↓ Skip to main content

Cancer heterogeneity: converting a limitation into a source of biologic information

Overview of attention for article published in Journal of Translational Medicine, September 2017
Altmetric Badge

Mentioned by

twitter
2 X users

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
94 Mendeley
citeulike
1 CiteULike
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
Cancer heterogeneity: converting a limitation into a source of biologic information
Published in
Journal of Translational Medicine, September 2017
DOI 10.1186/s12967-017-1290-9
Pubmed ID
Authors

Albert Rübben, Arturo Araujo

Abstract

Analysis of spatial and temporal genetic heterogeneity in human cancers has revealed that somatic cancer evolution in most cancers is not a simple linear process composed of a few sequential steps of mutation acquisitions and clonal expansions. Parallel evolution has been observed in many early human cancers resulting in genetic heterogeneity as well as multilineage progression. Moreover, aneuploidy as well as structural chromosomal aberrations seems to be acquired in a non-linear, punctuated mode where most aberrations occur at early stages of somatic cancer evolution. At later stages, the cancer genomes seem to get stabilized and acquire only few additional rearrangements. While parallel evolution suggests positive selection of driver mutations at early stages of somatic cancer evolution, stabilization of structural aberrations at later stages suggests that negative selection takes effect when cancer cells progressively lose their tolerance towards additional mutation acquisition. Mixing of genetically heterogeneous subclones in cancer samples reduces sensitivity of mutation detection. Moreover, driver mutations present only in a fraction of cancer cells are more likely to be mistaken for passenger mutations. Therefore, genetic heterogeneity may be considered a limitation negatively affecting detection sensitivity of driver mutations. On the other hand, identification of subclones and subclone lineages in human cancers may lead to a more profound understanding of the selective forces which shape somatic cancer evolution in human cancers. Identification of parallel evolution by analyzing spatial heterogeneity may hint to driver mutations which might represent additional therapeutic targets besides driver mutations present in a monoclonal state. Likewise, stabilization of cancer genomes which can be identified by analyzing temporal genetic heterogeneity might hint to genes and pathways which have become essential for survival of cancer cell lineages at later stages of cancer evolution. These genes and pathways might also constitute patient specific therapeutic targets.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 94 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 17%
Student > Ph. D. Student 14 15%
Student > Doctoral Student 10 11%
Researcher 8 9%
Student > Bachelor 6 6%
Other 14 15%
Unknown 26 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 28%
Medicine and Dentistry 17 18%
Agricultural and Biological Sciences 9 10%
Computer Science 6 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 3%
Other 7 7%
Unknown 26 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 11 September 2017.
All research outputs
#18,171,423
of 23,344,526 outputs
Outputs from Journal of Translational Medicine
#2,812
of 4,117 outputs
Outputs of similar age
#227,803
of 316,834 outputs
Outputs of similar age from Journal of Translational Medicine
#43
of 53 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,117 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 316,834 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.