In Vivo Optical Metabolic Imaging of Long-Chain Fatty Acid Uptake in Orthotopic Models of Triple-Negative Breast Cancer

FAP-Targeted Fluorescent Imaging Agents to Study Cancer-Associated Fibroblasts In Vivo

Cancer-associated fibroblasts (CAFs) expressing fibroblast activation protein alpha (FAP) are abundant in tumor microenvironments and represent an emerging target for PET cancer imaging. While different quinolone-based small molecule agents have been developed for whole-body imaging, there is a scarcity of well-validated fluorescent small molecule imaging agents to better study these cells in vivo. Here, we report the synthesis and characterization of a series of fluorescent FAP imaging agents based on the common quinolone azide inhibitor. Our data show excellent performance of some synthesized FAP Targeting Fluorescent probes (FTFs) for both topical application and intravenous delivery to label CAF populations in solid tumors. These results suggest that FTF can be used to study CAF biology and therapeutic targeting in vivo.

Optical imaging reveals chemotherapy-induced metabolic reprogramming of residual disease and recurrence

Fewer than 20% of triple-negative breast cancer patients experience long-term responses to mainstay chemotherapy. Resistant tumor subpopulations use alternative metabolic pathways to escape therapy, survive, and eventually recur. Here, we show in vivo, longitudinal metabolic reprogramming in residual disease and recurrence of triple-negative breast cancer xenografts with varying sensitivities to the chemotherapeutic drug paclitaxel. Optical imaging coupled with metabolomics reported an increase in non-glucose-driven mitochondrial metabolism and an increase in intratumoral metabolic heterogeneity during regression and residual disease in resistant MDA-MB-231 tumors. Conversely, sensitive HCC-1806 tumors were primarily reliant on glucose uptake and minimal changes in metabolism or heterogeneity were observed over the tumors' therapeutic life cycles. Further, day-matched resistant HCC-1806 tumors revealed a higher reliance on mitochondrial metabolism and elevated metabolic heterogeneity compared to sensitive HCC-1806 tumors. Together, metabolic flexibility, increased reliance on mitochondrial metabolism, and increased metabolic heterogeneity are defining characteristics of persistent residual disease, features that will inform the appropriate type and timing of therapies.

Single-cell mapping of lipid metabolites using an infrared probe in human-derived model systems

Understanding metabolic heterogeneity is the key to uncovering the underlying mechanisms of metabolic-related diseases. Current metabolic imaging studies suffer from limitations including low resolution and specificity, and the model systems utilized often lack human relevance. Here, we present a single-cell metabolic imaging platform to enable direct imaging of lipid metabolism with high specificity in various human-derived 2D and 3D culture systems. Through the incorporation of an azide-tagged infrared probe, selective detection of newly synthesized lipids in cells and tissue became possible, while simultaneous fluorescence imaging enabled cell-type identification in complex tissues. In proof-of-concept experiments, newly synthesized lipids were directly visualized in human-relevant model systems among different cell types, mutation status, differentiation stages, and over time. We identified upregulated lipid metabolism in progranulin-knockdown human induced pluripotent stem cells and in their differentiated microglia cells. Furthermore, we observed that neurons in brain organoids exhibited a significantly lower lipid metabolism compared to astrocytes.

Tumor Organoids: The Era of Personalized Medicine

The strategies of future medicine are aimed to modernize and integrate quality approaches including early molecular-genetic profiling, identification of new therapeutic targets and adapting design for clinical trials, personalized drug screening (PDS) to help predict and individualize patient treatment regimens. In the past decade, organoid models have emerged as an innovative in vitro platform with the potential to realize the concept of patient-centered medicine. Organoids are spatially restricted three-dimensional clusters of cells ex vivo that self-organize into complex functional structures through genetically programmed determination, which is crucial for reconstructing the architecture of the primary tissue and organs. Currently, there are several strategies to create three-dimensional (3D) tumor systems using (i) surgically resected patient tissue (PDTOs, patient-derived tumor organoids) or (ii) single tumor cells circulating in the patient's blood. Successful application of 3D tumor models obtained by co-culturing autologous tumor organoids (PDTOs) and peripheral blood lymphocytes have been demonstrated in a number of studies. Such models simulate a 3D tumor architecture in vivo and contain all cell types characteristic of this tissue, including immune system cells and stem cells. Components of the tumor microenvironment, such as fibroblasts and immune system cells, affect tumor growth and its drug resistance. In this review, we analyzed the evolution of tumor models from two-dimensional (2D) cell cultures and laboratory animals to 3D tissue-specific tumor organoids, their significance in identifying mechanisms of antitumor response and drug resistance, and use of these models in drug screening and development of precision methods in cancer treatment.

