Evidence map›Paper›PMID 33028804›Full record

ReviewSignal transduction and targeted therapy2020

Advances in targeted therapy for esophageal cancer.

Yan-Ming Yang, Pan Hong, Wen Wen Xu, Qing-Yu He, Bin Li

Open access · goldAbstract readReview
In one paragraph

Review in Signal transduction and targeted therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 280 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
280citing papers in PubMed, 3 pooled it
51.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

280 citing papers in PubMed, 3 syntheses or guidelines pooled it, 534 citations in OpenAlex.

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220 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Yan-Ming Yang *MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, Jinan University, Guangzhou, China.
Pan Hong *MOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, Jinan University, Guangzhou, China.
Wen Wen XuMOE Key Laboratory of Tumor Molecular Biology and Guangdong Provincial Key Laboratory of Bioengineering Medicine, National Engineering Research Center of Genetic Medicine, Institute of Biomedicine, College of Life Science and Technology, Jinan University, Guangzhou, China. xuwenwen@jnu.edu.cn.
Qing-Yu HeMOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, Jinan University, Guangzhou, China. tqyhe@jnu.edu.cn.ORCID http://orcid.org/0000-0003-0503-9492
Bin LiMOE Key Laboratory of Tumor Molecular Biology and Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, Jinan University, Guangzhou, China. libin2015@jnu.edu.cn.ORCID http://orcid.org/0000-0002-8979-610X
Jinan University · CN

Funding

National Natural Science Foundation of China (National Science Foundation of China) 31770888National Natural Science Foundation of China (National Science Foundation of China) 81672953National Natural Science Foundation of China (National Science Foundation of China) 81773085National Natural Science Foundation of China (National Science Foundation of China) 81803551National Natural Science Foundation of China (National Science Foundation of China) 81973339
6 · The paper itself

Abstract

Esophageal cancer (EC) is one of the most lethal cancers in the world, and its morbidity and mortality rates rank among the top ten in China. Currently, surgical resection, radiotherapy and chemotherapy are the primary clinical treatments for esophageal cancer. However, outcomes are still unsatisfactory due to the limited efficacy and severe adverse effects of conventional treatments. As a new type of approach, targeted therapies have been confirmed to play an important role in the treatment of esophageal cancer; these include cetuximab and bevacizumab, which target epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF), respectively. In addition, other drugs targeting surface antigens and signaling pathways or acting on immune checkpoints have been continuously developed. For example, trastuzumab, a monoclonal antibody targeting human epidermal growth factor receptor 2 (HER-2), has been approved by the Food and Drug Administration (FDA) as a first-line treatment of HER-2-positive cancer. Moreover, the PD-L1 inhibitor pembrolizumab has been approved as a highly efficient drug for patients with PD-L1-positive or advanced esophageal squamous cell carcinoma (ESCC). These novel drugs can be used alone or in combination with other treatment strategies to further improve the treatment efficacy and prognosis of cancer patients. Nevertheless, adverse events, optimal dosages and effective combinations still need further investigation. In this review, we expound an outline of the latest advances in targeted therapies of esophageal cancer and the mechanisms of relevant drugs, discuss their efficacy and safety, and provide a clinical rationale for precision medicine in esophageal cancer.

Indexed as

Drug Delivery SystemsPrecision MedicineAntigens, NeoplasmAntineoplastic Agents, ImmunologicalEsophageal NeoplasmsHumansImmune Checkpoint InhibitorsNeoplasm ProteinsAntigens, NeoplasmAntineoplastic Agents, ImmunologicalImmune Checkpoint InhibitorsNeoplasm Proteins

Identifiers

PMID33028804
PMCPMC7542465
OpenAlexW3092352594

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.