Single-cell transcriptomics reveals that tumor-infiltrating natural killer cells are activated by localized ablative immunotherapy and share anti-tumor signatures induced by immune checkpoint inhibitors.

Rationale: Natural killer (NK) cells provide protective anti-cancer immunity. However, the cancer therapy induced activation gene signatures and pathways in NK cells remain unclear. Methods: We applied a novel localized ablative immunotherapy (LAIT) by synergizing photothermal therapy (PTT) with intra-tumor delivering of the immunostimulant N-dihydrogalactochitosan (GC), to treat breast cancer using a mammary tumor virus-polyoma middle tumor-antigen (MMTV-PyMT) mouse model. We performed single-cell RNA sequencing (scRNAseq) analysis to unveil the cellular heterogeneity and compare the transcriptional alterations induced by PTT, GC, and LAIT in NK cells within the tumor microenvironment (TME). Results: ScRNAseq showed that NK subtypes, including cycling, activated, interferon-stimulated, and cytotoxic NK cells. Trajectory analysis revealed a route toward activation and cytotoxicity following pseudotime progression. Both GC and LAIT elevated gene expression associated with NK cell activation, cytolytic effectors, activating receptors, IFN pathway components, and cytokines/chemokines in NK subtypes. Single-cell transcriptomics analysis using immune checkpoint inhibitor (ICI)-treated animal and human samples revealed that ICI-induced NK activation and cytotoxicity across several cancer types. Furthermore, ICI-induced NK gene signatures were also induced by LAIT treatment. We also discovered that several types of cancer patients had significantly longer overall survival when they had higher expression of genes in NK cells that were also specifically upregulated by LAIT. Conclusion: Our findings show for the first time that LAIT activates cytotoxicity in NK cells and the upregulated genes positively correlate with beneficial clinical outcomes for cancer patients. More importantly, our results further establish the correlation between the effects of LAIT and ICI on NK cells, hence expanding our understanding of mechanism of LAIT in remodeling TME and shedding light on the potentials of NK cell activation and anti-tumor cytotoxic functions in clinical applications.

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.

BACKGROUND: Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi-view mammograms, most CAD schemes are single-view-based schemes, which limit CAD performance and clinical utility. PURPOSE: This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods. METHODS: An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model-generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images. RESULTS: Using a 10-fold cross-validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC = 0.876 ± 0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC = 0.817 ± 0.026 and 0.792 ± 0.026 for the CC and MLO view of bilateral mammograms, respectively (p < 0.001). CONCLUSIONS: The study demonstrates that the shift from single-view CAD to four-view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions.

Tumor volume doubling time estimated from digital breast tomosynthesis mammograms distinguishes invasive breast cancers from benign lesions.

OBJECTIVES: The aim of this study was to determine whether lesion size metrics on consecutive screening mammograms could predict malignant invasive carcinoma versus benign lesion outcome. METHODS: We retrospectively reviewed suspicious screen-detected lesions confirmed by biopsy to be invasive breast cancers or benign that were visible on current and in-retrospect prior screening mammograms performed with digital breast tomosynthesis from 2017 to 2020. Four experienced radiologists recorded mammogram dates, breast density, lesion type, lesion diameter, and morphology on current and prior exams. We used logistic regression models to evaluate the association of invasive breast cancer outcome with lesion size metrics such as maximum dimension, average dimension, volume, and tumor volume doubling time (TVDT). RESULTS: Twenty-eight patients with invasive ductal carcinoma or invasive lobular carcinoma and 40 patients with benign lesions were identified. The mean TVDT was significantly shorter for invasive breast cancers compared to benign lesions (0.84 vs. 2.5 years; p = 0.0025). Patients with a TVDT of less than 1 year were shown to have an odds ratio of invasive cancer of 6.33 (95% confidence interval, 2.18-18.43). Logistic regression adjusted for age, lesion maximum dimension, and lesion volume demonstrated that shorter TVDT was the size variable significantly associated with invasive cancer outcome. CONCLUSION: Invasive breast cancers detected on current and in-retrospect prior screening mammograms are associated with shorter TVDT compared to benign lesions. If confirmed to be sufficiently predictive of benignity in larger studies, lesions visible on mammograms which in comparison to prior exams have longer TVDTs could potentially avoid additional imaging and/or biopsy. KEY POINTS: • We propose tumor volume doubling time as a measure to distinguish benign from invasive breast cancer lesions. • Logistic regression results summarized the utility of the odds ratio in retrospective clinical mammography data.

