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Actionable gene-based classification toward precision medicine in gastric cancer

Overview of attention for article published in Genome Medicine, October 2017
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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)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
14 tweeters

Readers on

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59 Mendeley
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Title
Actionable gene-based classification toward precision medicine in gastric cancer
Published in
Genome Medicine, October 2017
DOI 10.1186/s13073-017-0484-3
Pubmed ID
Authors

Hiroshi Ichikawa, Masayuki Nagahashi, Yoshifumi Shimada, Takaaki Hanyu, Takashi Ishikawa, Hitoshi Kameyama, Takashi Kobayashi, Jun Sakata, Hiroshi Yabusaki, Satoru Nakagawa, Nobuaki Sato, Yuki Hirata, Yuko Kitagawa, Toshiyuki Tanahashi, Kazuhiro Yoshida, Ryota Nakanishi, Eiji Oki, Dana Vuzman, Stephen Lyle, Kazuaki Takabe, Yiwei Ling, Shujiro Okuda, Kohei Akazawa, Toshifumi Wakai

Abstract

Intertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC). To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed. A total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE) tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired with US Food and Drug Administration-approved targeted therapies, and the evaluation of Epstein-Barr virus (EBV) infection and microsatellite instability (MSI) status. Comprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7%) and of 69 actionable genes in 141 GCs (68.1%). We classified the 207 GCs into four The Cancer Genome Atlas (TCGA) subtypes using the genomic profiling data; EBV (N = 9), MSI (N = 17), chromosomal instability (N = 119), and genomically stable subtype (N = 62). Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32), while the others (N = 175) were sub-divided into six clusters including five with actionable gene alterations: ERBB2 (N = 25), CDKN2A, and CDKN2B (N = 10), KRAS (N = 10), BRCA2 (N = 9), and ATM cluster (N = 12). The clinical utility of this classification was demonstrated by a case of unresectable GC with a remarkable response to anti-HER2 therapy in the ERBB2 cluster. This actionable gene-based classification creates a framework for further studies for realizing precision medicine in GC.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 17%
Student > Ph. D. Student 8 14%
Other 7 12%
Student > Master 6 10%
Student > Doctoral Student 3 5%
Other 12 20%
Unknown 13 22%
Readers by discipline Count As %
Medicine and Dentistry 18 31%
Biochemistry, Genetics and Molecular Biology 14 24%
Agricultural and Biological Sciences 7 12%
Arts and Humanities 1 2%
Immunology and Microbiology 1 2%
Other 3 5%
Unknown 15 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 November 2017.
All research outputs
#2,245,049
of 13,784,591 outputs
Outputs from Genome Medicine
#530
of 969 outputs
Outputs of similar age
#72,060
of 315,760 outputs
Outputs of similar age from Genome Medicine
#55
of 81 outputs
Altmetric has tracked 13,784,591 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 969 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.7. This one is in the 45th percentile – i.e., 45% 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 315,760 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 81 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.