Evidence map›Paper›PMID 29951304›Full record

ReviewJournal of thoracic disease2018

Shining light on advanced NSCLC in 2017: combining immune checkpoint inhibitors.

Meng Qiao, Tao Jiang, Caicun Zhou

Abstract readReview
In one paragraph

Review in Journal of thoracic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Review
  2. Molecular Pathology of Lung Cancer.Cold Spring Harbor perspectives in medicine · 2022
    Review
  3. Article
  4. Article
  5. Article
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

3 authors.

Meng QiaoDepartment of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai 200433, China.
Tao JiangDepartment of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai 200433, China.
Caicun ZhouDepartment of Medical Oncology, Shanghai Pulmonary Hospital & Thoracic Cancer Institute, Tongji University School of Medicine, Shanghai 200433, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The treatment landscape has changed since the immune checkpoint inhibitors were approved in the treatment of non-small cell lung cancer (NSCLC). Although the promising clinical benefit from programmed death-1/programmed death ligand-1 (PD-1/PD-L1) inhibitors was observed in the second or subsequent line treatment of patients who progressed on chemotherapy, it has a long way for single PD-1/PD-L1 inhibitor to move forward to the frontline without a predictive biomarker. Tumor response is far from satisfactory without selection and primary or acquired resistance to PD-1/PD-L1 inhibitors hampered their utility. Therefore, it is crucial to determine a strategy that can optimize the application of immune checkpoint inhibitors and increase the numbers of the responders. Multiple combination approaches based on PD-1/PD-L1 inhibitors are designed and aimed to boost anti-tumor response and benefit a broader population. In this review, we will integrate the updated clinical data to highlight the four most promising combination strategies in advance NSCLC: combination of checkpoint inhibition with chemotherapy, anti-angiogenesis, immunotherapy and radiotherapy. We further discuss the issues needed to be addressed and perspectives in the context of "combination era".

Indexed as

combination strategyimmune checkpoint inhibitorNon-small cell lung cancer (NSCLC)programmed death-1 (PD-1)programmed death ligand-1 (PD-L1)

Identifiers

PMID29951304
PMCPMC5994489

What OpenQuestion holds

Textmetadata
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.