Immune modulations of the tumor microenvironment in response to phototherapy.

The tumor microenvironment (TME) promotes pro-tumor and anti-inflammatory metabolisms and suppresses the host immune system. It prevents immune cells from fighting against cancer effectively, resulting in limited efficacy of many current cancer treatment modalities. Different therapies aim to overcome the immunosuppressive TME by combining various approaches to synergize their effects for enhanced anti-tumor activity and augmented stimulation of the immune system. Immunotherapy has become a major therapeutic strategy because it unleashes the power of the immune system by activating, enhancing, and directing immune responses to prevent, control, and eliminate cancer. Phototherapy uses light irradiation to induce tumor cell death through photothermal, photochemical, and photo-immunological interactions. Phototherapy induces tumor immunogenic cell death, which is a precursor and enhancer for anti-tumor immunity. However, phototherapy alone has limited effects on long-term and systemic anti-tumor immune responses. Phototherapy can be combined with immunotherapy to improve the tumoricidal effect by killing target tumor cells, enhancing immune cell infiltration in tumors, and rewiring pathways in the TME from anti-inflammatory to pro-inflammatory. Phototherapy-enhanced immunotherapy triggers effective cooperation between innate and adaptive immunities, specifically targeting the tumor cells, whether they are localized or distant. Herein, the successes and limitations of phototherapy combined with other cancer treatment modalities will be discussed. Specifically, we will review the synergistic effects of phototherapy combined with different cancer therapies on tumor elimination and remodeling of the immunosuppressive TME. Overall, phototherapy, in combination with other therapeutic modalities, can establish anti-tumor pro-inflammatory phenotypes in activated tumor-infiltrating T cells and B cells and activate systemic anti-tumor immune responses.

A New Nrf2 Inhibitor Enhances Chemotherapeutic Effects in Glioblastoma Cells Carrying p53 Mutations.

TP53 tumor suppressor gene is a commonly mutated gene in cancer. p53 mediated senescence is critical in preventing oncogenesis in normal cells. Since p53 is a transcription factor, mutations in its DNA binding domain result in the functional loss of p53-mediated cellular pathways. Similarly, nuclear factor erythroid 2-related factor 2 (Nrf2) is another transcription factor that maintains cellular homeostasis by regulating redox and detoxification mechanisms. In glioblastoma (GBM), Nrf2-mediated antioxidant activity is upregulated while p53-mediated senescence is lost, both rendering GBM cells resistant to treatment. To address this, we identified novel Nrf2 inhibitors from bioactive compounds using a molecular imaging biosensor-based screening approach. We further evaluated the identified compounds for their in vitro and in vivo chemotherapy enhancement capabilities in GBM cells carrying different p53 mutations. We thus identified an Nrf2 inhibitor that is effective in GBM cells carrying the p53 (R175H) mutation, a frequent clinically observed hotspot structural mutation responsible for chemotherapeutic resistance in GBM. Combining this drug with low-dose chemotherapies can potentially reduce their toxicity and increase their efficacy by transiently suppressing Nrf2-mediated detoxification function in GBM cells carrying this important p53 missense mutation.

Inhaled Gold Nano-star Carriers for Targeted Delivery of Triple Suicide Gene Therapy and Therapeutic MicroRNAs to Lung Metastases: Development and Validation in a Small Animal Model.

Pulmonary metastases pose significant treatment challenges for many cancers, including triple-negative breast cancer (TNBC). We developed and tested a novel suicide gene and therapeutic microRNAs (miRs) combination therapy against lung metastases in vivo in mouse models after intranasal delivery using nontoxic gold nanoparticles (AuNPs) formulated to carry these molecular therapeutics. We used AuNPs coated with chitosan-ß-cyclodextrin (CS-CD) and functionalized with a urokinase plasminogen activator (uPA) peptide to carry triple cancer suicide genes (thymidine kinase-p53-nitroreductase: TK-p53-NTR) plus therapeutic miRNAs (antimiR-21, antimiR-10b and miR-100). We synthesized three AuNPs: 20nm nanodots (AuND), and 20nm or 50nm nanostars (AuNS), then surface coated these with CS-CD using a microfluidic-optimized method. We sequentially coated the resulting positively charged AuNP-CS-CD core with synthetic miRNAs followed by TK-p53-NTR via electrostatic interactions, and added uPA peptide through CD-adamantane host-guest chemistry. A comparison of transfection efficiencies for different AuNPs showed that the 50nm AuNS allowed ∼4.16-fold higher gene transfection than other NPs. The intranasal delivery of uPA-AuNS-TK-p53-NTR-microRNAs NPs (pAuNS@TK-p53-NTR-miRs) in mice predominantly accumulated in lungs and facilitated ganciclovir and CB1954 prodrug-mediated gene therapy against TNBC lung metastases. This new nanosystem may serve as an adaptable-across-cancer-type, facile, and clinically scalable platform to allow future inhalational suicide gene-miR combination therapy for patients harboring pulmonary metastases.

