@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 {326, title = {A new platform for ultra-high dose rate radiobiological research using the BELLA PW laser proton beamline.}, journal = {Sci Rep}, volume = {12}, year = {2022}, month = {2022 01 27}, pages = {1484}, abstract = {

Radiotherapy is the current standard of care for more than 50\% of all cancer patients. Improvements in radiotherapy (RT) technology have increased tumor targeting and normal tissue sparing. Radiations at ultra-high dose rates required for FLASH-RT effects have sparked interest in potentially providing additional differential therapeutic benefits. We present a new experimental platform that is the first one to deliver petawatt laser-driven proton pulses of 2\ MeV energy at 0.2\ Hz repetition rate by means of a compact, tunable active plasma lens beamline to biological samples. Cell monolayers grown over a 10\ mm diameter field were exposed to clinically relevant proton doses ranging from 7 to 35\ Gy at ultra-high instantaneous dose rates of 10\ Gy/s. Dose-dependent cell survival measurements of human normal and tumor cells exposed to LD protons showed significantly higher cell survival of normal-cells compared to tumor-cells for total doses of 7\ Gy and higher, which was not observed to the same extent for X-ray reference irradiations at clinical dose rates. These findings provide preliminary evidence that compact LD proton sources enable a new and promising platform for investigating the physical, chemical and biological mechanisms underlying the FLASH effect.

}, keywords = {Cell Line, Humans, Lasers, Monte Carlo Method, Neoplasms, Proton Therapy, Radiation Oncology, Radiobiology, Radiometry, Radiotherapy Dosage, Synchrotrons}, issn = {2045-2322}, doi = {10.1038/s41598-022-05181-3}, author = {Bin, Jianhui and Obst-Huebl, Lieselotte and Mao, Jian-Hua and Nakamura, Kei and Geulig, Laura D and Chang, Hang and Ji, Qing and He, Li and De Chant, Jared and Kober, Zachary and Gonsalves, Anthony J and Bulanov, Stepan and Celniker, Susan E and Schroeder, Carl B and Geddes, Cameron G R and Esarey, Eric and Simmons, Blake A and Schenkel, Thomas and Blakely, Eleanor A and Steinke, Sven and Snijders, Antoine M} } @article {323, title = {From Mouse to Human: Cellular Morphometric Subtype Learned From Mouse Mammary Tumors Provides Prognostic Value in Human Breast Cancer.}, journal = {Front Oncol}, volume = {11}, year = {2021}, month = {2021}, pages = {819565}, abstract = {

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H\&E) micrographs of orthotopic -null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan-Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H\&E preparations can provide unbiased biological stratification to inform patient care.

}, issn = {2234-943X}, doi = {10.3389/fonc.2021.819565}, author = {Chang, Hang and Yang, Xu and Moore, Jade and Liu, Xiao-Ping and Jen, Kuang-Yu and Snijders, Antoine M and Ma, Lin and Chou, William and Corchado-Cobos, Roberto and Garc{\'\i}a-Sancha, Natalia and Mendiburu-Eli{\c c}abe, Marina and Perez-Losada, Jesus and Barcellos-Hoff, Mary Helen and Mao, Jian-Hua} } @article {309, title = {Genetic and metabolic links between the murine microbiome and memory.}, journal = {Microbiome}, volume = {8}, year = {2020}, month = {2020 04 17}, pages = {53}, abstract = {

BACKGROUND: Recent evidence has linked the gut microbiome to host behavior via the gut-brain axis [1-3]; however, the underlying mechanisms remain unexplored. Here, we determined the links between host genetics, the gut microbiome and memory using the genetically defined Collaborative Cross (CC) mouse cohort, complemented with microbiome and metabolomic analyses in conventional and germ-free (GF) mice.

