Evidence map›Paper›PMID 35033985›Full record

ArticleTranslational oncology2022

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer.

Cecilia E Thomas, Leo Dahl, Sanna Byström, Yan Chen, Mathias Uhlén, Anders Mälarstig, Kamila Czene, Per Hall, Jochen M Schwenk, Marike Gabrielson

Open access · goldAbstract read
In one paragraph

Article in Translational oncology, 2022. 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
0.8field-weighted citation impact, top 35% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 3 institutions in 1 country.

Cecilia E ThomasScience for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden.
Leo DahlScience for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden.
Sanna ByströmScience for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden.
Yan ChenDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden; Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
Mathias UhlénScience for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden.
Anders MälarstigDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden; Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
Kamila CzeneDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden.
Per HallDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden; Department of Oncology, Södersjukhuset, Stockholm, Sweden.
Jochen M SchwenkScience for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden. Electronic address: jochen.schwenk@scilifelab.se.
Marike GabrielsonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden. Electronic address: marike.gabrielson@ki.se.
Karolinska Institutet · SEKTH Royal Institute of Technology · SEScience for Life Laboratory · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.

Indexed as

Affinity proteomicsArchetypal analysisBreast cancerClusteringKarma cohortMenopausal hormonal therapyPatient stratificationPlasma proteomicsRisk prediction

Identifiers

PMID35033985
PMCPMC8760550
OpenAlexW4226163603

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.