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Extracellular matrix (ECM) alignment contributes to metastasis in a number of cancers and is a known prognostic stromal factor; however, the mechanisms controlling matrix organization remain unclear. Cancer-associated fibroblasts (CAF) play a critical role in this process, particularly via matrix production and modulation of key signaling pathways controlling cell adhesion and contractility. Stroma normalization, as opposed to elimination, is a highly sought strategy, and screening for drugs that effectively alter ECM alignment is a practical way to identify novel CAF-normalizing targets that modulate ECM organization. To meet this need, we developed a novel high-throughput screening platform in which fibroblast-derived matrices were produced in 384-well plates, imaged with automated confocal microscopy, and analyzed using a customized MATLAB script. This platform is a technical advance because it miniaturizes the assay, eliminates costly and time-consuming experimental steps, and streamlines data acquisition and analysis to enable high-throughput screening applications. As a proof of concept, this platform was used to screen a kinase inhibitor library to identify modulators of matrix alignment. A number of novel potential regulators were identified, including several receptor tyrosine kinases [c-MET, tropomyosin receptor kinase 1 (NTRK1), HER2/ERBB2] and the serine/threonine kinases protein kinase A, C, and G. The expression of these regulators was analyzed in publicly available patient datasets to examine the association between stromal gene expression and patient outcomes. Significance: ECM fiber organization and alignment contribute to metastasis in a number of cancers and are a known prognostic stromal factor; however, the mechanisms controlling matrix organization remain unclear. Here, a high-throughput assay was developed to enable discovery-based screening for an in vitro ECM fiber alignment assay. As proof of concept, this platform was used to screen a kinase inhibitor library and identified several novel modulators of matrix alignment.

Self-assembling peptide (SAP) hydrogels provide a fibrous microenvironment to cells while also giving users control of biochemical and mechanical cues. Previously, biochemical cues were introduced by physically mixing them with SAPs prior to hydrogel assembly, or by incorporating them into the SAP sequence during peptide synthesis, which limited flexibility and increased costs. To circumvent these limitations, we developed "Click SAPs," a novel formulation that can be easily functionalized via click chemistry thiol-ene reaction. Due to its high cytocompatibility, the thiol-ene click reaction is currently used to crosslink and functionalize other types of polymeric hydrogels. In this study, we developed a click chemistry compatible SAP platform by addition of a modified lysine (lysine-alloc) to the SAP sequence, enabling effective coupling of thiol-containing molecules to the SAP hydrogel network. We demonstrate the flexibility of this approach by incorporating a fluorescent dye, a cellular adhesion peptide, and a matrix metalloproteinase-sensitive biosensor using the thiol-ene reaction in 3D Click SAPs. Using atomic force microscopy, we demonstrate that Click SAPs retain the ability to self-assemble into fibers, similar to previous systems. Additionally, a range of physiologically relevant stiffnesses can be achieved by adjusting SAP concentration. Encapsulated cells maintain high viability in Click SAPs and can interact with adhesion peptides and a matrix metalloproteinase biosensor, demonstrating that incorporated molecules retain their biological activity. The Click SAP platform supports easier functionalization with a wider array of bioactive molecules and enables new investigations with temporal and spatial control of the cellular microenvironment.

Figure S1, S2, S3, Table S1 and S2

Extracellular matrix (ECM) alignment contributes to metastasis in a number of cancers and is a known prognostic stromal factor; however, the mechanisms controlling matrix organization remain unclear. Cancer-associated fibroblasts (CAF) play a critical role in this process, particularly via matrix production and modulation of key signaling pathways controlling cell adhesion and contractility. Stroma normalization, as opposed to elimination, is a highly sought strategy, and screening for drugs that effectively alter ECM alignment is a practical way to identify novel CAF-normalizing targets that modulate ECM organization. To meet this need, we developed a novel high-throughput screening platform in which fibroblast-derived matrices were produced in 384-well plates, imaged with automated confocal microscopy, and analyzed using a customized MATLAB script. This platform is a technical advance because it miniaturizes the assay, eliminates costly and time-consuming experimental steps, and streamlines data acquisition and analysis to enable high-throughput screening applications. As a proof of concept, this platform was used to screen a kinase inhibitor library to identify modulators of matrix alignment. A number of novel potential regulators were identified, including several receptor tyrosine kinases [c-MET, tropomyosin receptor kinase 1 (NTRK1), HER2/ERBB2] and the serine/threonine kinases protein kinase A, C, and G. The expression of these regulators was analyzed in publicly available patient datasets to examine the association between stromal gene expression and patient outcomes.Significance:ECM fiber organization and alignment contribute to metastasis in a number of cancers and are a known prognostic stromal factor; however, the mechanisms controlling matrix organization remain unclear. Here, a high-throughput assay was developed to enable discovery-based screening for an in vitro ECM fiber alignment assay. As proof of concept, this platform was used to screen a kinase inhibitor library and identified several novel modulators of matrix alignment.

