Immunopeptidomics directly profiles peptides bound to human leukocyte antigens, or HLA molecules, capturing the antigen repertoire displayed on tumor-cell surfaces. The study’s authors identify several constraints in existing immunopeptidomics-based approaches to immunogenic peptide discovery: heavy reliance on sequence databases, dependence on matched normal controls and a lack of dedicated modules for predicting T-cell recognition. These limitations can restrict de novo peptide discovery, clinical applicability and the accuracy of immunogenicity prioritization, according to the research.

MSIPep is designed to address these gaps through an integrated analysis workflow. It combines database-based peptide searching with de novo sequencing strategies and supports both data-dependent acquisition, or DDA, and data-independent acquisition, or DIA, modes. The pipeline also applies false-discovery-rate control at multiple levels to improve the robustness of its results. Its source code is publicly available through a GitHub repository, and the developers provide a Docker container intended to support reproducible and scalable deployment.

For the prioritization stage, MSIPep incorporates ProTCR, an in-house deep-learning model developed to evaluate interactions between peptides and T-cell receptors. Rather than ranking candidates only by whether an HLA-bound peptide has been identified, the model provides a computational estimate of a peptide’s T-cell response potential. The combined framework therefore links identification of tumor-associated peptides with a receptor-interaction model intended to distinguish candidates with higher predicted immunogenicity, although that ranking remains a model-based estimate rather than a direct measurement of patient immune responses.

The evaluation used cancer datasets rather than a clinical trial. Acute myeloid leukemia applications highlighted MSIPep’s reported improvement in finding peptides with high predicted immunogenic potential, and analyses involving high-grade serous ovarian carcinoma and triple-negative breast cancer showed that the workflow could also be applied to solid-tumor data. These results position MSIPep as a reusable computational framework for narrowing large peptide-candidate spaces, but the supplied findings do not establish that prioritized peptides induce immune responses in people, validate them as cancer-vaccine targets or produce clinical benefit. The reported advance is therefore in the identification and computational prioritization of candidate immunogenic peptides across leukemia and solid-tumor datasets.

Subject of Research: Computational identification and prioritization of immunogenic tumor peptides

Article Title: MSIPep: A pipeline for immunogenic peptide identification and prioritization integrating immunopeptidomics and transcriptomics data

Article References: Liu, P., Zhang, S., Lu, M., Xu, M., Liu, Z., Chen, L., Xu, P., Tan, X., & Xie, L. (2026). MSIPep: A pipeline for immunogenic peptide identification and prioritization integrating immunopeptidomics and transcriptomics data. BMC Genomics. https://doi.org/10.1186/s12864-026-13481-5

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13481-5

Keywords: MSIPep, immunopeptidomics, transcriptomics, cancer peptides, neoantigens, T-cell receptors, deep learning, mass spectrometry, acute myeloid leukemia, solid tumors, pipeline, immunogenic