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At least 19 recordsLinked to original sources
PubMed2026

PMGen: from peptide-MHC structure prediction to peptide generation.

MOTIVATION: Accurate structural modeling of peptide-major histocompatibility complex (pMHC) complexes is essential for structure-driven immunotherapy design, yet current prediction tools suffer from narrow class coverage, restricted peptide lengths, insufficient accuracy, and a lack of built-in structure-aware peptide sampling. Consequently, most mimotope and altered peptide ligand designs rely solely on sequence substitution, leaving spatial and biophysical insights from pMHC structures largely unexploited. RESULTS: We introduce peptide-MHC generator (PMGen), an integrated framework for structure prediction and structure-guided design of variable-length peptides across MHC Class I and II. PMGen enforces anchor constraints within AlphaFold2 through two complementary strategies, initial guess and template engineering, achieving state-of-the-art structural fidelity without model fine-tuning. On a comprehensive benchmark, PMGen outperforms all existing methods, yielding median peptide-core Cα RMSDs of 0.62 Å for MHC-I and 0.33 Å for MHC-II. We show that PMGen can recover incorrectly predicted anchor positions and that AlphaFold pLDDT scores enable sequence-independent binding-core identification. Applied to a published neoantigen/wild-type pair, PMGen accurately captures mutation-induced conformational changes. Beyond structure prediction, we show that ProteinMPNN sampling on PMGen-predicted backbones yields higher affinity peptides while preserving the parental 3D conformation. Using PMGen to generate 63 817 high-confidence pMHC structures as training data, we further improve ProteinMPNN's peptide sequence recovery from 0.14 to 0.64 on a test set of 85 unseen MHC-I alleles, highlighting the value of accurate predicted structures for downstream machine learning tasks. AVAILABILITY AND IMPLEMENTATION: PMGen is freely available at https://github.com/soedinglab/PMGen, with an interactive Colab notebook at https://colab.research.google.com/github/soedinglab/PMGen/blob/master/colab.ipynb.

Peptides↗
PubMed2026

PepGen: conditional generation of peptides for MHC binding.

MOTIVATION: Peptide-MHC II binding drives adaptive immunity, yet discovery of novel binder peptides remains challenging due to open binding grooves of MHC-II that accommodate variable-length peptides. While discriminative models perform well, they are unfeasible for generation via enumeration due to vast peptide space (2013≈8×1016 for peptides of length 13 amino acids). Generative AI approaches could accelerate binder design to enable vaccines targeted to particular MHC-II alleles or optimize other peptide chemical properties. RESULTS: We introduce PepGen, the first protein language model for MHC II peptide generation building on Generalized Language Modeling. PepGen conditions on alleles, arbitrary partial peptides including putative TCR-interacting motifs, and continuous binding affinity. Across multiple benchmarks including infilling and de novo generation, PepGen outperformed frequency sampling, Gibbs clustering, and autoregressive baselines. Adjusted log-probabilities enable good classification performance. Experimental validation confirmed that the SARS-CoV-2 peptide TEGALNTPKDHIGTR binding the HLA-DQA101:03-DQB106:03 allele can be redesigned to bind the HLA-DQA101:02-DQB105:02 allele. PepGen generated three putative TCR-motif-preserving binders gaining up to 70% of original MFI. Overall, PepGen provides scalable, motif-constrained MHC II peptide redesign and de novo generation, validated through thorough benchmarks and functional assays. AVAILABILITY AND IMPLEMENTATION: Code and Data are available at https://github.com/DaniTheOrange/PepGen.

Peptides↗
PubMed2025

Proximity between LAG-3 and the T cell receptor guides suppression of T cell activation and autoimmunity.

