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Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients

Overview of attention for article published in BMC Genomics, August 2016
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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Title
Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2902-0
Pubmed ID
Authors

Jie Zhang, Zachary Abrams, Jeffrey D. Parvin, Kun Huang

Abstract

Somatic mutations can be used as potential biomarkers for subtyping and predicting outcomes for cancer patients. However, cancer patients often carry many somatic mutations, which do not always concentrate on specific genomic loci, suggesting that the mutations may affect common pathways or gene interaction networks instead of common genes. The challenge is thus to identify the functional relationships among the mutations using multi-modal data. We developed a novel approach for integrating patient somatic mutation, transcriptome and clinical data to mine underlying functional gene groups that can be used to stratify cancer patients into groups with different clinical outcomes. Specifically, we use distance correlation metric to mine the correlations between expression profiles of mutated genes from different patients. With this approach, we were able to cluster patients based on the functional relationships between the affected genes using their expression profiles, and to visualize the results using multi-dimensional scaling. Interestingly, we identified a stable subgroup of breast cancer patients that are highly enriched with ER-negative and triple-negative subtypes, and the somatic mutation genes they harbor were capable of acting as potential biomarkers to predict patient survival in several different breast cancer datasets, especially in ER-negative cohorts which has lacked reliable biomarkers. Our method provides a novel and promising approach for integrating genotyping and gene expression data in patient stratification in complex diseases.

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X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 17%
Student > Bachelor 4 14%
Researcher 4 14%
Student > Ph. D. Student 4 14%
Student > Doctoral Student 3 10%
Other 4 14%
Unknown 5 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 34%
Computer Science 5 17%
Engineering 3 10%
Agricultural and Biological Sciences 2 7%
Medicine and Dentistry 2 7%
Other 2 7%
Unknown 5 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 September 2016.
All research outputs
#6,058,371
of 24,171,551 outputs
Outputs from BMC Genomics
#2,394
of 10,913 outputs
Outputs of similar age
#92,439
of 349,600 outputs
Outputs of similar age from BMC Genomics
#60
of 273 outputs
Altmetric has tracked 24,171,551 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 10,913 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 349,600 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 273 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.