Title |
Lessons learned from the blockade of immune checkpoints in cancer immunotherapy
|
---|---|
Published in |
Journal of Hematology & Oncology, February 2018
|
DOI | 10.1186/s13045-018-0578-4 |
Pubmed ID | |
Authors |
Xiaolei Li, Changshun Shao, Yufang Shi, Weidong Han |
Abstract |
The advent of immunotherapy, especially checkpoint inhibitor-based immunotherapy, has provided novel and powerful weapons against cancer. Because only a subset of cancer patients exhibit durable responses, further exploration of the mechanisms underlying the resistance to immunotherapy in the bulk of cancer patients is merited. Such efforts may help to identify which patients could benefit from immune checkpoint blockade. Given the existence of a great number of pathways by which cancer can escape immune surveillance, and the complexity of tumor-immune system interaction, development of various combination therapies, including those that combine with conventional therapies, would be necessary. In this review, we summarize the current understanding of the mechanisms by which resistance to checkpoint blockade immunotherapy occurs, and outline how actionable combination strategies may be derived to improve clinical outcomes for patients. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 17% |
India | 1 | 17% |
Unknown | 4 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 67% |
Science communicators (journalists, bloggers, editors) | 1 | 17% |
Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 61 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 10 | 16% |
Student > Master | 5 | 8% |
Student > Ph. D. Student | 5 | 8% |
Other | 3 | 5% |
Researcher | 3 | 5% |
Other | 5 | 8% |
Unknown | 30 | 49% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 11 | 18% |
Medicine and Dentistry | 8 | 13% |
Immunology and Microbiology | 4 | 7% |
Agricultural and Biological Sciences | 2 | 3% |
Unspecified | 1 | 2% |
Other | 7 | 11% |
Unknown | 28 | 46% |