Researchers at the University of California, San Francisco, have developed a laboratory-based method that could help predict how individual breast tumors respond to cancer treatment. The approach combines molecular data from the I-SPY2 breast cancer trial with patient-derived organoids—three-dimensional “mini tumors” grown from a patient’s own cancer cells. In early testing, these organoids reproduced treatment responses observed in the corresponding tumors and helped identify drug combinations that may overcome resistance, including resistance in aggressive triple-negative breast cancer.
The study, published Aug. 6 in Cell Reports Medicine, addresses one of the central challenges in oncology: two tumors that appear similar under a microscope can respond very differently to the same therapy. Breast cancer treatment decisions are increasingly guided by biomarkers, such as hormone-receptor status, HER2 amplification and DNA-repair alterations, but these measurements do not always reveal which drug will work best in a particular patient. The UCSF team investigated whether living tumor models could provide a functional test of treatment response alongside genomic and clinical information.
To create the organoids, researchers placed cells taken from patient tumors into a specialized gel engineered to support the biological conditions of the original cancer. Over several weeks, the cells organized into compact, three-dimensional structures containing hundreds or thousands of cells. Unlike conventional cancer cell lines grown as flat layers, organoids preserve aspects of tumor architecture and retain many of the molecular features found in the tissue from which they were derived. Their small size also makes it possible to expose large numbers of organoids to multiple drugs in parallel.
The team established a biobank of organoids from early-stage invasive breast cancers and compared their behavior with clinical information from patients enrolled in the I-SPY2 trial. I-SPY2 is designed to accelerate the testing of therapies for high-risk breast cancer by evaluating several treatments simultaneously in biologically defined patient groups. The trial has generated “response predictive subtypes,” molecular classifications intended to estimate how tumors will respond to therapies such as immunotherapy, platinum chemotherapy, PARP inhibitors and dual-HER2-targeted drugs.
Using these predictive subtypes and additional tumor biomarkers, the researchers built a computational framework to forecast how individual organoids would react to specific treatments. They then tested the predictions experimentally. The model was especially evaluated in organoids derived from triple-negative breast cancers, a subtype that lacks estrogen and progesterone receptors and does not show elevated levels of HER2. Because triple-negative tumors have fewer established molecular targets and can rapidly develop treatment resistance, patients are often treated with intensive chemotherapy, including platinum-based drugs.
One treatment combination examined in the study was veliparib plus platinum chemotherapy, referred to as VP. Veliparib inhibits PARP proteins, which help repair certain forms of DNA damage, while platinum drugs damage DNA directly. The combination is intended to overwhelm the tumor’s repair machinery, but not every triple-negative tumor is vulnerable to it. Among the organoids, the model identified one sample, designated TORG40, as having a particularly high likelihood of resistance. Laboratory experiments subsequently confirmed that TORG40 showed limited sensitivity to VP, providing a test of the prediction system.
The researchers then used TORG40 to conduct a high-throughput drug screen involving 386 small-molecule inhibitors. The screen highlighted ABT-263, a compound that targets proteins involved in cellular survival and can promote the removal of damaged or stressed cells. When ABT-263 was combined with cisplatin, a platinum chemotherapy drug, the treatment produced a markedly stronger effect against the resistant TORG40 organoid than either agent alone. The result suggests that functional drug screening may uncover vulnerabilities that are not obvious from standard biomarkers, although the combination remains an experimental finding rather than an established treatment.
The organoid experiments also identified HSP90 inhibitors as potential candidates for further study. HSP90 is a molecular chaperone that helps stabilize and maintain numerous proteins, including proteins involved in cancer growth and survival. Blocking HSP90 can disrupt several signaling pathways at once, which may be useful in tumors driven by complex or overlapping mechanisms. The researchers linked the organoid findings to a subset of I-SPY patients who appeared to respond more favorably to drugs in this class, offering an example of how laboratory observations can be connected back to clinical trial data.
The investigators emphasize that the organoids do not reproduce the full environment of a tumor inside the body. They lack blood vessels, immune cells, stromal tissue and the broader organ systems that influence how cancer cells receive signals and how drugs are distributed. The study also did not determine whether organoid-guided treatment decisions would improve patient outcomes over months or years. Even so, the findings support a “reverse translational” strategy in which clinical trial data are used to generate laboratory predictions, and organoid experiments are then used to discover and prioritize therapies. With further validation in prospective clinical studies, patient-derived organoids could eventually become a practical bridge between molecular biomarkers and more individualized breast cancer treatment.
Subject of Research: Lab-produced tissue samples
Article Title: Biomarker-guided responses in patient-derived organoids predict effective therapies in breast cancer
News Publication Date: 6-Aug-2026
Web References: https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(26)00390-3
References: DOI: 10.1016/j.xcrm.2026.102973
Keywords: Breast cancer, triple-negative breast cancer, patient-derived organoids, tumor organoids, personalized medicine, biomarkers, drug resistance, combination therapy, cisplatin, veliparib, PARP inhibitors, HSP90 inhibitors, I-SPY2 trial, precision oncology
Tags: 3D tumor cell culturebiomarker-guided therapyBreast tumor treatment predictiondrug resistance in triple-negative breast cancerfunctional testing in oncologyminiature cancer modelsorganoid-based drug testingpatient-derived tumor organoidspersonalized breast cancer therapyprecision medicine in breast cancertumor response prediction methodsUCSF breast cancer research
