Optical Metabolic Imaging of Heterogeneous Drug Response in Pancreatic Cancer Patient Organoids

Sharick, Joe T.; Walsh, Christine M.; Sprackling, Carley M.; Pasch, Cheri A.; Parikh, Alexander A.; Deming, Dustin A.; Skala, Melissa C.

doi:10.1101/542167

Cited by 4 publications

(4 citation statements)

References 58 publications

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“…Treatment effect size was calculated with Glass's Δ [50], with directionality determined by the response of HER2+ cell line xenografts to trastuzumab [21]. Preliminary data from our group show that an OMI index treatment effect size threshold of 0.75 in tumor-derived organoids accurately classified pancreatic cancer patients based on their recurrence-free survival time during adjuvant therapy [51]. This is similar to a previously suggested cutoff of 0.8 for large effect sizes [52].…”

Section: Methodssupporting

confidence: 70%

Cellular Metabolic Heterogeneity In Vivo Is Recapitulated in Tumor Organoids

et al. 2019

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Heterogeneous populations within a tumor have varying metabolic profiles, which can muddle the interpretation of bulk tumor imaging studies of treatment response. Although methods to study tumor metabolism at the cellular level are emerging, these methods provide a single time point “snapshot” of tumor metabolism and require a significant time and animal burden while failing to capture the longitudinal metabolic response of a single tumor to treatment. Here, we investigated a novel method for longitudinal, single-cell tracking of metabolism across heterogeneous tumor cell populations using optical metabolic imaging (OMI), which measures autofluorescence of metabolic coenzymes as a report of metabolic activity. We also investigated whether in vivo cellular metabolic heterogeneity can be accurately captured using tumor-derived three-dimensional organoids in a genetically engineered mouse model of breast cancer. OMI measurements of response to paclitaxel and the phosphatidylinositol-3-kinase inhibitor XL147 in tumors and organoids taken at single cell resolution revealed parallel shifts in metaboltruic heterogeneity. Interestingly, these previously unappreciated heterogeneous metabolic responses in tumors and organoids could not be attributed to tumor cell fate or varying leukocyte content within the microenvironment, suggesting that heightened metabolic heterogeneity upon treatment is largely due to heterogeneous metabolic shifts within tumor cells. Together, these studies show that OMI revealed remarkable heterogeneity in response to treatment, which could provide a novel approach to predict the presence of potentially unresponsive tumor cell subpopulations lurking within a largely responsive bulk tumor population, which might otherwise be overlooked by traditional measurements.

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Section: Methodssupporting

confidence: 70%

Cellular Metabolic Heterogeneity In Vivo Is Recapitulated in Tumor Organoids

et al. 2019

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“…Thank you to Dr. Alex Walsh for invaluable discussion and advice on patient-derived organoid culture and imaging. This manuscript has been released as a pre-print at bioRxiv (74).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Metabolic Heterogeneity in Patient Tumor-Derived Organoids by Primary Site and Drug Treatment

et al. 2020

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New tools are needed to match cancer patients with effective treatments. Patient-derived organoids offer a high-throughput platform to personalize treatments and discover novel therapies. Currently, methods to evaluate drug response in organoids are limited because they overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity was characterized in breast cancer (BC) and pancreatic cancer (PC) patient-derived organoids. Baseline heterogeneity was analyzed for each patient, demonstrating that single-cell techniques, such as OMI, are required to capture the complete picture of heterogeneity present in a sample. Treatment-induced changes in heterogeneity were also analyzed, further demonstrating that these measurements greatly complement current techniques that only gauge average cellular response. Finally, OMI of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response for the first time. Organoids were treated with the same drugs as the patient's prescribed regimen, and OMI measurements of heterogeneity were compared to patient outcome. OMI distinguished subpopulations of cells with divergent and dynamic responses to treatment in living organoids without the use of labels or dyes. OMI of organoids agreed with long-term therapeutic response in patients. With these capabilities, OMI could serve as a sensitive high-throughput tool to identify optimal therapies for individual patients, and to develop new effective therapies that address cellular heterogeneity in cancer.

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“…Generally, cellular rates of glycolysis, and NAD(P)H and FAD protein-binding activity decrease with drug treatment in responsive cells [ 30 ] resulting in decreased optical redox ratios and NAD(P)H τ m , and increased FAD τ m . This leads to a decrease in the OMI index (OMI index = redox ratio + NAD(P)H τ m − FAD τ m ) with drug treatment in responsive cells [ 31 ]. Additionally, changes in NAD(P)H lifetimes correlate with changes in intracellular oxygen levels in organoids [ 32 ] while changes in the NAD(P)H/FAD ratio relate to changes in oxygen consumption [ 33 ] and glutamine consumption [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Autofluorescence Imaging of Treatment Response in Neuroendocrine Tumor Organoids

Gillette

Babiarz

VanDommelen

et al. 2021

Cancers

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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.

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Optical Metabolic Imaging of Heterogeneous Drug Response in Pancreatic Cancer Patient Organoids

Cited by 4 publications

References 58 publications

Cellular Metabolic Heterogeneity In Vivo Is Recapitulated in Tumor Organoids

Cellular Metabolic Heterogeneity In Vivo Is Recapitulated in Tumor Organoids

Metabolic Heterogeneity in Patient Tumor-Derived Organoids by Primary Site and Drug Treatment

Autofluorescence Imaging of Treatment Response in Neuroendocrine Tumor Organoids

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