O-GlcNAcylation is a dynamic post-translational modification consisting of the addition of a single N-acetylglucosamine sugar to serine and threonine residues in proteins by the enzyme O-linked β-N-acetylglucosamine transferase (OGT), whereas the enzyme O-GlcNAcase (OGA) removes the modification. In cancer, tumor samples present with altered O-GlcNAcylation; however, changes in O-GlcNAcylation are not consistent between tumor types. Interestingly, the tumor suppressor p53 is modified by O-GlcNAc, and most solid tumors contain mutations in p53 leading to the loss of p53 function. Because ovarian cancer has a high frequency of p53 mutation rates, we decided to investigate the relationship between O-GlcNAcylation and p53 function in ovarian cancer. We measured a significant decrease in O-GlcNAcylation of tumor tissue in an ovarian tumor microarray. Furthermore, O-GlcNAcylation was increased, and OGA protein and mRNA levels were decreased in ovarian tumor cell lines not expressing the protein p53. Treatment with the OGA inhibitor Thiamet-G (TMG), silencing of OGA, or overexpression of OGA and OGT led to p53 stabilization, increased nuclear localization, and increased protein and mRNA levels of p53 target genes. These data suggest that changes in O-GlcNAc homeostasis activate the p53 pathway. Combination treatment of the chemotherapeutic cisplatin with TMG decreased tumor cell growth and enhanced cell cycle arrest without impairing cytotoxicity. The effects of TMG on tumor cell growth were partially dependent on wild type p53 activation. In conclusion, changes in O-GlcNAc homeostasis activate the wild type p53 pathway in ovarian cancer cells, and OGA inhibition has the potential as an adjuvant treatment for ovarian carcinoma.

FoxM1 is an oncogenic Forkhead transcription factor that is overexpressed in ovarian cancer. However, the mechanisms by which FoxM1 is deregulated in ovarian cancer and the extent to which FoxM1 can be targeted in ovarian cancer have not been reported previously. In this study, we showed that MDM2 inhibitor Nutlin-3 upregulated p53 protein and downregulated FoxM1 expression in several cancer cell lines with wild type TP53 but not in cell lines with mutant TP53. FoxM1 downregulation was partially blocked by cycloheximide or actinomycin D, and pulse-chase studies indicate Nutlin-3 enhances FoxM1 mRNA decay. Knockdown of p53 using shRNAs abrogated the FoxM1 downregulation by Nutlin-3, indicating a p53-dependent mechanism. FoxM1 inhibitor, thiostrepton, induces apoptosis in cancer cell lines and enhances sensitivity to cisplatin in these cells. Thiostrepton downregulates FoxM1 expression in several cancer cell lines and enhances sensitivity to carboplatin in vivo. Finally, FoxM1 expression is elevated in nearly all (48/49) ovarian tumors, indicating that thiostrepton target gene is highly expressed in ovarian cancer. In summary, the present study provides novel evidence that both amorphic and neomorphic mutations in TP53 contribute to FoxM1 overexpression and that FoxM1 may be targeted for therapeutic benefits in cancers.

Background

Cyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo-ovarian high-grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large-scale, histotype-specific validation has been performed. The hypothesis was that high-level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC.

Methods

Within the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated.

Results

High-level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high-level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08-1.47, p = .034, and HR, 1.18; 95% CI, 1.05-1.32, p = .015, respectively). This was also true for cases with combined high-level amplification/overexpression (HR, 1.26; 95% CI, 1.09-1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1-SD increase; 95% CI, 0.94-1.06; p = .58). CCNE1 high-level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss.

Conclusion

This study provides large-scale validation that CCNE1 high-level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC.

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

Cervical cancer remains one of the leading causes of cancer-related deaths worldwide. Here we report the extensive molecular characterization of 228 primary cervical cancers, one of the largest comprehensive genomic studies of cervical cancer to date. We observed notable APOBEC mutagenesis patterns and identified SHKBP1, ERBB3, CASP8, HLA-A and TGFBR2 as novel significantly mutated genes in cervical cancer. We also discovered amplifications in immune targets CD274 (also known as PD-L1) and PDCD1LG2 (also known as PD-L2), and the BCAR4 long non-coding RNA, which has been associated with response to lapatinib. Integration of human papilloma virus (HPV) was observed in all HPV18-related samples and 76% of HPV16-related samples, and was associated with structural aberrations and increased target-gene expression. We identified a unique set of endometrial-like cervical cancers, comprised predominantly of HPV-negative tumours with relatively high frequencies of KRAS, ARID1A and PTEN mutations. Integrative clustering of 178 samples identified keratin-low squamous, keratin-high squamous and adenocarcinoma-rich subgroups. These molecular analyses reveal new potential therapeutic targets for cervical cancers.

(Cell Reports 23, 313–326; April 3, 2018) In the originally published version of this article, the author list contained two errors. Specifically, David J. Kwiatkowski was misspelled as David J. Kwaitkowski, and William Y. Kim was inadvertently written as William T. Kim. Both names have been corrected online. The authors regret this error.

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.”

(Cell 173, 371–385.e1–e9; April 5, 2018) It has come to our attention that we made two errors in preparation of this manuscript. First, in the STAR Methods, under the subheading of “Hypermutators and Immune Infiltrates” within the “Quantification and Statistical Analysis” section, we inadvertently referred to Figures S7A–S7C for data corresponding to sample stratification by hypermutator status alone in the last sentence. It should have referred to Figure S6A–S6C. Second, the lists of highly frequent missense mutations for COAD (colon adenocarcinoma) and READ (rectum adenocarcinoma) displayed in Figure S7 were incorrect because when we ordered the mutations in the initial analysis, we mistakenly combined the two cancer types COAD and READ for analysis, despite the fact that they were listed as two separate cancer types in the x-axis of the figure. After re-ordering the mutations by frequency for COAD and READ independently, information on highly frequent missense mutations for each of these cancer types is different and updated now in the revised Figure S7. These errors don't change the major conclusions of the paper and have been corrected online. We apologize for any confusion they may have caused. [Figure-presented]

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on "chromatin-state" to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.