Direct fluorescence in situ hybridization on human metaphase chromosomes using quantum dot-platinum labeled DNA probes.

The telomere shortening in chromosomes implies the senescence, apoptosis, or oncogenic transformation of cells. Since detecting telomeres in aging and diseases like cancer, is important, the direct detection of telomeres has been a very useful biomarker. We propose a telomere detection method using a newly synthesized quantum dot (QD) based probe with oligonucleotide conjugation and direct fluorescence in situ hybridization (FISH). QD-oligonucleotides were prepared with metal coordination bonding based on platinum-guanine binding reported in our previous work. The QD-oligonucleotide conjugation method has an advantage where any sequence containing guanine at the end can be easily bound to the starting QD-Pt conjugate. A synthesized telomeric oligonucleotide was bound to the QD-Pt conjugate successfully and this probe hybridized specifically on the telomere of fabricated MV-4-11 and MOLT-4 chromosomes. Additionally, the QD-telomeric oligonucleotide probe successfully detected the telomeres on the CGH metaphase slide. Due to the excellent photostability and high quantum yield of QDs, the QD-oligonucleotide probe has high fluorescence intensity when compared to the organic dye-oligonucleotide probe. Our QD-oligonucleotide probe, conjugation method of this QD probe, and hybridization protocol with the chromosomes can be a useful tool for chromosome painting and FISH.

One step synthesis of quantum dot-magnetic nanoparticle heterodimers for dual modal imaging applications.

Dual modal nanoprobes are promising tools for accurately detecting target molecules as part of the diagnosis of diseases including cancers. We have explored a new dual modal bioimaging probe that is comprised of a quantum dot (QD)-magnetic nanoparticle (MNP) hybrid. The MNP-QD heterodimers explored are fabricated by using a platinum-guanine coordination bonding guided self-assembly process, employing the metal-DNA conjugation method. Investigations utilizing energy dispersive spectroscopy (EDS) equipped high resolution transmission electron microscopy (HRTEM) demonstrate that the heterodimer contains an iron (Fe) dominant MNP and a cadmium (Cd) dominant QD. Finally, the results of cell studies show that the MNP-QD conjugates display good HeLa cell uptake in the absence of non-specific binding to the cell membrane and, as such, they can be used to label cells in vitro and in vivo as part of a new cell imaging technique.

Combination non-targeted and sGRP78-targeted nanoparticle drug delivery outperforms either component to treat metastatic ovarian cancer.

Metastatic ovarian cancer (MOC) is highly deadly, due in part to the limited efficacy of standard-of-care chemotherapies to metastatic tumors and non-adherent cancer cells. Here, we demonstrated the effectiveness of a combination therapy of GRP78-targeted (TNPGRP78pep) and non-targeted (NP) nanoparticles to deliver a novel DM1-prodrug to MOC in a syngeneic mouse model. Cell surface-GRP78 is overexpressed in MOC, making GRP78 an optimal target for selective delivery of nanoparticles to MOC. The NP + TNPGRP78pep combination treatment reduced tumor burden by 15-fold, compared to untreated control. Increased T cell and macrophage levels in treated groups also suggested antitumor immune system involvement. The NP and TNPGRP78pep components functioned synergistically through two proposed mechanisms of action. The TNPGRP78pep targeted non-adherent cancer cells in the peritoneal cavity, preventing the formation of new solid tumors, while the NP passively targeted existing solid tumor sites, providing a sustained release of the drug to the tumor microenvironment.

Disease-driven engineering of peptide-targeted DM1 loaded liposomal nanoparticles for enhanced efficacy in treating multiple myeloma by exploring DM1 prodrug chemistry.

Here, we report a CD138 receptor targeting liposomal formulation (TNP[Prodrug-4]) that achieved efficacious tumor growth inhibition in treating multiple myeloma by overcoming the dose limiting severe toxicity issues of a highly potent drug, Mertansine (DM1). Despite the promising potential to treat various cancers, due to poor solubility and pharmacokinetic profile, DM1's translation to the clinic has been unsatisfactory. We hypothesized that the optimal prodrug chemistry would promote efficient loading of the prodrug into targeted nanoparticles and achieve controlled release following endocytosis by the cancer cells, consequently, accomplish the most potent tumor growth inhibition. We evaluated four functional linker chemistries for synthesizing DM1-Prodrug molecules and evaluated their stability and cancer cell toxicity in vitro. It was determined that the phosphodiester moiety, as part of nanoparticle formulations, demonstrated most favorable characteristics with an IC50 of ∼16 nM. Nanoparticle formulations of Prodrug-4 enabled its administration at 8-fold higher dosage of equivalent free drug while remaining below maximum tolerated dose. Importantly, TNP[Prodrug-4] achieved near complete inhibition of tumor growth (∼99% by day 10) compared to control, without displaying noticeable systemic toxicity. TNP[Prodrug-4] promises a formulation that could potentially make DM1 treatment available for wider clinical applications with a long-term goal for better patient outcomes.

Peanut allergen inhibition prevents anaphylaxis in a humanized mouse model.

