Determination of Drug Sensitivity in Patient Derived Models of Breast Cancer by Multiparametric QPI

Functional precision oncology seeks to match patients to the most effective therapy by directing testing available therapies against material derived from patient samples. However, sample expansion is a key limitation of existing approaches, requiring both time and expense prior to sample analysis. Quantitative phase imaging (QPI) provides a potential path towards practical functional precision oncology by directly measuring the growth or death of single tumor cells when treated with chemotherapies or targeted drugs. In previous work, we demonstrated that multiple, independent parameters of response can be derived from QPI data, an approach we call multiparametric QPI (mQPI). Here, we demonstrate application of mQPI to cells from patient derived xenograft organoid (PDXO) models, as well as patient samples. We show that mQPI can distinguish responses from PDXO models derived from multiple tumor sites in the same patient, and resolve heterogeneity in a PDXO model of acquired therapeutic resistance. We also show that mQPI can differentiate responses in viably frozen primary patient samples, either direct from thaw or after a short-term, two-week expansion. Overall, these data provide proof-of-principle for application of mQPI in a range of sample types, including material from primary patient samples. This underscores the potential of mQPI as a time-efficient alternative to current methods in functional precision oncology.