Therapeutically targeting pathogenic T cells in autoimmune diseases has been challenging. Although LAG-3, an inhibitory checkpoint receptor specifically expressed on activated T cells, is known to bind to major histocompatibility complex class II (MHC class II), we demonstrate that MHC class II interaction alone is insufficient for optimal LAG-3 function. Instead, LAG-3's spatial proximity to T cell receptor (TCR) but not CD4 co-receptor, facilitated by cognate peptide-MHC class II, is crucial in mediating CD4+ T cell suppression. Mechanistically, LAG-3 forms condensate with TCR signaling component CD3ε through its intracellular FSAL motif, disrupting CD3ε/lymphocyte-specific protein kinase (Lck) association. To exploit LAG-3's proximity to TCR and maximize LAG-3-dependent T cell suppression, we develop an Fc-attenuated LAG-3/TCR inhibitory bispecific antibody to bypass the requirement of cognate peptide-MHC class II. This approach allows for potent suppression of both CD4+ and CD8+ T cells and effectively alleviates autoimmune symptoms in mouse models. Our findings reveal an intricate and conditional checkpoint modulatory mechanism and highlight targeting of LAG-3/TCR cis-proximity for T cell-driven autoimmune diseases lacking effective and well-tolerated immunotherapies.

Animals↗
PubMed2026

Comparative Genomics-Guided Epitope Prioritization and in Silico Design of a Multi-Epitope DNA Vaccine Candidate Against Megalocytivirus pagrus 1.

Megalocytivirus pagrus 1 infection is a World Organisation for Animal Health-listed aquatic animal disease caused by a virus species comprising the RSIV, ISKNV, and TRBIV genogroups. Here, we integrated comparative genomics and immunoinformatics to prioritize a multi-epitope protein construct, pMEV, and to design a DNA vaccine candidate encoding it, with emphasis on RSIV-type infection relevant to rock bream aquaculture. Analysis of 61 complete genomes identified 28 core gene clusters, from which myristoylated membrane protein (MMP) and major capsid protein (MCP) were prioritized as source antigens for epitope screening. Four cytotoxic T-cell, five helper T-cell, and five linear B-cell epitope candidates were selected based on sequence-based screening and exploratory peptide-MHC docking. The selected epitopes were assembled with rock bream beta-defensin-3, PADRE, and peptide linkers to generate the 283-aa pMEV construct. Sequence-based physicochemical analyses indicated properties relevant to subsequent structural and expression-based evaluation, while computationally refined structural modeling identified nine putative conformational B-cell epitope regions. TLR3 docking, normal mode analysis, and a 200-ns molecular dynamics simulation characterized the structural behavior of the selected computational complex without inferring receptor activation. C-ImmSim further generated model-dependent generic humoral and helper T-cell-associated response patterns within a mammalian-based simulation framework. Finally, the pMEV coding sequence was codon-optimized and incorporated into an in silico pcDNA3.1(+)-based DNA vaccine design. Collectively, this study provides a comparative genomics-guided framework for prioritizing an experimentally testable multi-epitope DNA vaccine candidate against M. pagrus 1, while construct expression, immunogenicity, and protective efficacy remain to be evaluated experimentally.

Animals↗
PubMed2026

Machine learning prognostic model and drug survival analysis for lung adenocarcinoma in the context of radiotherapy.

BACKGROUND: Patients with lung adenocarcinoma (LUAD) receiving radiotherapy represent an important but underexplored clinical subgroup. These patients often undergo concomitant pharmacologic treatments, yet the prognostic impact and underlying determinants of such combined regimens remain poorly understood. OBJECTIVE: This retrospective observational study aimed to develop and validate a radiotherapy-specific machine learning prognostic model for LUAD and to compare survival across concomitant pharmacologic regimens. METHODS: In this retrospective observational study, using genomic and clinical data from TCGA, a radiotherapy-specific prognostic model for LUAD was developed and validated through ten machine learning algorithms. Survival analyses were conducted across distinct concomitant pharmacologic strategies, followed by functional enrichment to elucidate molecular mechanisms underlying differential outcomes. RESULTS: Demonstrating robust prognostic abilities, the model efficiently sorted patients into high- and low-risk categories. Both treatment type and risk score independently predicted overall survival, with significant interaction effects. Low-risk patients receiving targeted or combination therapy-mainly erlotinib, gefitinib, or bevacizumab-exhibited substantially improved survival compared with those receiving conventional chemotherapy. Enrichment of "Exogenous peptide presentation," "MHC class II assembly," "Peptide-MHC II assembly," and "Symbiotic interaction" pathways indicated immune modulation and host-tumor crosstalk as key mediators of treatment efficacy. CONCLUSION: This study establishes a radiotherapy-specific prognostic model for lung adenocarcinoma, demonstrating distinct molecular and therapeutic heterogeneity and highlighting the superior survival benefit of targeted combination therapy in low-risk patients.

