Evidence map›Paper›PMID 31186680›Full record

ReviewOncology letters2019

Liquid biopsy for lung cancer immunotherapy.

Liang-Liang Cai, Jie Wang

Open access · diamondAbstract readReview
In one paragraph

Review in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

10 citing papers in PubMed, 18 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Liang-Liang CaiDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China.
Jie WangDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P.R. China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent successful use of the immune checkpoint inhibitors (CPIs) anti-programmed death receptor-1 (PD-1)/PD-1 ligand 1 in clinical trials indicates their crucial role in obtaining an effective cancer immune therapy. These CPIs have been identified to have an effective therapeutic response, particularly in tumors with high tumor mutation burden. Targeting private somatic mutations encoding immunogenic neoantigens (neo-Ags) has been developed as an autologous gene therapy. T-cell receptor-engineered T cells targeting neo-Ags are a novel option for adoptive cell therapy used for the treatment of lung cancer. However, not all patients experience an effective response from immunotherapy. Although the resistance mechanism of CPIs has been reported, its association with other treatment methods during systemic anticancer therapy remains unclear, particularly the treatment options following the emergence of drug resistance in lung cancer. The potential biomarkers used for liquid biopsy may assist in the identification of patients who would benefit the most from immunotherapy. Attempts to identify potential biomarkers for predicting clinical response to immunotherapy are underway. With regard to liquid biopsy, the present review summarizes and discusses the lung cancer management of immunotherapy for precision medicine by reviewing recent literature and associated clinical trials.

Indexed as

checkpoint inhibitorsliquid biopsylung cancerneoantigensprognosisT cellstumor mutation burden

Identifiers

PMID31186680
PMCPMC6507432
OpenAlexW2925302858

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.