Intron-Retention Neoantigen Load Predicts Favorable Prognosis in Pancreatic Cancer

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

Motivation

Neoantigens are promising targets for cancer immunotherapies and might arise from alternative splicing. However, detecting tumor-specific splicing is challenging because many non-canonical splice junctions identified in tumors also appear in healthy tissues. To increase tumor-specificity, we focused on splicing caused by somatic mutations as a source for neoantigen candidates in individual patients.

Results

We developed the tool splice2neo with multiple functionalities to integrate predicted splice effects from somatic mutations with splice junctions detected in tumor RNA-seq and to annotate the resulting transcript and peptide sequences. Additionally, we provide the tool EasyQuant for targeted RNA-seq read mapping to candidate splice junctions. Using a stringent detection rule, we predicted 1.7 splice junctions per patient as splice targets 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 major histocompatibility complex (MHC)-I or MHC-II binding. Compared to neoepitope candidates from non-synonymous point mutations, the splicing-derived MHC-I neoepitope candidates had lower self-similarity to corresponding wild-type peptides. In conclusion, we demonstrate that identifying mutation-derived, tumor-specific splice junctions can lead to additional neoantigen candidates to expand the target repertoire for cancer immunotherapies.

Availability and implementation

The R package splice2neo and the python package EasyQuant are available at https://github.com/TRON-Bioinformatics/splice2neo and https://github.com/TRON-Bioinformatics/easyquant, respectively.

Alternative splicing: a new breakthrough for understanding tumorigenesis and potential clinical applications

BACKGROUND

Alternative splicing (AS) is a post-transcriptional process that produces transcript variants, thus leading to transcriptome complexity. Recently, the scope of AS studies has been greatly expanded toward clinical applications owing to the abundance of RNA sequencing data.

OBJECTIVE

This review consists of two parts. We first summarize bioinformatic resources that are useful for large-scale cancer-related AS studies. We then highlight the research efforts to utilize AS events for predicting clinical outcomes and planning therapeutic strategies.

RESULTS

Computational approaches to interrogate AS events have been reviewed under three categories: (1) databases to provide functional and clinical annotation of AS events, (2) analytical tools to identify cancer-associated AS event, and (3) methods to identify splicing-related DNA variants and splicing-derived neoantigens. We also present the recent progress in exploring the clinical utility of AS under four categories: (1) identification of AS events for cancer prognosis, (2) utilization of AS events in molecular classification of various cancers, (3) regulatory mechanisms of AS underlying drug resistance, and (4) potential use of AS in cancer therapy.

CONCLUSION

This review will be helpful for understanding the biological implications of AS in cancer and facilitate the development of AS markers for cancer prognosis and treatment. We anticipate that future studies will lead to the application of genome-wide AS profiles in cancer precision medicine.

Retained introns in long RNA-seq reads are not reliably detected in sample-matched short reads

BACKGROUND

There is growing interest in retained introns in a variety of disease contexts including cancer and aging. Many software tools have been developed to detect retained introns from short RNA-seq reads, but reliable detection is complicated by overlapping genes and transcripts as well as the presence of unprocessed or partially processed RNAs.

RESULTS

We compared introns detected by 8 tools using short RNA-seq reads with introns observed in long RNA-seq reads from the same biological specimens. We found significant disagreement among tools (Fleiss' [Formula: see text]) such that 47.7% of all detected intron retentions were not called by more than one tool. We also observed poor performance of all tools, with none achieving an F1-score greater than 0.26, and qualitatively different behaviors between general-purpose alternative splicing detection tools and tools confined to retained intron detection.

CONCLUSIONS

Short-read tools detect intron retention with poor recall and precision, calling into question the completeness and validity of a large percentage of putatively retained introns called by commonly used methods.

Bioinformatics detection of modulators controlling splicing factor-dependent intron retention in the human brain

Alternative RNA splicing is an important means of genetic control and transcriptome diversity. However, when alternative splicing events are studied independently, coordinated splicing modulated by common factors is often not recognized. As a result, the molecular mechanisms of how splicing regulators promote or repress splice site recognition in a context-dependent manner are not well understood. The functional coupling between multiple gene regulatory layers suggests that splicing is modulated by additional genetic or epigenetic components. Here, we developed a bioinformatics approach to identify causal modulators of splicing activity based on the variation of gene expression in large RNA sequencing datasets. We applied this approach in a neurological context with hundreds of dorsolateral prefrontal cortex samples. Our model is strengthened with the incorporation of genetic variants to impute gene expression in a Mendelian randomization-based approach. We identified novel modulators of the splicing factor SRSF1, including UIMC1 and the long noncoding RNA CBR3-AS1, that function over dozens of SRSF1 intron retention splicing targets. This strategy can be widely used to identify modulators of RNA-binding proteins involved in tissue-specific alternative splicing.