Löwer, M. | bioRxiv Science

bioRxiv2023

Prediction of tumor-specific splicing from somatic mutations as a source of neoantigen candidates

Splicing is dysregulated in many tumors and may result in tumor-specific transcripts that can encode neoantigens, which are promising targets for cancer immunotherapy. Detecting tumor-specific splicing is challenging because many non-canonical splice junctions identified in tumor transcriptomes also appear in healthy tissues. Here, we developed splice2neo to integrate the predicted splice effects from somatic mutations with splice junctions detected in tumor RNA-seq for individual cancer patients. Splice2neo excludes splice junctions from healthy tissue samples, annotates resulting transcript and peptide sequences, and provides targeted re-quantification of supporting RNA-seq reads. We developed a stringent detection rule to predict splice junctions as mutation-derived targets and identified 1.7 target splice junctions per tumor with a false discovery rate below 5% in a melanoma cohort. We confirmed tumor-specificity using independent, healthy tissue samples. Furthermore, using tumor-derived RNA, we confirmed individual exon skipping events experimentally. Most target splice junctions encoded neoepitope candidates with predicted MHC-I or MHC-II binding. Compared to neoepitope candidates derived from non-synonymous point mutations, the splicing-derived MHC-I neoepitope candidates had a lower self-similarity to corresponding wild-type peptides. In conclusion, we demonstrate that identifying mutation-derived and tumor-specific splice junctions can lead to additional neoantigen candidates to expand the target repertoire for cancer immunotherapies. Key PointsO_LIsplice2neo is a versatile tool for identifying and analyzing of splice junctions as a source of neoantigen candidates C_LIO_LIWe identified mutation-retrieved splice junctions supported by RNA-seq in melanoma samples C_LIO_LIThe predicted target splice junctions exhibited a strong tumor-specificity as they were absent in healthy tissues. C_LIO_LITarget splice junctions often lead to frame-shift peptides and encode promising neoantigen candidates C_LI

bioinformatics↗

bioRxiv2022

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates

A successful response to immune checkpoint blockade treatment (ICB) depends on the functional re-invigoration of neoantigen-specific T cells and their anti-tumoral activity. Previous studies showed that the patients neoantigen candidate load is an imperfect predictor of the response to ICB. Further studies provided evidence that the overall response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. To our knowledge, this is the first study to predict the response to ICB therapy based on qualitative neoantigen candidate profiles in the context of the mutation type, using a multiple instance learning approach. Multiple instance learning is a special branch of machine learning which classifies labelled bags that are formed by a set of unlabeled instances. The multiple instance learning approach performed systematically better than random guessing and was independent of the neoantigen candidate load. Qualitative modeling performed better in comparison to the quantitative approach, in particular for modelling low-abundant fusion genes. Our findings suggest that multiple instance learning is an appropriate method to predict immunotherapy efficacy based on qualitative neoantigen candidate profiles without relying on direct T-cell response information and provide a foundation for future developments in the field.

bioinformatics↗