RESULTS: A genome-wide association analysis (GWAS) identified 715 of 76,080 single-nucleotide polymorphisms (SNPs) that were significantly associated with short-term memory using the passive avoidance model. The identified SNPs were enriched in genes known to be involved in learning and memory functions. By 16S rRNA gene sequencing of the gut microbial community in the same CC cohort, we identified specific microorganisms that were significantly correlated with longer latencies in our retention test, including a positive correlation with Lactobacillus. Inoculation of GF mice with individual species of Lactobacillus (L. reuteri F275, L. plantarum BDGP2 or L. brevis BDGP6) resulted in significantly improved memory compared to uninoculated or E. coli DH10B inoculated controls. Untargeted metabolomics analysis revealed significantly higher levels of several metabolites, including lactate, in the stools of Lactobacillus-colonized mice, when compared to GF control mice. Moreover, we demonstrate that dietary lactate treatment alone boosted memory in conventional mice. Mechanistically, we show that both inoculation with Lactobacillus or lactate treatment significantly increased the levels of the neurotransmitter, gamma-aminobutyric acid (GABA), in the hippocampus of the mice.

CONCLUSION: Together, this study provides new evidence for a link between Lactobacillus and memory and our results open possible new avenues for treating memory impairment disorders using specific gut microbial inoculants and/or metabolites. Video Abstract.

}, issn = {2049-2618}, doi = {10.1186/s40168-020-00817-w}, author = {Mao, Jian-Hua and Kim, Young-Mo and Zhou, Yan-Xia and Hu, Dehong and Zhong, Chenhan and Hang Chang and Brislawn, Colin J and Fansler, Sarah and Langley, Sasha and Wang, Yunshan and Peisl, B Y Loulou and Celniker, Susan E and Threadgill, David W and Wilmes, Paul and Orr, Galya and Metz, Thomas O and Jansson, Janet K and Snijders, Antoine M} } @article {300, title = {High-Dimensional Phenotyping Identifies Age-Emergent Cells in Human Mammary Epithelia.}, journal = {Cell Rep}, volume = {23}, year = {2018}, month = {2018 Apr 24}, pages = {1205-1219}, abstract = {

Aging is associated with tissue-level changes in\ cellular composition that are correlated with increased susceptibility to disease. Aging human mammary tissue shows skewed progenitor cell potency, resulting in diminished tumor-suppressive cell types and the accumulation of defective epithelial progenitors. Quantitative characterization of these age-emergent human cell subpopulations is lacking, impeding our understanding of the relationship between age and cancer susceptibility. We conducted single-cell resolution proteomic phenotyping of healthy breast epithelia from 57 women, aged 16-91 years, using mass cytometry. Remarkable heterogeneity was quantified within the two mammary epithelial lineages. Population partitioning identified a subset of aberrant basal-like luminal cells that accumulate with age and originate from age-altered progenitors. Quantification of age-emergent phenotypes\ enabled robust classification of breast tissues by age in healthy women. This high-resolution mapping highlighted specific epithelial subpopulations that\ change with age in a manner consistent with increased susceptibility to breast cancer.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2018.03.114}, author = {Pelissier Vatter, Fanny A and Schapiro, Denis and Hang Chang and Borowsky, Alexander D and Lee, Jonathan K and Parvin, Bahram and Stampfer, Martha R and LaBarge, Mark A and Bodenmiller, Bernd and Lorens, James B} } @article {303, title = {When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.}, journal = {Med Image Anal}, volume = {35}, year = {2017}, month = {2017 01}, pages = {530-543}, abstract = {

Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).

}, keywords = {Color, Histological Techniques, Humans, Pattern Recognition, Automated, Unsupervised Machine Learning}, issn = {1361-8423}, doi = {10.1016/j.media.2016.08.010}, author = {Zhong, Cheng and Han, Ju and Borowsky, Alexander and Parvin, Bahram and Wang, Yunfu and Hang Chang} } @article {79, title = {BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models}, journal = {PLoS One}, volume = {11}, year = {2016}, month = {2016}, pages = {e0148379}, abstract = {

