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Determination of drug sensitivity in patient derived models of breast cancer by multiparametric quantitative phase imaging
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DOI：10.1186/s12885-026-16752-2.png)
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Abstract
En 中文
Functional precision oncology aims to guide therapy selection by directly measuring drug sensitivity in patient-derived cancer samples. However, most existing functional assays require prolonged cell expansion limiting their feasibility for time-sensitive clinical decision-making. We evaluated whether multiparametric quantitative phase imaging (mQPI), a label-free single-cell imaging approach, could enable rapid assessment of therapeutic response in patient-derived breast cancer models. We applied mQPI to cells derived from patient-derived xenograft organoid (PDXO) models and viably cryopreserved primary breast cancer samples. Quantitative phase imaging was used to extract multiple orthogonal biophysical parameters describing cellular growth and response dynamics following drug exposure. Drug sensitivity, intrapatient heterogeneity, and resistance-associated phenotypes were quantified and compared across models. mQPI resolved distinct drug response profiles among cells derived from different anatomical sites within the same patient and revealed heterogeneous response dynamics in models of acquired therapeutic resistance. Importantly, drug responses were detected in a high-purity cryopreserved patient sample immediately after thawing, whereas a more heterogeneous sample required a short-term (2-week) expansion to enrich the viable tumor population before a response could be resolved, indicating that sample composition determines whether a direct-from-thaw or short-term expansion workflow is required. Across sample types, mQPI enabled robust single-cell measurements without the need for labeling or extensive culture. These findings establish mQPI as a rapid, label-free functional assay capable of quantifying therapeutic response and heterogeneity in patient-derived breast cancer samples. By reducing assay time and material requirements while preserving single-cell resolution, mQPI has the potential to complement genomic profiling and advance the clinical translation of functional precision oncology.
Keywords:
Quantitative phase imaging
Functional precision medicine
Breast cancer
In vitro models
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