An End-to-End Platform for Digital Pathology Using Hyperspectral Autofluorescence Microscopy and Deep Learning-Based Virtual Histology.

Conventional histopathology involves expensive and labor-intensive processes that often consume tissue samples, rendering them unavailable for other analyses. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to nondestructively image the autofluorescence of unstained tissue sections. We then trained a deep learning model to use autofluorescence to generate virtual histologic stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histologic features present in the real-stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study, where both real and virtual stains are scored by pathologists (D.T., A.D.B., R.K.P.). The test showed moderate-to-good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH Clinical Research Network score prediction. We showed that the end-to-end automated pathology platform is comparable with an independent panel of pathologists for NASH Clinical Research Network scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology and enable novel approaches to spatial biology research.

Digital spatial profiling of melanoma shows CD95 expression in immune cells is associated with resistance to immunotherapy.

Although immune checkpoint inhibitor (ICI) therapy has dramatically improved outcome for metastatic melanoma patients, many patients do not benefit. Since adverse events may be severe, biomarkers for resistance would be valuable, especially in the adjuvant setting. We performed high-plex digital spatial profiling (DSP) using the NanoString GeoMx® on 53 pre-treatment specimens from ICI-treated metastatic melanoma cases. We interrogated 77 targets simultaneously in four molecular compartments defined by S100B for tumor, CD68 for macrophages, CD45 for leukocytes, and nonimmune stromal cells defined as regions negative for all three compartment markers but positive for SYTO 13. For DSP validation, we confirmed the results obtained for some immune markers, such as CD8, CD4, CD20, CD68, CD45, and PD-L1, by quantitative immunofluorescence (QIF). In the univariable analysis, 38 variables were associated with outcome, 14 of which remained significant after multivariable adjustment. Among them, CD95 was further validated using multiplex immunofluorescence in the Discovery immunotherapy (ITX) Cohort and an independent validation cohort with similar characteristics, showing an association between high levels of CD95 and shorter progression-free survival. We found that CD95 in stroma was associated with resistance to ICI. With further validation, this biomarker could have value to select patients that will not benefit from immunotherapy.

Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade.

Treatment with immune checkpoint inhibitors has altered the course of malignant melanoma, with approximately half of the patients with advanced disease surviving for more than 5 years after diagnosis. Currently, there are no biomarker methods for predicting outcome from immunotherapy. Here, we obtained transcriptomic information from a total of 105 baseline tumor samples comprising two cohorts of patients with advanced melanoma treated with programmed cell death protein 1 (PD-1)-based immunotherapies. Gene expression profiles were correlated with progression-free survival (PFS) within consecutive clinical benefit intervals (i.e., 6, 12, 18, and 24 months). Elastic net binomial regression models with cross validation were utilized to compare the predictive value of distinct genes across time. Lasso regression was used to generate a signature predicting long-term benefit (LTB), defined as patients who remain alive and free of disease progression at 24 months post treatment initiation. We show that baseline gene expression profiles were consistently able to predict long-term immunotherapy outcomes with high accuracy. The predictive value of different genes fluctuated across consecutive clinical benefit intervals, with a distinct set of genes defining benefit at 24 months compared to earlier outcomes. A 12-gene signature was able to predict LTB following anti-PD-1 therapy with an area under the curve (AUC) equal to 0.92 and 0.74 in the training and validation set, respectively. Evaluation of LTB, via a unique signature may complement objective response classification and characterize the logistics of sustained antitumor immune responses.

Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade.

Next-generation tissue-based biomarkers for immunotherapy will likely include the simultaneous analysis of multiple cell types and their spatial interactions, as well as distinct expression patterns of immunoregulatory molecules. Here, we introduce a comprehensive platform for multispectral imaging and mapping of multiple parameters in tumor tissue sections with high-fidelity single-cell resolution. Image analysis and data handling components were drawn from the field of astronomy. Using this "AstroPath" whole-slide platform and only six markers, we identified key features in pretreatment melanoma specimens that predicted response to anti-programmed cell death-1 (PD-1)-based therapy, including CD163+PD-L1- myeloid cells and CD8+FoxP3+PD-1low/mid T cells. These features were combined to stratify long-term survival after anti-PD-1 blockade. This signature was validated in an independent cohort of patients with melanoma from a different institution.

Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality.

Immunotherapy has reshaped the field of cancer therapeutics but the population that benefits are small in many tumor types, warranting a companion diagnostic test. While immunohistochemistry (IHC) for programmed death-ligand 1 (PD-L1) or mismatch repair (MMR) and polymerase chain reaction (PCR) for microsatellite instability (MSI) are the only approved companion diagnostics others are under consideration. An optimal companion diagnostic test might combine the spatial information of IHC with the quantitative information from RNA expression profiling. Here, we show proof of concept for combination of spatially resolved protein information acquired by the NanoString GeoMx® Digital Spatial Profiler (DSP) with transcriptomic information from bulk mRNA gene expression acquired using NanoString nCounter® PanCancer IO 360™ panel on the same cohort of immunotherapy treated melanoma patients to create predictive models associated with clinical outcomes. We show that the combination of mRNA and spatially defined protein information can predict clinical outcomes more accurately (AUC 0.97) than either of these factors alone.

Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry.

PURPOSE: Imaging mass cytometry (IMC) is among the first tools with the capacity for multiplex analysis of more than 40 targets, which provides a novel approach to biomarker discovery. Here, we used IMC to characterize the tumor microenvironment of patients with metastatic melanoma who received immunotherapy in efforts to find indicative factors of treatment response. In spite of the new power of IMC, the image analysis aspects are still limited by the challenges of cell segmentation. EXPERIMENTAL DESIGN: Here, rather than segment, we performed image analysis using a newly designed version of the AQUA software to measure marker intensity in molecularly defined compartments: tumor cells, stroma, T cells, B cells, and macrophages. IMC data were compared with quantitative immunofluorescence (QIF) and digital spatial profiling. RESULTS: Validation of IMC results for immune markers was confirmed by regression with additional multiplexing methods and outcome assessment. Multivariable analyses by each compartment revealed significant associations of 12 markers for progression-free survival and seven markers for overall survival (OS). The most compelling indicative biomarker, beta2-microglobulin (B2M), was confirmed by correlation with OS by QIF in the discovery cohort and validated in an independent published cohort profiled by mRNA expression. CONCLUSIONS: Using digital image analysis based on pixel colocalization to assess IMC data allowed us to quantitively measure 25 markers simultaneously on formalin-fixed, paraffin-embedded tissue microarray samples. In addition to showing high concordance with other multiplexing technologies, we identified a series of potentially indicative biomarkers for immunotherapy in metastatic melanoma, including B2M.

Quantitative analysis of CMTM6 expression in tumor microenvironment in metastatic melanoma and association with outcome on immunotherapy.

Chemokine-like factor (CKLF)-like MARVEL transmembrane domain containing 6 (CMTM6) modulates degradation of a number of proteins, including programmed death ligand-1 (PD-L1) by protecting it from ubiquitin-mediated degradation. In this role, it could modulate the effectiveness of immunotherapy. Here, for the first time, we characterize CMTM6 expression in melanoma and evaluate its association with response to immune checkpoint inhibitors (ICI). We evaluated the expression of CMTM6, PD-L1 and other immune-related proteins in 60 pretreatment biopsies from metastatic melanoma patients who received immunotherapy, in a tissue microarray (TMA) using quantitative immunofluorescence (QIF). Expression of mRNA from control patients obtained from The Cancer Genome Atlas (TCGA) database was also compared. CMTM6 expression was positively correlated with PD-L1, CD3, CD20, and CD68 markers, at protein (Pearson's r = 0.53-0.81, all P < .0001) and mRNA (Spearman's r = 0.15-0.44, all P < .002, except for CD68 where P = .26) levels. CMTM6 protein was associated with longer survival after immunotherapy when measured in the stromal (P = .007) and all the immune compartments tested (T cells, B cells, and macrophages). Multivariable analyses also revealed significant CMTM6 survival associations when measured in stromal (Hazard Ratio (HR) = 0.12, P = .001) and CD68-positive (HR = 0.30, P = .043) compartments. Additionally, PD-L1 but not CMTM6 showed prognostic value in control patients. Finally, high CMTM6 and PD-L1 co-expression in the stromal compartment was significantly associated with longer survival in treated patients (P = .028). Consequently, CMTM6 expression shows potential as a predictive factor for ICI treatments.