Peanut-induced allergy is an immunoglobulin E (IgE)-mediated type I hypersensitivity reaction that manifests symptoms ranging from local edema to life-threatening anaphylaxis. Although there are treatments for symptoms in patients with allergies resulting from allergen exposure, there are few preventive therapies other than strict dietary avoidance or oral immunotherapy, neither of which are successful in all patients. We have previously designed a covalent heterobivalent inhibitor (cHBI) that binds in an allergen-specific manner as a preventive for allergic reactions. Building on previous in vitro testing, here, we developed a humanized mouse model to test cHBI efficacy in vivo. Nonobese diabetic-severe combined immunodeficient γc-deficient mice expressing transgenes for human stem cell factor, granulocyte-macrophage colony-stimulating factor, and interleukin-3 developed mature functional human mast cells in multiple tissues and displayed robust anaphylactic reactions when passively sensitized with patient-derived IgE monoclonal antibodies specific for peanut Arachis hypogaea 2 (Ara h 2). The allergic response in humanized mice was IgE dose dependent and was mediated by human mast cells. Using this humanized mouse model, we showed that cHBI prevented allergic reactions for more than 2 weeks when administered before allergen exposure. cHBI also prevented fatal anaphylaxis and attenuated allergic reactions when administered shortly after the onset of symptoms. cHBI impaired mast cell degranulation in vivo in an allergen-specific manner. cHBI rescued the mice from lethal anaphylactic responses during oral Ara h 2 allergen-induced anaphylaxis. Together, these findings suggest that cHBI has the potential to be an effective preventative for peanut-specific allergic responses in patients.

High Expression of PD-L1 Is Associated with Better Survival in Pancreatic/Periampullary Cancers and Correlates with Epithelial to Mesenchymal Transition.

Periampullary cancers (PACs) are characterized by tumor-infiltrating lymphocytes (TILs), severe fibrosis, and epithelial to mesenchymal transition (EMT). The immune checkpoint marker programmed death-1 (PD-1) and its ligands 1 and 2 have gained popularity in cancers with TILs. Evidence suggests a strong relationship between immune checkpoint markers and EMT in cancers. Here, we evaluated the expression and prognostic significance of immune checkpoint and EMT markers in PAC using an automated image analyzer. Formalin-fixed, paraffin-embedded surgically excised PAC tissues from laboratory archives (1998-2014) were evaluated by immunohistochemical staining for PD-1, PD-L1, and PD-L2 in a tissue microarray. In total, 115 PAC patients (70 males and 45 females) with an average age of 63 years were analyzed. Location, gross type, size, radial resection margin, N-M stage, lymphatic invasion, vascular invasion, perineural invasion, histologically well-differentiated severe inflammation, and high PD-L1 expression were significantly associated with recurrence. Higher PD-L1 expression, but not PD-1 and PD-L2, was significantly related to better overall survival (OS) and disease-free survival (DFS). PD-L1 and PD-L2 were significantly related to EMT markers. Aside from other clinicopathologic parameters, high PD-L1 expression was significantly related to better OS and DFS of PAC patients. Moreover, immune checkpoint markers were significantly associated with EMT markers. Therefore, PD-L1 expression can be a good prognostic marker to guide future immune target-based therapies in PAC patients.

Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.

Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.

Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation.

BACKGROUND: Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions. METHODS: We developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input. RESULTS: We trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases. CONCLUSION: The results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.

On-Demand Drug Release from Gold Nanoturf for a Thermo- and Chemotherapeutic Esophageal Stent.

Stimuli-responsive delivery systems for cancer therapy have been increasingly used to promote the on-demand therapeutic efficacy of anticancer drugs and, in some cases, simultaneously generate heat in response to a stimulus, resulting in hyperthermia. However, their application is still limited due to the systemic drawbacks of intravenous delivery, such as rapid clearance from the bloodstream and the repeat injections required for sustained safe dosage, which can cause overdosing. Here, we propose a gold (Au)-coated nanoturf structure as an implantable therapeutic interface for near-infrared (NIR)-mediated on-demand hyperthermia chemotherapy. The Au nanoturf possessed long-lasting doxorubicin (DOX) duration, which helps facilitate drug release in a sustained and prolonged manner. Moreover, the Au-coated nanoturf provides reproducible hyperthermia induced by localized surface plasmon resonances under NIR irradiation. Simultaneously, the NIR-mediated temperature increase can promote on-demand drug release at desired time points. For in vivo analysis, the Au nanoturf structure was applied on an esophageal stent, which needs sustained anticancer treatment to prevent tumor recurrence on the implanted surface. This thermo- and chemo-esophageal stent induced significant cancer cell death with released drug and hyperthermia. These phenomena were also confirmed by theoretical analysis. The proposed strategy provides a solution to achieve enhanced thermo-/chemotherapy and has broad applications in sustained cancer treatments.

In situ imaging of quantum dot-AZD4547 conjugates for tracking the dynamic behavior of fibroblast growth factor receptor 3.

Fibroblast growth factor receptors (FGFRs) play an important role in determining cell proliferation, differentiation, migration, and survival. Although a variety of small-molecule FGFR inhibitors have been developed for cancer therapeutics, the interaction between FGFRs and FGFR inhibitors has not been well characterized. The FGFR-inhibitor interaction can be characterized using a new imaging probe that has strong, stable signal properties for in situ cellular imaging of the interaction without quenching. We developed a kinase-inhibitor-modified quantum dot (QD) probe to investigate the interaction between FGFR and potential inhibitors. Especially, turbo-green fluorescent protein-FGFR3s were overexpressed in HeLa cells to investigate the colocalization of FGFR3 and AZD4547 using the QD-AZD4547 probe. The result indicates that this probe is useful for investigating the binding behaviors of FGFR3 with the FGFR inhibitor. Thus, this new inhibitor-modified QD probe is a promising tool for understanding the interaction between FGFR and inhibitors and for creating future high-content, cell-based drug screening strategies.