@article {335, title = {Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning.}, journal = {Neuro Oncol}, year = {2022}, month = {2022 Jun 18}, abstract = {

BACKGROUND: Lower grade gliomas (LGG) are heterogenous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.

METHODS: Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5- year overall survival (OS) of LGG patients. Tumor mutational burden (TMB), and immune cell infiltration between subtypes were analyzed using the Mann-Whitney test. The double-blinded validation for important immunotherapy-related biomarkers were executed using immunohistochemistry (IHC).

RESULTS: We developed a machine learning pipeline to extract CMBs from whole slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multi-center cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by immunohistochemical staining. In addition, the subtypes learned from LGG demonstrates translational impact on glioblastoma (GBM).

CONCLUSIONS: We developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune micro-environment, prognosis, and treatment response.

}, issn = {1523-5866}, doi = {10.1093/neuonc/noac154}, author = {Liu, Xiao-Ping and Jin, Xiaoqing and Ahmadian, Saman Seyed and Yang, Xu and Tian, Su-Fang and Cai, Yu-Xiang and Chawla, Kuldeep and Snijders, Antoine M and Xia, Yankai and van Diest, Paul J and Weiss, William A and Mao, Jian-Hua and Li, Zhi-Qiang and Vogel, Hannes and Chang, Hang} } @article {330, title = {Contribution of trace element exposure to gestational diabetes mellitus through disturbing the gut microbiome.}, journal = {Environ Int}, volume = {153}, year = {2021}, month = {2021 08}, pages = {106520}, abstract = {

BACKGROUND: A healthy gut microbiome is critical for glucose metabolism during pregnancy. In vivo studies indicate that trace element affects the composition and function of the gut microbiome and potentially leads to metabolic disorders but their relationships are largely unknown. We aimed to investigate whether the gut microbiome plays a role in the relationship between trace element exposure and gestational diabetes mellitus (GDM).

METHODS: In a prospective cohort study, serum levels of 22 trace elements and the fecal gut microbiome composition were assessed in 837 pregnant women in the second trimester between 22 and 24\ weeks of pregnancy prior to GDM diagnosis. Regression and mediation analysis were used to explore the link between element exposure, the gut microbiome, and GDM.

RESULTS: 128 pregnant women (15.3\%) were diagnosed with GDM. No individual trace elements were found significantly associated with GDM. In contrast, the composition of the gut microbiome was dramatically altered in women later diagnosed with GDM and characterized by lower alpha diversity and lower abundance of co-abundance groups (CAGs) composed of genera belonging to Ruminococcaceae, Coriobacteriales, and Lachnospiraceae. Rubidium (Rb) was positively associated with alpha diversity indices while mercury (Hg) and vanadium (V) showed negative associations. Elements including rubidium (Rb), thallium (Tl), arsenic (As), and antimony (Sb) were significantly correlated with GDM-related CAGs and mediation analysis revealed that Rb and Sb were inversely related to GDM risk by altering abundance levels of CAGs enriched for Lachnospiraceae, Coriobacteriales, and Ruminococcaceae.

CONCLUSION: Our study indicates that trace element exposure is associated with specific gut microbiome features that may contribute to GDM development, which could provide a new avenue for intervening in environmental exposure-related GDM.