Humans↗
PubMed2026

Immunopeptidomics-driven MHC class II peptide-binding motif discovery for 2 common canine DR alleles.

Despite the central role of major histocompatibility complex (MHC) class II in adaptive immunity, peptide-binding motifs have yet to be characterized for any canine MHC class II allele. Here, we report the first immunopeptidomics-derived binding motifs for DLA-DRB1*015:01 (DLA-DR15) and DLA-DRB1*012:01 (DLA-DR12), 2 alleles overrepresented in breeds predisposed to immune-mediated diseases. Because dogs co-express DLA-DR and DLA-DQ, the MHC class II Ab clone YKIX334.2 was validated to be DLA-DR-specific, enabling allele-selective immunoaffinity purification of DLA-DR molecules from homozygous DLA-DR15 and DLA-DR12 donor spleens. Mass spectrometry and GibbsCluster motif deconvolution of 838 DLA-DR15-associated and 644 DLA-DR12-associated peptides eluted from their respective peptide-binding grooves revealed distinct allele-specific binding motifs, with characterization of anchor residue preferences, peptide-length distributions, cross-species comparisons with human and murine MHC class II motifs, and source protein composition of the eluted self-peptidome. To evaluate the translational utility of these motifs, recombinant DLA-DR15 and DLA-DR12 molecules were used to screen rabies virus glycoprotein and nucleoprotein peptide libraries via fluorescence-based peptide competition assays, identifying high-affinity candidate binders for both alleles. Spearman rank correlation between immunopeptidomics-derived position-specific scoring matrix scores and peptide competition assay rankings demonstrated modest associations, consistent with these approaches capturing complementary dimensions of peptide-MHC class II interaction. Ultimately, these findings establish what we believe is the first allele-specific peptide-binding motif framework for canine MHC class II, providing a foundation for DLA-allele-informed CD4+ T-cell epitope discovery studies and Ag-specific immune response characterization in the dog.

Animals↗
PubMed2024

Innovations Toward Immunopeptidomics.

Over the past 30 years, immunopeptidomics has grown alongside improvements in mass spectrometry technology, genomics, transcriptomics, T cell receptor sequencing, and immunological assays to identify and characterize the targets of activated T cells. Together, multiple research groups with expertise in immunology, biochemistry, chemistry, and peptide mass spectrometry have come together to enable the isolation and sequence identification of endogenous major histocompatibility complex (MHC)-bound peptides. The idea to apply highly sensitive mass spectrometry techniques to study the landscape of peptide antigens presented by cell surface MHCs was innovative and continues to be successfully used and improved upon to deepen our understanding of how peptide antigens are processed and presented to T cells. Multiple research groups were involved in this bringing immunopeptidomics to the forefront of translational research, and we will highlight the contributions of one of the earliest developers, Professor Donald F. Hunt, and his research group at the University of Virginia. The Hunt laboratory applied cutting edge mass spectroscopy-based immunopeptidomics to study cancer, autoimmunity, transplant rejection, and infectious diseases. Across these diverse research areas, the Hunt laboratory and collaborators would characterize previously unknown MHC peptide-binding motifs and identify immunologically active antigens using ultra sensitive mass spectrometry techniques. Amazingly, many of the MHC-bound peptide antigens discovered in collaborations with the Hunt laboratory were sequenced by mass spectrometry before the completion of the human genome using manual de novo sequencing. In this perspective article, we will chronicle the work of the Hunt laboratory and their many collaborators that would be a major part of the foundation for mass spectrometry-based immunopeptidomics and its application to immunology research.