Dot plots corresponding to population distribution curves and graphs of individual replicates for bar graphs in Figure 4.

Purpose:Cancer treatment is limited by inaccurate predictors of patient-specific therapeutic response. Therefore, some patients are exposed to unnecessary side effects and delays in starting effective therapy. A clinical tool that predicts treatment sensitivity for individual patients is needed.Experimental Design:Patient-derived cancer organoids were derived across multiple histologies. The histologic characteristics, mutation profile, clonal structure, and response to chemotherapy and radiation were assessed using bright-field and optical metabolic imaging on spheroid and single-cell levels, respectively.Results:We demonstrate that patient-derived cancer organoids represent the cancers from which they were derived, including key histologic and molecular features. These cultures were generated from numerous cancers, various biopsy sample types, and in different clinical settings. Next-generation sequencing reveals the presence of subclonal populations within the organoid cultures. These cultures allow for the detection of clonal heterogeneity with a greater sensitivity than bulk tumor sequencing. Optical metabolic imaging of these organoids provides cell-level quantification of treatment response and tumor heterogeneity allowing for resolution of therapeutic differences between patient samples. Using this technology, we prospectively predict treatment response for a patient with metastatic colorectal cancer.Conclusions:These studies add to the literature demonstrating feasibility to grow clinical patient-derived organotypic cultures for treatment effectiveness testing. Together, these culture methods and response assessment techniques hold great promise to predict treatment sensitivity for patients with cancer undergoing chemotherapy and/or radiation.

Supplementary Methods

Success rates for growing human cancers in spheroid culture

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.

Triple-negative breast cancers (TNBCs) are heterogeneous and aggressive, with high mortality rates. TNBCs frequently respond to chemotherapy, yet many patients develop chemoresistance. The molecular basis and roles for tumor cell–stromal crosstalk in establishing chemoresistance are complex and largely unclear. Here we report molecular studies of paired TNBC patient–derived xenografts (PDXs) established before and after the development of chemoresistance. Interestingly, the chemoresistant model acquired a distinct KRASQ61R mutation that activates K-Ras. The chemoresistant KRAS-mutant model showed gene expression and proteomic changes indicative of altered tumor cell metabolism. Specifically, KRAS-mutant PDXs exhibited increased redox ratios and decreased activation of AMPK, a protein involved in responding to metabolic homeostasis. Additionally, the chemoresistant model exhibited increased immunosuppression, including expression of CXCL1 and CXCL2, cytokines responsible for recruiting immunosuppressive leukocytes to tumors. Notably, chemoresistant KRAS-mutant tumors harbored increased numbers of granulocytic myeloid-derived suppressor cells (gMDSCs). Interestingly, previously established Ras/MAPK-associated gene expression signatures correlated with myeloid/neutrophil-recruiting CXCL1/2 expression and negatively with T cell–recruiting chemokines (CXCL9/10/11) across patients with TNBC, even in the absence of KRAS mutations. MEK inhibition induced tumor suppression in mice while reversing metabolic and immunosuppressive phenotypes, including chemokine production and gMDSC tumor recruitment in the chemoresistant KRAS-mutant tumors. These results suggest that Ras/MAPK pathway inhibitors may be effective in some breast cancer patients to reverse Ras/MAPK-driven tumor metabolism and immunosuppression, particularly in the setting of chemoresistance., Longitudinally-derived TNBC xenografts identify acquired KRAS mutation/MEK activation as a driving feature of CXCL1/2-mediated MDSC recruitment.

While NAD(P)H fluorescence lifetime imaging (FLIM) can detect changes in flux through the TCA cycle and electron transport chain (ETC), it remains unclear whether NAD(P)H FLIM is sensitive to other potential fates of glucose. Glucose carbon can be diverted from mitochondria by the pentose phosphate pathway (via glucose 6-phosphate dehydrogenase, G6PDH), lactate production (via lactate dehydrogenase, LDH), and rejection of carbon from the TCA cycle (via pyruvate dehydrogenase kinase, PDK), all of which can be upregulated in cancer cells. Here, we demonstrate that multiphoton NAD(P)H FLIM can be used to quantify the relative concentrations of recombinant LDH and malate dehydrogenase (MDH) in solution. In multiple epithelial cell lines, NAD(P)H FLIM was also sensitive to inhibition of LDH and PDK, as well as the directionality of LDH in cells forced to use pyruvate versus lactate as fuel sources. Among the parameters measurable by FLIM, only the lifetime of protein-bound NAD(P)H (τ2) was sensitive to these changes, in contrast to the optical redox ratio, mean NAD(P)H lifetime, free NAD(P)H lifetime, or the relative amount of free and protein-bound NAD(P)H. NAD(P)H τ2 offers the ability to non-invasively quantify diversions of carbon away from the TCA cycle/ETC, which may support mechanisms of drug resistance.

