Evidence map›Paper›PMID 28057932›Full record

ReviewNature reviews. Drug discovery2017

Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity.

W Joost Lesterhuis, Anthony Bosco, Michael J Millward, Michael Small, Anna K Nowak, Richard A Lake

Open access · greenAbstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Drug discovery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
137citing papers in PubMed, 4 pooled it
12.5field-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

137 citing papers in PubMed, 4 syntheses or guidelines pooled it, 251 citations in OpenAlex.

  1. Pooled it
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  6. Serum IL-5 and IFN-Disease markers · 2021
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  10. Machine learning-based prediction of dynamic heterosis for plant height with pathway biomarkers in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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77 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

6 authors at 2 institutions in 1 country.

W Joost LesterhuisSchool of Medicine and Pharmacology and National Centre for Asbestos Related Diseases, University of Western Australia, 5th Floor QQ Block, 6 Verdun Street, Nedlands, Perth, Western Australia 6009, Australia.
Anthony BoscoTelethon Kids Institute, University of Western Australia, PO Box 855, West Perth, Western Australia 6872, Australia.
Michael J MillwardSchool of Medicine and Pharmacology and National Centre for Asbestos Related Diseases, University of Western Australia, 5th Floor QQ Block, 6 Verdun Street, Nedlands, Perth, Western Australia 6009, Australia.
Michael SmallSchool of Mathematics and Statistics, University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia.
Anna K NowakSchool of Medicine and Pharmacology and National Centre for Asbestos Related Diseases, University of Western Australia, 5th Floor QQ Block, 6 Verdun Street, Nedlands, Perth, Western Australia 6009, Australia.
Richard A LakeSchool of Medicine and Pharmacology and National Centre for Asbestos Related Diseases, University of Western Australia, 5th Floor QQ Block, 6 Verdun Street, Nedlands, Perth, Western Australia 6009, Australia.
University of Western Australia · AUSir Charles Gairdner Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, there has been a coordinated effort from academic institutions and the pharmaceutical industry to identify biomarkers that can predict responses to immune checkpoint blockade in cancer. Several biomarkers have been identified; however, none has reliably predicted response in a sufficiently rigorous manner for routine use. Here, we argue that the therapeutic response to immune checkpoint blockade is a critical state transition of a complex system. Such systems are highly sensitive to initial conditions, and critical transitions are notoriously difficult to predict far in advance. Nevertheless, warning signals can be detected closer to the tipping point. Advances in mathematics and network biology are starting to make it possible to identify such warning signals. We propose that these dynamic biomarkers could prove to be useful in distinguishing responding from non-responding patients, as well as facilitate the identification of new therapeutic targets for combination therapy.

Indexed as

Biomarkers, TumorAnimalsHumansImmune SystemMolecular Targeted TherapyNeoplasmsNonlinear DynamicsBiomarkers, Tumor

Identifiers

PMID28057932
OpenAlexW2570396690

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.