BioSig3D is a computational platform for high-content screening of three-dimensional (3D) cell culture models that are imaged in full 3D volume. It provides an end-to-end solution for designing high content screening assays, based on colony organization that is derived from segmentation of nuclei in each colony. BioSig3D also enables visualization of raw and processed 3D volumetric data for quality control, and integrates advanced bioinformatics analysis. The system consists of multiple computational and annotation modules that are coupled together with a strong use of controlled vocabularies to reduce ambiguities between different users. It is a web-based system that allows users to: design an experiment by defining experimental variables, upload a large set of volumetric images into the system, analyze and visualize the dataset, and either display computed indices as a heatmap, or phenotypic subtypes for heterogeneity analysis, or download computed indices for statistical analysis or integrative biology. BioSig3D has been used to profile baseline colony formations with two experiments: (i) morphogenesis of a panel of human mammary epithelial cell lines (HMEC), and (ii) heterogeneity in colony formation using an immortalized non-transformed cell line. These experiments reveal intrinsic growth properties of well-characterized cell lines that are routinely used for biological studies. BioSig3D is being released with seed datasets and video-based documentation.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0148379}, author = {Bilgin, Cemal Cagatay and Fontenay, Gerald and Cheng, Qingsu and Hang Chang and Ju Han and Parvin, Bahram} } @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 {241, title = {Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.}, journal = {Sci Rep}, volume = {6}, year = {2016}, month = {2016}, pages = {28987}, abstract = {

The effects of the stiffness of the microenvironment on the molecular response of 3D colony organization, at the maximum level of mammographic density (MD), are investigated. Phenotypic profiling reveals that 3D colony formation is heterogeneous and increased stiffness of the microenvironment, within the range of the MD, correlates with the increased frequency of aberrant 3D colony formation. Further integrative analysis of the genome-wide transcriptome and phenotypic profiling hypothesizes overexpression of ERBB2 in the premalignant MCF10A cell lines at a stiffness value that corresponds to the collagen component at high mammographic density. Subsequently, ERBB2 overexpression has been validated in the same cell line. Similar experiments with a more genetically stable cell line of 184A1 also revealed an increased frequency of aberrant colony formation with the increased stiffness; however, 184A1 did not demonstrate overexpression of ERBB2 at the same stiffness value of the high MD. These results suggest that stiffness exacerbates premalignant cell line of MCF10A.

}, issn = {2045-2322}, doi = {10.1038/srep28987}, author = {Cheng, Qingsu and Bilgin, Cemal Cagatay and Fonteney, Gerald and Hang Chang and Henderson, Matthew and Han, Ju and Parvin, Bahram} } @article {242, title = {When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.}, journal = {Med Image Anal}, volume = {35}, year = {2016}, month = {2016 Sep 9}, pages = {530-543}, abstract = {

Classification of histology sections in large cohorts, in terms of distinct regions of microanatomy (e.g., stromal) and histopathology (e.g., tumor, necrosis), enables the quantification of tumor composition, and the construction of predictive models of genomics and clinical outcome. To tackle the large technical variations and biological heterogeneities, which are intrinsic in large cohorts, emerging systems utilize either prior knowledge from pathologists or unsupervised feature learning for invariant representation of the underlying properties in the data. However, to a large degree, the architecture for tissue histology classification remains unexplored and requires urgent systematical investigation. This paper is the first attempt to provide insights into three fundamental questions in tissue histology classification: I. Is unsupervised feature learning preferable to human engineered features? II. Does cellular saliency help? III. Does the sparse feature encoder contribute to recognition? We show that (a) in I, both Cellular Morphometric Feature and features from unsupervised feature learning lead to superior performance when compared to SIFT and [Color, Texture]; (b) in II, cellular saliency incorporation impairs the performance for systems built upon pixel-/patch-level features; and (c) in III, the effect of the sparse feature encoder is correlated with the robustness of features, and the performance can be consistently improved by the multi-stage extension of systems built upon both Cellular Morphmetric Feature and features from unsupervised feature learning. These insights are validated with two cohorts of Glioblastoma Multiforme (GBM) and Kidney Clear Cell Carcinoma (KIRC).

