Abstract
The genetic and phenotypic heterogeneity of cancers can contribute to tumor aggressiveness, invasion, and resistance to therapy. Fluorescence imaging occupies a unique niche to investigate tumor heterogeneity due to its high resolution and molecular specificity. Here, heterogeneous populations are identified and quantified by combined optical metabolic imaging and subpopulation analysis (OMI-SPA). OMI probes the fluorescence intensities and lifetimes of metabolic enzymes in cells to provide images of cellular metabolism, and SPA models cell populations as mixed Gaussian distributions to identify cell subpopulations. In this study, OMI-SPA is characterized by simulation experiments and validated with cell experiments. To generate heterogeneous populations, two breast cancer cell lines, SKBr3 and MDA-MB-231, were co-cultured at varying proportions. OMI-SPA correctly identifies two populations with minimal mean and proportion error using the optical redox ratio (fluorescence intensity of NAD(P)H divided by the intensity of FAD), mean NAD(P)H fluorescence lifetime, and OMI index. Simulation experiments characterized the relationships between sample size, data standard deviation, and subpopulation mean separation distance required for OMI-SPA to identify subpopulations.
Highlights
Solid tumors are highly heterogeneous, both across patients and within individual tumors
The error of the flavin adenine dinucleotide (FAD) lifetime values for the modeled SKBr3 and MDA-MB-231 subpopulations was within 10% of the true FAD lifetime values except at sample sizes < 100 (Fig. 3(b)-3(c)); the error of the proportion of cells assigned to each subpopulation was greater than 10% at almost all sample sizes and proportions (Fig. 3(d))
Recent evidence suggests that tumor heterogeneity is a major source of drug resistance [1]
Summary
Solid tumors are highly heterogeneous, both across patients and within individual tumors. The OMI endpoints provide dynamic readouts of cellular metabolism and detect pre-malignant transformations within tissues [15, 16], classify subtypes of breast cancer cells [9, 13], and detect response to anti-cancer drugs [14]. Simulation experiments demonstrate the population characteristics required for robust SPA identification of two subpopulations These experiments were validated by OMI-SPA of heterogeneous samples created by co-culturing two different breast cancer cell lines, a triple negative breast cancer (MDA-MB-231) and a HER2 + cell line (SKBr3) at varying proportions. These two cell lines were chosen to represent cell populations responsive (SKBr3) and resistant (MDA-MB-231) to the antiHER2 antibody, trastuzumab. The OMI endpoints and morphology of these two cells are sufficiently different to allow computational separation and heterogeneity analysis
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