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Tumor-associated epitopes presented on MHC-I that can activate the immune system against cancer cells are typically identified from annotated protein-coding regions of the genome, but whether peptides originating from novel or unannotated open reading frames (nuORFs) can contribute to antitumor immune responses remains unclear. Here we show that peptides originating from nuORFs detected by ribosome profiling of malignant and healthy samples can be displayed on MHC-I of cancer cells, acting as additional sources of cancer antigens. We constructed a high-confidence database of translated nuORFs across tissues (nuORFdb) and used it to detect 3,555 translated nuORFs from MHC-I immunopeptidome mass spectrometry analysis, including peptides that result from somatic mutations in nuORFs of cancer samples as well as tumor-specific nuORFs translated in melanoma, chronic lymphocytic leukemia and glioblastoma. NuORFs are an unexplored pool of MHC-I-presented, tumor-specific peptides with potential as immunotherapy targets. New tumor epitopes are discovered by ribosome profiling and immunopeptidome mass spectrometry. [ABSTRACT FROM AUTHOR]

The aberrant activation of tyrosine kinases represents an important oncogenic mechanism, and yet the majority of such events remain undiscovered. Here we describe a bead-based method for detecting phosphorylation of both wild-type and mutant tyrosine kinases in a multiplexed, high-throughput and low-cost manner. With the aim of establishing a tyrosine kinase–activation catalog, we used this method to profile 130 human cancer lines. Follow-up experiments on the finding that SRC is frequently phosphorylated in glioblastoma cell lines showed that SRC is also activated in primary glioblastoma patient samples and that the SRC inhibitor dasatinib (Sprycel) inhibits viability and cell migration in vitro and tumor growth in vivo. Testing of dasatinib-resistant tyrosine kinase alleles confirmed that SRC is indeed the relevant target of dasatinib, which inhibits many tyrosine kinases. These studies establish the feasibility of tyrosine kinome–wide phosphorylation profiling and point to SRC as a possible therapeutic target in glioblastoma. [ABSTRACT FROM AUTHOR]

T cell alloreactivity against minor histocompatibility antigens (mHAgs)-polymorphic peptides resulting from donor-recipient (D-R) disparity at sites of genetic polymorphisms-is at the core of the therapeutic effect of allogeneic hematopoietic cell transplantation (allo-HCT). Despite the crucial role of mHAgs in graft-versus-leukemia (GvL) and graft-versus-host disease (GvHD) reactions, it remains challenging to consistently link patient-specific mHAg repertoires to clinical outcomes. Here we devise an analytic framework to systematically identify mHAgs, including their detection on HLA class I ligandomes and functional verification of their immunogenicity. The method relies on the integration of polymorphism detection by whole-exome sequencing of germline DNA from D-R pairs with organ-specific transcriptional- and proteome-level expression. Application of this pipeline to 220 HLA-matched allo-HCT D-R pairs demonstrated that total and organ-specific mHAg load could independently predict the occurrence of acute GvHD and chronic pulmonary GvHD, respectively, and defined promising GvL targets, confirmed in a validation cohort of 58 D-R pairs, for the prevention or treatment of post-transplant disease recurrence., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.