An engineered niche delineates metastatic potential of breast cancer

Metastatic breast cancer is often not diagnosed until secondary tumors have become macroscopically visible and millions of tumor cells have invaded distant tissues. Yet, metastasis is initiated by a cascade of events leading to formation of the pre-metastatic niche, which can precede tumor formation by a matter of years. We aimed to distinguish the potential for metastatic disease from nonmetastatic disease at early times in triple-negative breast cancer using sister cell lines 4T1 (metastatic), 4T07 (invasive, nonmetastatic), and 67NR (nonmetastatic). We used a porous, polycaprolactone scaffold, that serves as an engineered metastatic niche, to identify metastatic disease through the characteristics of the microenvironment. Analysis of the immune cell composition at the scaffold was able to distinguish noninvasive 67NR tumor-bearing mice from 4T07 and 4T1 tumor-bearing mice but could not delineate metastatic potential between the two invasive cell lines. Gene expression in the scaffolds correlated with the up-regulation of cancer hallmarks (e.g., angiogenesis, hypoxia) in the 4T1 mice relative to 4T07 mice. We developed a 9-gene signature (Dhx9, Dusp12, Fth1, Ifitm1, Ndufs1, Pja2, Slc1a3, Soga1, Spon2) that successfully distinguished 4T1 disease from 67NR or 4T07 disease throughout metastatic progression. Furthermore, this signature proved highly effective at distinguishing diseased lungs in publicly available datasets of mouse models of metastatic breast cancer and in human models of lung cancer. The early and accurate detection of metastatic disease that could lead to early treatment has the potential to improve patient outcomes and quality of life.

Metabolic Imaging as a Tool to Characterize Chemoresistance and Guide Therapy in Triple-Negative Breast Cancer (TNBC)

After an initial response to chemotherapy, tumor relapse is frequent. This event is reflective of both the spatiotemporal heterogeneities of the tumor microenvironment as well as the evolutionary propensity of cancer cell populations to adapt to variable conditions. Because the cause of this adaptation could be genetic or epigenetic, studying phenotypic properties such as tumor metabolism is useful as it reflects molecular, cellular, and tissue-level dynamics. In triple-negative breast cancer (TNBC), the characteristic metabolic phenotype is a highly fermentative state. However, during treatment, the spatial and temporal dynamics of the metabolic landscape are highly unstable, with surviving populations taking on a variety of metabolic states. Thus, longitudinally imaging tumor metabolism provides a promising approach to inform therapeutic strategies, and to monitor treatment responses to understand and mitigate recurrence. Here we summarize some examples of the metabolic plasticity reported in TNBC following chemotherapy and review the current metabolic imaging techniques available in monitoring chemotherapy responses clinically and preclinically. The ensemble of imaging technologies we describe has distinct attributes that make them uniquely suited for a particular length scale, biological model, and/or features that can be captured. We focus on TNBC to highlight the potential of each of these technological advances in understanding evolution-based therapeutic resistance.

Portable optical spectroscopic assay for non-destructive measurement of key metabolic parameters on in vitro cancer cells and organotypic fresh tumor slices