}, keywords = {Diabetes, Gestational, Feces, Female, Gastrointestinal Microbiome, Humans, Pregnancy, Prospective Studies, Trace Elements}, issn = {1873-6750}, doi = {10.1016/j.envint.2021.106520}, author = {Zhang, Yuqing and Chen, Ting and Zhang, Yiyun and Hu, Qi and Wang, Xu and Chang, Hang and Mao, Jian-Hua and Snijders, Antoine M and Xia, Yankai} } @article {22, title = {Coupled segmentation of nuclear and membrane-bound macromolecules through voting and multiphase level set}, journal = {Pattern Recognition}, volume = {48}, year = {2015}, pages = {882{\textendash}893}, doi = {10.1016/j.patcog.2014.10.005}, url = {http://dx.doi.org/10.1016/j.patcog.2014.10.005}, author = {Hang Chang and Quan Wen and Bahram Parvin} } @conference {25, title = {Classification of Histology Sections via Multispectral Convolutional Sparse Coding}, booktitle = {2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014}, year = {2014}, doi = {10.1109/CVPR.2014.394}, url = {http://dx.doi.org/10.1109/CVPR.2014.394}, author = {Yin Zhou and Hang Chang and Kenneth E. Barner and Paul T. Spellman and Bahram Parvin} } @conference {30, title = {Characterization of Tissue Histopathology via Predictive Sparse Decomposition and Spatial Pyramid Matching}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - {MICCAI} 2013 - 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part {II}}, year = {2013}, doi = {10.1007/978-3-642-40763-5_12}, url = {http://dx.doi.org/10.1007/978-3-642-40763-5_12}, author = {Hang Chang and Nandita Nayak and Paul T. Spellman and Bahram Parvin} } @conference {27, title = {Classification of Tumor Histology via Morphometric Context}, booktitle = {2013 {IEEE} Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013}, year = {2013}, doi = {10.1109/CVPR.2013.286}, url = {http://dx.doi.org/10.1109/CVPR.2013.286}, author = {Hang Chang and Alexander Borowsky and Paul T. Spellman and Bahram Parvin} } @conference {29, title = {Classification of tumor histopathology via sparse feature learning}, booktitle = {{ISBI}}, year = {2013}, doi = {10.1109/ISBI.2013.6556499}, url = {http://dx.doi.org/10.1109/ISBI.2013.6556499}, author = {Nandita Nayak and Hang Chang and Alexander Borowsky and Paul T. Spellman and Bahram Parvin} } @article {82, title = {CD36 repression activates a multicellular stromal program shared by high mammographic density and tumor tissues}, journal = {Cancer Discov}, volume = {2}, year = {2012}, month = {2012 Sep}, pages = {826-39}, abstract = {

UNLABELLED: Although high mammographic density is considered one of the strongest risk factors for invasive breast cancer, the genes involved in modulating this clinical feature are unknown. Tissues of high mammographic density share key histologic features with stromal components within malignant lesions of tumor tissues, specifically low adipocyte and high extracellular matrix (ECM) content. We show that CD36, a transmembrane receptor that coordinately modulates multiple protumorigenic phenotypes, including adipocyte differentiation, angiogenesis, cell-ECM interactions, and immune signaling, is greatly repressed in multiple cell types of disease-free stroma associated with high mammographic density and tumor stroma. Using both in vitro and in vivo assays, we show that CD36 repression is necessary and sufficient to recapitulate the above-mentioned phenotypes observed in high mammographic density and tumor tissues. Consistent with a functional role for this coordinated program in tumorigenesis, we observe that clinical outcomes are strongly associated with CD36 expression.

SIGNIFICANCE: CD36 simultaneously controls adipocyte content and matrix accumulation and is coordinately repressed in multiple cell types within tumor and high mammographic density stroma, suggesting that activation of this stromal program is an early event in tumorigenesis. Levels of CD36 and extent of mammographic density are both modifiable factors that provide potential for intervention.

}, keywords = {Adipocytes, Animals, Antigens, Breast Neoplasms, CD36, Cell Differentiation, Female, Humans, Knockout, Mammography, Mice, Risk Factors, Signal Transduction, Stromal Cells}, issn = {2159-8290}, doi = {10.1158/2159-8290.CD-12-0107}, author = {DeFilippis, Rosa Anna and Hang Chang and Dumont, Nancy and Rabban, Joseph T and Chen, Yunn-Yi and Fontenay, Gerald V and Berman, Hal K and Gauthier, Mona L and Zhao, Jianxin and Hu, Donglei and Marx, James J and Tjoe, Judy A and Ziv, Elad and Febbraio, Maria and Kerlikowske, Karla and Parvin, Bahram and Tlsty, Thea D} } @conference {36, title = {Comparison of sparse coding and kernel methods for histopathological classification of gliobastoma multiforme}, booktitle = {Proceedings of the 8th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2011, March 30 - April 2, 2011, Chicago, Illinois, {USA}}, year = {2011}, doi = {10.1109/ISBI.2011.5872505}, url = {http://dx.doi.org/10.1109/ISBI.2011.5872505}, author = {Ju Han and Hang Chang and Leandro A. Loss and Kai Zhang and Frederick L. Baehner and Joe W. Gray and Paul T. Spellman and Bahram Parvin} }