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  • br Despite these limitations this work

    2022-08-31

    
    Despite these limitations, this work provides an important foundation for future TSG investigation. First, we proposed a framework to define TSG inactivation events by combining somatic mutations and CNVs. This framework can be applied to studies with similar designs and can be extended to integrate germline mutations and epigenomic variants (e.g., methylation on promoters). Second, although more than 1,000 TSGs were re-ported, many of them did not show a significant inactivation event or had a measurable impact on the transcriptome. While several well-known TSGs (e.g., TP53, RB1, CDKN2A/CDKN2B, and PTEN) were prevalently inactivated in multiple cancer types, many others were only inactivated in few cancer types (e.g., APC in BLCA, COAD, and READ). Third, our analytical strategy could distinguish cis- from trans-effect of a TSG inactivation event. A number of TSGs, although genetically inactivated, showed no effect or only one way of effect on the transcriptome. As above, to further explore the features of TSG inactivation events, it is better to examine not only the genetic events but also their functional impacts using transcriptomic, proteomic, and functional genomic data.
    In summary, we presented a comprehensive framework to classify TSG inactivation events, revealed the landscape of these events, and extensively explored the potential functional impacts of TSG inactivation events in cancer.
    STAR+METHODS
    Detailed methods are provided in the online version of this paper and include the following:
    d KEY RESOURCES TABLE
    d CONTACT FOR REAGENT AND RESOURCE SHARING d METHOD DETAILS
    B Curation of tumor suppressor Blasticidin S B Multi-dimensional data
    B Statistical analysis of human TSGs B Transcriptomic impact
    B Filtering abnormal outliers at the transcriptomic level B Pathway enrichment analysis
    B Impacted network
    d QUANTIFICATION AND STATISTICAL ANALYSES
    SUPPLEMENTAL INFORMATION
    Supplemental Information includes seven figures and can be found with this article online at https://doi.org/10.1016/j.celrep.2018.12.066.
    ACKNOWLEDGMENTS
    We thank Drs. Dung-Fung Lee, Jeffrey Chang, and Da Yang and Mr. Mi Li for insightful discussion. We also thank the two reviewers for their constructive comments and Dr. Irmgard Willcockson for English editing that improved the manuscript. We thank the TCGA Research Network for making the TCGA data available to the research community and the UTHealth Cancer Geno-mics Core and Data Science and Informatics Core for Cancer Research, supported by CPRIT (RP180734 and RP170668). This work was partially supported by an NIH grant (R01LM012806) and an American Cancer Society Institutional Research Grant (IRG-58-009-55). The funders had no role in the study design, data collection and analysis, decision to publish, or prepara-tion of the manuscript.
    AUTHOR CONTRIBUTIONS
    P.J. and Z.Z. conceived of the project. P.J. performed the data analyses. P.J.
    and Z.Z. wrote the manuscript.
    DECLARATION OF INTERESTS
    The authors declare no Blasticidin S competing interests.
    REFERENCES
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    Cheng, F., Zhao, J., Fooksa, M., and Zhao, Z. (2016). A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes. J. Am. Med. Inform. Assoc. 23, 681ā€“691.
    Reva, B., Antipin, Y., and Sander, C. (2011). Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118.