Hybrid sequencing-based personal full-length transcriptomic analysis implicates proteostatic stress in metastatic ovarian cancer

Abstract

Comprehensive molecular characterization of myriad somatic alterations and aberrant gene expressions at personal level is key to precision cancer therapy, yet limited by current short-read sequencing technology, individualized catalog of complete genomic and transcriptomic features is thus far elusive. Here, we integrated second- and third-generation sequencing platforms to generate a multidimensional dataset on a patient affected by metastatic epithelial ovarian cancer. Whole-genome and hybrid transcriptome dissection captured global genetic and transcriptional variants at previously unparalleled resolution. Particularly, single-molecule mRNA sequencing identified a vast array of unannotated transcripts, novel long noncoding RNAs and gene chimeras, permitting accurate determination of transcription start, splice, polyadenylation and fusion sites. Phylogenetic and enrichment inference of isoform-level measurements implicated early functional divergence and cytosolic proteostatic stress in shaping ovarian tumorigenesis. A complementary imaging-based high-throughput drug screen was performed and subsequently validated, which consistently pinpointed proteasome inhibitors as an effective therapeutic regime by inducing protein aggregates in ovarian cancer cells. Therefore, our study suggests that clinical application of the emerging long-read full-length analysis for improving molecular diagnostics is feasible and informative. An in-depth understanding of the tumor transcriptome complexity allowed by leveraging the hybrid sequencing approach lays the basis to reveal novel and valid therapeutic vulnerabilities in advanced ovarian malignancies.