}, issn = {1361-8423}, doi = {10.1016/j.media.2016.08.010}, author = {Zhong, Cheng and Han, Ju and Borowsky, Alexander and Parvin, Bahram and Wang, Yunfu and Hang Chang} } @conference {24, title = {Nuclei segmentation via sparsity constrained convolutional regression}, booktitle = {12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015}, year = {2015}, doi = {10.1109/ISBI.2015.7164109}, url = {http://dx.doi.org/10.1109/ISBI.2015.7164109}, author = {Yin Zhou and Hang Chang and Kenneth E. Barner and Bahram Parvin} } @inbook {288, title = {Quantification of the Dynamics of DNA Repair to Ionizing Radiation via Colocalization of 53BP1 and {\^I}{\textthreesuperior}H2AX}, booktitle = {Video Bioinformatics: From Live Imaging to Knowledge}, year = {2015}, publisher = {Springer}, organization = {Springer}, isbn = {978-3-319-23723-7}, doi = {10.1007/978-3-319-23724-4}, author = {Groesser, Torsten and Fontenay, Gerald V. and Ju Han and Hang Chang and Janice Pluth and Bahram Parvin}, editor = {Bir Bhanu and Prue Talbot} } @article {19, title = {Stacked Predictive Sparse Decomposition for Classification of Histology Sections}, journal = {International Journal of Computer Vision}, volume = {113}, year = {2015}, pages = {3{\textendash}18}, doi = {10.1007/s11263-014-0790-9}, url = {http://dx.doi.org/10.1007/s11263-014-0790-9}, author = {Hang Chang and Yin Zhou and Alexander Borowsky and Kenneth E. Barner and Paul T. Spellman 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 {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 {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 {194, title = {Mass spectrometry imaging for in situ kinetic histochemistry.}, journal = {Sci Rep}, volume = {3}, year = {2013}, month = {2013}, pages = {1656}, abstract = {

Tissues are composed of diverse cell subpopulations each with distinct metabolic characteristics that influence overall behavior. Unfortunately, traditional histopathology imaging techniques are {\textquoteright}blind{\textquoteright} to the spatially ordered metabolic dynamics within tissue. While mass spectrometry imaging enables spatial mapping of molecular composition, resulting images are only a static snapshot in time of molecules involved in highly dynamic processes; kinetic information of flux through metabolic pathways is lacking. To address this limitation, we developed kinetic mass spectrometry imaging (kMSI), a novel technique integrating soft desorption/ionization mass spectrometry with clinically accepted in vivo metabolic labeling of tissue with deuterium to generate images of kinetic information of biological processes. Applied to a tumor, kMSI revealed heterogeneous spatial distributions of newly synthesized versus pre-existing lipids, with altered lipid synthesis patterns distinguishing region-specific intratumor subpopulations. Images also enabled identification and correlation of metabolic activity of specific lipids found in tumor regions of varying grade.

}, keywords = {Cell Line, Equipment Design, Equipment Failure Analysis, Experimental, Humans, Lipids, Mass Spectrometry, Molecular Imaging, Neoplasms, Tumor}, issn = {2045-2322}, doi = {10.1038/srep01656}, author = {Louie, Katherine B and Bowen, Benjamin P and McAlhany, Stephanie and Huang, Yurong and Price, John C and Jiang-Hua Mao and Hellerstein, Marc and Northen, Trent R} } @inbook {74, title = {Molecular Correlates of Morphometric Subtypes in Glioblastoma Multiforme}, booktitle = {Computational Systems Biology: From Molecular Mechanisms to Disease: Second Edition}, year = {2013}, pages = {423{\textendash}454}, publisher = {Elsevier Inc.}, organization = {Elsevier Inc.}, keywords = {Glioblastoma multiforme, Histology classification, Molecular pathology, Morphometric subtyping, Nuclear segmentation, Tumor heterogeneity, Tumor histopathology}, isbn = {9780124059269}, doi = {10.1016/B978-0-12-405926-9.00018-6}, author = {Hang Chang and Gerald Fontenay and Cemal Cagatay Bilgin and Alexander Borowsky and Paul Spellman and Bahram Parvin} } @conference {31, title = {Automatic segmentation and quantification of filamentous structures in electron tomography}, booktitle = {{ACM} International Conference on Bioinformatics, Computational Biology and Biomedicine, BCB{\textquoteright} 12, Orlando, FL, {USA} - October 08 - 10, 2012}, year = {2012}, doi = {10.1145/2382936.2382958}, url = {http://doi.acm.org/10.1145/2382936.2382958}, author = {Leandro A. Loss and George Bebis and Hang Chang and Manfred Auer and Purbasha Sarkar and Bahram Parvin} } @conference {32, title = {Batch-invariant nuclear segmentation in whole mount histology sections}, booktitle = {9th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2012, May 2-5, 2012, Barcelona, Spain, Proceedings}, year = {2012}, doi = {10.1109/ISBI.2012.6235683}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235683}, author = {Hang Chang and Leandro A. Loss and Paul T. Spellman and Alexander Borowsky 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 {57, title = {Learning invariant features of tumor signature with independent subspace analysis}, booktitle = {9th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2012, May 2-5, 2012, Barcelona, Spain, Proceedings}, year = {2012}, doi = {10.1109/ISBI.2012.6235544}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235544}, author = {Quoc V. Le and Ju Han and Joe W. Gray and Paul T. Spellman and Alexander Borowsky and Bahram Parvin} } @conference {34, title = {Molecular bases of morphometric composition in Glioblastoma multiforme}, booktitle = {9th {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, {ISBI} 2012, May 2-5, 2012, Barcelona, Spain, Proceedings}, year = {2012}, doi = {10.1109/ISBI.2012.6235889}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235889}, author = {Ju Han and Hang Chang and Gerald Fontenay and Paul T. Spellman and Alexander Borowsky and Bahram Parvin} } @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} } @article {35, title = {Morphometic analysis of {TCGA} glioblastoma multiforme}, journal = {{BMC} Bioinformatics}, volume = {12}, year = {2011}, pages = {484}, doi = {10.1186/1471-2105-12-484}, url = {http://dx.doi.org/10.1186/1471-2105-12-484}, author = {Hang Chang and Gerald Fontenay and Ju Han and Ge Cong and Frederick L. Baehner and Joe W. Gray and Paul T. Spellman and Bahram Parvin} } @article {85, title = {Multiscale iterative voting for differential analysis of stress response for 2D and 3D cell culture models}, journal = {J Microsc}, volume = {241}, year = {2011}, month = {2011 Mar}, pages = {315-26}, abstract = {

