Browsing by Author "Cheng, L."

Now showing 1 - 4 of 4
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    Differentiation of Prostate Cancer from Normal Tissue in Radical Prostatectomy Specimens by Desorption Electrospray Ionization and Touch Spray Ionization Mass Spectrometry.
    (RSC, 2015-02-21) Kerian, K. S.; Jarmusch, A. K.; Pirro, V.; Koch, M. O.; Masterson, T. A.; Cheng, L.; Cooks, R. G.; Department of Urology, IU School of Medicine
    Radical prostatectomy is a common treatment option for prostate cancer before it has spread beyond the prostate. Examination for surgical margins is performed post-operatively with positive margins reported to occur in 6.5 – 32% of cases. Rapid identification of cancerous tissue during surgery could improve surgical resection. Desorption electrospray ionization (DESI) is an ambient ionization method which produces mass spectra dominated by lipid signals directly from prostate tissue. With the use of multivariate statistics, these mass spectra can be used to differentiate cancerous and normal tissue. The method was applied to 100 samples from 12 human patients to create a training set of MS data. The quality of the discrimination achieved was evaluated using principal component analysis - linear discriminant analysis (PCA-LDA) and confirmed by histopathology. Cross validation (PCA-LDA) showed >95% accuracy. An even faster and more convenient method, touch spray (TS) mass spectrometry, not previously tested to differentiate diseased tissue, was also evaluated by building a similar MS data base characteristic of tumor and normal tissue. An independent set of 70 non-targeted biopsies from six patients was then used to record lipid profile data resulting in 110 data points for an evaluation dataset for TS-MS. This method gave prediction success rates measured against histopathology of 93%. These results suggest that DESI and TS could be useful in differentiating tumor and normal prostate tissue at surgical margins and that these methods should be evaluated intra-operatively.
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    Epigenetic Modulations and Lineage Plasticity in Advanced Prostate Cancer
    (Elsevier, 2020-04) Ge, R.; Wang, Z.; Montironi, R.; Jiang, Z.; Cheng, M.; Santoni, M.; Huang, K.; Massari, F.; Lu, X.; Cimadamore, A.; Lopez-Beltran, A.; Cheng, L.; Pathology and Laboratory Medicine, School of Medicine
    Prostate cancer is the most common cancer and second leading cause of cancer-related death in American men. Antiandrogen therapies are part of the standard of therapeutic regimen for advanced or metastatic prostate cancers; however, patients who receive these treatments are more likely to develop castration-resistant prostate cancer (CRPC) or neuroendocrine prostate cancer (NEPC). In the development of CRPC or NEPC, numerous genetic signaling pathways have been under preclinical investigations and in clinical trials. Accumulated evidence shows that DNA methylation, chromatin integrity, and accessibility for transcriptional regulation still play key roles in prostate cancer initiation and progression. Better understanding of how epigenetic change regulates the progression of prostate cancer and the interaction between epigenetic and genetic modulators driving NEPC may help develop a better risk stratification and more effective treatment regimens for prostate cancer patients.
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    Graphic Mining of High-Order Drug Interactions and Their Directional Effects on Myopathy Using Electronic Medical Records
    (Wiley, 2015-08) Du, L.; Chakraborty, A.; Chiang, C.-W.; Cheng, L.; Quinney, S.K.; Wu, H.; Zhang, P.; Li, L.; Shen, L.; Department of Medicine, IU School of Medicine
    We propose to study a novel pharmacovigilance problem for mining directional effects of high-order drug interactions on an adverse drug event (ADE). Our goal is to estimate each individual risk of adding a new drug to an existing drug combination. In this proof-of-concept study, we analyzed a large electronic medical records database and extracted myopathy-relevant case control drug co-occurrence data. We applied frequent itemset mining to discover frequent drug combinations within the extracted data, evaluated directional drug interactions related to these combinations, and identified directional drug interactions with large effect sizes. Furthermore, we developed a novel visualization method to organize multiple directional drug interaction effects depicted as a tree, to generate an intuitive graphical and visual representation of our data-mining results. This translational bioinformatics approach yields promising results, adds valuable and complementary information to the existing pharmacovigilance literature, and has the potential to impact clinical practice.
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    IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer
    (Wiley, 2017-03) Renbarger, J.; Radovich, M.; Vasudevaraja, V.; Kinnebrew, G.H.; Zhang, S.; Cheng, L.; Inavolu Mounika, S.; Department of Biohealth Informatics, School of Informatics and Computing
    Subnetwork analysis can explore complex patterns of entire molecular pathways for the purpose of drug target identification. In this article, the gene expression profiles of a cohort of patients with breast cancer are integrated with protein-protein interaction (PPI) networks using, simultaneously, both edge scoring and node scoring. A novel optimization algorithm, integrated optimization method to identify deregulated subnetwork (IODNE), is developed to search for the optimal dysregulated subnetwork of the merged gene and protein network. IODNE is applied to select subnetworks for Luminal-A breast cancer from The Cancer Genome Atlas (TCGA) data. A large fraction of cancer-related genes and the well-known clinical targets, ER1/PR and HER2, are found by IODNE. This validates the utility of IODNE. When applying IODNE to the triple-negative breast cancer (TNBC) subtype data, we identified subnetworks that contain genes such as ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.