@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 {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 {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 {327, title = {Development and Validation of an Unsupervised Feature Learning System for Leukocyte Characterization and Classification: A Multi-Hospital Study}, volume = {129}, year = {2021}, month = {2021/06/01}, pages = {1837 - 1856}, abstract = {

The characterization and classification of white blood cells (WBC) are critical for the diagnosis of anemia, leukemia, and many other hematologic diseases. We developed WBC-Profiler, an unsupervised feature learning system for quantitative analysis of leukocytes. We demonstrate, through independent validation, that WBC-Profiler enables automatic extraction of complex and robust signatures from microscopic images without human-intervention and, thereafter, effective construction of interpretable leukocyte profiles, which decouples large scale complex leukocyte characterization from limitations in both human-based feature engineering/optimization and the end-to-end solutions provided by many modern deep neural networks. Further evaluation in a real-world clinical setting confirms that, compared with 23 clinicians from 8 hospitals (class-average-sensitivity, 0.798; class-average-specificity, 0.963; cell-average-timecost: 3.158\  s), WBC-Profiler performs with significantly improved accuracy and speed (class-average-sensitivity, 0.890; class-average-specificity, 0.980; cell-average-timecost: 0.375\  s). Our findings suggest that WBC-Profiler has the potential clinical implications.

}, isbn = {1573-1405}, url = {https://doi.org/10.1007/s11263-021-01449-9}, author = {Yan, Hong and Mao, Xuanyu and Yang, Xu and Xia, Yongquan and Wang, Chengbin and Wang, Junjun and Xia, Rui and Xu, Xuejing and Wang, Zhiqiang and Li, Zhiyang and Zhao, Xie and Li, Yan and Liu, Guoye and He, Li and Wang, Zhongyu and Wang, Zhiqiong and Li, Zhiqiang and Cai, Weidong and Shen, Han and Chang, Hang} } @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 {329, title = {Genetic background influences the effect of thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice.}, journal = {Sci Rep}, volume = {11}, year = {2021}, month = {2021 06 24}, pages = {13285}, abstract = {

Growing evidence indicates that thirdhand smoke (THS) exposure induces many adverse health effects. However, it is unclear how THS exposure affects behavior and how host genetic background modulates phenotypic changes. Here we used the Collaborative Cross (CC) mouse population-based model to assess behavioral alterations immediately after THS exposure from 4 to 9\ weeks of age. We first measured anxiety-like behavior in six strains using light/dark box combined with a custom multivariate mouse tracking system. We developed an anxiety risk scoring system based on anxiety-related traits and then evaluated the THS impact on them. THS exposure significantly decreased anxiety risk in CC019 (P = 0.002) and CC051 (P = 0.009), but increased anxiety risk in CC036 (P \< 0.001), while the other three strains did not show significant changes in anxiety-related traits. Such differences were driven by female mice for the six measures of anxiety-like behavior. Memory potential was measured in the same cohort of mice using the passive avoidance assay. Both THS-exposed male and female CC019 mice displayed significant memory loss compared to controls while no significant changes were found in the other five strains. This study provides strong evidence that THS exposure leads to strain-dependent changes in anxiety-like behavior and memory, suggesting that host genetic variations play a critical role in individual susceptibility to THS-induced effects.

}, keywords = {Animals, Anxiety, Collaborative Cross Mice, Female, Humans, Male, Memory, Risk Factors, Tobacco Smoke Pollution}, issn = {2045-2322}, doi = {10.1038/s41598-021-92702-1}, author = {He, Li and Wang, Pin and Schick, Suzyann F and Huang, Abel and Jacob, Peyton and Yang, Xu and Xia, Yankai and Snijders, Antoine M and Mao, Jian-Hua and Chang, Hang and Hang, Bo} } @article {324, title = {Gut microbiome partially mediates and coordinates the effects of genetics on anxiety-like behavior in Collaborative Cross mice.}, journal = {Sci Rep}, volume = {11}, year = {2021}, month = {2021 01 11}, pages = {270}, abstract = {

