Miriam Brandl

Computational Prediction Models for Early Detection of Risk of Cardiovascular Events Using Mass Spectrometry Data

Early prediction of the risk of cardiovascular events in patients with chest pain is critical in order to provide appropriate medical care for those with positive diagnosis. This paper introduces a computational methodology for predicting such events in the context of robust computerized classification using mass spectrometry data of blood samples collected from patients in emergency departments. We applied the computational theories of statistical and geostatistical linear prediction models to extract effective features of the mass spectra and a simple decision logic to classify disease and control samples for the purpose of early detection. While the statistical and geostatistical techniques provide better results than those obtained from some other methods, the geostatistical approach yields superior results in terms of sensitivity and specificity in various designs of the data set for validation, training, and testing. The proposed computational strategies are very promising for predicting major adverse cardiac events within six months.

Dextran-Catechin: An anticancer chemically-modified natural compound targeting copper that attenuates neuroblastoma growth

Neuroblastoma is frequently diagnosed at advanced stage disease and treatment includes high dose chemotherapy and surgery. Despite the use of aggressive therapy survival rates are poor and children that survive their disease experience long term side effects from their treatment, highlighting the need for effective and less toxic therapies. Catechin is a natural polyphenol with anti-cancer properties and limited side effects, however its mechanism of action is unknown. Here we report that Dextran-Catechin, a conjugated form of catechin that increases serum stability, is preferentially and markedly active against neuroblastoma cells having high levels of intracellular copper, without affecting non-malignant cells. Copper transporter 1 (CTR1) is the main transporter of copper in mammalian cells and it is upregulated in neuroblastoma. Functional studies showed that depletion of CTR1 expression reduced intracellular copper levels and led to a decrease in neuroblastoma cell sensitivity to Dextran-Catechin, implicating copper in the activity of this compound. Mechanistically, Dextran-Catechin was found to react with copper, inducing oxidative stress and decreasing glutathione levels, an intracellular antioxidant and regulator of copper homeostasis. In vivo, Dextran-Catechin significantly attenuated tumour growth in human xenograft and syngeneic models of neuroblastoma. Thus, Dextran-Catechin targets copper, inhibits tumour growth, and may be valuable in the treatment of aggressive neuroblastoma and other cancers dependent on copper for their growth.

Novel functional cisplatin carrier based on carbon nanotubes–quercetin nanohybrid induces synergistic anticancer activity against neuroblastoma in vitro

The synergistic effects of a three-functional hybrid material composed of methacrylic acid (MAA), quercetin (Q) and carbon nanotubes (CNT), with cisplatin (CP) was evaluated in human neuroblastoma cells. A three-functional hybrid material suitable for CP combination therapy was synthesized through the free radical-induced reaction between methacrylic acid, quercetin and carbon nanotubes. Two-functional materials were prepared and fully characterised by coupling CNT and MAA, as well as MAA and Q. A Folin–Ciocalteu assay was used to assess the functionalisation degree expressed as mg of Q per gram of materials and we found to be 2.33 for CNT_PMAA_Q and 2.01 for PMAA_Q. The anticancer activity of CNT_PMAA_Q was shown in human neuroblastoma cells using the Alamar blue cell proliferation assay. Successively, cells were treated with a combination of CP and the nanocomposite showing strong synergistic anticancer effects in neuroblastoma cells. These studies showed that nanoparticle formulations incorporating quercetin and carbon nanotubes are good candidates for CP synergistic treatment against neuroblastoma.

Signal analysis for genome-wide maps of histone modifications measured by ChIP-seq

MOTIVATION Chromatin structure, including post-translational modifications of histones, regulates gene expression, alternative splicing and cell identity. ChIP-seq is an increasingly used assay to study chromatin function. However, tools for downstream bioinformatics analysis are limited and are only based on the evaluation of signal intensities. We reasoned that new methods taking into account other signal characteristics such as peak shape, location and frequencies might reveal new insights into chromatin function, particularly in situation where differences in read intensities are subtle. RESULTS We introduced an analysis pipeline, based on linear predictive coding (LPC), which allows the capture and comparison of ChIP-seq histone profiles. First, we show that the modeled signal profiles distinguish differentially expressed genes with comparable accuracy to signal intensities. The method was robust against parameter variations and performed well up to a signal-to-noise ratio of 0.55. Additionally, we show that LPC profiles of activating and repressive histone marks cluster into distinct groups and can be used to predict their function. AVAILABILITY AND IMPLEMENTATION http://www.cancerresearch.unsw.edu.au/crcweb.nsf/page/LPCHP A Matlab implementation along with usage instructions and an example input file are available from: http://www.cancerresearch.unsw.edu.au/crcweb.nsf/page/LPCHP.

Fuzzy declustering-based vector quantization

Vector quantization is a useful approach for multi-dimensional data compression and pattern classification. One of the most popular techniques for vector quantization design is the LBG (Linde, Buzo, Gray) algorithm. To address the problem of producing poor estimate of vector centroids which are subjected to biased data in vector quantization; we propose a fuzzy declustering strategy for the LBG algorithm. The proposed technique calculates appropriate declustering weights to adjust the global data distribution. Using the result of fuzzy declustering-based vector quantization design, we incorporate the notion of fuzzy partition entropy into the distortion measures that can be useful for classification of spectral features. Experimental results obtained from simulated and real data sets demonstrate the effective performance of the proposed approach.

