@article {333, title = {Identification of a novel cancer microbiome signature for predicting prognosis of human breast cancer patients.}, journal = {Clin Transl Oncol}, volume = {24}, year = {2022}, month = {2022 Mar}, pages = {597-604}, abstract = {

BACKGROUND: Prognosis of breast cancer (BC) patients differs considerably and identifying reliable prognostic biomarker(s) is imperative. With evidence that the microbiome plays a critical role in the response to cancer therapies, we aimed to identify a cancer microbiome signature for predicting the prognosis of BC patients.

METHODS: The TCGA BC microbiome data (TCGA-BRCA-microbiome) was downloaded from cBioPortal. Univariate and multivariate Cox regression analyses were used to examine association of microbial abundance with overall survival (OS) and to identify a microbial signature for creating a prognostic scoring model. The performance of the scoring model was assessed by the area under the ROC curve (AUC). Nomograms using the microbial signature, clinical factors, and molecular subtypes were established to predict OS and progression-free survival (PFS).

RESULTS: Among 1406 genera, the abundances of 94 genera were significantly associated with BC patient OS in TCGA-BRCA-microbiome dataset. From that set we identified a 15-microbe prognostic signature and developed a 15-microbial abundance prognostic scoring (MAPS) model. Patients in low-risk group significantly prolong OS and PFS as compared to those in high-risk group. The time-dependent ROC curves with MAPS showed good predictive efficacy both in OS and PFS. Moreover, MAPS is an independent prognostic factor for OS and PFS over clinical factors and PAM50-based molecular subtypes and superior to the previously published 12-gene signature. The integration of MAPS into nomograms significantly improved prognosis prediction.

CONCLUSION: MAPS was successfully established to have independent prognostic value, and our study provides a new avenue for developing prognostic biomarkers by microbiome profiling.

}, keywords = {Breast Neoplasms, Female, Humans, Microbiota, Prognosis, Survival Rate}, issn = {1699-3055}, doi = {10.1007/s12094-021-02725-3}, author = {Mao, A W and Barck, H and Young, J and Paley, A and Mao, J -H and Chang, Hang} } @article {322, title = {Identification of a novel 15-gene expression signature predicting overall survival of human colorectal cancer.}, journal = {Clin Transl Med}, volume = {10}, year = {2020}, month = {2020 Dec}, pages = {e258}, issn = {2001-1326}, doi = {10.1002/ctm2.258}, author = {Jiang, Chengfei and Zhao, Yue and Yuan, Binbin and Chang, Hang and Hang, Bo and Snijders, Antoine M and Mao, Jian-Hua and Zou, Xiaoping and Wang, Pin} } @conference {314, title = {Integrative Analysis of Cellular Morphometric Context Reveals Clinically Relevant Signatures in Lower Grade Glioma}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - {MICCAI} 2016 - 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part {I}}, year = {2016}, doi = {10.1007/978-3-319-46720-7_9}, url = {http://dx.doi.org/10.1007/978-3-319-46720-7_9}, author = {Ju Han and Yunfu Wang and Weidong Cai and Alexander Borowsky and Bahram Parvin and Hang Chang} } @article {26, title = {Integrated profiling of three dimensional cell culture models and 3D microscopy}, journal = {Bioinformatics}, volume = {29}, year = {2013}, pages = {3087{\textendash}3093}, doi = {10.1093/bioinformatics/btt535}, url = {http://dx.doi.org/10.1093/bioinformatics/btt535}, author = {Cemal Cagatay Bilgin and Sun Kim and Elle Leung and Hang Chang and Bahram Parvin} } @article {20, title = {Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association}, journal = {{IEEE} Trans. Med. Imaging}, volume = {32}, year = {2013}, pages = {670{\textendash}682}, doi = {10.1109/TMI.2012.2231420}, url = {http://dx.doi.org/10.1109/TMI.2012.2231420}, author = {Hang Chang and Ju Han and Alexander Borowsky and Leandro A. Loss and Joe W. Gray and Paul T. Spellman and Bahram Parvin} } @article {83, title = {Identification of fluorescent compounds with non-specific binding property via high throughput live cell microscopy}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e28802}, abstract = {

INTRODUCTION: Compounds exhibiting low non-specific intracellular binding or non-stickiness are concomitant with rapid clearing and in high demand for live-cell imaging assays because they allow for intracellular receptor localization with a high signal/noise ratio. The non-stickiness property is particularly important for imaging intracellular receptors due to the equilibria involved.

METHOD: Three mammalian cell lines with diverse genetic backgrounds were used to screen a combinatorial fluorescence library via high throughput live cell microscopy for potential ligands with high in- and out-flux properties. The binding properties of ligands identified from the first screen were subsequently validated on plant root hair. A correlative analysis was then performed between each ligand and its corresponding physiochemical and structural properties.

RESULTS: The non-stickiness property of each ligand was quantified as a function of the temporal uptake and retention on a cell-by-cell basis. Our data shows that (i) mammalian systems can serve as a pre-screening tool for complex plant species that are not amenable to high-throughput imaging; (ii) retention and spatial localization of chemical compounds vary within and between each cell line; and (iii) the structural similarities of compounds can infer their non-specific binding properties.

CONCLUSION: We have validated a protocol for identifying chemical compounds with non-specific binding properties that is testable across diverse species. Further analysis reveals an overlap between the non-stickiness property and the structural similarity of compounds. The net result is a more robust screening assay for identifying desirable ligands that can be used to monitor intracellular localization. Several new applications of the screening protocol and results are also presented.

}, keywords = {Animals, Arabidopsis, Cell Line, Cell Survival, Combinatorial Chemistry Techniques, Fluorescent Dyes, Humans, Ligands, Mice, Microscopy, Small Molecule Libraries}, issn = {1932-6203}, doi = {10.1371/journal.pone.0028802}, author = {Nath, Sangeeta and Spencer, Virginia A and Ju Han and Hang Chang and Zhang, Kai and Fontenay, Gerald V and Anderson, Charles and Hyman, Joel M and Nilsen-Hamilton, Marit and Chang, Young-Tae and Parvin, Bahram} } @conference {47, title = {Integrated profiling of cell surface protein and nuclear marker for discriminant analysis}, booktitle = {Proceedings of the 2008 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14-17, 2008}, year = {2008}, doi = {10.1109/ISBI.2008.4541253}, url = {http://dx.doi.org/10.1109/ISBI.2008.4541253}, author = {Ju Han and Hang Chang and Kumari L. Andarawewa and Paul Yaswen and Mary Helen Barcellos-Hoff and Bahram Parvin} } @article {49, title = {Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events}, journal = {{IEEE} Trans. Image Processing}, volume = {16}, year = {2007}, pages = {615{\textendash}623}, doi = {10.1109/TIP.2007.891154}, url = {http://dx.doi.org/10.1109/TIP.2007.891154}, author = {Bahram Parvin and Qing Yang and Ju Han and Hang Chang and Bjorn Rydberg and Mary Helen Barcellos-Hoff} } @conference {54, title = {An Iterative Bayesian Approach for Digital Matting}, booktitle = {18th International Conference on Pattern Recognition {(ICPR} 2006), 20-24 August 2006, Hong Kong, China}, year = {2006}, doi = {10.1109/ICPR.2006.259}, url = {http://dx.doi.org/10.1109/ICPR.2006.259}, author = {Hang Chang and Qing Yang and Chunhong Pan} }