Growing evidence suggests that the gut microbiome (GM) plays a critical role in health and disease. However, the contribution of GM to psychiatric disorders, especially anxiety, remains unclear. We used the Collaborative Cross (CC) mouse population-based model to identify anxiety associated host genetic and GM factors. Anxiety-like behavior of 445 mice across 30 CC strains was measured using the light/dark box assay and documented by video. A custom tracking system was developed to quantify seven anxiety-related phenotypes based on video. Mice were assigned to a low or high anxiety group by consensus clustering using seven anxiety-related phenotypes. Genome-wide association analysis (GWAS) identified 141 genes (264 SNPs) significantly enriched for anxiety and depression related functions. In the same CC cohort, we measured GM composition and identified five families that differ between high and low anxiety mice. Anxiety level was predicted with 79\% accuracy and an AUC of 0.81. Mediation analyses revealed that the genetic contribution to anxiety was partially mediated by the GM.\ Our findings indicate that GM partially mediates and coordinates the effects of genetics on anxiety.

}, keywords = {Animals, Anxiety, Behavior, Animal, Collaborative Cross Mice, Gastrointestinal Microbiome, Mice}, issn = {2045-2322}, doi = {10.1038/s41598-020-79538-x}, author = {Jin, X and Zhang, Y and Celniker, S E and Xia, Y and Mao, J-H and Snijders, A M and Chang, Hang} } @article {325, title = {Host genetics and gut microbiota cooperatively contribute to azoxymethane-induced acute toxicity in Collaborative Cross mice.}, journal = {Arch Toxicol}, volume = {95}, year = {2021}, month = {2021 03}, pages = {949-958}, abstract = {

Azoxymethane (AOM) is a widely used carcinogen to study chemical-induced colorectal carcinogenesis and is an agent for studying fulminant hepatic failure. The inter-strain susceptibility to acute toxicity by AOM has been reported, but its association with host genetics or gut microbiota remains largely unexplored. Here a cohort of genetically diverse Collaborative Cross (CC) mice was used to assess the contribution of host genetics and the gut microbiome to AOM-induced acute toxicity. We observed variation in AOM-induced acute liver failure across CC strains. Quantitative trait loci (QTL) analysis revealed three chromosome regions significantly associated with AOM toxicity. Genes located within these QTL, including peroxisome proliferator-activated receptor alpha (Ppara), were enriched for enzyme activator and nucleoside-triphosphatase regulator activity. We further demonstrated that the protein level of PPARα in liver tissues from sensitive strains was remarkably lower compared to levels in resistant strains, consistent with protective role of PPAR family in liver injury. We discovered that the abundance levels of gut microbial families Anaeroplasmataceae, Ruminococcaceae, Lactobacillaceae, Akkermansiaceae and Clostridiaceae were significantly higher in the sensitive strains\ compared to the resistant strains. Using a random forest classifier method, we determined that the relative abundance levels of these microbial families predicted AOM toxicity with the area under the receiver-operating curve (AUC) of 0.75. Combining the three genetic loci and five microbial families increased the predictive accuracy of AOM toxicity (AUC of 0.99). Moreover, we found that Ruminococcaceae and Lactobacillaceae acted as mediators between host genetics and AOM toxicity. In conclusion, this study shows that host genetics and specific microbiome members play a critical role in AOM-induced acute toxicity, which provides a framework for analysis of the health effects from environmental toxicants.