Computational analysis of image-based drug profiling predicts synergistic drug combinations: Applications in triple-negative breast cancer

An imaged‐based profiling and analysis system was developed to predict clinically effective synergistic drug combinations that could accelerate the identification of effective multi‐drug therapies for the treatment of triple‐negative breast cancer and other challenging malignancies. The identification of effective drug combinations for the treatment of triple‐negative breast cancer (TNBC) was achieved by integrating high‐content screening, computational analysis, and experimental biology. The approach was based on altered cellular phenotypes induced by 55 FDA‐approved drugs and biologically active compounds, acquired using fluorescence microscopy and retained in multivariate compound profiles. Dissimilarities between compound profiles guided the identification of 5 combinations, which were assessed for qualitative interaction on TNBC cell growth. The combination of the microtubule‐targeting drug vinblastine with KSP/Eg5 motor protein inhibitors monastrol or ispinesib showed potent synergism in 3 independent TNBC cell lines, which was not substantiated in normal fibroblasts. The synergistic interaction was mediated by an increase in mitotic arrest with cells demonstrating typical ispinesib‐induced monopolar mitotic spindles, which translated into enhanced apoptosis induction. The antitumour activity of the combination vinblastine/ispinesib was confirmed in an orthotopic mouse model of TNBC. Compared to single drug treatment, combination treatment significantly reduced tumour growth without causing increased toxicity. Image‐based profiling and analysis led to the rapid discovery of a drug combination effective against TNBC in vitro and in vivo, and has the potential to lead to the development of new therapeutic options in other hard‐to‐treat cancers.

Classification of Mass Spectrometry Based Protein Markers by Kriging Error Matching

Discovery of biomarkers using serum proteomic patterns is currently one of the most attractive interdisciplinary research areas in computational life science. This new proteomic approach has the clinical significance in being able to detect disease in its early stages and to develop new drugs for disease treatment and prevention. This paper introduces a novel pattern classification strategy for identifying protein biomarkers using mass spectrometry data of blood samples collected from patients in emergency department monitored for major adverse cardiac events within six months. We applied the theory of geostatistics and a kriging error matching scheme for identifying protein biomarkers that are able to provide an average classification rate superior to other current methods. The proposed strategy is very promising as a general computational bioinformatic model for proteomic-pattern based biomarker discovery.

Synthesis of isoflavene-thiosemicarbazone hybrids and evaluation of their anti-tumor activity

Phenoxodiol is an isoflavene with potent anti-tumor activity. In this study, a series of novel mono- and di-substituted phenoxodiol-thiosemicarbazone hybrids were synthesized via the condensation reaction between phenoxodiol with thiosemicarbazides. The in vitro anti-proliferative activities of the hybrids were evaluated against the neuroblastoma SKN-BE(2)C, the triple negative breast cancer MDA-MB-231, and the glioblastoma U87 cancer cell lines. The mono-substituted hybrids exhibited potent anti-proliferative activity against all three cancer cell lines, while the di-substituted hybrids were less active. Selected mono-substituted hybrids were further investigated for their cytotoxicity against normal MRC-5 human lung fibroblast cells, which identified two hybrids with superior selectivity for cancer cells over normal cells as compared to phenoxodiol. This suggests that mono-substituted phenoxodiol-thiosemicarbazone hybrids have promising potential for further development as anti-cancer agents.

Application of Fuzzy c‐Means and Joint‐Feature‐Clustering to Detect Redundancies of Image‐Features in Drug Combinations Studies of Breast Cancer

The high dimensionality of image‐based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c‐means clustering, cluster validity indices and the notation of a joint‐feature‐clustering matrix to find redundancies of image‐features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data‐derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

Effects of Rapamycin on Breast Cancer Cell Migration through the Cross-Talk of MAPK Pathway.

Phase I/II clinical studies with rapamycin analogs in breast and other cancers have demonstrated favorable responses. However, little is known on the effects of the mTOR inhibitor on breast cancer cell metastasis, which is a major cause of morbidity and death. We developed a highly sensitive 3-dimensional (3D) proliferation/invasion assay using quantitative bioluminescence (BL) imaging and applied this assay to evaluate the effects of rapamycin on the triple negative breast cancer cell line MDA-MB231. Without cytotoxicity of rapamycin on this cell line, rapamycin at 10nM inhibited the cell migration/invasion, but not at 1nM and 100nM, which was confirmed by the time-lapse single cell tracking analysis. The quantification of cytoskeleton changes showed most potent effects of 10nM rapamycin on the MDA-MB231 cells, with the formation and rearrangement of specialized cell membrane structures and actin fiber implicated in cell motility. Then, the Panorama Cell Signaling Antibody Microarray, enabling the global comparative analysis of cell signal proteins simultaneously, was exploited to analyze the effects of rapamycin on the cellular signaling network of the MDA-MB231 breast cancer cell line. 100nM rapamycin activated the MAPK pathway obviously, through the attenuated negative feedback of activated S6K1 to PI3K-Raf, which increased the expressions of activated Jun N-terminus kinase (JNK), Erk1/2, MEK-1, Raf-pS621, and MAPK-activated protein kinase 2 (MAPKAPK2) in the cells exposed to 100nM rapamycin. MEK inhibitor U0126 or PD98059 could restore the anti-migration effects of 100nM rapamycin on the MDA-MB231 cells. Furthermore, the combination of MEK inhibitors and rapamycin performed synergism on inhibiting the cell proliferation and migration/invasion. Accordingly, rapamycin at a certain dose suppresses MDA-MB231 cell migration/invasion, and the co-targeting of mTOR and MAPK pathways enhances the inhibition on cell proliferation and migration/invasion, underscoring the potential therapeutic utility of rapamycin, and rapamycin combining with MAPK inhibitors in triple negative breast cancer progression, and the results highlight the cross-talk homeostasis of mTOR and MAPK pathways in cancer treatment. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 5080.