Intratumoral heterogeneity, which is manifested in almost all of the hallmarks of cancer, including the significantly altered metabolic profiles of cancer cells, represents a challenge to effective cancer therapy. High-throughput measurements of the metabolism of individual cancer cells would allow direct visualization and quantification of intratumoral metabolic heterogeneity, yet the throughputs of current measurement techniques are limited to about 120 cells per hour. Here, we show that single-cell photoacoustic microscopy can reach throughputs of approximately 12,000 cells per hour by trapping single cells with blood in an oxygen-diffusion-limited high-density microwell array and by using photoacoustic imaging to measure the haemoglobin oxygen change (that is, the oxygen consumption rate) in the microwells. We demonstrate the capability of this label-free technique by performing high-throughput single-cell oxygen-consumption-rate measurements of cultured cells and by imaging intratumoral metabolic heterogeneity in specimens from patients with breast cancer. High-throughput single-cell photoacoustic microscopy of oxygen consumption rates should enable the faster characterization of intratumoral metabolic heterogeneity.

New tools are needed to match pancreatic 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 cannot be completed in a clinically relevant time frame, only evaluate response at one time point, and most importantly, overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response. Organoids were generated from fresh patient tissue samples acquired during surgery and treated with the same drugs as the patient's prescribed adjuvant treatment. OMI measurements of heterogeneity in response to this treatment were compared to later patient response, specifically to the time to recurrence following surgery. OMI was sensitive to patient-specific treatment response in as little as 24 hours. 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 pancreatic cancer patients, and to develop new effective therapies that address cellular heterogeneity in pancreatic cancer.

Optical coherence tomography (OCT) is an emerging technology for in vivo airway and lung imaging. However, OCT lacks sensitivity to the metabolic changes caused by inflammation, which drives chronic respiratory diseases such as asthma and chronic obstructive pulmonary disorder. Redox imaging (RI) is a label-free technique that uses the autofluorescence of the metabolic coenzymes NAD(P)H and flavin adenine dinucleotide (FAD) to probe cellular metabolism and could provide complimentary information to OCT for airway and lung imaging. We demonstrate OCT and RI of respiratory ciliated epithelial function in ex vivo mouse tracheae. We applied RI to measure cellular metabolism via the redox ratio [intensity of NAD(P)H divided by FAD] and particle tracking velocimetry OCT to quantify cilia-driven fluid flow. To model mitochondrial dysfunction, a key aspect of the inflammatory process, cyanide was used to inhibit oxidative metabolism and reduce ciliary motility. Cyanide exposure over 20 min significantly increased the redox ratio and reversed cilia-driven fluid flow. We propose that RI provides complementary information to OCT to assess inflammation in the airway and lungs.

OBJECTIVE: Cancer treatment is limited by inaccurate predictors of patient-specific therapeutic response. Therefore, some patients are exposed to unnecessary side effects and delays in starting effective therapy. A clinical tool that predicts treatment sensitivity for individual patients is needed. DESIGN: Patient-derived cancer organoids were derived across multiple histologies. The histological characteristics, mutation profile, clonal structure, and response to chemotherapy and radiation were assessed using bright-field and optical metabolic imaging on spheroid and single cell-levels, respectively. RESULTS: We demonstrate that patient-derived cancer organoids represent the cancers from which they were derived, including key histologic and molecular features. These cultures were generated from numerous cancers, various biopsy sample types, and in different clinical settings. Next-generation sequencing reveals the presence of subclonal populations within the organoid cultures. These cultures allow for the detection of clonal heterogeneity with a greater sensitivity than bulk tumor sequencing. Optical metabolic imaging of these organoids provides cell-level quantification of treatment response and tumor heterogeneity allowing for resolution of therapeutic differences between patient samples. Using this technology, we prospectively predict treatment response for a patient with metastatic colorectal cancer. CONCLUSION: These studies add to the literature demonstrating feasibility to grow clinical patient-derived organotypic cultures for treatment effectiveness testing. Together, these culture methods and response assessment techniques hold great promise to predict treatment sensitivity for patients with cancer undergoing chemotherapy and/or radiation. STATEMENT OF TRANSLATIONAL RELEVANCE: The lack of predictive biomarkers is a major limitation in clinical oncology. Patient-derived cancer organoids are a major advance as they are readily generated and represent the tumors from which they are derived. This study further demonstrates the ability of these cultures to represent the phenotypic and molecular heterogeneity within cancers. In addition, this study establishes therapeutic thresholds for further validation using changes in organoid growth rate and optical metabolic imaging. These techniques measure organoid-level and single cell-level therapeutic heterogeneity, respectively. Here these techniques are applied for determining differential response of colorectal cancers to chemotherapy and radiation. In addition, this is one of the first studies to prospectively examine the use these cultures to predict response for an individual patient with cancer.