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

PURPOSE: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction. EXPERIMENTAL DESIGN: The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT). A receiver operating characteristic (ROC) curve was generated on the basis of vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS). RESULTS: Area under the curve (AUC) in the YSM patients was 0.905 (P < 0.0001). AUC in the GHS patients was 0.880 (P < 0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan-Meier (KM) analysis (P < 0.0001). CONCLUSIONS: The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.

An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma.

Assessment of tumor infiltrating lymphocytes (TILs) as a prognostic variable in melanoma has not seen broad adoption due to lack of standardization. Automation could represent a solution. Here, using open source software, we build an algorithm for image-based automated assessment of TILs on hematoxylin-eosin stained sections in melanoma. Using a retrospective collection of 641 melanoma patients comprising four independent cohorts; one training set (N = 227) and three validation cohorts (N = 137, N = 201, N = 76) from 2 institutions, we show that the automated TIL scoring algorithm separates patients into favorable and poor prognosis cohorts, where higher TILs scores were associated with favorable prognosis. In multivariable analyses, automated TIL scores show an independent association with disease-specific overall survival. Therefore, the open source, automated TIL scoring is an independent prognostic marker in melanoma. With further study, we believe that this algorithm could be useful to define a subset of patients that could potentially be spared immunotherapy.

Closed system RT-qPCR as a potential companion diagnostic test for immunotherapy outcome in metastatic melanoma.

BACKGROUND: In melanoma, there is no companion diagnostic test to predict response to programmed cell death 1 (PD-1) axis immune checkpoint inhibitor (ICI) therapy. In the adjuvant setting, only one in five patients may benefit from ICI, so a biomarker is needed to select those that may or may not benefit. Here, we test a new 4-gene multiplex immunotherapy panel with research use only (RUO) prototype mRNA expression profile on the GeneXpert closed system using real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) for association with clinical benefit after treatment with ICI therapy in metastatic melanoma patients. METHODS: Pretreatment formalin-fixed paraffin-embedded (FFPE) tissue sections from melanoma patients treated with anti-PD-1 therapy (pembrolizumab, nivolumab, or ipilimumab plus nivolumab) between 2011 and 17 were selected from the Yale Pathology archives. FFPE sections were macrodissected to enrich for tumor for quantitative assessment of CD274 (PD-L1), PDCD1LG2 (PD-L2), CD8A, and IRF1 by RT-qPCR multiplex mRNA panel. Multiplex panel transcript levels were correlated with clinical benefit (complete response [CR], partial response [PR], stable disease [SD]); disease outcomes (progression-free survival [PFS] and overall survival [OS]); and protein levels assessed by quantitative immunofluorescence (QIF). RESULTS: Transcript levels were significantly higher in responders (CR/PR/SD) than in nonresponders (PD) for CD8A (p = 0.0001) and IRF1 (p = 0.0019). PFS was strongly associated with high CD274 (p = 0.0046), PDCD1LG2 (p = 0.0039), CD8A (p = 0.0002), and IRF1 (p = 0.0030) mRNA expression. Similar associations were observed for OS with high CD274 (p = 0.0004), CD8A (p = 0.0030), and IRF1 (p = 0.0096) mRNA expression. Multivariate analyses revealed significant PFS and OS associations with immunotherapy panel markers independent of baseline variables. Exploratory analyses revealed a novel significant association of high combined CD274 & PDCD1LG2 (L1/L2) transcript expression with PFS (p < 0.0001) and OS (p = 0.0011), which remained significant at a multivariate level for both PFS (HR = 0.31) and OS (HR = 0.39). CONCLUSIONS: Individual immunotherapy panel markers CD274, PDCD1LG2, CD8A, IRF1 and a combined L1/L2 mRNA levels show promising associations with melanoma immunotherapy outcome. The turnaround time of the test (2 h) and easy standardization of the platform makes this an attractive approach for further study in the search for predictive biomarkers for ICI.