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.

Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.

A rationally identified panel of microRNAs targets multiple oncogenic pathways to enhance chemotherapeutic effects in glioblastoma models.

Glioblastoma (GBM) is the most common malignant brain tumor. Available treatments have limited success because most patients develop chemoresistance. Alternative strategies are required to improve anticancer effects of current chemotherapeutics while limiting resistance. Successful targeting of microRNAs (miRNAs) as regulators of gene expression can help reprogram GBM cells to better respond to chemotherapy. We aimed to identify a panel of miRNAs that target multiple oncogenic pathways to improve GBM therapy. We first identified differentially expressed miRNAs and tested if their target genes play central roles in GBM signaling pathways by analyzing data in the Gene Expression Omnibus and The Cancer Genome Atlas databases. We then studied the effects of different combinations of these miRNAs in GBM cells by delivering synthetic miRNAs using clinically compatible PLGA-PEG nanoparticles prior to treatment with temozolomide (TMZ) or doxorubicin (DOX). The successful miRNA panel was tested in mice bearing U87-MG cells co-treated with TMZ. We identified a panel of five miRNAs (miRNA-138, miRNA-139, miRNA-218, miRNA-490, and miRNA-21) and their oncogenic targets (CDK6, ZEB1, STAT3, TGIF2, and SMAD7) that cover four different signaling pathways (cell proliferation and apoptotic signaling, invasion and metastasis, cytokine signaling, and stemness) in GBM. We observed significant in vitro and in vivo enhancement of therapeutic efficiency of TMZ and DOX in GBM models. The proposed combination therapy using rationally selected miRNAs and chemotherapeutic drugs is effective owing to the ability of this specific miRNA panel to better target multiple genes associated with the hallmarks of cancer.

Compact Eight-Channel Light-Sensing System for Bioassays.

The present protocol introduces a new instrumental setup as a luminometer to simultaneously measure eight light samples with high sensitivity. The system consists of 8-channel photomultiplier tubes (8-PMTs) with different sensitivities to light. Therefore, it is critical to normalize the sensitivities of PMTs to light samples and integrate them as a system. We first introduce how to normalize the diverse light sensitivity among the PMTs using placental alkaline phosphatase (PLAP) as a model chemiluminescence light source. The normalized BBI system shows a statistically strong linear correlation graph to photon counts. The biomedical utility of this system is exemplified by (i) determining the alkaline phosphatase (AP) activities in mouse plasma samples as a cancer biomarker and (ii) diagnosing metastatic tissues during cancer progression using bioluminescent reporter.

Examining the Feasibility of Quantifying Receptor Availability Using Cross-Modality Paired-Agent Imaging.

PURPOSE: The ability to noninvasively quantify receptor availability (RA) in solid tumors is an aspirational goal of molecular imaging, often challenged by the influence of non-specific accumulation of the contrast agent. Paired-agent imaging (PAI) techniques aim to compensate for this effect by imaging the kinetics of a targeted agent and an untargeted isotype, often simultaneously, and comparing the kinetics of the two agents to estimate RA. This is usually accomplished using two spectrally distinct fluorescent agents, limiting the technique to superficial tissues and/or preclinical applications. Applying the approach in humans using conventional imaging modalities is generally infeasible since most modalities are unable to routinely image multiple agents simultaneously. We examine the ability of PAI to be implemented in a cross-modality paradigm, in which the targeted and untargeted agent kinetics are imaged with different modalities and used to recover receptor availability. PROCEDURES: Eighteen mice bearing orthotopic brain tumors were administered a solution containing three contrast agents: (1) a fluorescent agent targeted to epidermal growth factor receptor (EGFR), (2) an untargeted fluorescent isotype, and (3) a gadolinium-based contrast agent (GBCA) for MRI imaging. The kinetics of all three agents were imaged for 1 h after administration using an MRI-coupled fluorescence tomography system. Paired-agent receptor availability was computed using (1) the conventional all-optical approach using the targeted and untargeted optical agent images and (2) the cross-modality approach using the targeted optical and untargeted MRI-GBCA images. Receptor availability estimates between the two methods were compared. RESULTS: Receptor availability values using the cross-modality approach were highly correlated to the conventional, single-modality approach (r = 0.94; p < 0.00001). CONCLUSION: These results suggest that cross-modality paired-agent imaging for quantifying receptor availability is feasible. Ultimately, cross-modality paired-agent imaging could facilitate rapid, noninvasive receptor availability quantification in humans using hybrid clinical imaging modalities.