Animals↗
PubMed2024

Mass Spectrometry-Based Proteomics for the Masses: Peptide and Protein Identification in the Hunt Laboratory During the 2000's.

There has been a rapid increase in the number of individuals utilizing mass spectrometry-based proteomics to study complex biological systems and questions since the start of the 2000's. Building off the advancements in ionization and liquid chromatography scientists continued to push towards technology that would enable in-depth analysis of biological specimen. Donald F Hunt and the Hunt laboratory were major contributors to this effort with their work on improving upon existing Fourier Transform MS, development of electron transfer dissociation, and continued work on ion-ion reactions to improve intact protein analysis. Collaboration with other instrumentation laboratories and instrument companies led to the sharing of technology and eventual commercialization providing greater access. Additionally, the Hunt laboratory spread the gospel of MS-based proteomics through collaborations that lasted decades with other scientists who were experts in immunology, cellular signaling, epigenetics, and other fascinating fields. This article attempts to highlight the many contributions of Don and the Hunt laboratory to peptide and protein identification since the year 2000.

Humans↗
PubMed2024

Targeting peptide antigens using a multiallelic MHC I-binding system.

Identifying highly specific T cell receptors (TCRs) or antibodies against epitopic peptides presented by class I major histocompatibility complex (MHC I) proteins remains a bottleneck in the development of targeted therapeutics. Here, we introduce targeted recognition of antigen-MHC complex reporter for MHC I (TRACeR-I), a generalizable platform for targeting peptides on polymorphic HLA-A*, HLA-B* and HLA-C* allotypes while overcoming the cross-reactivity challenges of TCRs. Our TRACeR-MHC I co-crystal structure reveals a unique antigen recognition mechanism, with TRACeR forming extensive contacts across the entire peptide length to confer single-residue specificity at the accessible positions. We demonstrate rapid screening of TRACeR-I against a panel of disease-relevant HLAs with peptides derived from human viruses (human immunodeficiency virus, Epstein-Barr virus and severe acute respiratory syndrome coronavirus 2), and oncoproteins (Kirsten rat sarcoma virus, paired-like homeobox 2b and New York esophageal squamous cell carcinoma 1). TRACeR-based bispecific T cell engagers and chimeric antigen receptor T cells exhibit on-target killing of tumor cells with high efficacy in the low nanomolar range. Our platform empowers the development of broadly applicable MHC I-targeting molecules for research, diagnostic and therapeutic applications.

Humans↗
PubMed2025

PepMapViz: a versatile toolkit for peptide mapping, visualization, and comparative exploration.

SUMMARY: PepMapViz is a versatile R package that provides flexible peptide mapping and visualization capabilities. PepMapViz can import peptide data output from multiple popular mass spectrometry analysis tools, map peptides to their parent protein sequences, highlight protein domains and modifications, and enable comparative visualization across multiple experimental conditions. Beyond enabling visualization of MHC-presented peptide clusters in different antibody regions to predict potential immunogenicity of antibody-based therapies, PepMapViz can also aid in the visualization of cross-software mass spectrometry results at the peptide level for specific proteins, domain details in a linearized format, and post-translational modification coverage across different experimental conditions. AVAILABILITY AND IMPLEMENTATION: PepMapViz is freely available on GitHub at https://github.com/Genentech/PepMapViz and on CRAN. The package is implemented in R and includes documentation and example datasets.

Software↗
PubMed2025

STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features.