Multiplex quantitative analysis of cancer-associated fibroblasts and immunotherapy outcome in metastatic melanoma.

BACKGROUND: The cancer-associated fibroblast (CAF) population is implicated in immune dysregulation. Here, we test the hypothesis that CAF profiles in pretreatment tumor specimens are associated with response to immune checkpoint blockade of programmed cell death 1 (PD-1). METHODS: Pretreatment whole tissue sections from 117 melanoma patients treated with anti-PD-1 therapy were assessed by multiplex immunofluorescence to detect CAFs defined by Thy1, smooth muscle actin (SMA), and fibroblast activation protein (FAP). Two independent image analysis technologies were used: inForm software (PerkinElmer) to quantify cell counts, and AQUA™ to measure protein by quantitative immunofluorescence (QIF). CAF parameters by both methodologies were assessed for association with previously measured immune markers (CD3, CD4, CD8, CD20, CD68, PD-L1), best overall response, progression-free survival (PFS), and overall survival (OS). RESULTS: CAF parameters, by cell counts or QIF, did not correlate with immune markers nor with best overall response. However, both Thy1 and FAP cell counts had significant positive associations with PFS (all P < 0.05) and OS (all P < 0.003). SMA cell counts showed negative associations with outcome in anti-PD-1 treated patients. Similar associations were not observed in a control cohort of historical melanoma patients predating immunotherapy. Instead, FAP was a negative prognostic biomarker (P = 0.01) in the absence of immunotherapy. Multivariable analyses revealed significant PFS and OS associations with the CAF parameters were independent of baseline variables. CONCLUSIONS: Pretreatment CAF profiles are associated with melanoma immunotherapy outcome. Multiplex CAF analysis has potential as an objective companion diagnostic in immuno-oncology.

High-Plex Predictive Marker Discovery for Melanoma Immunotherapy-Treated Patients Using Digital Spatial Profiling.

PURPOSE: Protein expression in formalin-fixed, paraffin-embedded tissue is routinely measured by IHC or quantitative fluorescence (QIF) on a handful of markers on a single section. Digital spatial profiling (DSP) allows spatially informed simultaneous assessment of multiple biomarkers. Here we demonstrate the DSP technology using a 44-plex antibody cocktail to find protein expression that could potentially be used to predict response to immune therapy in melanoma.Experimental Design: The NanoString GeoMx DSP technology is compared with automated QIF (AQUA) for immune marker compartment-specific measurement and prognostic value in non-small cell lung cancer (NSCLC). Then we use this tool to search for novel predictive markers in a cohort of 60 patients with immunotherapy-treated melanoma on a tissue microarray using a 44-plex immune marker panel measured in three compartments (macrophage, leukocyte, and melanocyte) generating 132 quantitative variables. RESULTS: The spatially informed variable assessment by DSP validates by both regression and variable prognostication compared with QIF for stromal CD3, CD4, CD8, CD20, and PD-L1 in NSCLC. From the 132 variables, 11 and 15 immune markers were associated with prolonged progression-free survival (PFS) and overall survival (OS). Notably, we find PD-L1 expression in CD68-positive cells (macrophages) and not in tumor cells was a predictive marker for PFS, OS, and response. CONCLUSIONS: DSP technology shows high concordance with QIF and validates based on both regression and outcome assessment. Using the high-plex capacity, we found a series of expression patterns associated with outcome, including that the expression of PD-L1 in macrophages is associated with response.

Multiplex Quantitative Analysis of Tumor-Infiltrating Lymphocytes and Immunotherapy Outcome in Metastatic Melanoma.