A Microfluidics-Based Scalable Approach to Generate Extracellular Vesicles with Enhanced Therapeutic MicroRNA Loading for Intranasal Delivery to Mouse Glioblastomas.

Extracellular vesicles (EVs), including exosomes and microvesicles derived from different cell sources, are used as promising nanovesicles for delivering therapeutic microRNAs (miRNAs) and drugs in cancer therapy. However, their clinical translation is limited by the quantity, size heterogeneity, and drug or small RNA loading efficiency. Herein, we developed a scalable microfluidic platform that can load therapeutic miRNAs (antimiRNA-21 and miRNA-100) and drugs while controlling the size of microfluidically processed EVs (mpEVs) using a pressure-based disruption and reconstitution process. We prepared mpEVs of optimal size using microvesicles isolated from neural stem cells engineered to overexpress CXCR4 receptor and characterized them for charge and miRNA loading efficiency. Since the delivery of therapeutic miRNAs to brain cancer is limited by the blood-brain barrier (BBB), we adopted intranasal administration of miRNA-loaded CXCR4-engineered mpEVs in orthotopic GBM mouse models and observed a consistent pattern of mpEVs trafficking across the nasal epithelia, bypassing the BBB into the intracranial compartment. In addition, the CXCR4-engineered mpEVs manifested selective tropism toward GBMs by stromal-derived factor-1 chemotaxis to deliver their miRNA cargo. The delivered miRNAs sensitized GBM cells to temozolomide, resulting in prominent tumor regression, and improved the overall survival of mice. A simple and efficient approach of packaging miRNAs in mpEVs using microfluidics, combined with a noninvasive nose-to-brain delivery route presents far-reaching potential opportunities to improve GBM therapy in clinical practice.

Prediction of optimal contrast times post-imaging agent administration to inform personalized fluorescence-guided surgery.

SIGNIFICANCE: Fluorescence guidance in cancer surgery (FGS) using molecular-targeted contrast agents is accelerating, yet the influence of individual patients' physiology on the optimal time to perform surgery post-agent-injection is not fully understood. AIM: Develop a mathematical framework and analytical expressions to estimate patient-specific time-to-maximum contrast after imaging agent administration for single- and paired-agent (coadministration of targeted and control agents) protocols. APPROACH: The framework was validated in mouse subcutaneous xenograft studies for three classes of imaging agents: peptide, antibody mimetic, and antibody. Analytical expressions estimating time-to-maximum-tumor-discrimination potential were evaluated over a range of parameters using the validated framework for human cancer parameters. RESULTS: Correlations were observed between simulations and matched experiments and metrics of tumor discrimination potential (p < 0.05). Based on human cancer physiology, times-to-maximum contrast for peptide and antibody mimetic agents were <200 min, >15 h for antibodies, on average. The analytical estimates of time-to-maximum tumor discrimination performance exhibited errors of <10 % on average, whereas patient-to-patient variance is expected to be greater than 100%. CONCLUSION: We demonstrated that analytical estimates of time-to-maximum contrast in FGS carried out patient-to-patient can outperform the population average time-to-maximum contrast used currently in clinical trials. Such estimates can be made with preoperative DCE-MRI (or similar) and knowledge of the targeted agent's binding affinity.

Noninvasive quantification of target availability during therapy using paired-agent fluorescence tomography.

