| Title |
Cancer pharmacogenomics, challenges in implementation, and patient-focused perspectives
|
|---|---|
| Published in |
Pharmacogenomics and Personalized Medicine, July 2016
|
| DOI | 10.2147/pgpm.s62918 |
| Pubmed ID | |
| Authors |
Jai N Patel |
| Abstract |
Cancer pharmacogenomics is an evolving landscape and has the potential to significantly impact cancer care and precision medicine. Harnessing and understanding the genetic code of both the patient (germline) and the tumor (somatic) provides the opportunity for personalized dose and therapy selection for cancer patients. While germline DNA is useful in understanding the pharmacokinetic and pharmacodynamic disposition of a drug, somatic DNA is particularly useful in identifying drug targets and predicting drug response. Molecular profiling of somatic DNA has resulted in the current breadth of targeted therapies available, expanding the armamentarium to battle cancer. This review provides an update on cancer pharmacogenomics and genomics-based medicine, challenges in applying pharmacogenomics to the clinical setting, and patient perspectives on the use of pharmacogenomics to personalize cancer therapy. |
X Demographics
The data shown below were collected from the profiles of 6 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 | 1 | 17% |
| Montenegro | 1 | 17% |
| Unknown | 4 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 50% |
| Practitioners (doctors, other healthcare professionals) | 2 | 33% |
| Members of the public | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 138 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 33 | 24% |
| Researcher | 20 | 14% |
| Student > Master | 18 | 13% |
| Student > Ph. D. Student | 12 | 9% |
| Student > Doctoral Student | 6 | 4% |
| Other | 14 | 10% |
| Unknown | 35 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 30 | 22% |
| Biochemistry, Genetics and Molecular Biology | 30 | 22% |
| Medicine and Dentistry | 23 | 17% |
| Agricultural and Biological Sciences | 9 | 7% |
| Computer Science | 2 | 1% |
| Other | 6 | 4% |
| Unknown | 38 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 29 December 2016.
All research outputs
#8,689,826
of 25,748,735 outputs
Outputs from Pharmacogenomics and Personalized Medicine
#1
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#133,374
of 368,515 outputs
Outputs of similar age from Pharmacogenomics and Personalized Medicine
#1
of 1 outputs
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