The adaptive immune system monitors cellular integrity by recognizing short peptides from intracellular proteins presented on Major Histocompatibility Complex class I (MHC-I) molecules, collectively termed peptide-MHC complexes (pMHC), enabling detection of foreign or mutated proteins. With the rising importance of immunotherapies targeting neoantigens in cancers, the ability to accurately predict which peptides will bind to the diverse population of MHC alleles is critically important. Current computational methods for pMHC-I prediction fall broadly into sequence-based methods, which rely heavily on large training datasets, and structure-based methods that leverage structural modeling and energetics of pMHC binding. While sequence-based methods have been popularly used, their performance is dependent on the size and quality of training data. On the other hands, while structure-based approaches can generalize better across diverse MHC alleles, they traditionally depend on identifying a single global minimum energy conformation, an assumption that often fails due to the inherent binding promiscuity of MHC-I molecules. To address these limitations, we developed a STRUMP-I (STRUcture-based pMHC Prediction (for class I)), a novel pMHC binding prediction tool that directly leverages a broad set of force-field-derived energy terms as machine-learning features. STRUMP-I achieves performance comparable to state-of-the-art sequence-based models while significantly outperforming them on MHC alleles with limited representation in training data. Furthermore, STRUMP-I demonstrates strong synergy when integrated with sequence-based methods, notably enhancing prediction precision. The robustness and generalizability of STRUMP-I were confirmed by evaluating its predictive performance on independent, previously unseen datasets, including an experimentally validated cancer neoantigen dataset. This combined approach advances our capability to reliably identify clinically relevant neoantigen targets. The source code and trained models are available at https://github.com/yoonjoolab/STRUMP-I.

energy optimization↗
PubMed2026

Noncanonical Transcription and Splicing Shape the Colorectal Cancer Immunopeptidome in MSI and MSS Tumors.

Treatment with immune checkpoint inhibitors in colorectal cancer (CRC) has largely benefited patients with microsatellite instability-high (MSI-H) and not the larger proportion of patient with microsatellite-stable (MSS) tumors. This clinical dichotomy has fueled the view that high mutational burden is the dominant driver of tumor immunogenicity and that MSS CRC fails to respond because it is "antigen poor". To directly test this premise and define the origins of presented tumor antigens, we integrated HLA class I immunopeptidomics and matched RNA-seq from 26 primary CRC tumors spanning MSI-H and MSS subtypes. Using patient-specific canonical and cancer-specific proteogenomic databases, we identified 115,292 unique major histocompatibility complex (MHC)-associated peptides (MAPs) across 61 HLA alleles, with a mean of 9292 MAPs per tumor and no significant difference in MAP counts between MSI-H and MSS tumors. In toto, we identified 266 tumor antigens, all coded by unmutated genomic sequences, comprising 70 aberrantly expressed tumor-specific antigens (aeTSAs) and 196 tumor-associated antigens (TAAs). In our cohort, MSS tumors presented more TAAs and a comparable number of aeTSAs per tumor relative to MSI-H tumors. In TCGA-COAD stratified analyses (483 tumors), MSS tumors yielded more presentable aeTSAs and TAAs per patient than MSI-H tumors. Across both subtypes, aeTSAs arose predominantly from intronic translation, UTR usage, retroelement activation, and germline-like transcription, including recurrent aeTSAs from PIWIL1, L1TD1, and endogenous retroviral loci. Together, these data demonstrate that MSS CRC is not antigen poor and highlight noncanonical translation as a major, previously underappreciated contributor to the CRC immunopeptidome.

Humans↗
PubMed2024

Immunopeptidomics Mapping of Listeria monocytogenes T Cell Epitopes in Mice.

Listeria monocytogenes is a foodborne intracellular bacterial model pathogen. Protective immunity against Listeria depends on an effective CD8+ T cell response, but very few T cell epitopes are known in mice as a common animal infection model for listeriosis. To identify epitopes, we screened for Listeria immunopeptides presented in the spleen of infected mice by mass spectrometry-based immunopeptidomics. We mapped more than 6000 mouse self-peptides presented on MHC class I molecules, including 12 high confident Listeria peptides from 12 different bacterial proteins. Bacterial immunopeptides with confirmed fragmentation spectra were further tested for their potential to activate CD8+ T cells, revealing VTYNYINI from the putative cell wall surface anchor family protein LMON_0576 as a novel bona fide peptide epitope. The epitope showed high biological potency in a prime boost model and can be used as a research tool to probe CD8+ T cell responses in the mouse models of Listeria infection. Together, our results demonstrate the power of immunopeptidomics for bacterial antigen identification.