Collagen deposition contributes to both high mammographic density and breast cancer progression. Low stromal PTEN expression has been observed in as many as half of breast tumors and is associated with increases in collagen deposition, however the mechanism connecting PTEN loss to increased collagen deposition remains unclear. Here, we demonstrate thatPtenknockout in fibroblasts using anFsp-Cre;PtenloxP/loxPmouse model increases collagen fiber number and fiber size within the mammary gland.Ptenknockout additionally upregulatedSparctranscription in fibroblasts and promoted collagen shuttling out of the cell. Interestingly,SPARCmRNA expression was observed to be significantly elevated in the tumor stroma as compared to the normal breast in several patient cohorts. While SPARC knockdown via shRNA did not affect collagen shuttling, it notably decreased assembly of exogenous collagen. In addition, SPARC knockdown decreased fibronectin assembly and alignment of the extracellular matrix in anin vitrofibroblast-derived matrix model. Overall, these data indicate upregulation of SPARC is a mechanism by which PTEN regulates collagen deposition in the mammary gland stroma.

Significance: Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to distinguish the unique molecular environment of fluorophores. FLIM measures the time a fluorophore remains in an excited state before emitting a photon, and detects molecular variations of fluorophores that are not apparent with spectral techniques alone. FLIM is sensitive to multiple biomedical processes including disease progression and drug efficacy. Aim: We provide an overview of FLIM principles, instrumentation, and analysis while highlighting the latest developments and biological applications. Approach: This review covers FLIM principles and theory, including advantages over intensity-based fluorescence measurements. Fundamentals of FLIM instrumentation in time- and frequency-domains are summarized, along with recent developments. Image segmentation and analysis strategies that quantify spatial and molecular features of cellular heterogeneity are reviewed. Finally, representative applications are provided including high-resolution FLIM of cell- and organelle-level molecular changes, use of exogenous and endogenous fluorophores, and imaging protein-protein interactions with Förster resonance energy transfer (FRET). Advantages and limitations of FLIM are also discussed. Conclusions: FLIM is advantageous for probing molecular environments of fluorophores to inform on fluorophore behavior that cannot be elucidated with intensity measurements alone. Development of FLIM technologies, analysis, and applications will further advance biological research and clinical assessments.

Patient-derived 3D organoids show great promise for understanding patient heterogeneity and chemotherapy response in human-derived tissue. The combination of organoid culture techniques with mass spectrometry imaging provides a label-free methodology for characterizing drug penetration, patient-specific response, and drug biotransformation. However, current methods used to grow tumor organoids employ extracellular matrices that can produce small molecule background signal during mass spectrometry imaging analysis. Here, we develop a method to isolate 3D human tumor organoids out of a Matrigel extracellular matrix into gelatin mass spectrometry compatible microarrays for high-throughput mass spectrometry imaging analysis. The alignment of multiple organoids in the same z-axis is essential for sectioning organoids together and for maintaining reproducible sample preparation on a single glass slide for up to hundreds of organoids. This method successfully removes organoids from extracellular matrix interference and provides an organized array for high-throughput imaging analysis to easily identify organoids by eye for area selection and further analysis. With this method, mass spectrometry imaging can be readily applied to organoid systems for preclinical drug development and personalized medicine research initiatives.

While NAD(P)H fluorescence lifetime imaging (FLIM) can detect changes in flux through the TCA cycle and electron transport chain (ETC), it remains unclear whether NAD(P)H FLIM is sensitive to other potential fates of glucose. Glucose carbon can be diverted from mitochondria by the pentose phosphate pathway (via glucose 6-phosphate dehydrogenase, G6PDH), lactate production (via lactate dehydrogenase, LDH), and rejection of carbon from the TCA cycle (via pyruvate dehydrogenase kinase, PDK), all of which can be upregulated in cancer cells. Here, we demonstrate that NAD(P)H FLIM can be used to quantify the relative concentrations of recombinant LDH and malate dehydrogenase (MDH) in solution. In multiple epithelial cell lines, NAD(P)H FLIM was also sensitive to inhibition of LDH and PDK, as well as the directionality of LDH in cells forced to use pyruvate versus lactate as a fuel source. Among the parameters measurable by FLIM, only the lifetime of protein-bound NAD(P)H (τ_2) was sensitive to these changes, in contrast to the optical redox ratio, mean NAD(P)H lifetime, free NAD(P)H lifetime, or the relative amount of free and protein-bound NAD(P)H. NAD(P)H τ_2 offers the ability to non-invasively quantify diversions of carbon away from the TCA cycle/ETC, which may support mechanisms of drug resistance. It remains unclear whether NAD(P)H FLIM is sensitive to potential fates of glucose other than the TCA cycle and electron transport chain. We show that the lifetime of protein-bound NAD(P)H (τ_2) can distinguish the relative concentrations of malate dehydrogenase (TCA cycle enzyme) and lactate dehydrogenase (diverts carbon to/from lactate production) in solutions containing both enzymes. In cells, NAD(P)H FLIM was also sensitive to alterations in the path of carbon from glucose uptake to mitochondrial activity. Additionally, τ_2 was found to be the FLIM parameter best-suited for detecting these carbon-diverting shifts in cell metabolism, which may support mechanisms of drug resistance.

