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Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types.

ABSTRACT: We systematically investigated the landscape of tumor-suppressor gene (TSG) inactivation events in 33 cancer types by quantitatively measuring their global and local genomic features and their transcriptional and signaling footprints. Using The Cancer Genome Atlas data, we identified with high confidence 337 TSG × cancer events in 30 cancer types, of which 277 were unique events. The majority (91.0%) of these events had a significant downstream impact measured by reduced expression of the TSG itself (cis-effect), disturbance of the transcriptome (trans-effect), or combinatorial effects. Importantly, the transcriptomic changes associated with TSG inactivation events were stronger than the cancer lineage difference, and the same TSGs inactivated in different cancer types tended to cluster together. Several TSGs (e.g., RB1, TP53, and CDKN2A) involved in the regulation of the cell-cycle-formed clusters. Finally, we constructed subnetworks of the TSG × cancer inactivation events, including the local genes frequently disturbed upon the inactivation events.

SUBMITTER: Jia P

PROVIDER: S-EPMC7375892 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types.

Jia Peilin P Zhao Zhongming Z

Cell reports 20190101 2

We systematically investigated the landscape of tumor-suppressor gene (TSG) inactivation events in 33 cancer types by quantitatively measuring their global and local genomic features and their transcriptional and signaling footprints. Using The Cancer Genome Atlas data, we identified with high confidence 337 TSG × cancer events in 30 cancer types, of which 277 were unique events. The majority (91.0%) of these events had a significant downstream impact measured by reduced expression of the TSG it ...[more]

PMID: 30625331

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Project description:In attempt to discover novel aberrantly hypermethylated genes with putative tumor suppressor function in epithelial ovarian cancer (EOC), we applied expression profiling following pharmacologic inhibition of DNA methylation in EOC cell lines. Among the genes identified, one of particular interest was DOK1, or downstream of tyrosine kinase 1, previously recognized as a candidate tumor suppressor gene (TSG) for leukemia and other human malignancies. Using bisulfite sequencing, we determined that a 5'-non-coding DNA region (located at nt -1158 to -850, upstream of the DOK1 translation start codon) was extensively hypermethylated in primary serous EOC tumors compared with normal ovarian specimens; however, this hypermethylation was not associated with DOK1 suppression. On the contrary, DOK1 was found to be strongly overexpressed in serous EOC tumors as compared to normal tissue and importantly, DOK1 overexpression significantly correlated with improved progression-free survival (PFS) values of serous EOC patients. Ectopic modulation of DOK1 expression in EOC cells and consecutive functional analyses pointed toward association of DOK1 expression with increased EOC cell migration and proliferation, and better sensitivity to cisplatin treatment. Gene expression profiling and consecutive network and pathway analyses were also confirmative for DOK1 association with EOC cell migration and proliferation. These analyses were also indicative for DOK1 protective role in EOC tumorigenesis, linked to DOK1-mediated induction of some tumor suppressor factors and its suppression of pro-metastasis genes. Taken together, our findings are suggestive for a possible tumor suppressor role of DOK1 in EOC; however its implication in enhanced EOC cell migration and proliferation restrain us to conclude that DOK1 represents a true TSG in EOC. Further studies are needed to more completely elucidate the functional implications of DOK1 and other members of the DOK gene family in ovarian tumorigenesis.

Project description:In recent years, with the development of transcriptome sequencing, the molecular characteristics of tumors are gradually revealed. Because of the complexity of tumor transcriptome, there is a need to look for the molecular signatures which can be used to evaluate the tissue origin and cell stemness of tumors in order to promote the diagnosis and treatment of tumors. Tumor tissue-specific gene sets (TTSGs) consisting of 200 genes were selected using RNA expression data of 9,875 patients from 33 tumor types. t-distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction and visualization of TTSGs in each tumor type. To evaluate oncogenic dedifferentiation and loss of cell stemness, Euclidean distance from each sample to a human embryo single-cell RNA-seq dataset (GSE36552) of TTSGs was calculated as TTSGs index indicating dissimilarity of tumors and embryo. TTSGs index was evaluated for prognosis in each tumor type. Two published signature indexes, the mRNA signature index (mRNAsi) and CIBERSORT, were compared to assess the correlation between the TTSGs index with cell stemness and immune microenvironment. Finally, the difference of prognosis, immune microenvironment and radiotherapy outcomes were compared between patients with high and low TTSGs index. In this study, all 33 tumor types in The Cancer Genome Atlas (TCGA) were embedded into isolated clusters by t-SNE and confirmed by k-nearest neighbors (kNN) algorithm. Clusters of squamous-cell carcinoma were adjacent to each other revealing similar histologic origin. Basal-like breast cancer was separated from luminal and HER-2-amplified subtypes and closed to squamous-cell carcinoma. TTSGs index was related to overall survival outcomes in cancers derived from liver, thyroid, brain, cervical and kidney. There was a positive correlation between mRNAsi and TTSGs index in pan-kidney and pan-neuronal cancers. Furthermore, cell fractions of M2 macrophages and total leukocytes increased in the group with higher TTSGs index. Patients with higher TTSGs index had longer overall survival time and less radiation therapy resistance compared to patients with lower TTSGs index. The signature of TTSGs is related to tumor expression features that distinguish tumors of different histologic origin using t-SNE. The signature also relates to prognosis of certain kinds of tumors.

Dataset Information

Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types.

Publications

Characterization of Tumor-Suppressor Gene Inactivation Events in 33 Cancer Types.

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