Evidence map›Paper›PMID 40405296›Full record

ArticleBreast cancer research : BCR2025

miRNA panel from HER2+ and CD24+ plasma extracellular vesicle subpopulations as biomarkers of early-stage breast cancer.

Griffin B Spychalski, Andrew A Lin, Stephanie J Yang, Hanfei Shen, Jean Rosario, Kyle Tien, Kate French, Miriyam Ghali, Stephanie Yee, Melinda Yin and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer research : BCR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. 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

16 authors.

Griffin B SpychalskiPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Andrew A LinPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Stephanie J YangDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
Hanfei ShenDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
Jean RosarioDepartment of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.
Kyle TienDivision of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Kate FrenchDivision of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Miriyam GhaliDivision of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Stephanie YeeDivision of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Melinda YinDivision of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Michael D FeldmanDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce St., Philadelphia, PA, 19104, USA.
Emily F ConantDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Susan P WeinsteinDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Erica L CarpenterDivision of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
David IssadoreDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
Anupma NayakDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce St., Philadelphia, PA, 19104, USA. anupma.nayak@pennmedicine.upenn.edu.

Funding

Nanomagnetic isolation and sensing for mobile HIV-1 self-testingR33AI147406 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI ISSADORE, DAVID AARON · 2022 to 2023
$1.6M
Combining Machine Learning and Nanofluidic Technology for The Multiplexed Diagnosis of Pancreatic AdenocarcinomaR33CA278551 · NCI · UNIVERSITY OF PENNSYLVANIA · PI CARPENTER, ERICA, ISSADORE, DAVID AARON · 2023 to 2025
$1.1M
National Institute of Allergy and Infectious Diseases 5-R33-AI-147406-03NCI NIH HHS R33CA278551NIAID NIH HHS R33 AI147406Penn Center for Precision Medicine, University of Pennsylvania Accelerator Fund
6 · The paper itself

Abstract

backgroundMammography screening has improved early breast cancer detection, leading to reduced mortality and lower rates of advanced breast cancer. However, mammography has a high false positive rate that results in over a million invasive breast biopsies of benign lesions in the US each year. Therefore, there is a need for noninvasive, blood-based diagnostics that can accurately assess risk of malignancy for women with indeterminate lesions identified by mammography, such as BI-RADS category 4 breast lesions. The aim of this study is to identify biomarkers from multiplexed extracellular vesicle liquid biopsy that can accurately classify mammographically detected BI-RADS 4 lesions.

methodsWe analyzed plasma from 113 prospectively enrolled subjects with BI-RADS 4 breast lesions, including 86 women with benign lesions and 27 women with malignant lesions (including 12 with stage I invasive carcinoma and 14 with ductal carcinoma in situ). None of the invasive carcinomas were metastatic. From each plasma sample, we used track etched magnetic nanopore technology to separately isolate HER2 and CD24 expressing extracellular vesicles (EVs) and measured their miRNA cargo using next-generation sequencing. We evaluated the performance of EV-miRNA biomarkers for classifying malignancy and applied LASSO classification to identify a panel of four complementary EV miRNA biomarkers that we validated by qPCR.

resultsWe identified 19 differentially enriched miRNA from HER2+ EVs and 11 differentially enriched miRNA from CD24+ EVs of women with malignant lesions compared to benign lesions. We observed individual miRNA with an AUC of up to 0.87 for miR-340-5p from HER2+ EVs and 0.75 for miR-223-3p from CD24+ EVs. LASSO classification selected a panel of four complementary EV miRNA for classifying breast cancer: miR-340-5p (HER2+ EVs), miR-598-3p (CD24+), miR-15b-5p (HER2+), and miR-126-3p (CD24+).

conclusionsHER2+ and CD24+ EV subpopulations contain complementary biomarkers suitable for validation in larger studies that can accurately detect early-stage breast cancer among women with BI-RADS category 4 breast lesions.

Indexed as

Biomarkers, TumorBreast NeoplasmsCD24 AntigenErb-b2 Receptor Tyrosine KinasesExtracellular VesiclesMicroRNAsAdultAgedEarly Detection of CancerFemaleHumansLiquid BiopsyMammographyMiddle AgedNeoplasm StagingProspective StudiesBiomarkers, TumorCD24 AntigenCD24 protein, humanERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesMicroRNAsBI-RADS 4 breast lesionsEarly detection biomarkersExtracellular vesiclesLiquid biopsyMiRNA sequencing

Identifiers

PMID40405296
PMCPMC12096773

What Socratic holds

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LicenceCC BY
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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.