Three-dimensional (2D) cell culture models have emerged as the basis for improved cell systems biology. However, there is a gap in robust computational techniques for segmentation of these model systems that are imaged through confocal or deconvolution microscopy. The main issues are the volume of data, overlapping subcellular compartments and variation in scale or size of subcompartments of interest, which lead to ambiguities for quantitative analysis on a cell-by-cell basis. We address these ambiguities through a series of geometric operations that constrain the problem through iterative voting and decomposition strategies. The main contributions of this paper are to (i) extend the previously developed 2D radial voting to an efficient 3D implementation, (ii) demonstrate application of iterative radial voting at multiple subcellular and molecular scales, and (iii) investigate application of the proposed technology to two endpoints between 2D and 3D cell culture models. These endpoints correspond to kinetics of DNA damage repair as measured by phosphorylation of γH2AX, and the loss of the membrane-bound E-cadherin protein as a result of ionizing radiation. Preliminary results indicate little difference in the kinetics of the DNA damage protein between 2D and 3D cell culture models; however, differences between membrane-bound E-cadherin are more pronounced.

}, keywords = {Cell Culture Techniques, Cell Line, Computer-Assisted, Epithelial Cells, Humans, Image Processing, Microscopy, Organ Culture Techniques, Physiological, Stress}, issn = {1365-2818}, doi = {10.1111/j.1365-2818.2010.03442.x}, author = {Ju Han and Hang Chang and Yang, Q and Fontenay, G and Groesser, Torsten and Barcellos-Hoff, M Helen and Parvin, B} } @article {84, title = {Persistence of γ-H2AX and 53BP1 foci in proliferating and non-proliferating human mammary epithelial cells after exposure to γ-rays or iron ions}, journal = {Int J Radiat Biol}, volume = {87}, year = {2011}, month = {2011 Jul}, pages = {696-710}, abstract = {

PURPOSE: To investigate γ-H2AX (phosphorylated histone H2AX) and 53BP1 (tumour protein 53 binding protein No. 1) foci formation and removal in proliferating and non-proliferating human mammary epithelial cells (HMEC) after exposure to sparsely and densely ionising radiation under different cell culture conditions.

MATERIAL AND METHODS: HMEC cells were grown either as monolayers (2D) or in extracellular matrix to allow the formation of acinar structures in vitro (3D). Foci numbers were quantified by image analysis at various time points after exposure.