Immuno-oncological treatment strategies that target abnormal receptor profiles of tumors are an increasingly important feature of cancer therapy. Yet, assessing receptor availability (RA) and drug-target engagement, important determinants of therapeutic efficacy, is challenging with current imaging strategies, largely due to the complex nonspecific uptake behavior of imaging agents in tumors. Herein, we evaluate whether a quantitative noninvasive imaging approach designed to compensate for nonspecific uptake, MRI-coupled paired-agent fluorescence tomography (MRI-PAFT), is capable of rapidly assessing the availability of epidermal growth factor receptor (EGFR) in response to one dose of anti-EGFR antibody therapy in orthotopic brain tumor models. Methods: Mice bearing orthotopic brain tumor xenografts with relatively high EGFR expression (U251) (N=10) or undetectable human EGFR (9L) (N=9) were considered in this study. For each tumor type, mice were either treated with one dose of cetuximab, or remained untreated. All animals were scanned using MRI-PAFT, which commenced immediately after paired-agent administration, and values of RA were recovered using a model-based approach, which uses the entire dynamic sequence of agent uptake, as well as a simplified "snapshot" approach which requires uptake measurements at only two time points. Recovered values of RA were evaluated between groups and techniques. Hematoxylin & eosin (H&E) and immunohistochemical (IHC) staining was performed on tumor specimens from every animal to confirm tumor presence and EGFR status. Results: In animals bearing EGFR(+) tumors, a significant difference in RA values between treated and untreated animals was observed (RA = 0.24 ± 0.15 and 0.61 ± 0.18, respectively, p=0.027), with an area under the curve - receiver operating characteristic (AUC-ROC) value of 0.92. We did not observe a statistically significant difference in RA values between treated and untreated animals bearing EGFR(-) tumors (RA = 0.18 ± 0.19 and 0.27 ± 0.21, respectively; p = 0.89; AUC-ROC = 0.55), nor did we observe a difference between treated EGFR(+) tumors compared to treated and untreated EGFR(-) tumors. Notably, the snapshot paired-agent strategy quantified drug-receptor engagement within just 30 minutes of agent administration. Examination of the targeted agent alone showed no capacity to distinguish tumors either by treatment or receptor status, even 24h after agent administration. Conclusions: This study demonstrated that a noninvasive imaging strategy enables rapid quantification of receptor availability in response to therapy, a capability that could be leveraged in preclinical drug development, patient stratification, and treatment monitoring.

Highly sensitive eight-channel light sensing system for biomedical applications.

We demonstrate the potential of an eight-channel light sensing platform system, named Black Box I (BBI), for rapid and highly sensitive measurement of low-level light using a nonradioactive optical readout. We developed, normalized, and characterized the photon sensitivities of the eight channels of the BBI using placental alkaline phosphatase (PLAP) as a model imaging reporter. We found that the BBI system had a statistically strong linear correlation with the reference IVIS Lumina II system. When we applied normalization constants, we were able to optimize the photomultiplier tubes (PMT) of all eight channels of the BBI (up to r2 = 0.998). We investigated the biomedical utilities of BBI by: (i) determining alkaline phosphatase activities in mouse plasma samples as a diagnostic secretory biomarker of cancer, and (ii) diagnosing cancer metastases in the organs of mice bearing triple negative breast cancer. We provide an important new addition to low-cost biomedical instruments intended for pre-clinical diagnostic imaging with high sensitivity, high sample throughput, portability, and rapid on-site analysis of low-level light.

A paired-agent fluorescent molecular imaging strategy for quantifying antibody drug target engagement in in vivo window chamber xenograft models.

A paired-agent fluorescent molecular imaging strategy is presented as a method to measure drug target engagement in whole tumor imaging. The protocol involves dynamic imaging of a pair of targeted and control imaging agents prior to and following antibody therapy. Simulations demonstrated that antibody "drug target engagement" can be estimated within a 15%-error over a wide range of tumor physiology (blood flow, vascular permeability, target density) and antibody characteristics (affinity, binding rates). Experimental results demonstrated the first in vivo detection of binding site barrier, highlighting the potential for this methodology to provide novel insights in drug distribution/binding imaging.

Noninvasive imaging of dual-agent uptake in glioma and normal tissue using MRI-coupled fluorescence tomography.