Efficient mucociliary clearance is necessary to protect the respiratory tract from infection. Mucociliary dysfunction is common in respiratory diseases including asthma, chronic obstructive pulmonary disease, and cystic fibrosis. Rescuing mucociliary clearance by stimulating the metabolism of respiratory ciliated epithelia could offer new treatments for respiratory diseases. However, the coupling between cellular metabolism and mechanical output in respiratory ciliated epithelia is poorly understood. We propose to study this coupling with autofluorescence microscopy and optical coherence tomography (OCT), to measure cellular metabolism and ciliary motility, respectively. The autofluorescent metabolic co-enzymes NAD(P)H and FAD provide non-invasive measures of metabolism through the optical redox ratio (NAD(P)H intensity divided by FAD intensity), while OCT measures both the frequency of ciliary beating and cilia-driven fluid flow. Preliminary experiments were performed in ex vivo mouse trachea using an epifluorescence microscope and a spectral-domain OCT system. Cilia-driven fluid flow was quantified using 2D particle tracking velocimetry (PTV-OCT) and TrackMate, a particle-tracking tool. PTV-OCT was validated by manual particle tracking (within 4% agreement) and a calibrated flow phantom (r=0.998, p

Pancreatic cancer has the worst prognosis of all cancers (5-year survival rate of 7%). Patients that are eligible for surgery often receive adjuvant chemotherapy to improve survival. There is a critical need for a tool to match individual patients with optimal drugs for their cancer. The goal of this work is to validate Optical Metabolic Imaging (OMI) of patient-derived pancreatic tumor organoids as a high-throughput predictive drug screen for patients. Three-dimensional organoids were successfully generated from surgically resected pancreatic tumors. These organoids were treated with the patient’s prescribed adjuvant therapy, and early metabolic changes were measured using multiphoton fluorescence lifetime imaging microscopy (FLIM) of the metabolic co-enzymes NAD(P)H and FAD. Changes at the single-cell level were quantified using the OMI Index, a linear combination of the optical redox ratio (ratio of the fluorescence intensities of NAD(P)H to FAD), and the mean NAD(P)H and FAD fluorescence lifetimes. Population density modeling on the OMI Index was used to evaluate cell-level heterogeneities in drug responses. Additionally, mass spectrometry imaging (MSI) was used to map metabolites in organoids. Combining multiphoton OMI images and MSI images provides a detailed map of the complex metabolic changes that occur in response to treatment, which may be used to identify additional drug targets. Patient survival data after surgery was used as validation of drug response in organoids. This platform shows promise for predicting long-term response to therapy in pancreatic cancer patients.

Previous work has shown that cellular-level Optical Metabolic Imaging (OMI) of organoids derived from human breast cancer cell-line xenografts accurately and rapidly predicts in vivo response to therapy. To validate OMI as a predictive measure of treatment response in an immune-competent model, we used the polyomavirus middle-T (PyVmT) transgenic mouse breast cancer model. The PyVmT model includes intra-tumoral heterogeneity and a complex tumor microenvironment that can influence treatment responses. Three-dimensional organoids generated from primary PyVmT tumor tissue were treated with a chemotherapy (paclitaxel) and a PI3K inhibitor (XL147), each alone or in combination. Cellular subpopulations of response were measured using the OMI Index, a composite endpoint of metabolic response comprised of the optical redox ratio (ratio of the fluorescence intensities of metabolic co-enzymes NAD(P)H to FAD) as well as the fluorescence lifetimes of NAD(P)H and FAD. Combination treatment significantly decreased the OMI Index of PyVmT tumor organoids (p

The health of the tracheal mucosa is an important, but poorly understood, aspect of critical care medicine. Many critical care patients are mechanically ventilated through an endotracheal tube that can cause local inflammation and blunt damage to the ciliated epithelial cells lining the trachea. These cilia clear mucus and infectious agents from the respiratory tract, so impaired ciliary function may lead to increased susceptibility to respiratory infection. Therefore, a minimally-invasive method to monitor mucosal health and ciliary function in intubated patients would be valuable to critical care medicine. Optical metabolic imaging (OMI) can quantitatively assess the metabolic state of cells by measuring the fluorescence intensities of endogenous metabolic co-enzymes NAD(P)H and FAD. OMI is especially attractive for assessing tracheal health because OMI is label-free, and ciliary function is tightly linked to the levels of NAD(P)H and FAD. In this study, we apply widefield OMI to ex vivo mouse tracheae (n=6), and demonstrate that the optical redox ratio (fluorescence intensity of NAD(P)H divided by the intensity of FAD) is sensitive to changes in the cellular metabolism of the tracheal mucosa. We observed a 46% increase in the redox ratio 20 minutes after treatment with 10mM of sodium cyanide (p

