TOX3 Mutations in Breast Cancer

Jones, James O.; Chin, Suet-Feung; Wong-Taylor, Li-An; Leaford, Donna; Ponder, Bruce A.J.; Caldas, Carlos; Maia, Ana-Teresa

doi:10.1371/journal.pone.0074102

Cited by 26 publications

(22 citation statements)

References 20 publications

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“…TOX3 is involved in bending and unwinding of DNA and alteration of chromatin structure 28 . It is a known risk gene for breast cancer 29 , where it is also somatically mutated at a moderate rate 30 . In line with this, we observed the most SNVs in breast cancer (n=5).…”

mentioning

confidence: 99%

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Hornshøj

Nielsen

Sinnott‐Armstrong

et al. 2017

Preprint

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Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Noncoding mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC wholegenomes from ten cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g. TERT and CDH10) and survival (e.g. CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4,128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217 and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. This screen for significant mutation patterns followed by correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

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mentioning

confidence: 99%

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Hornshøj

Nielsen

Sinnott‐Armstrong

et al. 2017

Preprint

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“…BRCA1 and BRCA2 genes encode tumor suppressing proteins, and it has been shown that a mutation in these genes increases susceptibility to breast cancer [21], [22]. FGFR2 and TOX3 mutations have both been linked to an increase in breast cancer risk though their molecular mechanisms are not well understood [23], [24]. Finally, TP53 is an important gene in many cancers, and it has been shown that a change in the p53 pathway leads to more aggressive breast cancer.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Integrated Theory-and Data-Driven Feature Selection in Gene Expression Data Analysis

Raghu

Chrysanthis

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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The exponential growth of high dimensional biological data has led to a rapid increase in demand for automated approaches for knowledge production. Existing methods rely on two general approaches to address this challenge: 1) the Theory-driven approach, which utilizes prior accumulated knowledge, and 2) the Data-driven approach, which solely utilizes the data to deduce scientific knowledge. Both of these approaches alone suffer from bias toward past/present knowledge, as they fail to incorporate all of the current knowledge that is available to make new discoveries. In this paper, we show how an integrated method can effectively address the high dimensionality of big biological data, which is a major problem for pure data-driven analysis approaches. We realize our approach in a novel two-step analytical workflow that incorporates a new feature selection paradigm as the first step to handling high-throughput gene expression data analysis and that utilizes graphical causal modeling as the second step to handle the automatic extraction of causal relationships. Our results, on real-world clinical datasets from The Cancer Genome Atlas (TCGA), demonstrate that our method is capable of intelligently selecting genes for learning effective causal networks.

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“…Multiple studies have demonstrated that the overregulation of TOX is associated with tumor progression [21]. Deregulation of TOX expression in cancer can be roughly attributed to two mechanisms: genetic alteration [22,23] and epigenetic events [24].…”

Section: Introductionmentioning

confidence: 99%

Clinical characterization, genetic profiling, and immune infiltration of TOX in diffuse gliomas

Zhang

Fan

et al. 2020

Preprint

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Background: Immunotherapies targeting glioblastoma (GBM) have led to significant improvements in patient outcomes. TOX is closely associated with the immune environment surrounding tumors, but its role in gliomas is not fully understood. Methods: Using data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we analyzed the transcriptomes of 1691 WHO grade I-IV human glioma samples. The R language was used to perform most of the statistical analyses. Somatic mutations and somatic copy number variation (CNV) were analyzed using GISTIC 2.0. Results: TOX was down-regulated in malignant gliomas compared to low grade gliomas, and upregulated in the proneural and IDH mutant subtypes of GBM. TOXlow tumours are associated with the loss of PTEN and amplification of EGFR, while TOXhigh tumours harbor frequent mutations in IDH1 (91%). TOX was highly expressed in leading edge regions of tumours. Gene ontology and pathway analyses demonstrated that TOX was enriched in multiple immune related processes including lymphocyte migration in GBM. Finally, TOX had a negative association with the infiltration of several immune cell types in the tumour microenvironment. Conclusion: TOX has the potential to be a new prognostic marker for GBM.

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TOX3 Mutations in Breast Cancer

Cited by 26 publications

References 20 publications

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Integrated Theory-and Data-Driven Feature Selection in Gene Expression Data Analysis

Clinical characterization, genetic profiling, and immune infiltration of TOX in diffuse gliomas

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