As the role of immuno-oncological therapeutics expands, the capacity to noninvasively quantify molecular targets and drug-target engagement is increasingly critical to drug development efforts and treatment monitoring. Previously, we showed that MRI-coupled dual-agent fluorescence tomography (FMT) is capable of estimating the concentration of epidermal growth factor receptor (EGFR) in orthotopic glioma models noninvasively. This approach uses the dynamic information of two fluorescent agents (a targeted agent and untargeted isotype) to estimate tumor receptor concentration in vivo. This approach generally relies on the two tracers having similar kinetics in normal tissues, which may not always be the case. Herein, we describe an additional channel added to the MRI-FMT system which measures the uptake of both agents in the normal muscle, data which can be used to compensate for differing kinetic behavior.

Design, Synthesis, and Biological Evaluation of Polyaminocarboxylate Ligand-Based Theranostic Conjugates for Antibody-Targeted Cancer Therapy and Near-Infrared Optical Imaging.

We report the design, synthesis, and evaluation of polyaminocarboxylate ligand-based antibody conjugates for potential application in targeted cancer therapy and near-infrared (NIR) fluorescence imaging. We synthesized a new polyaminocarboxylate chelate (CAB-NE3TA) as a potential anticancer agent. CAB-NE3TA displayed potent inhibitory activities against various cancer cell lines. We then designed a multifunctional theranostic platform (CAB-NE3TA-PAN-IR800) constructed on an epidermal growth factor receptor (EGFR)-targeted antibody (panitumumab, PAN) labeled with a NIR fluorescent dye. We also built the first atomistic model of the EGFR-PAN complex and loaded it with the cytotoxic CAB-NE3TA and the NIR dye. The therapeutic (CAB-NE3TA-PAN) and theranostic (CAB-NE3TA-PAN-IR800) conjugates were evaluated using an EGFR-positive A431 (human skin cancer) cell xenograft mouse model. Biodistribution studies using NIR fluorescence imaging demonstrated that the CAB-NE3TA-PAN labeled with the IR800 dye selectively targeted the A431 tumors in mice and resulted in prolonged retention in the tumor tissue and displayed excellent clearance in blood and normal organs. The therapeutic conjugate was capable of significantly inhibiting tumor growth, leading to nearly complete disappearance of tumors in the mice. The results of our pilot in vivo studies support further evaluation of the novel ligand-based therapeutic and theranostic conjugates for targeted iron chelation cancer therapy and imaging applications.

Quantifying cancer cell receptors with paired-agent fluorescent imaging: a novel method to account for tissue optical property effects.

Dynamic fluorescence imaging approaches can be used to estimate the concentration of cell surface receptors in vivo. Kinetic models are used to generate the final estimation by taking the targeted imaging agent concentration as a function of time. However, tissue absorption and scattering properties cause the final readout signal to be on a different scale than the real fluorescent agent concentration. In paired-agent imaging approaches, simultaneous injection of a suitable control imaging agent with a targeted one can account for non-specific uptake and retention of the targeted agent. Additionally, the signal from the control agent can be a normalizing factor to correct for tissue optical property differences. In this study, the kinetic model used for paired-agent imaging analysis (i.e., simplified reference tissue model) is modified and tested in simulation and experimental data in a way that accounts for the scaling correction within the kinetic model fit to the data to ultimately extract an estimate of the targeted biomarker concentration.

Correcting for targeted and control agent signal differences in paired-agent molecular imaging of cancer cell-surface receptors.

Paired-agent kinetic modeling protocols provide one means of estimating cancer cell-surface receptors with in vivo molecular imaging. The protocols employ the coadministration of a control imaging agent with one or more targeted imaging agent to account for the nonspecific uptake and retention of the targeted agent. These methods require the targeted and control agent data be converted to equivalent units of concentration, typically requiring specialized equipment and calibration, and/or complex algorithms that raise the barrier to adoption. This work evaluates a kinetic model capable of correcting for targeted and control agent signal differences. This approach was compared with an existing simplified paired-agent model (SPAM), and modified SPAM that accounts for signal differences by early time point normalization of targeted and control signals (SPAMPN). The scaling factor model (SPAMSF) outperformed both SPAM and SPAMPN in terms of accuracy and precision when the scale differences between targeted and imaging agent signals (α) were not equal to 1, and it matched the performance of SPAM for α = 1. This model could have wide-reaching implications for quantitative cancer receptor imaging using any imaging modalities, or combinations of imaging modalities, capable of concurrent detection of at least two distinct imaging agents (e.g., SPECT, optical, and PET/MR).