Browsing by Author "Fernández, Facundo M."

Now showing 1 - 8 of 8
  • Thumbnail Image
    Item
    Deep Metabolomics of a High-Grade Serous Ovarian Cancer Triple-Knockout Mouse Model
    (ACS, 2019) Huang, Danning; Gaul, David A.; Nan, Hongmei; Kim, Jaeyeon; Fernández, Facundo M.; Epidemiology, School of Public Health
    High-grade serous carcinoma (HGSC) is the most common and deadliest ovarian cancer (OC) type, accounting for 70–80% of OC deaths. This high mortality is largely due to late diagnosis. Early detection is thus crucial to reduce mortality, yet the tumor pathogenesis of HGSC remains poorly understood, making early detection exceedingly difficult. Faithfully and reliably representing the clinical nature of human HGSC, a recently developed triple-knockout (TKO) mouse model offers a unique opportunity to examine the entire disease spectrum of HGSC. Metabolic alterations were investigated by applying ultra-performance liquid chromatography–mass spectrometry (UPLC–MS) to serum samples collected from these mice at premalignant, early, and advanced stages of HGSC. This comprehensive analysis revealed a panel of 29 serum metabolites that distinguished mice with HGSC from controls and mice with uterine tumors with over 95% accuracy. Meanwhile, our panel could further distinguish early-stage HGSC from controls with 100% accuracy and from advanced-stage HGSC with over 90% accuracy. Important identified metabolites included phospholipids, sphingomyelins, sterols, N-acyltaurine, oligopeptides, bilirubin, 2(3)-hydroxysebacic acids, uridine, N-acetylneuraminic acid, and pyrazine derivatives. Overall, our study provides insights into dysregulated metabolism associated with HGSC development and progression, and serves as a useful guide toward early detection.
  • Thumbnail Image
    Item
    In vivo modeling of metastatic human high-grade serous ovarian cancer in mice
    (PLOS, 2020-06-04) Kim, Olga; Park, Eun Young; Klinkebiel, David L.; Pack, Svetlana D.; Shin, Yong-Hyun; Abdullaev, Zied; Emerson, Robert E.; Coffey, Donna M.; Kwon, Sun Young; Creighton, Chad J.; Kwon, Sanghoon; Chang, Edmund C.; Chiang, Theodore; Yatsenko, Alexander N.; Chien, Jeremy; Cheon, Dong-Joo; Yang-Hartwich, Yang; Nakshatri, Harikrishna; Nephew, Kenneth P.; Behringer, Richard R.; Fernández, Facundo M.; Cho, Chi-Heum; Vanderhyden, Barbara; Drapkin, Ronny; Bast, Robert C., Jr.; Miller, Kathy D.; Karpf, Adam R.; Kim, Jaeyeon; Biochemistry and Molecular Biology, School of Medicine
    Metastasis is responsible for 90% of human cancer mortality, yet it remains a challenge to model human cancer metastasis in vivo. Here we describe mouse models of high-grade serous ovarian cancer, also known as high-grade serous carcinoma (HGSC), the most common and deadliest human ovarian cancer type. Mice genetically engineered to harbor Dicer1 and Pten inactivation and mutant p53 robustly replicate the peritoneal metastases of human HGSC with complete penetrance. Arising from the fallopian tube, tumors spread to the ovary and metastasize throughout the pelvic and peritoneal cavities, invariably inducing hemorrhagic ascites. Widespread and abundant peritoneal metastases ultimately cause mouse deaths (100%). Besides the phenotypic and histopathological similarities, mouse HGSCs also display marked chromosomal instability, impaired DNA repair, and chemosensitivity. Faithfully recapitulating the clinical metastases as well as molecular and genomic features of human HGSC, this murine model will be valuable for elucidating the mechanisms underlying the development and progression of metastatic ovarian cancer and also for evaluating potential therapies.
  • Thumbnail Image
    Item
    Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer
    (Cold Spring Harbor Laboratory, 2023-01-04) Bifarin, Olatomiwa O.; Sah, Samyukta; Gaul, David A.; Moore, Samuel G.; Chen, Ruihong; Palaniappan, Murugesan; Kim, Jaeyeon; Matzuk, Martin M.; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.
  • Thumbnail Image
    Item
    Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women
    (American Association for Cancer Research, 2024) Sah, Samyukta; Bifarin, Olatomiwa O.; Moore, Samuel G.; Gaul, David A.; Chung, Hyewon; Kwon, Sun Young; Cho, Hanbyoul; Cho, Chi-Heum; Kim, Jae-Hoon; Kim, Jaeyeon; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    Background: Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention. Methods: Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches. Results: We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set. Conclusions: This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Targeting progesterone signaling prevents metastatic ovarian cancer
    (National Academy of Science, 2020-12-15) Kim, Olga; Park, Eun Young; Kwon, Sun Young; Shin, Sojin; Emerson, Robert E.; Shin, Yong-Hyun; DeMayo, Francesco J.; Lydon, John P.; Coffey, Donna M.; Hawkins, Shannon M.; Quilliam, Lawrence A.; Cheon, Dong-Joo; Fernández, Facundo M.; Nephew, Kenneth P.; Karpf, Adam R.; Widschwendter, Martin; Sood, Anil K.; Bast, Robert C., Jr.; Godwin, Andrew K.; Miller, Kathy D.; Cho, Chi-Heum; Kim, Jaeyeon; Biochemistry and Molecular Biology, School of Medicine
    Effective cancer prevention requires the discovery and intervention of a factor critical to cancer development. Here we show that ovarian progesterone is a crucial endogenous factor inducing the development of primary tumors progressing to metastatic ovarian cancer in a mouse model of high-grade serous carcinoma (HGSC), the most common and deadliest ovarian cancer type. Blocking progesterone signaling by the pharmacologic inhibitor mifepristone or by genetic deletion of the progesterone receptor (PR) effectively suppressed HGSC development and its peritoneal metastases. Strikingly, mifepristone treatment profoundly improved mouse survival (∼18 human years). Hence, targeting progesterone/PR signaling could offer an effective chemopreventive strategy, particularly in high-risk populations of women carrying a deleterious mutation in the BRCA gene.
  • Thumbnail Image
    Item
    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.