PURPOSE: Because durable response to programmed cell death 1 (PD-1) inhibition is limited to a subset of melanoma patients, new predictive biomarkers could have clinical utility. We hypothesize that pretreatment tumor-infiltrating lymphocyte (TIL) profiles could be associated with response. EXPERIMENTAL DESIGN: Pretreatment whole tissue sections from 94 melanoma patients treated with anti-PD-1 therapy were profiled by multiplex immunofluorescence to perform TIL quantification (CD4, CD8, CD20) and assess TIL activation (CD3, GZMB, Ki67). Two independent image analysis technologies were used: inForm (PerkinElmer) to determine cell counts, and AQUA to measure protein by quantitative immunofluorescence (QIF). TIL parameters by both methodologies were correlated with objective response or disease control rate (ORR/DCR) by RECIST 1.1 and survival outcome. RESULTS: Pretreatment lymphocytic infiltration, by cell counts or QIF, was significantly higher in complete or partial response than in stable or progressive disease, particularly for CD8 (P < 0.0001). Neither TIL activation nor dormancy was associated with outcome. CD8 associations with progression-free survival (HR > 3) were independently significant in multivariable analyses and accounted for similar CD3 associations in anti-PD-1-treated patients. CD8 was not associated with melanoma prognosis in the absence of immunotherapy. Predictive performance of CD8 cell count (and QIF) had an area under the ROC curve above 0.75 (ORR/DCR), which reached 0.83 for ipilimumab plus nivolumab. CONCLUSIONS: Pretreatment lymphocytic infiltration is associated with anti-PD-1 response in metastatic melanoma. Quantitative TIL analysis has potential for application in digital precision immuno-oncology as an "indicative" companion diagnostic.

Nuclear IRF-1 expression as a mechanism to assess "Capability" to express PD-L1 and response to PD-1 therapy in metastatic melanoma.

BACKGROUND: Predictive biomarkers for antibodies against programmed death 1 (PD-1) remain a major unmet need in metastatic melanoma. Specifically, response is seen in tumors that do not express programmed death ligand 1 (PD-L1), highlighting the need for a more sensitive biomarker. We hypothesize that capacity to express PD-L1, as assessed by an assay for a PD-L1 transcription factor, interferon regulatory factor 1 (IRF-1), may better distinguish patients likely to benefit from anti-PD-1 immunotherapy. METHODS: Samples from 47 melanoma patients that received nivolumab, pembrolizumab, or combination ipilimumab/nivolumab at Yale New Haven Hospital from May 2013 to March 2016 were collected. Expression of IRF-1 and PD-L1 in archival pre-treatment formalin-fixed, paraffin-embedded tumor samples were assessed by the AQUA method of quantitative immunofluorescence. Objective radiographic response (ORR) and progression-free survival (PFS) were assessed using modified RECIST v1.1 criteria. RESULTS: Nuclear IRF-1 expression was higher in patients with partial or complete response (PR/CR) than in patients with stable or progressive disease (SD/PD) (p = 0.044). There was an insignificant trend toward higher PD-L1 expression in patients with PR/CR (p = 0.085). PFS was higher in the IRF-1-high group than the IRF-1-low group (p = 0.017), while PD-L1 expression had no effect on PFS (p = 0.83). In a subset analysis, a strong association with PFS is seen in patients treated with combination ipilimumab and nivolumab (p = 0.0051). CONCLUSIONS: As a measure of PD-L1 expression capability, IRF-1 expression may be a more valuable predictive biomarker for anti-PD-1 therapy than PD-L1 itself.

Neuropeptide Y is a physiological substrate of fibroblast activation protein: Enzyme kinetics in blood plasma and expression of Y2R and Y5R in human liver cirrhosis and hepatocellular carcinoma.

