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundNeoantigen based personalized immune therapies achieve promising results in melanoma and lung cancer, but few neoantigen based models perform well in IDH wild-type GBM, and the association between neoantigen intrinsic features and prognosis remain unclear in IDH wild-type GBM. We presented a novel neoantigen intrinsic feature-based deep learning model (neoDL) to stratify IDH wild-type GBMs into subgroups with different survivals.
resultsWe first derived intrinsic features for each neoantigen associated with survival, followed by applying neoDL in TCGA data cohort(AUC = 0.988, p value < 0.0001). Leave one out cross validation (LOOCV) in TCGA demonstrated that neoDL successfully classified IDH wild-type GBMs into different prognostic subgroups, which was further validated in an independent data cohort from Asian population. Long-term survival IDH wild-type GBMs identified by neoDL were found characterized by 12 protective neoantigen intrinsic features and enriched in development and cell cycle.
conclusionsThe model can be therapeutically exploited to identify IDH wild-type GBM with good prognosis who will most likely benefit from neoantigen based personalized immunetherapy. Furthermore, the prognostic intrinsic features of the neoantigens inferred from this study can be used for identifying neoantigens with high potentials of immunogenicity.
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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 Socratic graph.