Biology subjects

Moore, S. G.

Publications and source records attributed to Moore, S. G..

5 recordsLinked to original sources

bioRxiv2023

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women

Distinguishing ovarian cancer (OC) from other gynecological malignancies remains a critical unmet medical need with significant implications for patient survival. However, non-specific symptoms along with our lack of understanding of OC pathogenesis hinder its diagnosis, preventing many women from receiving appropriate medical assistance. Accumulating evidence suggests a link between OC and deregulated lipid metabolism. Most studies, however, are limited by small sample size, particularly for early-stage cases. Furthermore, racial/ethnic differences in OC survival and incidence have been reported, yet most of the studies consist largely of non-Hispanic white women or women with European ancestry. Studies of more diverse racial/ethnic populations are needed to make OC diagnosis and prevention more inclusive. Here, we profiled the serum lipidome of 208 OC, including 93 patients with early-stage OC, and 117 non-OC (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 approaches. Results show that lipidome alterations unique to OC 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 OC in comparison to other gynecological diseases. Machine learning methods selected a panel of 17 lipids that discriminated OC from non-OC cases with an AUC of 0.85 for an independent test set. This study provides a systemic analysis of lipidome alterations in human OC, specifically in Korean women, emphasizing the potential of circulating lipids in distinguishing OC from non-OC conditions.

cancer biology↗

bioRxiv2023

Constitutively synergistic multiagent drug formulations targeting MERTK, FLT3, and BCL-2 for treatment of AML

Although high-dose, multi-agent chemotherapy has improved leukemia survival rates in recent years, treatment outcomes remain poor in high-risk subsets, including acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) in infants. Development of new, more effective therapies for these patients is therefore an urgent, unmet clinical need. To address this challenge, we developed a nanoscale combination drug formulation that exploits ectopic expression of MERTK tyrosine kinase and dependency on BCL-2 family proteins for leukemia cell survival in pediatric AML and MLL-rearranged precursor B-cell ALL (infant ALL). In a novel, high-throughput combination drug screen, the MERTK/FLT3 inhibitor MRX-2843 synergized with venetoclax and other BCL-2 family protein inhibitors to reduce AML cell density in vitro. Neural network models based on drug exposure and target gene expression were used to identify a classifier predictive of drug synergy in AML. To maximize the therapeutic potential of these findings, we developed a combination monovalent liposomal drug formulation that maintains ratiometric drug synergy in cell-free assays and following intracellular delivery. The translational potential of these nanoscale drug formulations was confirmed in a genotypically diverse set of primary AML patient samples and both the magnitude and frequency of synergistic responses were not only maintained but were improved following drug formulation. Together, these findings demonstrate a systematic, generalizable approach to combination drug screening, formulation, and development that maximizes therapeutic potential, was effectively applied to develop a novel nanoscale combination therapy for treatment of AML, and could be extended to other drug combinations or diseases in the future.

bioengineering↗

bioRxiv2023

Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high potency MSC lines and metabolic engineering.

bioengineering↗

bioRxiv2023

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

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. TeaserTime-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

cancer biology↗

bioRxiv2022

Screening and Development of Constitutively Synergistic Combination Drug Formulations for T Cell Acute Lymphoblastic Leukemia

Advances in multiagent chemotherapy have led to recent improvements in overall survival for patients with acute lymphoblastic leukemia (ALL); however, a significant fraction do not respond to frontline chemotherapy or later relapse with recurrent disease, after which long-term survival rates remain low. To address the challenge of developing new, effective treatment options for these patients, we conducted a series of high-throughput combination drug screens to identify chemotherapies that synergize in a lineage-specific manner with MRX-2843, a small molecule dual MERTK and FLT3 kinase inhibitor currently in clinical testing for treatment of relapsed/refractory leukemias and solid tumors. Using experimental and computational approaches, we found that MRX-2843 synergized strongly - and in a ratio-dependent manner - with vincristine chemotherapy to inhibit T-ALL cell expansion and, based on these findings, we developed multiagent lipid nanoparticle formulations of these drugs that not only constitutively maintained ratiometric drug synergy following T-ALL cell delivery, but also improved anti-leukemic activity following drug encapsulation. To determine the clinical relevance of these combination drug formulations and the therapeutic impact of ratiometric drug synergy, we compared the efficacy of lipid nanoparticles comprising synergistic, additive, and antagonistic ratios of MRX-2843 and vincristine, and observed that trends in in vitro synergy were directly recapitulated in primary T-ALL patient samples. Together, these findings present a systematic approach to high-throughput combination drug screening and multiagent drug delivery that maximizes the therapeutic potential of combined MRX-2843 and vincristine in T-ALL. This broadly generalizable approach could lead to the development of constitutively synergistic combination products for the treatment of cancer and other diseases.

bioengineering↗