| Title |
Drug targets and predictive biomarkers in the management of metastatic melanoma
|
|---|---|
| Published in |
Pharmacogenomics and Personalized Medicine, September 2012
|
| DOI | 10.2147/pgpm.s25100 |
| Pubmed ID | |
| Authors |
Jaykumar Thumar, Eva Giesen, Harriet M Kluger |
| Abstract |
Melanoma is the leading cause of fatal skin cancer, and in the past few decades, there has been an increase in the incidence of and mortality from metastatic melanoma. Until recently, the therapeutic options for treatment of metastatic melanoma were limited. The approval of ipilimumab (an anti-CTLA-4 antibody) and vemurafenib (mutant B-RAF(V600E) kinase inhibitor) by the Federal Drug Administration has led to a new era in melanoma treatment, and additional promising drugs and drug combinations are currently being investigated. As the choices of treatment for melanoma have expanded, the need to identify predictive biomarkers to tailor treatment strategies to individual tumor or immune system characteristics has become necessary. Such strategies have the potential of maximizing antitumor effect while minimizing toxicity and improving clinical benefit. In this article, we review the currently approved targeted therapies in melanoma and discuss the future of personalized therapy for this disease. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 25% |
| Student > Bachelor | 4 | 20% |
| Student > Master | 3 | 15% |
| Other | 2 | 10% |
| Student > Doctoral Student | 2 | 10% |
| Other | 2 | 10% |
| Unknown | 2 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 40% |
| Medicine and Dentistry | 7 | 35% |
| Chemistry | 1 | 5% |
| Biochemistry, Genetics and Molecular Biology | 1 | 5% |
| Unknown | 3 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 26 March 2019.
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#5,269,238
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Outputs from Pharmacogenomics and Personalized Medicine
#1
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#36,862
of 188,906 outputs
Outputs of similar age from Pharmacogenomics and Personalized Medicine
#1
of 1 outputs
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So far Altmetric has tracked 1 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one scored the same or higher as 0 of them.
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