The unique metabolic demands of cancer cells underscore potentially fruitful opportunities for drug discovery in the era of precision medicine. However, therapeutic targeting of cancer metabolism has led to surprisingly few new drugs to date. The neutral amino acid glutamine serves as a key intermediate in numerous metabolic processes leveraged by cancer cells, including biosynthesis, cell signaling, and oxidative protection. Herein we report the preclinical development of V-9302, a competitive small molecule antagonist of transmembrane glutamine flux that selectively and potently targets the amino acid transporter ASCT2. Pharmacological blockade of ASCT2 with V-9302 resulted in attenuated cancer cell growth and proliferation, increased cell death, and increased oxidative stress, which collectively contributed to antitumor responses in vitro and in vivo. This is the first study, to our knowledge, to demonstrate the utility of a pharmacological inhibitor of glutamine transport in oncology, representing a new class of targeted therapy and laying a framework for paradigm-shifting therapies targeting cancer cell metabolism.

Triple-negative breast cancers (TNBCs) are highly heterogeneous and aggressive, with high mortality rates. Although TNBC is typically more responsive to chemotherapy than other breast cancer subtypes, many patients develop chemo-resistance. The molecular processes between tumor and stromal cells involved in developing chemo-resistance are under-explored. Here we report studies of paired TNBC patient-derived xenografts (PDX) established before and after chemo-resistance. Despite significant genetic similarities, the chemo-resistant PDX model harbored a rare constitutively-active KRASQ61R mutation which was not present in the chemo-naive PDX. Further analysis demonstrated that the chemo-resistant KRAS-mutant model exhibited altered gene expression changes including increased expression of CXCR2-ligands CXCL1 and CXCL2, which are responsible for recruiting immune cells to tumors. These expression patterns were largely inhibited in vivo by MEK inhibitor (MEKi) treatment. Moreover, in breast cancer cell lines, CXCL1, CXCL2, and granulocyte macrophage-colony stimulating factor (CSF2, stimulates granulocyte and macrophage differentiation from hematopoietic precursor cells, including immunosuppressive myeloid cells) transcripts were also downregulated by MEKi. Notably, chemo-resistant KRAS-mutant tumors harbored increased Gr1+ and Arginase-1+ cells, consistent with recruitment of immunosuppressive M2-like macrophages and/or myeloid-derived suppressor cells (MDSCs), which was inhibited by MEKi. Further experiments demonstrate that CD45+CD11b+Ly6G+ MDSC accumulation in tumors can be inhibited by MEKi treatment alone, or by CXCR2 inhibition, suggesting that the effects of MEK inhibition on MDSC recruitment are CXCL1/2-dependent. Confirming the translational relevance of these findings, in >200 murine and >1000 human breast tumors, Ras/MAPK transcriptional activity correlated with myeloid-recruiting CXCL1/2 expression and negatively with T-cell recruiting chemokines (CXCL9/10/11), even in the absence of activating KRAS mutations. The association with Ras/MAPK activity was also confirmed using immunofluorescence to quantify MHC-II-low myeloid cells in 80 post-chemotherapy TNBC tumors. Importantly, MEKi and chemotherapy combination treatment reversed immunosuppressive cell accumulation and metabolic phenotypes exemplified by altered optical redox ratios (NAD(P)H/FAD) in the chemo-resistant KRAS mutant tumors, resulting in tumor growth suppression in mice. MEKi treatment also reduced redox ratios in 3D cultures of breast cancer cell lines further suggesting that MEK inhibition targets multiple oncogenic processes in breast cancer. These results suggest that Ras/MAPK pathway inhibitors may be effective in some breast cancer patients to reverse Ras/MAPK-driven tumor metabolism and immunosuppression, particularly in the setting of chemo-resistant disease. Citation Format: Derek A. Franklin, Joe T. Sharick, Paula I. Ericsson-Gonzalez, Violeta Sanchez, Phillip T. Dean, Susan R. Opalenik, Stefano Cairo, Jean-Gabriel Judde, Michael T. Lewis, Jenny C. Chang, Melinda E. Sanders, Rebecca S. Cook, Melissa C. Skala, Jennifer Bordeaux, Jehovana Orozco Bender, Christine Vaupel, Gary Geiss, Douglas Hinerfeld, Justin M. Balko. MEK activation modulates immunosuppressive MDSCs and metabolic phenotypes in TNBC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1511.

The health of the tracheal mucosa and submucosa is a vital yet poorly understood component of critical care medicine, and a minimally-invasive method is needed to monitor tracheal health in patients. Of particular interest are the ciliated cells of the tracheal epithelium that move mucus away from the lungs and prevent respiratory infection. Optical metabolic imaging (OMI) allows cellular-level measurement of metabolism, and is a compelling method for assessing tracheal health because ciliary motor proteins require ATP to function. In this pilot study, we apply multiphoton imaging of the fluorescence intensities and lifetimes of metabolic co-enzymes NAD(P)H and FAD to the mucosa and submucosa of ex vivo mouse trachea. We demonstrate the feasibility and potential diagnostic utility of these measurements for assessing tracheal health and pathophysiology at the single-cell level.

