Evidence map›Paper›PMID 41366250›Full record

ReviewNPJ breast cancer2025

Biomarker prediction of immunotherapy response in breast cancer: from single markers to multi-omics integration.

Ali Sanjari Moghaddam, Kit Y Lu, Elham Nasrollahi, Lovette Oji, Anna Homeniuk, Oyindamola Amosu, Daria Chelysheva, Adam M Brufsky

Abstract readReview
In one paragraph

Review in NPJ breast cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Association of PD-L1 combined positive score with disease control and response kinetics with first-line pembrolizumab-based therapy in metastatic triple-negative breast cancer: results from routine clinical practice in Poland.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  4. Review
  5. Article
  6. Review
  7. Review
  8. Review
  9. Review
  10. Article
  11. Review
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

8 authors.

Ali Sanjari MoghaddamDepartment of Internal Medicine, University of Pittsburgh Medical Center, Harrisburg, PA, USA.
Kit Y LuDivision of Hematology/Oncology, Department of Medicine, Hillman Cancer Center, University of Pittsburgh Medical Center, Harrisburg, PA, USA.
Elham NasrollahiDepartment of Internal Medicine, University of Pittsburgh Medical Center, Harrisburg, PA, USA.
Lovette OjiDepartment of Internal Medicine, University of Pittsburgh Medical Center, Harrisburg, PA, USA.
Anna HomeniukDepartment of Internal Medicine, University of Pittsburgh Medical Center, Harrisburg, PA, USA.
Oyindamola AmosuDepartment of Internal Medicine, University of Pittsburgh Medical Center, Harrisburg, PA, USA.
Daria ChelyshevaDepartment of Internal Medicine, University of Pittsburgh Medical Center, Harrisburg, PA, USA.
Adam M BrufskyDivision of Hematology/Oncology, Department of Medicine, Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA. brufskyam@upmc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy has redefined treatment paradigms across several malignancies, yet its application in breast cancer remains limited to select subtypes, particularly triple-negative breast cancer. As immune checkpoint inhibitors advance into earlier disease stages and broader clinical use, the identification of predictive biomarkers is essential to optimize patient selection and therapeutic outcomes. This review critically examines the current landscape of immunotherapy biomarkers in breast cancer and discusses their predictive value across breast cancer subtypes and treatment settings, highlighting both their clinical relevance and limitations. While conventional single biomarkers have demonstrated clinical relevance, they remain limited in sensitivity and specificity. Recent data suggest that gene expression signatures may offer superior predictive power. Moreover, emerging evidence supports the utility of integrated, multi-parametric biomarker strategies that combine genomic, transcriptomic, spatial, and dynamic immune profiling. Ongoing endeavors in standardization, validation, and the incorporation of artificial intelligence-driven analytics are critical to translating these biomarkers into precision immuno-oncology for breast cancer.

Identifiers

PMID41366250
PMCPMC12800120

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.