Animals↗
PubMed2026

GAMMA: gap-aware motif mining under incomplete labeling with applications to MHC motifs.

MOTIVATION: Sequence motif identification is crucial for understanding molecular recognition, particularly in immune responses involving peptide binding to major histocompatibility complex (MHC) Class I molecules for antigen presentation to T cells. Traditionally, MHC Class I binding motifs are assumed to be contiguous and span nine amino acids. However, structural evidence suggests that binding may involve nonadjacent residues, challenging the assumptions of existing methods. RESULTS: In this study, we propose Gap-Aware Motif Mining Algorithm (GAMMA), a probabilistic framework designed to identify noncontiguous motifs under conditions of incomplete labeling. GAMMA employs Bayesian inference with Markov chain Monte Carlo sampling to jointly estimate motif parameters, binding locations, and the relative spacing between binding positions. Through extensive simulations and real-world applications to MHC Class I peptide datasets, GAMMA outperforms existing motif discovery tools such as GLAM2 in accurately localizing binding residues and identifying the underlying motifs. Notably, our results suggest that the true number of binding residues may be eight, fewer than the commonly assumed nine. In addition, for longer peptides, the model captures increased flexibility in the central region, consistent with structural observations that peptides may bulge in the middle. AVAILABILITY AND IMPLEMENTATION: The raw data and the source codes are available on GitHub (https://github.com/RanLIUaca/GAMMAmotif).

Amino Acid Motifs↗
PubMed2026

PRMT5-mediated intron retention triggers innate and adaptive immunity against cancer.

PRMT5 is expressed at high levels in many cancers, where it regulates diverse cellular pathways that contribute to oncogenesis. Here, we have defined a new role for PRMT5 in regulating and coordinating the interplay between the innate and adaptive immune response. This occurs, in part, through the influence of PRMT5 and E2F1 on RNA splicing and the presence of retained introns (RIs). We found that RIs have a propensity to form double-stranded RNAs that contribute to the innate response. Furthermore, many RIs contain non-canonical open-reading frames (ncORFs), which can be translated and then processed into small peptides that assemble with the MHC class I complex. Significantly, RI-derived peptides are highly immunogenic and, as a murine cancer vaccine, carrying a string of antigenic RI peptides, delayed tumour growth and enhanced survival. RIs are present in human tumour cells, and we identified T lymphocytes in human cancer patients, with antigen specificity for RI-derived peptides, that killed human tumour cells in vitro. Regulating intron retention thus offers a new therapeutic approach to enhance tumour immunogenicity.

Animals↗
PubMed2025

Characterization of Tumor Antigens from Multi-omics Data: Computational Approaches and Resources.

Tumor-specific antigens, also known as neoantigens, have potential utility in anti-cancer immunotherapy, including immune checkpoint blockade (ICB), neoantigen-specific T cell receptor-engineered T (TCR-T), chimeric antigen receptor T (CAR-T), and therapeutic cancer vaccines (TCVs). After recognizing presented neoantigens, the immune system becomes activated and triggers the death of tumor cells. Neoantigens may be derived from multiple origins, including somatic mutations (single nucleotide variants, insertions/deletions, and gene fusions), circular RNAs, alternative splicing, RNA editing, and polymorphic microbiomes. An increasing amount of bioinformatics tools and algorithms are being developed to predict tumor neoantigens derived from different sources, which may require inputs from different multi-omics data. In addition, calculating the peptide-major histocompatibility complex (MHC) affinity can aid in selecting putative neoantigens, as high binding affinities facilitate antigen presentation. Based on these approaches and previous experiments, many resources have been developed to reveal the landscape of tumor neoantigens across multiple cancer types. Herein, we summarize these tools, algorithms, and resources to provide an overview of computational analysis for neoantigen discovery and prioritization, as well as the future development of potential clinical utilities in this field.

