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Browsing by Author "Cao, Sha"
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Item Aberrant epigenetic and transcriptional events associated with breast cancer risk(BMC, 2022-02-09) Marino, Natascia; German, Rana; Podicheti, Ram; Rusch, Douglas B.; Rockey, Pam; Huang, Jie; Sandusky, George E.; Temm, Constance J.; Althouse, Sandra; Nephew, Kenneth P.; Nakshatri, Harikrishna; Liu, Jun; Vode, Ashley; Cao, Sha; Storniolo, Anna Maria V.; Medicine, School of MedicineBackground: Genome-wide association studies have identified several breast cancer susceptibility loci. However, biomarkers for risk assessment are still missing. Here, we investigated cancer-related molecular changes detected in tissues from women at high risk for breast cancer prior to disease manifestation. Disease-free breast tissue cores donated by healthy women (N = 146, median age = 39 years) were processed for both methylome (MethylCap) and transcriptome (Illumina's HiSeq4000) sequencing. Analysis of tissue microarray and primary breast epithelial cells was used to confirm gene expression dysregulation. Results: Transcriptomic analysis identified 69 differentially expressed genes between women at high and those at average risk of breast cancer (Tyrer-Cuzick model) at FDR < 0.05 and fold change ≥ 2. Majority of the identified genes were involved in DNA damage checkpoint, cell cycle, and cell adhesion. Two genes, FAM83A and NEK2, were overexpressed in tissue sections (FDR < 0.01) and primary epithelial cells (p < 0.05) from high-risk breasts. Moreover, 1698 DNA methylation changes were identified in high-risk breast tissues (FDR < 0.05), partially overlapped with cancer-related signatures, and correlated with transcriptional changes (p < 0.05, r ≤ 0.5). Finally, among the participants, 35 women donated breast biopsies at two time points, and age-related molecular alterations enhanced in high-risk subjects were identified. Conclusions: Normal breast tissue from women at high risk of breast cancer bears molecular aberrations that may contribute to breast cancer susceptibility. This study is the first molecular characterization of the true normal breast tissues, and provides an opportunity to investigate molecular markers of breast cancer risk, which may lead to new preventive approaches.Item ADAM8 is expressed widely in breast cancer and predicts poor outcome in hormone receptor positive, HER-2 negative patients(BMC, 2023-08-11) Pianetti, Stefania; Miller, Kathy D.; Chen, Hannah H.; Althouse, Sandra; Cao, Sha; Michael, Steven J.; Sonenshein, Gail E.; Mineva, Nora D.; Biostatistics and Health Data Science, School of MedicineBackground: Breast malignancies are the predominant cancer-related cause of death in women. New methods of diagnosis, prognosis and treatment are necessary. Previously, we identified the breast cancer cell surface protein ADAM8 as a marker of poor survival, and a driver of Triple-Negative Breast Cancer (TNBC) growth and spread. Immunohistochemistry (IHC) with a research-only anti-ADAM8 antibody revealed 34.0% of TNBCs (17/50) expressed ADAM8. To identify those patients who could benefit from future ADAM8-based interventions, new clinical tests are needed. Here, we report on the preclinical development of a highly specific IHC assay for detection of ADAM8-positive breast tumors. Methods: Formalin-fixed paraffin-embedded sections of ADAM8-positive breast cell lines and patient-derived xenograft tumors were used in IHC to identify a lead antibody, appropriate staining conditions and controls. Patient breast cancer samples (n = 490) were used to validate the assay. Cox proportional hazards models assessed association between survival and ADAM8 expression. Results: ADAM8 staining conditions were optimized, a lead anti-human ADAM8 monoclonal IHC antibody (ADP2) identified, and a breast staining/scoring control cell line microarray (CCM) generated expressing a range of ADAM8 levels. Assay specificity, reproducibility, and appropriateness of the CCM for scoring tumor samples were demonstrated. Consistent with earlier findings, 36.1% (22/61) of patient TNBCs expressed ADAM8. Overall, 33.9% (166/490) of the breast cancer population was ADAM8-positive, including Hormone Receptor (HR) and Human Epidermal Growth Factor Receptor-2 (HER2) positive cancers, which were tested for the first time. For the most prevalent HR-positive/HER2-negative subtype, high ADAM8 expression identified patients at risk of poor survival. Conclusions: Our studies show ADAM8 is widely expressed in breast cancer and provide support for both a diagnostic and prognostic value of the ADP2 IHC assay. As ADAM8 has been implicated in multiple solid malignancies, continued development of this assay may have broad impact on cancer management.Item Asparagine starvation suppresses histone demethylation through iron depletion(Elsevier, 2023-03-16) Jiang, Jie; Srivastava, Sankalp; Liu, Sheng; Seim, Gretchen; Claude, Rodney; Zhong, Minghua; Cao, Sha; Davé, Utpal; Kapur, Reuben; Mosley, Amber L.; Zhang, Chi; Wan, Jun; Fan, Jing; Zhang, Ji; Pediatrics, School of MedicineIntracellular α-ketoglutarate is an indispensable substrate for the Jumonji family of histone demethylases (JHDMs) mediating most of the histone demethylation reactions. Since α-ketoglutarate is an intermediate of the tricarboxylic acid cycle and a product of transamination, its availability is governed by the metabolism of several amino acids. Here, we show that asparagine starvation suppresses global histone