Xing, Yi

Diagnosing rare genetic diseases remains a major challenge despite widespread clinical testing. Long-read RNA sequencing (RNA-seq) offers a powerful approach to capturing the effects of genetic variants on the transcriptome, yet challenges with sequencing coverage, cost, tissue selection, and scalability have limited its clinical adoption. To address this, we developed STRIPE (Sequencing Targeted RNAs Identifies Pathogenic Events), a targeted long-read RNA-seq–based strategy for rare disease diagnosis and variant interpretation. STRIPE enables deep sequencing of full-length transcripts for any customized disease-specific gene panel such that a wide range of clinically informative readouts, including transcript aberrations and sequence variants, can be detected at haplotype-level resolution. Applying STRIPE to 88 individuals spanning two major rare disease groups, we accurately reidentified known pathogenic variants and revealed their transcript consequences, including many unexpected ones. For 8 of 15 splice site region variants, we observed more complex RNA processing defects beyond single exon skipping or cryptic splice site activation. Notably, we find that donor splice site variants frequently activate cryptic intronic polyadenylation sites, leading to premature transcript termination. Leveraging unique strengths of long-read RNA-seq, STRIPE also resolved variants of uncertain significance and uncovered disease-causing variants in five previously undiagnosed individuals. Overall, STRIPE is a powerful, adaptable, and scalable strategy with broad potential to improve clinical variant interpretation and advance genetic diagnosis of rare diseases.

Abstract The prognosis for pediatric high-grade gliomas associated with mutations in the H3-A3 histone gene is very poor. To investigate whether peptide-pulsed dendritic cells (DC) together with checkpoint blockade might be a potential treatment modality for diffuse hemispheric glioma, H3 G34-mutant (DHG), we have developed a novel mouse model, and utilized a novel bioinformatics pipeline to identify tumor-associated peptide antigens (TAs). We used the RCAS/tv-A system to target the expression of H3/G34R and PDGFb, and to knock out p53 in neural progenitors in transgenic C57BL/6 neonatal mice. Three independent cell lines were obtained that expressed transcripts associated with oligodendrocyte and interneuron lineages, and formed lethal tumors after intracranial implantation. To identify TAs, we implemented a new computational workflow to identify nonapeptides generated from alternative splicing of mRNAs that were highly enriched in the tumor transcriptomes of two DHG patients, and predicted to bind the HLA-A*2:01 class I allele carried by these patients. Three of the candidate peptides were conserved in the mouse and also predicted to bind the H2-Kb MHC I allele carried by C57/BL6 mice. Using our syngeneic mouse model, we showed that DCs pulsed with these peptides together with anti-PD1 mAb provided a significant survival benefit compared with checkpoint inhibitor and DCs pulsed with peptides that were not predicted to bind the H2-Kb allele. Splenocytes isolated from successfully treated mice were analyzed through bulk TCR sequencing, and in comparison with control mice and publicly-accessible TCR libraries, 6 highly-represented TCR clonotypes were identified only in treated mice. In summary, we demonstrate that effective TAs can be extracted from RNAseq data, and can be targeted with DC vaccination to generate a systemic anti-tumor cytotoxic T cell response to eliminate DHG in a novel immunocompetent mouse model.