Humans↗
PubMed2026

A modular γδ TCR-T platform combining KRAS pMHC targeting with re-dosable mRNA engager redirection.

Solid tumors often evade TCR-engineered αβ T cells when antigen expression varies or when the restricting Human Leukocyte Antigen (HLA) allele is lost. γδ T cells, in contrast, detect cellular dysregulation through non-peptide/Major Histocompatibility Complex (MHC) cues, including phosphoantigens and stress ligands, and can be developed as allogeneic therapies. Although intratumoral γδ T cell signatures are associated with improved outcome across cancers, γδ recognition itself is broad and still selected within the thymus just as αβ T cell receptors (TCRs) are. It does not, however, anchor specificity to a defined driver-mutation pMHC epitope. We therefore asked whether a high-affinity, co-receptor-independent αβ TCR could graft oncogenic-driver specificity onto γδ T cells while leaving the endogenous γδ TCR intact. We knocked the KRASG12V/HLA-A*11:01 TCR A11v into primary human γδ T cells. Engineered cells co-expressed the transgenic αβ TCR and the endogenous γδ TCR and lysed KRASG12V/HLA-A*11:01+ tumor cells in vitro and in vivo. To cover potential resistance through loss of HLA-A*11:01, we delivered an mRNA lipid nanoparticle (LNP) encoding a secreted mesothelin×CD3 (M5) bispecific T cell engager (TCE). LNP-M5 produced circulating TCE that redirected γδ A11v T cells and polyclonal bystander T cells to kill mesothelin+ targets, accompanied by development of higher γδ A11v T cell counts in vivo. In humanized mice bearing mixed HLA-A*11:01+ and HLA-A*11:01 - KRASG12V tumors, γδ A11v T cells produced transient control, whereas adding LNP-M5 yielded complete responses and prolonged survival. Thus, this two-part therapy couples invariant driver targeting to tunable redirection and addresses loss of the restricting HLA allele, a central escape route for TCR-based therapy. It provides an off-the-shelf reagent to enable KRAS-anchored treatment with the ability to redeliver the reagent.

Humans↗
PubMed2026

Integrated molecular and immune profiling identifies FOXA1 as a complementary co-target to MUC1 for bispecific immunotherapy in breast cancer.

In breast cancer immunotherapy, Mucin 1 (MUC1) is a well-established target with promising preclinical results; however, single targeting of MUC1 has demonstrated limited efficacy in clinical trials, largely due to tumor heterogeneity, diverse glycosylation patterns, and an immunosuppressive TME. Identification of complementary co-targets enables bi-specific or dual-target immunotherapy, limiting antigen escape, improving specificity, and reducing relapse. Here, we employed a comprehensive multi-layered analytical approach to evaluate MUC1 expression, clinical relevance, and methylation status, followed by systematic screening of MUC1-correlated genes. Antigenicity prediction and protein-protein interaction analyses identified Forkhead Box A1 (FOXA1) as a potential functional partner. Expression analysis revealed concordant patterns of MUC1 and FOXA1 across breast cancer samples, while network mapping demonstrated shared interactions with adhesion-associated proteins, including CTNNB1, CTNND1, and CDH1, suggesting roles in epithelial organization and tumor progression. Further validation using gene expression datasets from Indian breast cancer cohorts confirmed consistent expression and correlation patterns, supporting reproducibility across populations. Immune profiling revealed an inverse association between MUC1-FOXA1 co-expression and immune-related gene signatures, with high co-expression linked to reduced infiltration of dendritic cells, CD4⁺ and CD8⁺ T cells, macrophages, and natural killer cells, indicative of an immunosuppressive microenvironment. Negative correlations with MHC Class I genes further suggested impaired antigen presentation. Epitope prediction identified high-affinity peptides from both targets with strong MHC Class I binding potential. Collectively, these findings support the associated role of MUC1 and FOXA1 as dual immunotherapeutic targets in breast cancer.

Hepatocyte Nuclear Factor 3-alpha↗