Fibroblast activation protein (FAP) is a dipeptidyl peptidase (DPP) and endopeptidase that is weakly expressed in normal adult human tissues but is greatly up-regulated in activated mesenchymal cells of tumors and chronically injured tissue. The identities and locations of target substrates of FAP are poorly defined, in contrast to the related protease DPP4. This study is the first to characterize the physiological substrate repertoire of the DPP activity of endogenous FAP present in plasma. Four substrates, neuropeptide Y (NPY), peptide YY, B-type natriuretic peptide and substance P, were analyzed by mass spectrometry following proteolysis in human or mouse plasma, and by in vivo localization in human liver tissues with cirrhosis and hepatocellular carcinoma (HCC). NPY was the most efficiently cleaved substrate of both human and mouse FAP, whereas all four peptides were efficiently cleaved by endogenous DPP4, indicating that the in vivo degradomes of FAP and DPP4 differ. All detectable DPP-specific proteolysis and C-terminal processing of these neuropeptides was attributable to FAP and DPP4, and plasma kallikrein, respectively, highlighting their combined physiological significance in the regulation of these neuropeptides. In cirrhotic liver and HCC, NPY and its receptor Y2R, but not Y5R, were increased in hepatocytes near the parenchymal-stromal interface where there is an opportunity to interact with FAP expressed on nearby activated mesenchymal cells in the stroma. These novel findings provide insights into the substrate specificity of FAP, which differs greatly from DPP4, and reveal a potential function for FAP in neuropeptide regulation within liver and cancer biology.

Icaritin attenuates cigarette smoke-mediated oxidative stress in human lung epithelial cells via activation of PI3K-AKT and Nrf2 signaling.

Icariin is the major active ingredient of Herba Epimedii. Icaritin (ICT) is a hydrolytic product of Icariin. In the present study, we investigated the protective role of ICT against cigarette smoke extract (CSE)-mediated oxidative stress in human lung epithelial A549 cells. As demonstrated by the WST-8 assay, exposure to CSE (2.5%, 5%, and 10%) reduced the cell viability of A549 cells (84%, 64% and 53%) in a dose-dependent manner and treatment with ICT 10 µM dramatically attenuated CSE induced cytotoxicity (73% and 64%). The MFI data suggested that CSE induced oxidative stress by generating ROS (230) and 10 µM ICT treatment attenuated CSE-induced ROS production (90). 10 µM ICT treatment resulted in significant AKT activation, Nrf2 nuclear translocation, increased GCL transcription and GSH levels, as compared with CSE exposure alone. However, ICT-mediated upregulation of GCL transcription in CSE-treated cells were lost in Nrf2 siRNA-transfected cells. Furthermore, inhibition of PI3K/AKT signaling by LY294002 partially prevents ICT-induced nuclear translocation of Nrf2 and GCL transcription. These findings suggest that ICT attenuates CS-induced oxidative stress by quenching ROS and also by upregulating GSH via a PI3K-AKT-Nrf2-dependent mechanism. Further studies are required to confirm that a similar protective effect of ICT occurs in the lungs in vivo in response to CS exposure.

Icariside II induces apoptosis via inhibition of the EGFR pathways in A431 human epidermoid carcinoma cells.

Improvements in skin cancer treatment are likely to derive from novel agents targeting the molecular pathways that promote tumor cell growth and survival. Icariside II (IS) is a metabolite of icariin, which is derived from Herba Epimedii. The aim of the present study was to evaluate the antitumor effects of IS and to determine the mechanism of apoptosis in A431 human epidermoid carcinoma cells. A431 cells were treated with IS (0­100 µM) for 24 or 48 h and cell viability was detected using the WST­8 assay. Apoptosis was measured by the Annexin­V/propidium iodide (PI) flow cytometric assay. Western blot analysis was used to measure the expression of cleaved caspase­9, cleaved poly ADP ribose polymerase (PARP), phosphorylated signal transducer and activator of transcription 3 (P­STAT3), phosphorylated extracellular signal-regulated kinase (P­ERK), and P­AKT. A431 cells were also pretreated with IS (0­100 µM) 2 h prior to treatment with epidermal growth factor (EGF; 100 ng/ml) for 10 min. Phosphorylated EGF receptor (P­EGFR), P­STAT3, P­ERK and P­AKT were detected by western blot analysis. The results demonstrated that IS inhibited the cell viability of the A431 cells in a dose­dependent manner. Pretreatment with LY294002 [a phosphatidylinositol 3-kinase (PI3K) inhibitor], EGF (an EGFR agonist) and AG1478 (an EGFR inhibitor) partially reversed IS­induced decreases in cell viability. Treatment with 50 µm IS resulted in an increased number of apoptotic cells mirrored by increases in cleaved caspase­9 and cleaved PARP. In addition, treatment with 50 µM IS significantly inhibited the activation of the Janus kinase (JAK)­STAT3 and mitogen­activated protein kinase (MAPK)­ERK pathways, but promoted the activation of the PI3K­AKT pathway. Furthermore, IS effectively inhibited the EGF-induced activation of the EGFR pathways. In conclusion, IS inhibited the cell viability of the A431 cells through the regulation of apoptosis. These effects were mediated, at least in part, by inhibiting the activation of the EGFR pathways.

