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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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (81472537 and 81672714 to GZ; 81502597 to YJ; 81472426 to WD), the Grants from the State Key Laboratory of Oncogenes and Related Genes (SB17-06 to M-CC), the grants from Shanghai Jiao Tong University School of Medicine (DLY201505 to WD; YG2016MS51 to XY), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (20161313 to GZ), the Shanghai Institutions of Higher Learning (Eastern Scholar to GZ), Shanghai Rising-Star Program (16QA1403600 to GZ), Shanghai Municipal Commission of Health and Family Planning (2013ZYJB0202 and 15GWZK0701 to WD; 20174Y0189 to YJ; 20174Y0043 to M-CC), the grant from Shanghai Key Laboratory of Gynecologic Oncology (FKZL-2017-01 to YJ), and the grant from Science and Technology Commission of Shanghai Municipality (16140904401 to XY).

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Jing, Y., Zhang, Y., Zhu, H. et al. Hybrid sequencing-based personal full-length transcriptomic analysis implicates proteostatic stress in metastatic ovarian cancer. Oncogene 38, 3047–3060 (2019). https://doi.org/10.1038/s41388-018-0644-y

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