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Network perturbation by recurrent regulatory variants in cancer

View Article: PubMed Central - PubMed

ABSTRACT

Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes.

No MeSH data available.


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Network module of coding drivers and transcriptional drivers.(A) Schematic view of a network module consisting of the central CD and its partner TDs. (B) ROC graphs for the prediction of the 20/20 CD (left) and CGC CD (right) based on the modular recurrence level. The gray curves are results when the cis-regulatory recurrence level of the CD alone was used. The colored curves are resulted from a modular extension of recurrence based on the average, sum, or maximum of the neighbor TDs (see Methods for detail). (C) Network-level recurrence patterns of the TP53 module. The yellow and blue bars at the center indicate the coding recurrence levels of TP53 in breast cancer and liver cancer, respectively. The violet and green bars at the circumferences represent the regulatory recurrence levels of TP53-interacting genes in the functional network in breast cancer and liver cancer, respectively.
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pcbi.1005449.g003: Network module of coding drivers and transcriptional drivers.(A) Schematic view of a network module consisting of the central CD and its partner TDs. (B) ROC graphs for the prediction of the 20/20 CD (left) and CGC CD (right) based on the modular recurrence level. The gray curves are results when the cis-regulatory recurrence level of the CD alone was used. The colored curves are resulted from a modular extension of recurrence based on the average, sum, or maximum of the neighbor TDs (see Methods for detail). (C) Network-level recurrence patterns of the TP53 module. The yellow and blue bars at the center indicate the coding recurrence levels of TP53 in breast cancer and liver cancer, respectively. The violet and green bars at the circumferences represent the regulatory recurrence levels of TP53-interacting genes in the functional network in breast cancer and liver cancer, respectively.

Mentions: This finding, in concert with the high degree of the CDs in the protein interactome, led us to test whether the CDs have modular relationships with the TDs (Fig 3A). For a given gene and all its neighbors in the network, we computed the combinatorial chromatin-based measure of cis-recurrence as described above. Then, we examined the degree to which the cis-recurrence levels of the given gene itself and all its neighbors can predict the coding driver status of the given gene (see Methods). The CDs themselves had a higher level of cis-recurrence than other genes as indicated by the gray receiver operating characteristic (ROC) curves in Fig 3B. This is consistent with the high connectivity of the CDs in the regulatory network. However, the modular extension of the recurrence levels considerably improved the performance of CD prediction (colored ROC curves in Fig 3B). The TP53 network module is illustrated in Fig 3C with the coding recurrence levels in breast and liver cancer (yellow and blue bars at the center) and regulatory recurrence levels in each cancer (violet and green bars at the circumferences).


Network perturbation by recurrent regulatory variants in cancer
Network module of coding drivers and transcriptional drivers.(A) Schematic view of a network module consisting of the central CD and its partner TDs. (B) ROC graphs for the prediction of the 20/20 CD (left) and CGC CD (right) based on the modular recurrence level. The gray curves are results when the cis-regulatory recurrence level of the CD alone was used. The colored curves are resulted from a modular extension of recurrence based on the average, sum, or maximum of the neighbor TDs (see Methods for detail). (C) Network-level recurrence patterns of the TP53 module. The yellow and blue bars at the center indicate the coding recurrence levels of TP53 in breast cancer and liver cancer, respectively. The violet and green bars at the circumferences represent the regulatory recurrence levels of TP53-interacting genes in the functional network in breast cancer and liver cancer, respectively.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5383347&req=5

pcbi.1005449.g003: Network module of coding drivers and transcriptional drivers.(A) Schematic view of a network module consisting of the central CD and its partner TDs. (B) ROC graphs for the prediction of the 20/20 CD (left) and CGC CD (right) based on the modular recurrence level. The gray curves are results when the cis-regulatory recurrence level of the CD alone was used. The colored curves are resulted from a modular extension of recurrence based on the average, sum, or maximum of the neighbor TDs (see Methods for detail). (C) Network-level recurrence patterns of the TP53 module. The yellow and blue bars at the center indicate the coding recurrence levels of TP53 in breast cancer and liver cancer, respectively. The violet and green bars at the circumferences represent the regulatory recurrence levels of TP53-interacting genes in the functional network in breast cancer and liver cancer, respectively.
Mentions: This finding, in concert with the high degree of the CDs in the protein interactome, led us to test whether the CDs have modular relationships with the TDs (Fig 3A). For a given gene and all its neighbors in the network, we computed the combinatorial chromatin-based measure of cis-recurrence as described above. Then, we examined the degree to which the cis-recurrence levels of the given gene itself and all its neighbors can predict the coding driver status of the given gene (see Methods). The CDs themselves had a higher level of cis-recurrence than other genes as indicated by the gray receiver operating characteristic (ROC) curves in Fig 3B. This is consistent with the high connectivity of the CDs in the regulatory network. However, the modular extension of the recurrence levels considerably improved the performance of CD prediction (colored ROC curves in Fig 3B). The TP53 network module is illustrated in Fig 3C with the coding recurrence levels in breast and liver cancer (yellow and blue bars at the center) and regulatory recurrence levels in each cancer (violet and green bars at the circumferences).

View Article: PubMed Central - PubMed

ABSTRACT

Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes.

No MeSH data available.


Related in: MedlinePlus