}, keywords = {Animals, Azoxymethane, Carcinogens, Chemical and Drug Induced Liver Injury, Collaborative Cross Mice, Gastrointestinal Microbiome, Liver Failure, Acute, Male, Mice, Quantitative Trait Loci, Species Specificity}, issn = {1432-0738}, doi = {10.1007/s00204-021-02972-x}, author = {Zhong, Chenhan and He, Li and Lee, Sun-Young and Chang, Hang and Zhang, Yuqing and Threadgill, David W and Yuan, Ying and Zhou, Fuling and Celniker, Susan E and Xia, Yankai and Snijders, Antoine M and Mao, Jian-Hua} } @article {328, title = {Thirdhand cigarette smoke leads to age-dependent and persistent alterations in the cecal microbiome of mice.}, journal = {Microbiologyopen}, volume = {10}, year = {2021}, month = {2021 06}, pages = {e1198}, abstract = {

The gut microbiome composition is influenced by many factors including environmental exposures. Here, we investigated the effect of thirdhand cigarette smoke (THS) and exposure age on gut microbiome diversity. C57BL/6 mice were exposed to THS at human exposure relevant levels for three weeks during three different life stages: postnatal (0-3\ weeks of age), pubescent (4-7\ weeks of age), and adult (9-12\ weeks of age), respectively. Cecal microbiome profiles were assessed at 17\ weeks of age by 16S rRNA gene sequencing. We found that age at THS exposure strongly influenced the cecal microbiome composition. Although postnatal THS exposure significantly influenced the microbial composition, pubescent and adulthood exposures only had minor effects. The microbiome of postnatally THS-exposed mice significantly increased several degradation pathways that regulate glycolysis and pyruvate decarboxylation, and significantly decreased coenzyme A biosynthesis and pyrimidine deoxyribonucleoside salvage. Our results indicate that mouse postnatal development is particularly susceptible to persistent THS exposure effects on the gut microbiome.

}, keywords = {Age Factors, Animals, Bacteria, Cecum, Environmental Exposure, Female, Gastrointestinal Microbiome, Humans, Male, Mice, Mice, Inbred C57BL, Smoke, Tobacco, Tobacco Smoke Pollution}, issn = {2045-8827}, doi = {10.1002/mbo3.1198}, author = {He, Li and Zhou, Yan-Xia and Zhang, Yuqing and Hang, Bo and Chang, Hang and Schick, Suzaynn F and Celniker, Susan E and Xia, Yankai and Snijders, Antoine M 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 {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} } @article {307, title = {Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles.}, journal = {mSphere}, volume = {5}, year = {2020}, month = {2020 01 08}, abstract = {

The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended. The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile.

}, issn = {2379-5042}, doi = {10.1128/mSphere.00763-19}, author = {Liang, Yali and Dong, Tianyu and Chen, Minjian and He, Lianping and Wang, Tingzhang and Liu, Xingyin and Hang Chang and Mao, Jian-Hua and Hang, Bo and Snijders, Antoine M and Xia, Yankai} } @article {308, title = {Thirdhand smoke: Genotoxicity and carcinogenic potential.}, journal = {Chronic Dis Transl Med}, volume = {6}, year = {2020}, month = {2020 Mar}, pages = {27-34}, abstract = {

Thirdhand smoke (THS), the residual tobacco smoke remaining in the environment after tobacco has been smoked, represents a hidden and underestimated public health hazard. Evidence supports its widespread presence in indoor environments. Exposure to secondhand smoke (SHS), a precursor of THS, has been well documented as a risk factor for human cancers, especially lung cancer. However, the concept of THS as a distinct entity that poses health risks for small children has developed only recently and the associations of THS with cancer risk and other chronic diseases are poorly understood due to limited numbers of studies to date. In this perspective, we mainly summarize all published studies on the genotoxicity and carcinogenic potential of THS exposure. These studies begin to fill the knowledge gap in our understanding of cancer risk of THS. Accumulating data from existing and future studies will help reduce the tobacco-related cancer incidence through changes in lifestyle and tobacco control policies.

}, issn = {2589-0514}, doi = {10.1016/j.cdtm.2019.08.002}, author = {Hang, Bo and Wang, Pin and Zhao, Yue and Hang Chang and Mao, Jian-Hua 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 {302, title = {Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications.}, journal = {IEEE Trans Pattern Anal Mach Intell}, volume = {40}, year = {2018}, month = {2018 May}, pages = {1182-1194}, abstract = {

The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains.

