↓ Skip to main content

Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility

Overview of attention for article published in BMC Medical Genomics, January 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

twitter
6 tweeters
patent
1 patent

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
33 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility
Published in
BMC Medical Genomics, January 2015
DOI 10.1186/s12920-015-0145-6
Pubmed ID
Authors

Stephen R Piccolo, Irene L Andrulis, Adam L Cohen, Thomas Conner, Philip J Moos, Avrum E Spira, Saundra S Buys, W. E Johnson, Andrea H Bild, Piccolo, Stephen R, Andrulis, Irene L, Cohen, Adam L, Conner, Thomas, Moos, Philip J, Spira, Avrum E, Buys, Saundra S, Johnson, W Evan, Bild, Andrea H, Stephen R. Piccolo, Irene L. Andrulis, Adam L. Cohen, Philip J. Moos, Avrum E. Spira, Saundra S. Buys, W. Evan Johnson, Andrea H. Bild

Abstract

Women with a family history of breast cancer face considerable uncertainty about whether to pursue standard screening, intensive screening, or prophylactic surgery. Accurate and individualized risk-estimation approaches may help these women make more informed decisions. Although highly penetrant genetic variants have been associated with familial breast cancer (FBC) risk, many individuals do not carry these variants, and many carriers never develop breast cancer. Common risk variants have a relatively modest effect on risk and show limited potential for predicting FBC development. As an alternative, we hypothesized that additional genomic data types, such as gene-expression levels, which can reflect genetic and epigenetic variation, could contribute to classifying a person's risk status. Specifically, we aimed to identify common patterns in gene-expression levels across individuals who develop FBC. We profiled peripheral blood mononuclear cells from women with a family history of breast cancer (with or without a germline BRCA1/2 variant) and from controls. We used the support vector machines algorithm to differentiate between patients who developed FBC and those who did not. Our study used two independent datasets, a training set of 124 women from Utah (USA) and an external validation (test) set from Ontario (Canada) of 73 women (197 total). We controlled for expression variation associated with clinical, demographic, and treatment variables as well as lymphocyte markers. Our multigene biomarker provided accurate, individual-level estimates of FBC occurrence for the Utah cohort (AUC = 0.76 [0.67-84]) . Even at their lower confidence bounds, these accuracy estimates meet or exceed estimates from alternative approaches. Our Ontario cohort resulted in similarly high levels of accuracy (AUC = 0.73 [0.59-0.86]), thus providing external validation of our findings. Individuals deemed to have "high" risk by our model would have an estimated 2.4 times greater odds of developing familial breast cancer than individuals deemed to have "low" risk. Together, these findings suggest that gene-expression levels in peripheral blood cells reflect genomic variation associated with breast cancer risk and that such data have potential to be used as a non-invasive biomarker for familial breast cancer risk.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 24%
Researcher 7 21%
Professor > Associate Professor 4 12%
Student > Bachelor 2 6%
Student > Master 2 6%
Other 5 15%
Unknown 5 15%
Readers by discipline Count As %
Medicine and Dentistry 8 24%
Agricultural and Biological Sciences 6 18%
Biochemistry, Genetics and Molecular Biology 5 15%
Computer Science 1 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 6 18%
Unknown 6 18%

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 14 September 2016.
All research outputs
#1,851,868
of 9,464,402 outputs
Outputs from BMC Medical Genomics
#113
of 494 outputs
Outputs of similar age
#56,097
of 249,841 outputs
Outputs of similar age from BMC Medical Genomics
#5
of 32 outputs
Altmetric has tracked 9,464,402 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 494 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 249,841 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.