Related Experiment Video
Updated: Jan 7, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
OmniNeo: a multi-omics pipeline incorporating proteomics and AI selection for neoantigen optimization in tumor
Manman Lu1,2, Yang Liu2, Linfeng Xu3
1College of Food Science and Technology, Shanghai Ocean University, Shanghai, China.
Abstract:
Neoantigen-based vaccines represent a promising approach in cancer immunotherapy, with the key to their effective clinical application lying in the precise identification of immunogenic neoantigens. Existing methods primarily focus on genomic variations, lacking integration of multi-omics data and essential filtering steps, which limits comprehensive assessment of immunogenicity and results in only a small subset of neoantigens capable of eliciting effective immune responses. Moreover, the complexity and poor portability further hinder the clinical applicability. To address these limitations, we developed OmniNeo, an automated multi-omics-based neoantigen discovery framework. 1) OmniNeo integrates whole-genome/exome sequencing (WGS/WES), transcriptomic, and proteomics data to simultaneously identify neoantigenic epitopes derived from SNVs/Indels, frameshift mutations, gene fusions, and non-coding region variations; 2) The pipeline incorporates a convolutional neural network-based model, OmniNeo-CNN along with multiple filtering mechanisms to quantify the immunogenicity and T-cell receptor (TCR) recognition potential of predicted neoantigen candidates through multiple features; 3) The workflow is built on nextflow, offering a one-stop, scalable, and portable solution for rapid and efficient neoantigen prediction. Finally, we demonstrated the practical application procedures of this workflow in potential tumor immunotherapy through case study analyses of liver cancer samples. The tool is freely accessible as an open-source resource via https://github.com/linfengxu/OmniNeo, https://zenodo.org/records/15340824.
Insights
OmniNeo is a new framework for discovering cancer neoantigens using multi-omics data. It improves immunogenicity prediction and offers a scalable, portable solution for cancer immunotherapy development.
Area of Science:
- Cancer immunotherapy
- Genomics and Bioinformatics
- Precision Medicine
Background:
- Neoantigen-based vaccines are promising for cancer immunotherapy.
- Current neoantigen identification methods lack multi-omics integration and robust filtering, limiting immunogenicity assessment and clinical applicability.
- Complexity and poor portability of existing tools hinder widespread clinical use.
Purpose of the Study:
- To develop an automated, multi-omics framework for comprehensive neoantigen discovery.
- To enhance the prediction of immunogenic neoantigens by integrating diverse data types and advanced computational models.
- To provide a scalable, portable, and user-friendly solution for rapid neoantigen identification.
Main Methods:
- OmniNeo integrates whole-genome/exome sequencing (WGS/WES), transcriptomic, and proteomics data.
- Identifies neoantigenic epitopes from various genetic variations including SNVs/Indels, frameshift mutations, gene fusions, and non-coding region variations.
- Employs a convolutional neural network (OmniNeo-CNN) and multiple filtering steps to quantify immunogenicity and T-cell receptor (TCR) recognition potential.
- Utilizes Nextflow for a scalable and portable workflow.
Main Results:
- OmniNeo successfully integrates multi-omics data for comprehensive neoantigen identification.
- The OmniNeo-CNN model effectively quantifies neoantigen immunogenicity and TCR recognition potential.
- The Nextflow-based workflow provides a rapid, scalable, and portable solution for neoantigen prediction.
- Demonstrated practical application in liver cancer samples for potential immunotherapy.
Conclusions:
- OmniNeo offers an advanced, automated framework for discovering immunogenic neoantigens by leveraging multi-omics data.
- The tool addresses limitations of existing methods, improving the accuracy and efficiency of neoantigen prediction for cancer immunotherapy.
- OmniNeo is an open-source resource, facilitating its accessibility and application in clinical research and development.
More Related Videos
Related Concept Videos
01:27Tumor Immunotherapy
02:50Combination Therapies and Personalized Medicine
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...