To enable non-destructive metabolic characterizations on in vitro cancer cells and organotypic tumor models for therapeutic studies in an easy-to-access way, we report a highly portable optical spectroscopic assay for simultaneous measurement of glucose uptake and mitochondrial function on various cancer models with high sensitivity. Well-established breast cancer cell lines (MCF-7 and MDA-MB-231) were used to validate the optical spectroscopic assay for metabolic characterizations, while fresh tumor samples harvested from both animals and human cancer patients were used to test the feasibility of our optical metabolic assay for non-destructive measurement of key metabolic parameters on organotypic tumor slices. Our optical metabolic assay captured that MCF-7 cells had higher mitochondrial metabolism, but lower glucose uptake compared to the MDA-MB-231 cells, which is consistent with our microscopy imaging and flow cytometry data, as well as the published Seahorse Assay data. Moreover, we demonstrated that our optical assay could non-destructively measure both glucose uptake and mitochondrial metabolism on the same cancer cell samples at one time, which remains challenging by existing metabolic tools. Our pilot tests on thin fresh tumor slices showed that our optical assay captured increased metabolic activities in tumors compared to normal tissues. Our non-destructive optical metabolic assay provides a cost-effective way for future longitudinal therapeutic studies using patient-derived organotypic fresh tumor slices through the lens of tumor energetics, which will significantly advance translational cancer research.

The Importance of Animal Models in Biomedical Research: Current Insights and Applications

Animal research is considered a key element in advance of biomedical science. Although its use is controversial and raises ethical challenges, the contribution of animal models in medicine is essential for understanding the physiopathology and novel treatment alternatives for several animal and human diseases. Current pandemics’ pathology, such as the 2019 Coronavirus disease, has been studied in primate, rodent, and porcine models to recognize infection routes and develop therapeutic protocols. Worldwide issues such as diabetes, obesity, neurological disorders, pain, rehabilitation medicine, and surgical techniques require studying the process in different animal species before testing them on humans. Due to their relevance, this article aims to discuss the importance of animal models in diverse lines of biomedical research by analyzing the contributions of the various species utilized in science over the past five years about key topics concerning human and animal health.

Adaptive Design of Fluorescence Imaging Systems for Custom Resolution, Fields of View, and Geometries

Objective and Impact Statement: We developed a generalized computational approach to design uniform, high-intensity excitation light for low-cost, quantitative fluorescence imaging of in vitro, ex vivo, and in vivo samples with a single device. Introduction: Fluorescence imaging is a ubiquitous tool for biomedical applications. Researchers extensively modify existing systems for tissue imaging, increasing the time and effort needed for translational research and thick tissue imaging. These modifications are application-specific, requiring new designs to scale across sample types. Methods: We implemented a computational model to simulate light propagation from multiple sources. Using a global optimization algorithm and a custom cost function, we determined the spatial positioning of optical fibers to generate 2 illumination profiles. These results were implemented to image core needle biopsies, preclinical mammary tumors, or tumor-derived organoids. Samples were stained with molecular probes and imaged with uniform and nonuniform illumination. Results: Simulation results were faithfully translated to benchtop systems. We demonstrated that uniform illumination increased the reliability of intraimage analysis compared to nonuniform illumination and was concordant with traditional histological findings. The computational approach was used to optimize the illumination geometry for the purposes of imaging 3 different fluorophores through a mammary window chamber model. Illumination specifically designed for intravital tumor imaging generated higher image contrast compared to the case in which illumination originally optimized for biopsy images was used. Conclusion: We demonstrate the significance of using a computationally designed illumination for in vitro, ex vivo, and in vivo fluorescence imaging. Application-specific illumination increased the reliability of intraimage analysis and enhanced the local contrast of biological features. This approach is generalizable across light sources, biological applications, and detectors.

Fatty acid transport proteins (FATPs) in cancer

Lipids play pivotal roles in cancer biology. Lipids have a wide range of biological roles, especially in cell membrane synthesis, serve as energetic molecules in regulating energy-demanding processes; and they play a significant role as signalling molecules and modulators of numerous cellular functions. Lipids may participate in the development of cancer through the fatty acid signalling pathway. Lipids consumed in the diet act as a key source of extracellular pools of fatty acids transported into the cellular system. Increased availability of lipids to cancer cells is due to increased uptake of fatty acids from adipose tissues. Lipids serve as a source of energy for rapidly dividing cancerous cells. Surviving requires the swift synthesis of biomass and membrane matrix to perform exclusive functions such as cell proliferation, growth, invasion, and angiogenesis. FATPs (fatty acid transport proteins) are a group of proteins involved in fatty acid uptake, mainly localized within cells and the cellular membrane, and have a key role in long-chain fatty acid transport. FATPs are composed of six isoforms that are tissue-specific and encoded by a specific gene. Previous studies have reported that FATPs can alter fatty acid metabolism, cell growth, and cell proliferation and are involved in the development of various cancers. They have shown increased expression in most cancers, such as melanoma, breast cancer, prostate cancer, renal cell carcinoma, hepatocellular carcinoma, bladder cancer, and lung cancer. This review introduces a variety of FATP isoforms and summarises their functions and their possible roles in the development of cancer.