RESULTS: Our results reveal that in non-proliferating cells under 2D and 3D cell culture conditions, iron-ion induced γ-H2AX foci were still present at 72 h after exposure, although 53BP1 foci returned to control levels at 48 h. In contrast in proliferating HMEC, both γ-H2AX and 53BP1 foci decreased to control levels during the 24-48 h time interval after irradiation under 2D conditions. Foci numbers decreased faster after γ-ray irradiation and returned to control levels by 12 h regardless of marker, cell proliferation status, and cell culture condition.

CONCLUSIONS: The disappearance of radiation-induced γ-H2AX and 53BP1 foci in HMEC has different dynamics that depend on radiation quality and proliferation status. Notably, the general patterns do not depend on the cell culture condition (2D versus 3D). We speculate that the persistent γ-H2AX foci in iron-ion irradiated non-proliferating cells could be due to limited availability of double-strand break (DSB) repair pathways in G0/G1-phase, or that repair of complex DSB requires replication or chromatin remodelling.

}, keywords = {Cell Line, Cell Proliferation, Gamma Rays, Heavy Ions, Histones, Human, Humans, Iron, Mammary Glands, Tumor Suppressor Protein p53}, issn = {1362-3095}, doi = {10.3109/09553002.2010.549535}, author = {Groesser, Torsten and Hang Chang and Gerald Fontenay and Chen, James and Costes, Sylvain V and Helen Barcellos-Hoff, Mary and Parvin, Bahram and Rydberg, Bjorn} } @article {179, title = {Progressive genomic instability in the FVB/Kras(LA2) mouse model of lung cancer.}, journal = {Mol Cancer Res}, volume = {9}, year = {2011}, month = {2011 Oct}, pages = {1339-45}, abstract = {

Alterations in DNA copy number contribute to the development and progression of cancers and are common in epithelial tumors. We have used array Comparative Genomic Hybridization (aCGH) to visualize DNA copy number alterations across the genomes of lung tumors in the Kras(LA2) model of lung cancer. Copy number gain involving the Kras locus, as focal amplification or whole chromosome gain, is the most common alteration in these tumors and with a prevalence that increased significantly with increasing tumor size. Furthermore, Kras amplification was the only major genomic event among the smallest lung tumors, suggesting that this alteration occurs early during the development of mutant Kras-driven lung cancers. Recurring gains and deletions of other chromosomes occur progressively more frequently among larger tumors. These results are in contrast to a previous aCGH analysis of lung tumors from Kras(LA2) mice on a mixed genetic background, in which relatively few DNA copy number alterations were observed regardless of tumor size. Our model features the Kras(LA2) allele on the inbred FVB/N mouse strain, and in this genetic background, there is a highly statistically significant increase in level of genomic instability with increasing tumor size. These data suggest that recurring DNA copy alterations are important for tumor progression in the Kras(LA2) model of lung cancer and that the requirement for these alterations may be dependent on the genetic background of the mouse strain.

}, keywords = {Animal, Animals, Cell Line, Chromosome Aberrations, Disease Models, Disease Progression, DNA, DNA Copy Number Variations, Gene Dosage, Gene Expression Regulation, Genes, Genomic Instability, Inbred C57BL, Lung Neoplasms, Mice, Neoplasm, Neoplastic, Proto-Oncogene Proteins p21(ras), ras, Tumor}, issn = {1557-3125}, doi = {10.1158/1541-7786.MCR-11-0219}, author = {To, Minh D and Quigley, David A and Jiang-Hua Mao and Del Rosario, Reyno and Hsu, Jeff and Hodgson, Graeme and Jacks, Tyler and Balmain, Allan} } @article {177, title = {Radiation acts on the microenvironment to affect breast carcinogenesis by distinct mechanisms that decrease cancer latency and affect tumor type.}, journal = {Cancer Cell}, volume = {19}, year = {2011}, month = {2011 May 17}, pages = {640-51}, abstract = {

Tissue microenvironment is an important determinant of carcinogenesis. We demonstrate that ionizing radiation, a known carcinogen, affects cancer frequency and characteristics by acting on the microenvironment. Using a mammary chimera model in which an irradiated host is transplanted with oncogenic Trp53 null epithelium, we show accelerated development of aggressive tumors whose molecular signatures were distinct from tumors arising in nonirradiated hosts. Molecular and genetic approaches show that TGFβ mediated tumor acceleration. Tumor molecular signatures implicated TGFβ, and genetically reducing TGFβ abrogated the effect on latency. Surprisingly, tumors from irradiated hosts were predominantly estrogen receptor negative. This effect was TGFβ independent and linked to mammary stem cell activity. Thus, the irradiated microenvironment affects latency and clinically relevant features of cancer through distinct and unexpected mechanisms.

