Evidence map›Paper›PMID 34430923›Full record

ArticlePatterns (New York, N.Y.)2021

Interpretable systems biomarkers predict response to immune-checkpoint inhibitors.

Óscar Lapuente-Santana, Maisa van Genderen, Peter A J Hilbers, Francesca Finotello, Federica Eduati

Open access · goldAbstract read
In one paragraph

Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed, 2 pooled it
8.3field-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

105 citing papers in PubMed, 2 syntheses or guidelines pooled it, 131 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Systematic Analysis of Tumor Microenvironment Using IOBR.Methods in molecular biology (Clifton, N.J.) · 2027
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  13. CD20Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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45 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 2 institutions in 2 countries.

Óscar Lapuente-SantanaDepartment of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands.
Maisa van GenderenDepartment of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands.
Peter A J HilbersDepartment of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands.
Francesca FinotelloBiocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Federica EduatiDepartment of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands.
Eindhoven University of Technology · NLInnsbruck Medical University · AT

Funding

Austrian Science Fund FWF T 974
6 · The paper itself

Abstract

Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-sequencing (RNA-seq) data combined with different sources of prior knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and intercellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1/PD-L1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.

Indexed as

biomarkersimmuno-oncologymachine learningprecision oncologyRNA-seqsystems biologytumor microenvironment

Identifiers

PMID34430923
PMCPMC8369166
OpenAlexW3175295347

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.