Identification of Prognostic Fatty Acid Metabolism lncRNAs and Potential Molecular Targeting Drugs in Uveal Melanoma

Background

The aim of this study was to identify prognostic fatty acid metabolism lncRNAs and potential molecular targeting drugs in uveal melanoma through integrated bioinformatics analysis.

Methods

In the present study, we obtained the expression matrix of 309 FAM-mRNAs and identified 225 FAM-lncRNAs by coexpression network analysis. We then performed univariate Cox analysis, LASSO regression analysis, and cross-validation and finally obtained an optimized UVM prognosis prediction model composed of four PFAM-lncRNAs (AC104129.1, SOS1-IT1, IDI2-AS1, and DLGAP1-AS2).

Results

The survival curves showed that the survival time of UVM patients in the high-risk group was significantly lower than that in the low-risk group in the train cohort, test cohort, and all patients in the prognostic prediction model (P < 0.05). We further performed risk prognostic assessment, and the results showed that the risk scores of the high-risk group in the train cohort, test cohort, and all patients were significantly higher than those of the low-risk group (P < 0.05), patient survival decreased and the number of deaths increased with increasing risk scores, and AC104129.1, SOS1-IT1, and DLGAP1-AS2 were high-risk PFAM-lncRNAs, while IDI2-AS1 were low-risk PFAM-lncRNAs. Afterwards, we further verified the accuracy and the prognostic value of our model in predicting prognosis by PCA analysis and ROC curves.

Conclusion

We identified 24 potential molecularly targeted drugs with significant sensitivity differences between high- and low-risk UVM patients, of which 13 may be potential targeted drugs for high-risk patients. Our findings have important implications for early prediction and early clinical intervention in high-risk UVM patients.

In vivo metabolic imaging identifies lipid vulnerability in a preclinical model of Her2+/Neu breast cancer residual disease and recurrence

Recurrent cancer cells that evade therapy is a leading cause of death in breast cancer patients. This risk is high for women showing an overexpression of human epidermal growth factor receptor 2 (Her2). Cells that persist can rely on different substrates for energy production relative to their primary tumor counterpart. Here, we characterize metabolic reprogramming related to tumor dormancy and recurrence in a doxycycline-induced Her2+/Neu model of breast cancer with varying times to recurrence using longitudinal fluorescence microscopy. Glucose uptake (2-NBDG) and mitochondrial membrane potential (TMRE) imaging metabolically phenotype mammary tumors as they transition to regression, dormancy, and recurrence. “Fast-recurrence” tumors (time to recurrence ~55 days), transition from glycolysis to mitochondrial metabolism during regression and this persists upon recurrence. “Slow-recurrence” tumors (time to recurrence ~100 days) rely on both glycolysis and mitochondrial metabolism during recurrence. The increase in mitochondrial activity in fast-recurrence tumors is attributed to a switch from glucose to fatty acids as the primary energy source for mitochondrial metabolism. Consequently, when fast-recurrence tumors receive treatment with a fatty acid inhibitor, Etomoxir, tumors report an increase in glucose uptake and lipid synthesis during regression. Treatment with Etomoxir ultimately prolongs survival. We show that metabolic reprogramming reports on tumor recurrence characteristics, particularly at time points that are essential for actionable targets. The temporal characteristics of metabolic reprogramming will be critical in determining the use of an appropriate timing for potential therapies; namely, the notion that metabolic-targeted inhibition during regression reports long-term therapeutic benefit.

