Browsing by Subject "High-grade serous ovarian cancer"

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    Deubiquitinase UCHL1 Maintains Protein Homeostasis through the PSMA7–APEH–Proteasome Axis in High-grade Serous Ovarian Carcinoma
    (AACR, 2021-07) Tangri, Apoorva; Lighty, Kinzie; Loganathan, Jagadish; Mesmar, Fahmi; Podicheti, Ram; Zhang, Chi; Iwanicki, Marcin; Drapkin, Ronny; Nakshatri, Harikrishna; Mitra, Sumegha; Obstetrics and Gynecology, School of Medicine
    High-grade serous ovarian cancer (HGSOC) is characterized by chromosomal instability, DNA damage, oxidative stress, and high metabolic demand that exacerbate misfolded, unfolded, and damaged protein burden resulting in increased proteotoxicity. However, the underlying mechanisms that maintain protein homeostasis to promote HGSOC growth remain poorly understood. This study reports that the neuronal deubiquitinating enzyme, ubiquitin carboxyl-terminal hydrolase L1 (UCHL1), is overexpressed in HGSOC and maintains protein homeostasis. UCHL1 expression was markedly increased in HGSOC patient tumors and serous tubal intraepithelial carcinoma (HGSOC precursor lesions). High UCHL1 levels correlated with higher tumor grade and poor patient survival. UCHL1 inhibition reduced HGSOC cell proliferation and invasion, as well as significantly decreased the in vivo metastatic growth of ovarian cancer xenografts. Transcriptional profiling of UCHL1-silenced HGSOC cells revealed downregulation of genes implicated with proteasome activity along with upregulation of endoplasmic reticulum stress–induced genes. Reduced expression of proteasome subunit alpha 7 (PSMA7) and acylaminoacyl peptide hydrolase (APEH), upon silencing of UCHL1, resulted in a significant decrease in proteasome activity, impaired protein degradation, and abrogated HGSOC growth. Furthermore, the accumulation of polyubiquitinated proteins in the UCHL1-silenced cells led to attenuation of mTORC1 activity and protein synthesis, and induction of terminal unfolded protein response. Collectively, these results indicate that UCHL1 promotes HGSOC growth by mediating protein homeostasis through the PSMA7–APEH–proteasome axis.This study identifies the novel links in the proteostasis network to target protein homeostasis in HGSOC and recognizes the potential of inhibiting UCHL1 and APEH to sensitize cancer cells to proteotoxic stress in solid tumors.
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    Emerging and Evolving Ovarian Cancer Animal Models
    (Libertas Academica, 2015-08-12) Bobbs, Alexander S.; Cole, Jennifer M.; Dahl, Karen D. Cowden; Department of Biochemistry and Molecular Biology, IU School of Medicine
    Ovarian cancer (OC) is the leading cause of death from a gynecological malignancy in the United States. By the time a woman is diagnosed with OC, the tumor has usually metastasized. Mouse models that are used to recapitulate different aspects of human OC have been evolving for nearly 40 years. Xenograft studies in immunocompromised and immunocompetent mice have enhanced our knowledge of metastasis and immune cell involvement in cancer. Patient-derived xenografts (PDXs) can accurately reflect metastasis, response to therapy, and diverse genetics found in patients. Additionally, multiple genetically engineered mouse models have increased our understanding of possible tissues of origin for OC and what role individual mutations play in establishing ovarian tumors. Many of these models are used to test novel therapeutics. As no single model perfectly copies the human disease, we can use a variety of OC animal models in hypothesis testing that will lead to novel treatment options. The goal of this review is to provide an overview of the utility of different mouse models in the study of OC and their suitability for cancer research.
