Browsing by Author "Cao, Sha"
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Item A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data(Cold Spring Harbor Laboratory, 2021) Alghamdi, Norah; Chang, Wennan; Dang, Pengtao; Lu, Xiaoyu; Wan, Changlin; Gampala, Silpa; Huang, Zhi; Wang, Jiashi; Ma, Qin; Zang, Yong; Fishel, Melissa; Cao, Sha; Zhang, Chi; Medical and Molecular Genetics, School of MedicineThe metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.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 Amplification-Free, High-Throughput Nanoplasmonic Quantification of Circulating MicroRNAs in Unprocessed Plasma Microsamples for Earlier Pancreatic Cancer Detection(ACS, 2023-03) Masterson, Adrianna N.; Chowdhury, Nayela N.; Yang, Yue; Yip-Schneider, Michele T.; Hati, Sumon; Gupta, Prashant; Cao, Sha; Wu, Huangbing; Schmidt, C. Max; Fishel, Melissa L.; Sardar, Rajesh; Chemistry, School of SciencePancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy that is often detected at an advanced stage. Earlier diagnosis of PDAC is key to reducing mortality. Circulating biomarkers such as microRNAs are gaining interest, but existing technologies require large sample volumes, amplification steps, extensive biofluid processing, lack sensitivity, and are low-throughput. Here, we present an advanced nanoplasmonic sensor for the highly sensitive, amplification-free detection and quantification of microRNAs (microRNA-10b, microRNA-let7a) from unprocessed plasma microsamples. The sensor construct utilizes uniquely designed −ssDNA receptors attached to gold triangular nanoprisms, which display unique localized surface plasmon resonance (LSPR) properties, in a multiwell plate format. The formation of −ssDNA/microRNA duplex controls the nanostructure–biomolecule interfacial electronic interactions to promote the charge transfer/exciton delocalization processes and enhance the LSPR responses to achieve attomolar (10–18 M) limit of detection (LOD) in human plasma. This improve LOD allows the fabrication of a high-throughput assay in a 384-well plate format. The performance of nanoplasmonic sensors for microRNA detection was further assessed by comparing with the qRT-PCR assay of 15 PDAC patient plasma samples that shows a positive correlation between these two assays with the Pearson correlation coefficient value >0.86. Evaluation of >170 clinical samples reveals that oncogenic microRNA-10b and tumor suppressor microRNA-let7a levels can individually differentiate PDAC from chronic pancreatitis and normal controls with >94% sensitivity and >94% specificity at a 95% confidence interval (CI). Furthermore, combining both oncogenic and tumor suppressor microRNA levels significantly improves differentiation of PDAC stages I and II versus III and IV with >91% and 87% sensitivity and specificity, respectively, in comparison to the sensitivity and specificity values for individual microRNAs. Moreover, we show that the level of microRNAs varies substantially in pre- and post-surgery PDAC patients (n = 75). Taken together, this ultrasensitive nanoplasmonic sensor with excellent sensitivity and specificity is capable of assaying multiple biomarkers simultaneously and may facilitate early detection of PDAC to improve patient care.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 Association of amyloid and cardiovascular risk with cognition: Findings from KBASE(Wiley, 2024) Chaudhuri, Soumilee; Dempsey, Desarae A.; Huang, Yen-Ning; Park, Tamina; Cao, Sha; Chumin, Evgeny J.; Craft, Hannah; Crane, Paul K.; Mukherjee, Shubhabrata; Choi, Seo-Eun; Scollard, Phoebe; Lee, Michael; Nakano, Connie; Mez, Jesse; Trittschuh, Emily H.; Klinedinst, Brandon S.; Hohman, Timothy J.; Lee, Jun-Young; Kang, Koung Mi; Sohn, Chul-Ho; Kim, Yu Kyeong; Yi, Dahyun; Byun, Min Soo; Risacher, Shannon L.; Nho, Kwangsik; Saykin, Andrew J.; Lee, Dong Young; KBASE Research Group; Radiology and Imaging Sciences, School of MedicineBackground: Limited research has explored the effect of cardiovascular risk and amyloid interplay on cognitive decline in East Asians. Methods: Vascular burden was quantified using Framingham's General Cardiovascular Risk Score (FRS) in 526 Korean Brain Aging Study (KBASE) participants. Cognitive differences in groups stratified by FRS and amyloid positivity were assessed at baseline and longitudinally. Results: Baseline analyses revealed that amyloid-negative (Aβ-) cognitively normal (CN) individuals with high FRS had lower cognition compared to Aβ- CN individuals with low FRS (p < 0.0001). Longitudinally, amyloid pathology predominantly drove cognitive decline, while FRS alone had negligible effects on cognition in CN and mild cognitive impairment (MCI) groups. Conclusion: Our findings indicate that managing vascular risk may be crucial in preserving cognition in Aβ- individuals early on and before the clinical manifestation of dementia. Within the CN and MCI groups, irrespective of FRS status, amyloid-positive individuals had worse cognitive performance than Aβ- individuals. Highlights: Vascular risk significantly affects cognition in amyloid-negative older Koreans. Amyloid-negative CN older adults with high vascular risk had lower baseline cognition. Amyloid pathology drives cognitive decline in CN and MCI, regardless of vascular risk. The study underscores the impact of vascular health on the AD disease spectrum.Item Author Correction: Upregulation of lipid metabolism genes in the breast prior to cancer diagnosis(Springer Nature, 2024-06-17) Marino, Natascia; German, Rana; Rao, Xi; Simpson, Ed; Liu, Sheng; Wan, Jun; Liu, Yunlong; Sandusky, George; Jacobsen, Max; Stovall, Miranda; Cao, Sha; Storniolo, Anna Maria V.; Medicine, School of MedicineCorrection to: npj Breast Cancer 10.1038/s41523-020-00191-8, published online 06 October 2020 In this article, the author name Miranda Stovall was incorrectly written as Miranda Stoval. The original article has been corrected.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.