| Title |
Precision medicine in immune checkpoint blockade therapy for non‐small cell lung cancer
|
|---|---|
| Published in |
Clinical and Translational Medicine, January 2017
|
| DOI | 10.1186/s40169-017-0136-7 |
| Pubmed ID | |
| Authors |
Xiaoming Liu, William C. Cho |
| Abstract |
Immune checkpoint blockade therapy by targeting the programmed death protein 1/programmed death ligand 1 (PD-L1) axis using antibodies has yielded promising clinical responses in patients with non-small cell lung cancer (NSCLC). However, owing to the dynamic expression of PD-L1, degree of mutational/neoantigen load, intratumoral heterogeneity, infiltrated immune cells of tumor microenvironment of NSCLC, the response rates to these agents are limited, despite several companion diagnostic assays by detecting PD-L1 in tumor cells have been introduced into clinical practice. Therefore, in this era of precision medicine, there is an urgent need for predictive biomarkers to identify NSCLC patients likely to benefit from this novel therapy. |
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 % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Unknown | 53 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 15% |
| Student > Bachelor | 7 | 13% |
| Other | 6 | 11% |
| Student > Ph. D. Student | 6 | 11% |
| Student > Postgraduate | 4 | 7% |
| Other | 8 | 15% |
| Unknown | 15 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 17 | 31% |
| Biochemistry, Genetics and Molecular Biology | 6 | 11% |
| Agricultural and Biological Sciences | 4 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 6% |
| Immunology and Microbiology | 2 | 4% |
| Other | 4 | 7% |
| Unknown | 18 | 33% |
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 25 January 2017.
All research outputs
#20,657,128
of 25,377,790 outputs
Outputs from Clinical and Translational Medicine
#752
of 1,060 outputs
Outputs of similar age
#319,348
of 420,809 outputs
Outputs of similar age from Clinical and Translational Medicine
#8
of 8 outputs
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