Biotransformations of anticancer ruthenium(III) complexes: an X-ray absorption spectroscopic study.

An anti-metastatic drug, NAMI-A ((ImH)[Ru(III) Cl4 (Im)(dmso)]; Im=imidazole, dmso=S-bound dimethylsulfoxide), and a cytotoxic drug, KP1019 ((IndH)[Ru(III) Cl4 (Ind)2 ]; Ind=indazole), are two Ru-based anticancer drugs in human clinical trials. Their reactivities under biologically relevant conditions, including aqueous buffers, protein solutions or gels (e.g, albumin, transferrin and collagen), undiluted blood serum, cell-culture medium and human liver (HepG2) cancer cells, were studied by Ru K-edge X-ray absorption spectroscopy (XAS). These XAS data were fitted from linear combinations of spectra of well-characterised Ru compounds. The absence of XAS data from the parent drugs in these fits points to profound changes in the coordination environments of Ru(III) . The fits point to the presence of Ru(IV/III) clusters and binding of Ru(III) to S-donor groups, amine/imine and carboxylato groups of proteins. Cellular uptake of KP1019 is approximately 20-fold higher than that of NAMI-A under the same conditions, but it diminishes drastically after the decomposition of KP1019 in cell-culture media, which indicate that the parent complex is taken in by cells through passive diffusion.

Icariside II potentiates paclitaxel-induced apoptosis in human melanoma A375 cells by inhibiting TLR4 signaling pathway.

Combination therapy of paclitaxel (taxol) with natural anti-tumor agents that are capable of inhibiting survival signals may provide a rational molecular basis for novel chemotherapeutic strategies. Our previous study showed that icariside II (IS), derived from Herba Epimedii, inhibited the proliferation of melanoma cells in vivo and in vitro through the regulation of apoptosis. In this report, the combination effects of paclitaxel and IS were investigated in human melanoma A375 cells. As compared to the treatment with paclitaxel alone, the co-administration of IS and paclitaxel resulted in an enhancement of apoptosis as revealed by WST-8 and PI assays. Meanwhile, Western blot analysis showed that the co-administration of IS and paclitaxel resulted in increases of cleaved caspase-3, one of the terminal pro-apoptotic proteins. In melanoma, IL-8 and VEGF are positively correlated with disease stage and a high probability of progression. We demonstrated that treatment of A375 cells with IS in combination with paclitaxel resulted in a significant decrease in the production of IL-8 and VEGF, compared with paclitaxel alone. Recent studies suggest that TLR4-MyD88-ERK signaling may be a novel target for reversing chemoresistance to paclitaxel. Our flow cytometry and Western blot data showed that paclitaxel activated TLR4-MyD88-ERK signaling and that IS treatment could effectively inhibit this paclitaxel-induced activation of TLR4-MyD88-ERK signaling. In conclusion, this study demonstrated for the first time that IS could potentiate paclitaxel-induced apoptosis in melanoma cells. These effects were mediated, at least in part, by inhibiting the activation of the TLR4 signal transduction pathways. These findings support further preclinical evaluation of IS as a new potential anti-tumor agent.