ArticleCancer cell international2026
Molecular subtype-specific breast cancer organoids: development of an FGF-free estradiol valerate culture system for precision drug screening.
Article in Cancer cell international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Construction and application of mammary and breast cancer organoids.Cell insight · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Breast cancer, a prevalent malignancy among women, exhibits high heterogeneity, highlighting the critical need for personalized treatment strategies. Breast cancer organoids (BCOs), an emerging in vitro research tool, closely mimic the biological characteristics of tumors and demonstrate distinct differences in therapeutic response, prognosis, and biological features. However, the incomplete culture system of BCOs is still a challenging aspect of current research. In order to further optimize the existing culture formula of BCOs, molecular subtype characteristics in most cases. Through literature review and screening of key factors, we developed an FGF-free estradiol we collected tumor tissue samples from breast cancer patients with various molecular subtypes, including Luminal A, Luminal B, HER2 overexpressing (HER2+), and triple-negative breast cancer (TNBC). The samples were extracted and cultured using commercially available media. We observed that some media failed to sustain the growth of breast cancer organoids, while others, although supporting organoid survival, led to the loss of parental valerate (EV) culture system. Using triple-positive breast cancer samples, we optimized the concentration of EV. Our results indicated that 0, 10, and 20 µM EV maintained normal organoid growth, whereas 50 µM EV promoted adherent differentiation, which is detrimental to long-term organoid culture. Further analysis revealed that 20 µM EV not only preserved the organoid cellular morphology but also retained the parental molecular subtype features. Using this optimized culture system, we successfully cultivated breast cancer organoids from TNBC, Luminal, and HER2+ samples. We then developed a drug sensitivity prediction model using our established organoid culture system, which included triple-negative, Luminal, and HER2-positive breast cancer subtypes. By comparing imaging data before and after clinical treatment, we found a high concordance between drug sensitivity in the organoid model and clinical efficacy. This study provides an optimized culture medium system for the cultivation of breast cancer organoids and validates its potential application in precision drug screening. This model promises to deliver precise and effective treatment strategies for breast cancer patients, further improving their prognosis and quality of life, and ushering in a new chapter in personalized breast cancer treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.