}, keywords = {Animal, Animals, Breast Neoplasms, Cell Transformation, Dose-Response Relationship, Epithelial Cells, Estrogen, Female, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Inbred BALB C, Knockout, Mammary Glands, Mice, Neoplasms, Neoplastic, Radiation, Radiation Chimera, Radiation-Induced, Reaction Time, Receptors, Time Factors, Transforming Growth Factor beta1, Tumor Burden, Tumor Microenvironment, Tumor Suppressor Protein p53, Whole-Body Irradiation}, issn = {1878-3686}, doi = {10.1016/j.ccr.2011.03.011}, author = {Nguyen, David H and Oketch-Rabah, Hellen A and Illa-Bochaca, Irineu and Geyer, Felipe C and Reis-Filho, Jorge S and Jiang-Hua Mao and Ravani, Shraddha A and Zavadil, Jiri and Borowsky, Alexander D and Jerry, D Joseph and Dunphy, Karen A and Seo, Jae Hong and Haslam, Sandra and Medina, Daniel and Barcellos-Hoff, Mary Helen} } @article {86, title = {Linking changes in epithelial morphogenesis to cancer mutations using computational modeling}, journal = {PLoS Comput Biol}, volume = {6}, year = {2010}, month = {2010}, abstract = {

Most tumors arise from epithelial tissues, such as mammary glands and lobules, and their initiation is associated with the disruption of a finely defined epithelial architecture. Progression from intraductal to invasive tumors is related to genetic mutations that occur at a subcellular level but manifest themselves as functional and morphological changes at the cellular and tissue scales, respectively. Elevated proliferation and loss of epithelial polarization are the two most noticeable changes in cell phenotypes during this process. As a result, many three-dimensional cultures of tumorigenic clones show highly aberrant morphologies when compared to regular epithelial monolayers enclosing the hollow lumen (acini). In order to shed light on phenotypic changes associated with tumor cells, we applied the bio-mechanical IBCell model of normal epithelial morphogenesis quantitatively matched to data acquired from the non-tumorigenic human mammary cell line, MCF10A. We then used a high-throughput simulation study to reveal how modifications in model parameters influence changes in the simulated architecture. Three parameters have been considered in our study, which define cell sensitivity to proliferative, apoptotic and cell-ECM adhesive cues. By mapping experimental morphologies of four MCF10A-derived cell lines carrying different oncogenic mutations onto the model parameter space, we identified changes in cellular processes potentially underlying structural modifications of these mutants. As a case study, we focused on MCF10A cells expressing an oncogenic mutant HER2-YVMA to quantitatively assess changes in cell doubling time, cell apoptotic rate, and cell sensitivity to ECM accumulation when compared to the parental non-tumorigenic cell line. By mapping in vitro mutant morphologies onto in silico ones we have generated a means of linking the morphological and molecular scales via computational modeling. Thus, IBCell in combination with 3D acini cultures can form a computational/experimental platform for suggesting the relationship between the histopathology of neoplastic lesions and their underlying molecular defects.

}, keywords = {Apoptosis, Biological, Breast Neoplasms, Cell Proliferation, Computer Simulation, Epithelium, ErbB-2, Extracellular Matrix, Female, Human, Humans, Mammary Glands, Models, Morphogenesis, Mutation, Receptor}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000900}, author = {Rejniak, Katarzyna A and Wang, Shizhen E and Bryce, Nicole S and Hang Chang and Parvin, Bahram and Jourquin, Jerome and Estrada, Lourdes and Gray, Joe W and Arteaga, Carlos L and Weaver, Alissa M and Quaranta, Vito and Anderson, Alexander R A} } @article {87, title = {Molecular predictors of 3D morphogenesis by breast cancer cell lines in 3D culture}, journal = {PLoS Comput Biol}, volume = {6}, year = {2010}, month = {2010 Feb}, pages = {e1000684}, abstract = {

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associations with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPARgamma has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPARgamma has been validated through two supporting biological assays.

