Evidence map›Paper›PMID 37828984›Full record

ReviewFrontiers in immunology2023

Combination of multiple omics techniques for a personalized therapy or treatment selection.

Chiara Massa, Barbara Seliger

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

9 citing papers in PubMed, 11 citations in OpenAlex.

  1. Review
  2. Progress and prospects of metal-based immunotherapy in breast cancer.Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine · 2025
    Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. MiDNE a tool for Multi-omics genes and drugs interactions discovery.Computational and structural biotechnology journal · 2025
    Article
  8. Review
  9. Translational pathology in drug discovery.Frontiers in pharmacology · 2024
    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

2 authors at 1 institution in 1 country.

Chiara MassaInstitute for Translational Immunology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany.
Barbara SeligerInstitute for Translational Immunology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany.
Martin Luther University Halle-Wittenberg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its

Indexed as

ImmunotherapyNeoplasmsBiomarkers, TumorHumansTumor MicroenvironmentBiomarkers, Tumorbiomarkercancerhigh throughput technologiespatient stratificationpersonalized therapy

Identifiers

PMID37828984
PMCPMC10565668
OpenAlexW4387117073

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.