Time-correlated single photon counting (TCSPC) enables acquisition of fluorescence lifetime decays with high temporal resolution within the fluorescence decay. However, many thousands of photons per pixel are required for accurate lifetime decay curve representation, instrument response deconvolution, and lifetime estimation, particularly for two-component lifetimes. TCSPC imaging speed is inherently limited due to the single photon per laser pulse nature and low fluorescence event efficiencies (

Pancreatic cancer has the worst prognosis of all cancers (5-year survival rate of 7%). There is a critical need for a tool to match individual patients with optimal drugs for their cancer. The goal of this work is to validate Optical Metabolic Imaging (OMI) of patient-derived pancreatic tumor organoids as a high-throughput predictive drug screen for patients. Drug induced changes in cell metabolism precede changes in tumor volume and thus present an earlier marker of treatment response. OMI is sensitive to cellular metabolism because it measures the intrinsic fluorescent properties of NAD(P)H and FAD, coenzymes of metabolic reactions. OMI endpoints include the optical redox ratio (the fluorescence intensity of NAD(P)H divided by the fluorescence intensity of FAD), and the fluorescence lifetimes of NAD(P)H and FAD. The redox ratio reflects the cellular redox balance, and the fluorescence lifetimes report on the binding activity of these coenzymes. OMI, performed using high-resolution multiphoton microscopy, has the unique ability to non-invasively monitor metabolism in living, intact 3D samples on the single-cell level, and can thus quantify inter-cellular heterogeneity in drug response. OMI also allows for high-throughput screening of potential cancer drugs and drug combinations on patient biopsy samples cultured ex vivo. These patient-specific organoids are grown in a 3D matrix that mimics the natural tumor environment. Drug response can be quantified separately in tumor cells and tumor-associated fibroblasts. Organoids were successfully generated from 9 of 11 surgically resected pancreatic tumors and 2 of 2 pancreatic intraepithelial neoplasias. These organoids were treated with a panel of standard and experimental therapies, and early metabolic changes were measured using OMI. Changes at the single-cell level were quantified using the OMI Index, a linear combination of the optical redox ratio and the mean NAD(P)H and FAD fluorescence lifetimes. Patient survival data after surgery and drug treatment was compared to drug response in organoids to that same treatment. Early results have focused on the standard gemcitabine + 5-fluorouracil (G+F) adjuvant treatment. Organoids generated from a patient whose CA19-9 biomarker levels increased while on G+F adjuvant therapy contained a subpopulation of 68% of the cells which were resistant to G+F therapy. Moreover, no G+F resistant cell subpopulations were found in organoids generated from a patient with no signs of disease recurrence over 14 months of G+F adjuvant therapy. Follow-up with the remaining patients is on-going. The ability of OMI to capture heterogeneous cell-level drug responses shows promise for predicting treatment response of individual pancreatic cancer patients. Citation Format: Joe T. Sharick, Tongcheng Qian, Jillian K. Johnson, Lingjun Li, Cheri A. Pasch, Dustin A. Deming, Alexander A. Parikh, Melissa C. Skala. Personalized medicine in pancreatic cancer using cellular-resolution optical metabolic imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4100.

Currently, there are no reliable methods to optimize treatment regimens for individual breast cancer patients. Oncologists choose drug treatments based on expression levels of tumor cell signaling receptors (i.e. HER2, ER, PR) and other factors, and assess whether the treatment is effective after significant time has passed. Unfortunately, over one third of patients exhibit resistance to their initial treatment, increasing their risk of future metastasis and death. Morbidities from sub-optimal drug regimens could be reduced with a personalized drug screen for breast cancer at the time of diagnosis. With the vast number of therapeutic options available to patients (>50 drugs approved with more on the way), a high-throughput screening technology is needed to accurately evaluate how a patient will respond to these options. Here we present Optical Metabolic Imaging (OMI) of tumor-derived organoids as a predictive drug screening platform for individual breast cancer patients. Changes in cell metabolism precede changes in tumor volume and thus present an earlier marker of treatment response. OMI is sensitive to these early changes by exploiting the intrinsic fluorescent properties of NAD(P)H and FAD, coenzymes of metabolic reactions. OMI endpoints include the optical redox ratio (the fluorescence intensity of NAD(P)H divided by the fluorescence intensity of FAD), as well as the fluorescence lifetimes of NAD(P)H and FAD. The redox ratio reflects the cellular redox balance, and the fluorescence lifetimes report on the binding activity of these coenzymes. OMI has the unique ability to non-invasively monitor metabolism in living, intact samples on the single-cell level, and can thus quantify heterogeneity in drug response. Changes were quantified at the single-cell level using the OMI Index, a linear combination of the optical redox ratio and the mean NAD(P)H and FAD fluorescence lifetimes. This index was derived using a multivariate analysis of variance and has been shown previously to correlate with treatment response in human cancer cells. OMI also allows for high-throughput screening of potential cancer drugs and drug combinations on patient biopsy samples cultured ex vivo. These samples are grown as organoids in a 3D matrix that mimics the natural tumor environment. Organoids were successfully generated from core needle biopsies of untreated breast tumors. These organoids were treated with the patient's prescribed neoadjuvant therapy, and early metabolic changes were quantified using OMI. Organoids grew from a variety of untreated breast tumor subtypes, and early metabolic changes could be resolved at the single-cell level after only 24 hours of treatment in vitro. In parallel, each patient's Residual Cancer Burden (RCB) score was quantified by a surgical pathologist after neoadjuvant treatment and served as gold standard validation of tumor drug response. Results from an early cohort of patients suggest that OMI could be used to predict patient clinical response to therapy. A linear combination of OMI variables measured in vitro in only 48 hours correlated strongly with patient RCB score (Pearson correlation coefficient=0.97, n=5). This methodology could allow oncologists to determine the ideal treatment regimen for their patients at the time of diagnosis. Citation Format: Sharick JT, Walsh AJ, Sanders ME, Kelley MC, Meszoely IM, Hooks MA, Burkard ME, Esbona K, Choudhary A, Skala MC. Personalized neoadjuvant treatment planning using optical metabolic imaging [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-01-01.