A Spectroscopic Technique to Simultaneously Characterize Fatty Acid Uptake, Mitochondrial Activity, Vascularity, and Oxygen Saturation for Longitudinal Studies In Vivo

Aggressive breast cancer has been shown to shift its metabolism towards increased lipid catabolism as the primary carbon source for oxidative phosphorylation. In this study, we present a technique to longitudinally monitor lipid metabolism and oxidative phosphorylation in pre-clinical tumor models to investigate the metabolic changes with mammary tissue development and characterize metabolic differences between primary murine breast cancer and normal mammary tissue. We used optical spectroscopy to measure the signal of two simultaneously injected exogenous fluorescent metabolic reporters: TMRE (oxidative phosphorylation surrogate) and Bodipy FL C16 (lipid catabolism surrogate). We leverage an inverse Monte Carlo algorithm to correct for aberrations resulting from tissue optical properties and to extract vascular endpoints relevant to oxidative metabolism, specifically oxygen saturation (SO2) and hemoglobin concentration ([Hb]). We extensively validated our optical method to demonstrate that our two fluorescent metabolic endpoints can be measured without chemical or optical crosstalk and that dual measurements of both fluorophores in vivo faithfully recapitulate the measurements of each fluorophore independently. We then applied our method to track the metabolism of growing 4T1 and 67NR breast tumors and aging mammary tissue, all highly metabolic tissue types. Our results show the changes in metabolism as a function of mammary age and tumor growth, and these changes can be best distinguished through the combination of endpoints measured with our system. Clustering analysis incorporating both Bodipy FL C16 and TMRE endpoints combined with either SO2 or [Hb] proved to be the most effective in minimizing intra-group variance and maximizing inter-group differences. Our platform can be extended to applications in which long-term metabolic flexibility is important to study, for example in tumor regression, recurrence following dormancy, and responses to cancer treatment.

Systematic interaction of plasma albumin with the efficacy of chemotherapeutic drugs

Albumin, as the most abundant plasma protein, plays an integral role in the transport of a variety of exogenous and endogenous ligands in the bloodstream and extravascular spaces. For exogenous drugs, especially chemotherapeutic drugs, binding to and being delivered by albumin can significantly affect their efficacy. Meanwhile, albumin can also bind to many endogenous ligands, such as fatty acids, with important physiological significance that can affect tumor proliferation and metabolism. In this review, we summarize how albumin with unique properties affects chemotherapeutic drugs efficacy from the aspects of drug outcome in blood, toxicity, tumor accumulation and direct or indirect interactions with fatty acids, plus application of albumin-based carriers for anti-tumor drug delivery.

Empirical method for rapid quantification of intrinsic fluorescence signals of key metabolic probes from optical spectra measured on tissue-mimicking turbid medium

SIGNIFICANCE

Optical fluorescence spectroscopy technique has been explored extensively to quantify both glucose uptake and mitochondrial metabolism with proper fluorescent probes in small tumor models in vivo. However, it remains a great challenge to rapidly quantify the intrinsic metabolic fluorophores from the optically measured fluorescence spectra that contain significant distortions due to tissue absorption and scattering.

AIM

To enable rapid spectral data processing and quantify the in vivo metabolic parameters in real-time, we present an empirical ratio-metric method for rapid fluorescence spectra attenuation correction with high accuracy.

APPROACH

A first-order approximation of intrinsic fluorescence spectra can be obtained by dividing the fluorescence spectra by diffuse reflectance spectra with some variable powers. We further developed this approximation for rapid extraction of intrinsic key metabolic probes (2-NBDG for glucose uptake and TMRE for mitochondrial function) by dividing the distorted fluorescence spectra by diffuse reflectance intensities recorded at excitation and emission peak with a pair of system-dependent powers. Tissue-mimicking phantom studies were conducted to evaluate the method.

RESULTS

The tissue-mimicking phantom studies demonstrated that our empirical method could quantify the key intrinsic metabolic probes in near real-time with an average percent error of ∼5  %  .

CONCLUSIONS

An empirical method was demonstrated for rapid quantification of key metabolic probes from fluorescence spectra measured on a tissue-mimicking turbid medium. The proposed method will potentially facilitate real-time monitoring of key metabolic parameters of tumor models in vivo using optical spectroscopy, which will significantly advance translational cancer research.