Evidence map›Paper›PMID 25189716›Full record

ReviewChinese journal of cancer2014

Immune checkpoint inhibitors in clinical trials.

Elad Sharon, Howard Streicher, Priscila Goncalves, Helen X Chen

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Chinese journal of cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 5 pooled it
–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

73 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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13 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

4 authors.

Elad SharonCancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD 20892, USA. sharone@mail.nih.gov.
Howard Streicher
Priscila Goncalves
Helen X Chen

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunology-based therapy is rapidly developing into an effective treatment option for a surprising range of cancers. We have learned over the last decade that powerful immunologic effector cells may be blocked by inhibitory regulatory pathways controlled by specific molecules often called "immune checkpoints." These checkpoints serve to control or turn off the immune response when it is no longer needed to prevent tissue injury and autoimmunity. Cancer cells have learned or evolved to use these mechanisms to evade immune control and elimination. The development of a new therapeutic class of drugs that inhibit these inhibitory pathways has recently emerged as a potent strategy in oncology. Three sets of agents have emerged in clinical trials exploiting this strategy. These agents are antibody-based therapies targeting cytotoxic T-lymphocyte antigen 4 (CTLA4), programmed cell death 1 (PD-1), and programmed cell death ligand 1 (PD-L1). These inhibitors of immune inhibition have demonstrated extensive activity as single agents and in combinations. Clinical responses have been seen in melanoma, renal cell carcinoma, non-small cell lung cancer, and several other tumor types. Despite the autoimmune or inflammatory immune-mediated adverse effects which have been seen, the responses and overall survival benefits exhibited thus far warrant further clinical development.

Indexed as

B7-H1 AntigenCarcinoma, Non-Small-Cell LungCarcinoma, Renal CellCell Cycle CheckpointsCTLA-4 AntigenImmunotherapyMelanomaNeoplasmsProgrammed Cell Death 1 ReceptorB7-H1 AntigenCD274 protein, humanCTLA-4 AntigenPDCD1 protein, humanProgrammed Cell Death 1 Receptor

Identifiers

PMID25189716
PMCPMC4190433

What OpenQuestion holds

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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.