demethylation. This process is neither due to the change of expression of histone-modifying enzymes nor due to the change of intracellular levels of α-ketoglutarate. Rather, asparagine starvation reduces the intracellular pool of labile iron, a key co-factor for the JHDMs to function. Mechanistically, asparagine starvation suppresses the expression of the transferrin receptor to limit iron uptake. Furthermore, iron supplementation to the culture medium restores histone demethylation and alters gene expression to accelerate cell death upon asparagine depletion. These results suggest that suppressing iron-dependent histone demethylation is part of the cellular adaptive response to asparagine starvation.Item A Bayesian adaptive marker‐stratified design for molecularly targeted agents with customized hierarchical modeling(Wiley, 2019-07) Zang, Yong; Guo, Beibei; Han, Yan; Cao, Sha; Zhang, Chi; Biostatistics, School of Public HealthIt is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker‐stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta‐binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a “customized” equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs.Item A Bayesian Adaptive Phase I/II Clinical Trial Design with Late-onset Competing Risk Outcomes(Wiley, 2021-09) Zhang, Yifei; Cao, Sha; Zhang, Chi; Jin, Ick Hoon; Zang, Yong; Biostatistics, School of Public HealthEarly-phase dose-finding clinical trials are often subject to the issue of late-onset outcomes. In phase I/II clinical trials, the issue becomes more intractable because toxicity and efficacy can be competing risk outcomes such that the occurrence of the first outcome will terminate the other one. In this paper, we propose a novel Bayesian adaptive phase I/II clinical trial design to address the issue of late-onset competing risk outcomes. We use the continuation-ratio model to characterize the trinomial response outcomes and the cause-specific hazard rate method to model the competing-risk survival outcomes. We treat the late-onset outcomes as missing data and develop a Bayesian data augmentation method to impute the missing data from the observations. We also propose an adaptive dose-finding algorithm to allocate patients and identify the optimal biological dose during the trial. Simulation studies show that the proposed design yields desirable operating characteristics.Item Bias Aware Probabilistic Boolean Matrix Factorization(PMLR, 2022-08) Wan, Changlin; Dang, Pengtao; Zhao, Tong; Zang, Yong; Zhang, Chi; Cao, Sha; Biostatistics, School of Public HealthBoolean matrix factorization (BMF) is a combinatorial problem arising from a wide range of applications including recommendation system, collaborative filtering, and dimensionality reduction. Currently, the noise model of existing BMF methods is often assumed to be homoscedastic; however, in real world data scenarios, the deviations of observed data from their true values are almost surely diverse due to stochastic noises, making each data point not equally suitable for fitting a model. In this case, it is not ideal to treat all data points as equally distributed. Motivated by such observations, we introduce a probabilistic BMF model that recognizes the object- and feature-wise bias distribution respectively, called bias aware BMF (BABF). To the best of our knowledge, BABF is the first approach for Boolean decomposition with consideration of the feature-wise and object-wise bias in binary data. We conducted experiments on datasets with different levels of background noise, bias level, and sizes of the signal patterns, to test the effectiveness of our method in various scenarios. We demonstrated that our model outperforms the state-of-the-art factorization methods in both accuracy and efficiency in recovering the original datasets, and the inferred bias level is highly significantly correlated with true existing bias in both simulated and real world datasets.Item Celltyper: A Single-Cell Sequencing Marker Gene Tool Suite(2023-05) Paisley, Brianna Meadow; Liu, Yunlong; Yan, Jingwen; Cao, Sha; Wang, Juexin; Carfagna, MarkSingle-cell RNA-sequencing (scRNA-seq) has enabled researchers to study interindividual cellular heterogeneity, to explore disease impact on cellular composition of tissue, and to identify novel cell subtypes. However, a major challenge in scRNA-seq analysis is to identify the cell type of individual cells. Accurate cell type identification is crucial for any scRNA-seq analysis to be valid as incorrect cell type assignment will reduce statistical robustness and may lead to incorrect biological conclusions. Therefore, accurate and comprehensive cell type assignment is necessary for reliable biological insights into scRNA-seq datasets. With over 200 distinct cell types in humans alone, the concept of cell identity is large. Even within the same cell type there exists heterogeneity due to cell cycle phase, cell state, cell subtypes, cell health and the tissue microenvironment. This makes cell type classification a complicated biological problem requiring bioinformatics. One approach to classify cell type identity is using marker genes. Marker genes are genes specific for one or a few cell types. When coupled with bioinformatic methods, marker genes show promise of improving cell type classification. However, current scRNA-seq classification methods and databases use marker genes that are non-specific across sources, samples, and/or species leading to bias and errors. Furthermore, many existing tools require manual intervention