Autofluorescence intensity and lifetime imaging of NAD(P)H yields quantitative, non-invasive measurements of cellular metabolism. NAD(P)H is a coenzyme involved in cellular metabolism processes including glycolysis and oxidative phosphorylation. The NAD(P)H fluorescence lifetime includes a short and long lifetime component due to the two possible physiological conditions of NAD(P)H, free or protein-bound (to an enzyme and/or substrate). Fluorescence lifetimes of NAD(P)H have been imaged in cells, ex vivo tissues, and in vivo tissues to investigate cellular metabolism at basal conditions and with perturbations. In particular, NAD(P)H fluorescence lifetimes are altered in pre-malignant and malignant cells and tissues compared with non-malignant cells and tissues across several cancers including head and neck cancers, breast cancer, and skin cancer. Additionally, NAD(P)H fluorescence lifetimes decrease in cancer cells and tumors following drug treatment and therefore, these metabolic endpoints show potential for drug monitoring and screening.

While over 50 drugs have been approved by the FDA to treat breast cancer, there are no reliable methods for optimizing treatment regimens for individual patients. Currently, oncologists choose drug treatments based on expression levels of tumor cell signaling receptors (i.e. HER2, ER) and assess whether the treatment is effective after weeks or months of precious time have passed. Unfortunately, over one third of patients exhibit resistance to their initial treatment. The toxic side effects and morbidities resulting from suboptimal drug regimens could be eradicated by applying a personalized medicine approach to breast cancer treatment. This approach would allow clinicians to determine the optimal treatment plan for individual patients early on, at the time of diagnosis. While current methods track therapy response via changes in tumor size (i.e. MRI, mammography, ultrasound), changes in cell metabolism precede changes in tumor size and thus present an earlier marker of treatment response. Optical metabolic imaging (OMI) is sensitive to these early changes in metabolism by exploiting the intrinsic fluorescent properties of NAD(P)H and FAD, coenzymes of metabolic reactions. OMI endpoints include the optical redox ratio (the fluorescence intensity of NAD(P)H divided by the fluorescence intensity of FAD), as well as the fluorescence lifetimes of NAD(P)H and FAD. The redox ratio reflects the cellular redox state, and the fluorescence lifetimes of NAD(P)H and FAD report on the binding activity of these coenzymes. Additionally, OMI has the unique ability to measure these endpoints in individual cells, which allows for the detection of heterogeneous subpopulations of responsive or resistant cells within a tumor. OMI also allows for high-throughput screening of potential cancer drugs and drug combinations on patient biopsy samples cultured ex vivo. These samples are grown as “organoids” in a 3D matrix that mimics the natural tumor environment. We have demonstrated that OMI accurately predicts treatment response in organoids derived from breast cancer xenografts compared with gold standard tumor growth curves in vivo. We have also shown that OMI can measure drug response and detect heterogeneous cell populations in organoids derived from triple negative, ER+, and HER2+ human breast tumors. The ability of OMI to predict treatment response has also been demonstrated in the polyoma middle-T mouse model of breast cancer, which exhibits more cellular heterogeneity than cell line xenografts and also incorporates the influence of the immune system on cancer drug response. Preliminary data shows that OMI of organoids generated from biopsies of newly diagnosed breast cancer patients can accurately predict how the patient clinically responds to neoadjuvant treatment. This methodology could allow oncologists to determine the ideal treatment regimen for their patients at the time of diagnosis. Citation Format: Joe T. Sharick, Alex J. Walsh, Melinda E. Sanders, Ingrid Meszoely, Mary A. Hooks, Mark C. Kelley, Melissa C. Skala. Predicting clinical response in breast cancer using cellular-resolution optical metabolic imaging. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4241.