Researchers from the National Research Council (CNR), Italy have introduced a new computational tool called ENEO that could simplify and speed up the process of identifying cancer neoantigens, small mutated proteins that the immune system can recognize as a threat. Neoantigen detection is crucial for creating personalized immunotherapies, such as cancer vaccines, but current methods are slow and require multiple types of data from both tumor and normal tissues.
ENEO offers a more efficient solution by using only tumor RNA sequencing data, without needing matched normal samples or exome sequencing. The tool works by applying a Bayesian model to filter out background noise and identify mutations that result in abnormal proteins. These mutated proteins, if confirmed to be presented on the cell surface, can serve as ideal targets for cancer immunotherapy.
Overview of the ENEO computational workflow
The pipeline is composed of three main phases: reads processing (first step), variant calling (second step), and neoepitopes prediction (third step). Input files required by the workflow are indicated on the top gray box, together with the employed public resources whose first download and setup is guided. Intermediate post-processed output files (i.e. alignment (BAM), variant calling file (VCF), HLA genotyping) used throughout the pipeline are saved and made available for further user inspection.
When tested on the TESLA benchmark dataset and validated across two additional patient cohorts with different cancer types, ENEO demonstrated accuracy on par with traditional, more data-intensive methods. In some cases, it even identified additional neoantigens caused by RNA-specific mutations that DNA-based methods would miss.
This RNA-only approach not only reduces costs and simplifies the analysis pipeline but also expands the potential pool of therapeutic targets. With tumor RNA sequencing already a common practice in cancer research and diagnostics, ENEO may help speed up the development of customized cancer treatments and make them more broadly accessible.
Availability – ENEO is freely available at the URL: https://github.com/ctglab/ENEO.