by the user to provide training datasets or the expected number and name of cell types, which can introduce selection bias. The selection bias negatively impacts the accuracy of cell type classification methods as the model cannot extrapolate outside of the user inputs even when it is biologically meaningful to do so. In this dissertation I developed CellTypeR, a suite of tools to explore the biology governing cell identity in a “normal” state for humans and mice. The work presented here accomplishes three aims: 1. Develop an ontology standardized database of published marker gene literature; 2. Develop and apply a marker gene classification algorithm; and 3. Create user interface and input data structure for scRNA-seq cell type prediction.Item Co-expression based cancer staging and application(Nature Publishing group, 2020-06-30) Yu, Xiangchun; Cao, Sha; Zhou, Yi; Yu, Zhezhou; Xu, Ying; Biochemistry and Molecular Biology, School of MedicineA novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample.Item Competition between DNA Methylation, Nucleotide Synthesis, and Antioxidation in Cancer versus Normal Tissues(AACR, 2017-08) Cao, Sha; Zhu, Xiwen; Zhang, Chi; Qian, Hong; Schuttler, Heinz-Bernd; Gong, Jianping; Xu, Ying; Medical and Molecular Genetics, School of MedicineGlobal DNA hypomethylation occurs in many cancer types, but there is no explanation for its differential occurrence or possible impact on cancer cell physiology. Here we address these issues with a computational study of genome-scale DNA methylation in 16 cancer types. Specifically, we identified (i) a possible determinant for global DNA methylation in cancer cells and (ii) a relationship between levels of DNA methylation, nucleotide synthesis, and intracellular oxidative stress in cells. We developed a system of kinetic equations to capture the metabolic relations among DNA methylation, nucleotide synthesis, and antioxidative stress response, including their competitions for methyl and sulfur groups, based on known information about one-carbon metabolism and trans-sulfuration pathways. We observed a kinetic-based regulatory mechanism that controls reaction rates of the three competing processes when their shared resources are limited, particularly when the nucleotide synthesis rates or oxidative states are high. The combination of this regulatory mechanism and the need for rapid nucleotide synthesis, as well as high production of glutathione dictated by cancer-driving forces, led to the nearly universal observations of reduced global DNA methylation in cancer. Our model provides a natural explanation for differential global DNA methylation levels across cancer types and supports the observation that more malignant cancers tend to exhibit reduced DNA methylation levels. Insights obtained from this work provide useful information about the complexities of cancer due to interplays among competing, dynamic biological processes.Item Computational Modeling of Cell and Tissue Level Metabolic Characterization of the Human Metabolic Network by Using scRNA-seq Data(2022-06) Alghamdi, Norah Saeed; Zhang, Chi; Cao, Sha; Yan, Jingwen; Jones, JosetteThe heterogeneity of metabolic pathways is a hallmark of many common disease types. Nowadays, there are several sources of knowledge on the core components of metabolic networks and sub-networks we have obtained, however, there are still limitations in our knowledge of the integrated behavior and metabolic reprogramming of cells microenvironment. Basically, the metabolic changes can be characterized by different factors, and the changes are different from one cell to another cell because of their high plasticity. The large amount of single-cell and tissue data gained from disease tissue has the potential to provide information on a cell functioning state and its underlying phenotypic changes. Hence, advanced systems biology models and computational tools are in pressing need to empower reliable characterization of metabolic variations in disease by using scRNA-seq data. Our preliminary data include (1) a new computational method to estimate cell-wise metabolic flux and states from single-cell and tissue transcriptomics data, and (2) matched scRNA-seq data and metabolomics experiment on cells under perturbed biochemical conditions and knock-down of metabolic genes, both of which form the computational and experimental foundations of this project. In this dissertation, we proposed to develop a suite of novel computational methods, systems biology models, and quantitative metrics to bring the following unmet capabilities: (1) reconstruction of context-specific and subcellular-resolution metabolic network for different disease types, (2) estimation of cell-/sample-wise metabolic flux by considering metabolic imbalance, metabolic exchange between cells in the disease microenvironment, (3) a systematic evaluation of the functional impact of variations in gene expression, metabolite availability and network structure on the context-specific metabolic network and flux. By implementing these methods using scRNA-seq data, we addressed the following outstanding biological questions: (i) identification of genes, metabolites, and network topology with high impact on metabolic variations, (ii) estimation of metabolic flux, and (iv) assessment of metabolic changes over metabolic network. Successful execution of the proposed research provides a suite of computational capabilities to analyze metabolic variations that could be broadly utilized by the biomedical research community.