Autofluorescence Imaging of Treatment Response in Neuroendocrine Tumor Organoids

Abstract

Gastroenteropancreatic neuroendocrine tumors (GEP-NET) account for roughly 60% of all neuroendocrine tumors. Low/intermediate grade human GEP-NETs have relatively low proliferation rates that animal models and cell lines fail to recapitulate. Short-term patient-derived cancer organoids (PDCOs) are a 3D model system that holds great promise for recapitulating well-differentiated human GEP-NETs. However, traditional measurements of drug response (i.e., growth, proliferation) are not effective in GEP-NET PDCOs due to the small volume of tissue and low proliferation rates that are characteristic of the disease. Here, we test a label-free, non-destructive optical metabolic imaging (OMI) method to measure drug response in live GEP-NET PDCOs. OMI captures the fluorescence lifetime and intensity of endogenous metabolic cofactors NAD(P)H and FAD. OMI has previously provided accurate predictions of drug response on a single cell level in other cancer types, but this is the first study to apply OMI to GEP-NETs. OMI tested the response to novel drug combination on GEP-NET PDCOs, specifically ABT263 (navitoclax), a Bcl-2 family inhibitor, and everolimus, a standard GEP-NET treatment that inhibits mTOR. Treatment response to ABT263, everolimus, and the combination were tested in GEP-NET PDCO lines derived from seven patients, using two-photon OMI. OMI measured a response to the combination treatment in 5 PDCO lines, at 72 h post-treatment. In one of the non-responsive PDCO lines, heterogeneous response was identified with two distinct subpopulations of cell metabolism. Overall, this work shows that OMI provides single-cell metabolic measurements of drug response in PDCOs to guide drug development for GEP-NET patients.

Keywords: NAD(P)H; autofluorescence; fluorescence lifetime imaging; neuroendocrine tumor; organoid.

Figure 1

ABT263 and everolimus combination therapy increases cell death and decreases optical metabolic imaging (OMI) index. (A) Representative images of STC-1 cells stained for Ki67 (green) and CC3 (red), with DAPI stained nuclei (blue), (B) Quantified results from the stain show the number of DAPI cells that were also positive for Ki67 or CC3. There was a significant increase in the percent positive CC3 cells with combination treatment vs. control; * indicates p < 0.05, using an ANOVA (n = 24 regions of interest), (C) Quantified OMI index [linear combination of NAD(P)H mean lifetime (τ_m), FAD τ_m, and redox ratio with the coefficients [1, 1, −1], respectively from STC-1 cells treated with ABT263 and everolimus. Redox Ratio is defined as NAD(P)H intensity/FAD Intensity. Bars indicate p < 0.01 from an ANOVA (n = 2500 cells). Glass’s delta (Δ) values are also shown for treatment vs. control conditions, n.a. indicates non-applicable Glass Δ due to increase in OMI index (decrease in OMI index indicates drug response, increase or no change in OMI index indicates non-response [20]).

Figure 2

Generation of gastroenteropancreatic neuroendocrine tumors (GEP-NET) patient-derived cancer organoids (PDCOs). (A) Patients present with Gastroenteropancreatic neuroendocrine tumors, (B) tumor is surgically removed as part of standard treatment, (C) a portion of the resected tumor is taken to the lab and digested using dispase and collagenase, (D) after the digestion buffer is removed, cells are resuspended in culture medium and combined with Matrigel before plating on glass bottom imaging dishes. PDCOs are maintained in culture for at least 1 month, and re-plated prior to experiments. Due to low proliferation rates, GEP-NET PDCOs do not repopulate dishes and are therefore not passaged, (E) some control dishes are used for standard staining when enough material is present (Hematoxylin & Eosin, Synaptophysin, DAPI + Ki67; scale bars 100 µm). All other dishes are treated with drugs and monitored for drug response using brightfield imaging of PDCO diameter change and optical metabolic imaging of metabolic response.

Figure 3

GEP-NET PDCOs maintain key phenotypic characteristics of patient GEP-NETs. (A) Comparison of H&E and GEP-NET specific stains, synaptophysin and chromogranin A, between tumor slices and PDCOs from a single patient; scale bars 100 µm, (B) Ki67 and DAPI stained PDCOs show slow low percentage of cells with Ki67 staining. Ki67+ cells are marked by white arrow; scale bar 100 µm, (C) percent Ki67 positive cells assessed from PDCOs and the patient tissue from which they were derived, (D) scatter plots of PDCO growth after 7 days of culture. Growth % = [(day 7 diameter − day 1 diameter)/day 1 diameter] × 100. Each datapoint is one organoid. GEP-NET PDCOs have relatively low growth rates compared to fast growing colorectal cancer (diamonds). See Table S1 for sample sizes, (E) optical metabolic imaging of the NAD(P)H mean lifetime (τ_m), FAD τ_m, and the Redox Ratio (NAD(P)H intensity/FAD Intensity) for a representative GEP-NET PDCO sample, which indicates that the cells are metabolically active even with low proliferation rates; scale bar 50 µm; ns indicates “nanoseconds”.

Figure 4

Optical metabolic imaging of drug response in GEP-NET PDCOs. (A) Representative images of the NAD(P)H mean lifetime (τ_m), FAD τ_m, and the Redox Ratio (NAD(P)H intensity/FAD Intensity) for three PDCO lines; scale bar 50 µm; ns indicates “nanoseconds”, (B) OMI Index (linear combination of NAD(P)H τ_m FAD τ_m, and redox ratio with the coefficients [1, 1, −1], respectively) for each patient on a single cell level (each datapoint is a cell), unpaired t-test for each treatment compared to control; bars indicate p < 0.01. See Table S1 for sample sizes, (C) Glass’s Δ heat map of effect size for the OMI Index of each treatment relative to control; * indicates Glass’s Δ greater than 1.0, n.a. indicates non-applicable Glass Δ due to increase in OMI index (decrease in OMI index indicates drug response, increase or no change in OMI index indicates non-response [20]).

Figure 5

Single-cell subpopulation analysis of the OMI Index (same data shown in Figure 4B) shows differences in heterogeneity between three patients due to treatment. Single cell distributions were fit to a Gaussian mixture model to summarize heterogeneity (see Methods), and the resulting Gaussian fits are shown in (A–C). (A) Patient 1 was best fit with a single Gaussian for all conditions, with a wide variance for everolimus treatment alone, (B) patient 6 was best fit with two Gaussians for all conditions, indicating high heterogeneity, (C) patient 7 was best fit with a single Gaussian for all conditions, with a narrow variance for each condition, (D) heat map of the weighted heterogeneity index (wH-index), which accounts for variance in Gaussian fits and the presence of multiple Gaussians to quantify cellular heterogeneity in a sample (see Methods). Patient 1 has more cellular heterogeneity with everolimus treatment compared to the other conditions. Patient 6 has heterogeneous distributions for all conditions due to two distinct populations for each condition. Patient 7 has low cellular heterogeneity consistent with narrow single population distributions.