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neoANT-HILL: an integrated tool for identification of potential neoantigens
Ana Carolina M F Coelho1, André L Fonseca1, Danilo L Martins1
1Bioinformatics Multidisciplinary Enviroment (BioME), Institute Metropolis Digital, Federal University of Rio Grande do Norte, UFRN, Natal, Brazil.
Background:
Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives.
Results:
We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity.
Conclusion:
neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available through github at https://github.com/neoanthill/neoANT-HILL.
Insights
Identifying cancer neoantigens is crucial for immunotherapy. Our new tool, neoANT-HILL, efficiently detects potential neoantigens from Next Generation Sequencing (NGS) data, improving accuracy and reducing false positives.
Area of Science:
- Immunogenomics
- Cancer Research
- Bioinformatics
Background:
- Cancer neoantigens are key targets for immunotherapy due to their ability to trigger anti-tumor responses.
- Neoantigens arise from somatic mutations in cancer cells, leading to altered proteins.
- Accurate neoantigen identification is challenging due to a high rate of false positives.
Purpose of the Study:
- To develop an efficient and automated pipeline for identifying potential cancer neoantigens.
- To improve neoantigen detection from Next Generation Sequencing (NGS) data using integrated immunogenomic analyses.
Main Methods:
- Developed neoANT-HILL, an automated pipeline integrated with immunogenomic analyses.
- Packaged the pipeline in a Docker image for simplified setup and use.
- Applied neoANT-HILL to The Cancer Genome Atlas (TCGA) melanoma dataset and RNA-Seq data.
Main Results:
- neoANT-HILL successfully identified putative neoantigens, including those from RAC1:P29S and SERPINB3:E250K mutations in melanoma.
- The pipeline demonstrated high sensitivity and specificity in identifying potential neoantigens from RNA-Seq data.
- The tool is user-friendly with a graphical interface, capable of processing multiple samples.
Conclusions:
- neoANT-HILL provides an efficient and user-friendly solution for neoantigen prediction.
- The tool integrates multiple analyses, including RNA-Seq data and immune cell quantification, for enhanced neoantigen discovery.
- neoANT-HILL is available via GitHub, facilitating its adoption in cancer immunotherapy research.
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