Evidence map›Paper›PMID 41649611›Full record

ReviewJournal of mammary gland biology and neoplasia2026

Insights from Genomic Sequencing of Preclinical Breast Cancer Models Establish Human Parallels to Increase Therapeutic Applicability.

Anthony J Schulte, Eran R Andrechek

Abstract readReview
In one paragraph

Review in Journal of mammary gland biology and neoplasia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Anthony J SchulteDepartment of Pharmacology and Toxicology, Michigan State University, 1355 Bogue St, East Lansing, MI, 48824, USA.
Eran R AndrechekDepartment of Physiology, Michigan State University, 2194 BPS Building, 567 Wilson Road, East Lansing, MI, 48824, USA. Andrech1@msu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of breast cancer is complicated by the heterogeneity inherent within the disease. Numerous models have been developed to study the initiation, progression, and treatment of breast cancer. These include carcinogen induced mouse models, genetically engineered mouse models, and patient derived xenografts. The relevance of these mouse models to humans must be precisely defined for appropriate understanding of disease mechanisms to derive intervening treatments. Sequencing projects such as The Cancer Genome Atlas Project (TCGA) and Catalogue Of Somatic Mutations In Cancer (COSMIC) were pivotal developments in understanding driving events in human cancers. These studies have revealed that in addition to activation of strong oncogenes, or loss of tumor suppressors, that secondary events are necessary for tumor development and progression. These techniques should also be applied to mouse models of human breast cancer. For all the available models studied and reviewed here, whole genome sequencing (WGS) in conjunction with gene expression analysis has revealed conserved events between human and mouse model systems. This identification of conserved, critical events driving breast cancer has led to novel targets based on breast cancer subtype, ultimately resulting in new therapeutic opportunities. The combination of sequencing and choice of the appropriate mouse model can provide a powerful tool in developing appropriate pre-clinical models of breast cancer.

Indexed as

Breast NeoplasmsGenomicsMammary Neoplasms, ExperimentalAnimalsDisease Models, AnimalFemaleHumansMiceWhole Genome SequencingBreast cancerMouse modelsSequencingTherapeutic targeting

Identifiers

PMID41649611
PMCPMC12971849

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.