Evidence map›Paper›PMID 37463882›Full record

ArticleNature communications2023

Next generation pan-cancer blood proteome profiling using proximity extension assay.

María Bueno Álvez, Fredrik Edfors, Kalle von Feilitzen, Martin Zwahlen, Adil Mardinoglu, Per-Henrik Edqvist, Tobias Sjöblom, Emma Lundin, Natallia Rameika, Gunilla Enblad and 16 more

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing 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

48 citing papers in PubMed.

  1. Towards liquid biopsy-based analysis of antitumour immunity.Nature reviews. Clinical oncology · 2026
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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

26 authors.

María Bueno ÁlvezScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-2669-7796
Fredrik EdforsScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-0017-7987
Kalle von FeilitzenScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-0257-7554
Martin ZwahlenScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-0064-4776
Adil MardinogluScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-4254-6090
Per-Henrik EdqvistDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-8330-0134
Tobias SjöblomDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-6668-4140
Emma LundinDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0009-0000-7711-1962
Natallia RameikaDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-7835-4357
Gunilla EnbladDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-0594-724X
Henrik LindmanDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Martin HöglundDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-2468-0226
Göran HesselagerDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-9613-1344
Karin StålbergDepartment of Women's and Children's Health, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-5527-8796
Malin EnbladDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-5900-0900
Oscar E SimonsonDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-3552-5558
Michael HäggmanDepartment of Surgical Sciences, Uppsala University, Uppsala, Sweden.
Tomas AxelssonDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.
Mikael ÅbergDepartment of Medical Sciences, Clinical Chemistry and SciLifeLab Affinity Proteomics, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-7858-8233
Jessica NordlundDepartment of Medical Sciences, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-8699-9959
Wen ZhongScience for Life Laboratory, Department of Biomedical and Clinical Sciences (BKV), Linköping University, Linköping, Sweden.ORCID 0000-0002-7422-6104
Max KarlssonScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0002-7000-4416
Ulf GyllenstenDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0000-0002-6316-3355
Fredrik PontenDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-0703-3940
Linn FagerbergScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID 0000-0003-0198-7137
Mathias UhlénScience for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden. mathias.uhlen@scilifelab.se.ORCID 0000-0002-4858-8056

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.

Indexed as

Hematologic NeoplasmsNeoplasmsHumansMachine LearningPrecision MedicineProteomeProteome

Identifiers

PMID37463882
PMCPMC10354027

What OpenQuestion holds

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Registered trials

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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.