}, keywords = {Biological, Biomarkers, Breast Neoplasms, Cell Culture Techniques, Cell Line, Computer-Assisted, ErbB-2, Female, Gene Expression Profiling, Histocytochemistry, Humans, Image Processing, Models, Phenotype, PPAR gamma, Receptor, Reproducibility of Results, Tumor}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000684}, author = {Han, Ju and Hang Chang and Giricz, Orsi and Lee, Genee Y and Baehner, Frederick L and Gray, Joe W and Bissell, Mina J and Kenny, Paraic A and Parvin, Bahram} } @article {88, title = {Multidimensional profiling of cell surface proteins and nuclear markers}, journal = {IEEE/ACM Trans Comput Biol Bioinform}, volume = {7}, year = {2010}, month = {2010 Jan-Mar}, pages = {80-90}, abstract = {

Cell membrane proteins play an important role in tissue architecture and cell-cell communication. We hypothesize that segmentation and multidimensional characterization of the distribution of cell membrane proteins, on a cell-by-cell basis, enable improved classification of treatment groups and identify important characteristics that can otherwise be hidden. We have developed a series of computational steps to 1) delineate cell membrane protein signals and associate them with a specific nucleus; 2) compute a coupled representation of the multiplexed DNA content with membrane proteins; 3) rank computed features associated with such a multidimensional representation; 4) visualize selected features for comparative evaluation through heatmaps; and 5) discriminate between treatment groups in an optimal fashion. The novelty of our method is in the segmentation of the membrane signal and the multidimensional representation of phenotypic signature on a cell-by-cell basis. To test the utility of this method, the proposed computational steps were applied to images of cells that have been irradiated with different radiation qualities in the presence and absence of other small molecules. These samples are labeled for their DNA content and E-cadherin membrane proteins. We demonstrate that multidimensional representations of cell-by-cell phenotypes improve predictive and visualization capabilities among different treatment groups, and identify hidden variables.

}, keywords = {Biological, Cadherins, Cell Membrane, Cell Nucleus, Computer Simulation, DNA, Gene Expression Profiling, Membrane Proteins, Models}, issn = {1557-9964}, doi = {10.1109/TCBB.2008.134}, author = {Ju Han and Hang Chang and Andarawewa, Kumari and Yaswen, Paul and Barcellos-Hoff, Mary Helen 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 {53, title = {Perceptual Grouping of Membrane Signals in Cell-Based Assays}, booktitle = {Proceedings of the 2007 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Washington, DC, USA, April 12-16, 2007}, year = {2007}, doi = {10.1109/ISBI.2007.356906}, url = {http://dx.doi.org/10.1109/ISBI.2007.356906}, author = {Hang Chang and Kumari L. Andarawewa and Ju Han and Mary Helen Barcellos-Hoff and Bahram Parvin} } @conference {52, title = {Segmentation of Mammosphere Structures from Volumetric Data}, booktitle = {Proceedings of the 2007 {IEEE} International Symposium on Biomedical Imaging: From Nano to Macro, Washington, DC, USA, April 12-16, 2007}, year = {2007}, doi = {10.1109/ISBI.2007.356904}, url = {http://dx.doi.org/10.1109/ISBI.2007.356904}, author = {Ju Han and Hang Chang and Qing Yang and Mary Helen Barcellos-Hoff and Bahram Parvin} } @conference {55, title = {3D Segmentation of Mammospheres for Localization Studies}, booktitle = {Advances in Visual Computing, Second International Symposium, {ISVC} 2006, Lake Tahoe, NV, USA, November 6-8, 2006 Proceedings, Part {I}}, year = {2006}, doi = {10.1007/11919476_52}, url = {http://dx.doi.org/10.1007/11919476_52}, author = {Ju Han and Hang Chang and Qing Yang and Mary Helen Barcellos-Hoff and Bahram Parvin} }