Regulators of genetic risk of breast cancer identified by integrative network analysis

Castro, Mauro A. A.; Santiago, Inês de; Campbell, Thomas M.; Vaughn, Courtney M.; Hickey, Theresa E.; Ross, Edith; Tilley, Wayne D.; Markowetz, Florian; Ponder, Bruce A.J.; Meyer, Kerstin B.

doi:10.1038/ng.3458

Cited by 167 publications

(191 citation statements)

References 53 publications

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“…In this analysis, regulator activity was associated with survival, as described previously for breast cancer (Castro et al, 2016a). Certain regulon activity profiles varied greatly between the different coding and noncoding gene expression subtypes, suggesting that the regulators are key drivers of those expression subtypes.…”

Section: Discussionmentioning

confidence: 99%

“…Certain regulon activity profiles varied greatly between the different coding and noncoding gene expression subtypes, suggesting that the regulators are key drivers of those expression subtypes. These findings provide potential targets for intervention, and could be used for subtype discrimination and therapy selection (Castro et al, 2016a). …”

Section: Discussionmentioning

confidence: 99%

“…By ‘regulator’ we mean a gene whose product induces and/or represses a target gene set, which we call a ‘regulon’ (Castro et al, 2016a). …”

Section: Star Methodsmentioning

confidence: 99%

“…By ‘regulator’ we mean a gene whose product induces and/or represses a target gene set, which we refer to as a ‘regulon’ (Castro et al, 2016a). We performed the analysis for this cohort of 412 samples, and also for an independent, mixed non-muscle invasive/MIBC cohort (n = 308) (Sjodahl et al, 2012).…”

Section: Regulon Activity Differences Among Rna Subtypesmentioning

confidence: 99%

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Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Robertson¹,

Kim²,

Al‐Ahmadie³

et al. 2017

Cell

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1,448

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Summary We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival ‘neuronal’ subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, lncRNA, and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma-in-situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…By ‘regulator’ we mean a gene whose product induces and/or represses a target gene set, which we call a ‘regulon’ (Castro et al, 2016a). …”

Section: Star Methodsmentioning

confidence: 99%

Section: Regulon Activity Differences Among Rna Subtypesmentioning

confidence: 99%

See 2 more Smart Citations

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Robertson¹,

Kim²,

Al‐Ahmadie³

et al. 2017

Cell

1,675

1,448

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show abstract

“…Bruce Ponder (Cancer Research UK Cambridge Institute, Cambridge, UK) looked for better ways of classifying single-patient samples by regulators of genetic risk of breast cancer identified by integrative network analysis [4] . A breast cancer gene regulatory network was established comprising transcription factors and groups of putative target genes (regulons), which allowed to examine whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci.…”

Section: Genetic Profiling Of Patients and Risk Predictionmentioning

confidence: 99%

Personalized Cancer Care: Risk Prediction, Early Diagnosis, Progression, and Therapy

Zänker

Børresen‐Dale

2016

Biomed Hub

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At the annual prestigious International Symposium of the Fritz-Bender Foundation, Munich, 18-20 May, 2016, researchers, clinicians, and students discussed the state of the art and future perspectives of genomic medicine in cancer. Genomic medicine (also known as precision medicine/oncology) should help clinicians to provide a more precise diagnosis and therapy in oncology for individual patients. The meeting focused on next-generation sequencing methods, analytical computational analysis of big data, and data mining on the way to translational and evidence-based medicine. The meeting covered the social and ethical impact of genomic medicine as well as news and views on antibody targeting of intracellular proteins, on the architecture of intracellular proteins and their impact on carcinogenesis, and on the adaptation of tumor therapy in due consideration of tumor evolution. Subheadings like "Genetic Profiling of Patients and Risk Prediction," "Molecular Profiling of Tumors and Metastases," "Tumor-Host Microenvironment Interaction and Metabolism," and "Targeted Therapy" were subsumed under the main heading of "Personalized Cancer Care." What Is It about?This meeting report is a follow-up progress report from a previous one in 2013. It summarizes at the cutting edge how cancer manifests in the human body. Basic scientists and clinicians shared their views on personal cancer care and precision cancer medicine with special reference to epigenetic, genetic, and novel therapy strategies.

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Development of a miRNA‐based classifier for detection of colorectal cancer molecular subtypes

Adam

Poel²,

Moreno

et al. 2022

Molecular Oncology

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Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA‐based CMS classifier by converting the existing mRNA‐based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA‐assigned CMS classifier (CMS‐miRaCl) was evaluated in several datasets, achieving an overall accuracy of ~ 0.72 (0.6329–0.7987) in the largest dataset (n = 158). To gain insight into the biological relevance of CMS‐miRaCl, we evaluated the most important features in the classifier. We found that miRNAs previously reported to be relevant in microsatellite‐instable CRCs or Wnt signaling were important features for CMS‐miRaCl. Following further studies to validate its robustness, this miRNA‐based alternative might simplify the implementation of CMS classification in clinical workflows.

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Regulators of genetic risk of breast cancer identified by integrative network analysis

Cited by 167 publications

References 53 publications

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Personalized Cancer Care: Risk Prediction, Early Diagnosis, Progression, and Therapy

Development of a miRNA‐based classifier for detection of colorectal cancer molecular subtypes

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