Glycosphingolipid-Glycan Signatures of Acute Myeloid Leukemia Cell Lines Reflect Hematopoietic Differentiation

Comprehensive O-Glycan Analysis by Porous Graphitized Carbon Nanoliquid Chromatography-Mass Spectrometry

The diverse and unpredictable structures of O-GalNAc-type protein glycosylation present a challenge for its structural and functional characterization in a biological system. Porous graphitized carbon (PGC) liquid chromatography (LC) coupled to mass spectrometry (MS) has become one of the most powerful methods for the global analysis of glycans in complex biological samples, mainly due to the extensive chromatographic separation of (isomeric) glycan structures and the information delivered by collision induced fragmentation in negative mode MS for structural elucidation. However, current PGC-based methodologies fail to detect the smaller glycan species consisting of one or two monosaccharides, such as the Tn (single GalNAc) antigen, which is broadly implicated in cancer biology. This limitation is caused by the loss of small saccharides during sample preparation and LC. Here, we improved the conventional PGC nano-LC-MS/MS-based strategy for O-glycan analysis, enabling the detection of truncated O-glycan species and improving isomer separation. This was achieved by the implementation of 2.7 μm PGC particles in both the trap and analytical LC columns, which provided an enhanced binding capacity and isomer separation for O-glycans. Furthermore, a novel mixed-mode PGC-boronic acid-solid phase extraction during sample preparation was established to purify a broad range of glycans in an unbiased manner, including the previously missed mono- and disaccharides. Taken together, the optimized PGC nano-LC-MS/MS platform presents a powerful component of the toolbox for comprehensive O-glycan characterization.

Linking aberrant glycosylation of plasma glycoproteins with progression of myelodysplastic syndromes: a study based on plasmonic biosensor and lectin array

Aberrant glycosylation of glycoproteins has been linked with various pathologies. Therefore, understanding the relationship between aberrant glycosylation patterns and the onset and progression of the disease is an important research goal that may provide insights into cancer diagnosis and new therapy development. In this study, we use a surface plasmon resonance imaging biosensor and a lectin array to investigate aberrant glycosylation patterns associated with oncohematological disease-myelodysplastic syndromes (MDS). In particular, we detected the interaction between the lectins and glycoproteins present in the blood plasma of patients (three MDS subgroups with different risks of progression to acute myeloid leukemia (AML) and AML patients) and healthy controls. The interaction with lectins from Aleuria aurantia (AAL) and Erythrina cristagalli was more pronounced for plasma samples of the MDS and AML patients, and there was a significant difference between the sensor response to the interaction of AAL with blood plasma from low and medium-risk MDS patients and healthy controls. Our data also suggest that progression from MDS to AML is accompanied by sialylation of glycoproteins and increased levels of truncated O-glycans and that the number of lectins that allow discriminating different stages of disease increases as the disease progresses.

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.

Transcriptionally imprinted glycomic signatures of acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous disease that has been suffering from stagnant survival curves for decades. In the endeavor toward improved diagnosis and treatment, cellular glycosylation has emerged as an interesting focus area in AML. While mechanistic insights are still limited, aberrant glycosylation may affect intracellular signaling pathways of AML blasts, their interactions within the microenvironment, and even promote chemoresistance. Here, we performed a meta-omics study to portray the glycomic landscape of AML, thereby screening for potential subtypes and responsible glyco-regulatory networks.

RESULTS

Initially, by integrating comprehensive N-, O-, and glycosphingolipid (GSL)-glycomics of AML cell lines with transcriptomics from public databases, we were able to pinpoint specific glycosyltransferases (GSTs) and upstream transcription factors (TFs) associated with glycan phenotypes. Intriguingly, subtypes M5 and M6, as classified by the French-American-British (FAB) system, emerged with distinct glycomic features such as high (sialyl) Lewis^x/a ((s)Le^x/a) and high sialylation, respectively. Exploration of transcriptomics datasets of primary AML cells further substantiated and expanded our findings from cell lines as we observed similar gene expression patterns and regulatory networks that were identified to be involved in shaping AML glycan signatures.

CONCLUSIONS

Taken together, our data suggest transcriptionally imprinted glycomic signatures of AML, reflecting their differentiation status and FAB classification. This study expands our insights into the emerging field of AML glycosylation and paves the way for studies of FAB class-associated glycan repertoires of AML blasts and their functional implications.

Lipids and the cancer stemness regulatory system in acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous disease of impaired myeloid differentiation and a caricature of normal hematopoiesis. Leukemic stem cells (LSCs) are responsible for long-term clonal propagation in AML just as hematopoietic stem cells (HSCs) sustain lifelong hematopoiesis. LSCs are often resistant to standard chemotherapy and are responsible for clinical relapse. Although AML is highly heterogeneous, determinants of stemness are prognostic for AML patient survival and can predict AML drug sensitivity. Therefore, one way to overcome challenges preventing efficacious treatment outcomes is to target LSC stemness. Metabolomic and lipidomic studies of serum and cells from AML patients are emerging to complement genomic, transcriptomic, epigenetic, and proteomic data sets to characterize and stratify AML. Recent studies have shown the value of fractionating LSCs versus blasts when characterizing metabolic pathways and implicate the importance of lipid balance to LSCs function. As more extensive metabolic studies coupled to functional in vivo assays are conducted on highly purified HSCs, bulk AML, and LSCs, the similarities and differences in lipid homeostasis in stem-like versus more mature AML subtypes as well as from normal HSCs are emerging. Here, we discuss the latest findings from studies of lipid function in LSCs, with a focus on sphingolipids (SLs) as stemness/lineage fate mediators in AML, and the balance of fatty acid anabolism and catabolism fueling metabolic flexibility and drug resistance in AML. We also discuss how designing successful strategies to target lipid vulnerabilities and improve AML patient survival should take into consideration the hierarchical nature of AML.

Bridging Glycomics and Genomics: New Uses of Functional Genetics in the Study of Cellular Glycosylation

All living cells are coated with a diverse collection of carbohydrate molecules called glycans. Glycans are key regulators of cell behavior and important therapeutic targets for human disease. Unlike proteins, glycans are not directly templated by discrete genes. Instead, they are produced through multi-gene pathways that generate a heterogenous array of glycoprotein and glycolipid antigens on the cell surface. This genetic complexity has sometimes made it challenging to understand how glycosylation is regulated and how it becomes altered in disease. Recent years, however, have seen the emergence of powerful new functional genomics technologies that allow high-throughput characterization of genetically complex cellular phenotypes. In this review, we discuss how these techniques are now being applied to achieve a deeper understanding of glyco-genomic regulation. We highlight specifically how methods like ChIP-seq, RNA-seq, CRISPR genomic screening and scRNA-seq are being used to map the genomic basis for various cell-surface glycosylation states in normal and diseased cell types. We also offer a perspective on how emerging functional genomics technologies are likely to create further opportunities for studying cellular glycobiology in the future. Taken together, we hope this review serves as a primer to recent developments at the glycomics-genomics interface.