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    Space- and Time-Resolved Metabolomics of a High-Grade Serous Ovarian Cancer Mouse Model
    (MDPI, 2022-04-30) Sah, Samyukta; Ma, Xin; Botros, Andro; Gaul, David A.; Yun, Sylvia R.; Park, Eun Young; Kim, Olga; Moore, Samuel G.; Kim, Jaeyeon; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    The dismally low survival rate of ovarian cancer patients diagnosed with high-grade serous carcinoma (HGSC) emphasizes the lack of effective screening strategies. One major obstacle is the limited knowledge of the underlying mechanisms of HGSC pathogenesis at very early stages. Here, we present the first 10-month time-resolved serum metabolic profile of a triple mutant (TKO) HGSC mouse model, along with the spatial lipidome profile of its entire reproductive system. A high-coverage liquid chromatography mass spectrometry-based metabolomics approach was applied to longitudinally collected serum samples from both TKO (n = 15) and TKO control mice (n = 15), tracking metabolome and lipidome changes from premalignant stages to tumor initiation, early stages, and advanced stages until mouse death. Time-resolved analysis showed specific temporal trends for 17 lipid classes, amino acids, and TCA cycle metabolites, associated with HGSC progression. Spatial lipid distributions within the reproductive system were also mapped via ultrahigh-resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry and compared with serum lipid profiles for various lipid classes. Altogether, our results show that the remodeling of lipid and fatty acid metabolism, amino acid biosynthesis, TCA cycle and ovarian steroidogenesis are critical components of HGSC onset and development. These metabolic alterations are accompanied by changes in energy metabolism, mitochondrial and peroxisomal function, redox homeostasis, and inflammatory response, collectively supporting tumorigenesis.
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    SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression
    (Oxford University Press, 2022) Liu, Yusong; Wang, Tongxin; Duggan, Ben; Sharpnack, Michael; Huang, Kun; Zhang, Jie; Ye, Xiufen; Johnson, Travis S.; Biostatistics and Health Data Science, School of Medicine
    High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).
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    Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression
    (MDPI, 2022-06-09) Sah, Samyukta; Yun, Sylvia R.; Gaul, David A.; Botros, Andro; Park, Eun Young; Kim, Olga; Kim, Jaeyeon; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    The lack of effective screening strategies for high-grade serous carcinoma (HGSC), a subtype of ovarian cancer (OC) responsible for 70-80% of OC related deaths, emphasizes the need for new diagnostic markers and a better understanding of disease pathogenesis. Capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) offers high selectivity and sensitivity for ionic compounds, thereby enhancing biomarker discovery. Recent advances in CE-MS include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI) to provide rapid, high-resolution analysis of biological specimens. Here, we describe the development of a targeted microchip (µ) CE-HRMS method, with an acquisition time of only 3 min and sample injection volume of 4nL, to analyze 40 target metabolites in serum samples from a triple-mutant (TKO) mouse model of HGSC. Extracted ion electropherograms showed sharp, baseline resolved peak shapes, even for structural isomers such as leucine and isoleucine. All calibration curves of the analytes maintained good linearity with an average R2 of 0.994, while detection limits were in the nM range. Thirty metabolites were detected in mouse serum with recoveries ranging from 78 to 120%, indicating minimal ionization suppression and good accuracy. We applied the µCE-HRMS method to biweekly-collected serum samples from TKO and TKO control mice. A time-resolved analysis revealed characteristic temporal trends for amino acids, nucleosides, and amino acid derivatives. These metabolic alterations are indicative of altered nucleotide biosynthesis and amino acid metabolism in HGSC development and progression. A comparison of the µCE-HRMS dataset with non-targeted ultra-high performance liquid chromatography (UHPLC)-MS results showed identical temporal trends for the five metabolites detected with both platforms, indicating the µCE-HRMS method performed satisfactorily in terms of capturing metabolic reprogramming due to HGSC progression while reducing the total data collection time three-fold.
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    Ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models
    (Frontiers Media, 2024-01-08) Ma, Xin; Botros, Andro; Yun, Sylvia R.; Park, Eun Young; Kim, Olga; Park, Soojin; Pham, Thu-Huyen; Chen, Ruihong; Palaniappan, Murugesan; Matzuk, Martin M.; Kim, Jaeyeon; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering that little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights into how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSOC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes were compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.