}, issn = {1939-3539}, doi = {10.1109/TPAMI.2017.2656884}, author = {Hang Chang and Han, Ju and Zhong, Cheng and Snijders, Antoine M and Mao, Jian-Hua} } @conference {23, title = {Predictive sparse morphometric context for classification of histology sections}, booktitle = {12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015}, year = {2015}, doi = {10.1109/ISBI.2015.7164040}, url = {http://dx.doi.org/10.1109/ISBI.2015.7164040}, author = {Hang Chang and Paul T. Spellman and Bahram Parvin} } @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 {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 {204, title = {Hematein, a casein kinase II inhibitor, inhibits lung cancer tumor growth in a murine xenograft model.}, journal = {Int J Oncol}, volume = {43}, year = {2013}, month = {2013 Nov}, pages = {1517-22}, abstract = {

Casein kinase II (CK2) inhibitors suppress cancer cell growth. In this study, we examined the inhibitory effects of a novel CK2 inhibitor, hematein, on tumor growth in a murine xenograft model. We found that in lung cancer cells, hematein inhibited cancer cell growth, Akt/PKB Ser129 phosphorylation, the Wnt/TCF pathway and increased apoptosis. In a murine xenograft model of lung cancer, hematein inhibited tumor growth without significant toxicity to the mice tested. Molecular docking showed that hematein binds to CK2α in durable binding sites. Collectively, our results suggest that hematein is an allosteric inhibitor of protein kinase CK2 and has antitumor activity to lung cancer.

}, keywords = {Animals, Apoptosis, Blotting, Casein Kinase II, Cultured, Enzyme Inhibitors, Female, Hematoxylin, Humans, Immunoenzyme Techniques, Inbred BALB C, Lung Neoplasms, Mice, Phosphorylation, Proto-Oncogene Proteins c-akt, T Cell Transcription Factor 1, Tumor Cells, Western, Wnt Proteins, Xenograft Model Antitumor Assays}, issn = {1791-2423}, doi = {10.3892/ijo.2013.2087}, author = {Hung, Ming-Szu and Xu, Zhidong and Chen, Yu and Smith, Emmanuel and Jiang-Hua Mao and Hsieh, David and Lin, Yu-Ching and Yang, Cheng-Ta and Jablons, David M and You, Liang} } @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} } @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 {28, title = {Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology}, booktitle = {{IEEE} International Conference on Computer Vision, {ICCV} 2013, Sydney, Australia, December 1-8, 2013}, year = {2013}, doi = {10.1109/ICCV.2013.28}, url = {http://dx.doi.org/10.1109/ICCV.2013.28}, author = {Hang Chang and Yin Zhou and Paul T. 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} } @conference {33, title = {Detection of 3D filamentous networks from tomographic electron microscopy}, 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.2012.6235826}, url = {http://dx.doi.org/10.1109/ISBI.2012.6235826}, author = {Leandro A. Loss and Hang Chang and Purbasha Sarkar and Manfred Auer 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 {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} } @article {21, title = {Multireference Level Set for the Characterization of Nuclear Morphology in Glioblastoma Multiforme}, journal = {{IEEE} Trans. Biomed. Engineering}, volume = {59}, year = {2012}, pages = {3460{\textendash}3467}, doi = {10.1109/TBME.2012.2218107}, url = {http://dx.doi.org/10.1109/TBME.2012.2218107}, author = {Hang Chang and Ju Han and Paul T. Spellman 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 {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 {296, title = {Active mask segmentation of fluorescence microscope images.}, journal = {IEEE Trans Image Process}, volume = {18}, year = {2009}, month = {2009 Aug}, pages = {1817-29}, abstract = {

We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.

}, keywords = {Algorithms, Automated, Computer-Assisted, Cytological Techniques, Fluorescence, HeLa Cells, Humans, Image Processing, Microscopy, Pattern Recognition}, issn = {1057-7149}, doi = {10.1109/TIP.2009.2021081}, author = {Srinivasa, Gowri and Fickus, Matthew C and Yusong Guo and Linstedt, Adam D and Kovacevi{\'c}, Jelena} }