| Title |
Cancer systems biology of TCGA SKCM: Efficient detection of genomic drivers in melanoma
|
|---|---|
| Published in |
Scientific Reports, January 2015
|
| DOI | 10.1038/srep07857 |
| Pubmed ID | |
| Authors |
Jian Guan, Rohit Gupta, Fabian V. Filipp |
| Abstract |
We characterized the mutational landscape of human skin cutaneous melanoma (SKCM) using data obtained from The Cancer Genome Atlas (TCGA) project. We analyzed next-generation sequencing data of somatic copy number alterations and somatic mutations in 303 metastatic melanomas. We were able to confirm preeminent drivers of melanoma as well as identify new melanoma genes. The TCGA SKCM study confirmed a dominance of somatic BRAF mutations in 50% of patients. The mutational burden of melanoma patients is an order of magnitude higher than of other TCGA cohorts. A multi-step filter enriched somatic mutations while accounting for recurrence, conservation, and basal rate. Thus, this filter can serve as a paradigm for analysis of genome-wide next-generation sequencing data of large cohorts with a high mutational burden. Analysis of TCGA melanoma data using such a multi-step filter discovered novel and statistically significant potential melanoma driver genes. In the context of the Pan-Cancer study we report a detailed analysis of the mutational landscape of BRAF and other drivers across cancer tissues. Integrated analysis of somatic mutations, somatic copy number alterations, low pass copy numbers, and gene expression of the melanogenesis pathway shows coordination of proliferative events by Gs-protein and cyclin signaling at a systems level. |
X Demographics
The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 27% |
| United Kingdom | 5 | 19% |
| Spain | 3 | 12% |
| Belgium | 1 | 4% |
| Brazil | 1 | 4% |
| Unknown | 9 | 35% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 18 | 69% |
| Science communicators (journalists, bloggers, editors) | 3 | 12% |
| Scientists | 3 | 12% |
| Practitioners (doctors, other healthcare professionals) | 2 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| France | 2 | 1% |
| Hungary | 1 | <1% |
| Sweden | 1 | <1% |
| Germany | 1 | <1% |
| Denmark | 1 | <1% |
| Belgium | 1 | <1% |
| Unknown | 125 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 36 | 26% |
| Researcher | 30 | 22% |
| Student > Master | 19 | 14% |
| Student > Doctoral Student | 9 | 7% |
| Student > Bachelor | 7 | 5% |
| Other | 18 | 13% |
| Unknown | 17 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 38 | 28% |
| Agricultural and Biological Sciences | 38 | 28% |
| Medicine and Dentistry | 19 | 14% |
| Computer Science | 9 | 7% |
| Mathematics | 2 | 1% |
| Other | 9 | 7% |
| Unknown | 21 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 147. 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 20 October 2018.
All research outputs
#280,542
of 25,463,724 outputs
Outputs from Scientific Reports
#3,243
of 141,203 outputs
Outputs of similar age
#3,263
of 360,371 outputs
Outputs of similar age from Scientific Reports
#25
of 1,112 outputs
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