Single-cell and spatially resolved omics: Advances and limitations

A pan-cancer analysis of the oncogenic role of N-acetyltransferase 8 like in human cancer

BACKGROUND

N-Acetyltransferase 8 Like (NAT8L) inhibits natural killer (NK)/T-cell cytotoxicity by impairing the formation of the immunological synapse via N-acetylaspartate (NAA). Existing research has predominantly focused on the metabolic functions of NAT8L, particularly in adipose tissues and myelination in the brain. However, in contrast to other N-acetyltransferases such as NAT1 and NAT2, the role of NAT8L in cancer has been less extensively studied. In this study, we conducted a comprehensive pan-cancer analysis to investigate the carcinogenic role of NAT8L in human cancers.

METHODS

We utilized the standardized TCGA pan-cancer dataset to analyze differential expression, clinical prognosis, gene mutation, immune infiltration, epigenetic modification, tumor stemness, and heterogeneity. Additionally, we evaluated the sensitivity of NAT8L to small molecule drugs using the GDSC and CTRP databases.

RESULTS

In this study, we identified that NAT8L expression was upregulated in 6 cancers and downregulated in 12 compared to normal tissues. We analyzed its prognostic value in 5 tumor types (KIRP, COAD, COADREAD, GBMLGG, LUSC) and found correlations with overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Furthermore, NAT8L expression was significantly correlated with levels of most immune checkpoints, immunomodulators, and immune cell infiltration. The mutation frequencies for bladder cancer (BLCA), glioblastoma multiforme and glioma (GBMLGG), lower-grade glioma (LGG), and KIRP were 1.2%, 0.9%, 0.8%, and 0.4%, respectively.

CONCLUSION

Our findings suggest that NAT8L may serve as a potential prognostic marker and therapeutic target across a variety of cancers, particularly in KIRP, COAD, COADREAD, GBMLGG, and lung squamous cell carcinoma (LUSC).

The molecular features of lung cancer stem cells in dedifferentiation process-driven epigenetic alterations

Cancer stem cells (CSCs) may be dedifferentiated somatic cells following oncogenic processes, representing a subpopulation of cells able to promote tumor growth with their capacities for proliferation and self-renewal, inducing lineage heterogeneity, which may be a main cause of resistance to therapies. It has been shown that the "less differentiated process" may have an impact on tumor plasticity, particularly when non-CSCs may dedifferentiate and become CSC-like. Bidirectional interconversion between CSCs and non-CSCs has been reported in other solid tumors, where the inflammatory stroma promotes cell reprogramming by enhancing Wnt signaling through nuclear factor kappa B activation in association with intracellular signaling, which may induce cells' pluripotency, the oncogenic transformation can be considered another important aspect in the acquisition of "new" development programs with oncogenic features. During cell reprogramming, mutations represent an initial step toward dedifferentiation, in which tumor cells switch from a partially or terminally differentiated stage to a less differentiated stage that is mainly manifested by re-entry into the cell cycle, acquisition of a stem cell-like phenotype, and expression of stem cell markers. This phenomenon typically shows up as a change in the form, function, and pattern of gene and protein expression, and more specifically, in CSCs. This review would highlight the main epigenetic alterations, major signaling pathways and driver mutations in which CSCs, in tumors and specifically, in lung cancer, could be involved, acting as key elements in the differentiation/dedifferentiation process. This would highlight the main molecular mechanisms which need to be considered for more tailored therapies.

The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma

Ovarian cancer is the deadliest gynecologic malignancy, and therapeutic options and mortality rates over the last three decades have largely not changed. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes. To improve spatial understanding of the TIME, we performed multiplexed ion beam imaging on 83 human high-grade serous carcinoma tumor samples, identifying approximately 160,000 cells across 23 cell types. From the 77 of these samples that met inclusion criteria, we generated composition features based on cell type proportions, spatial features based on the distances between cell types, and spatial network features representing cell interactions and cell clustering patterns, which we linked to traditional clinical and IHC variables and patient overall survival (OS) and progression-free survival (PFS) outcomes. Among these features, we found several significant univariate correlations, including B-cell contact with M1 macrophages (OS HR = 0.696; P = 0.011; PFS HR = 0.734; P = 0.039). We then used high-dimensional random forest models to evaluate out-of-sample predictive performance for OS and PFS outcomes and to derive relative feature importance scores for each feature. The top model for predicting low or high PFS used TIME composition and spatial features and achieved an average AUC score of 0.71. The results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

Spatially resolved metabolomics visualizes heterogeneous distribution of metabolites in lung tissue and the anti-pulmonary fibrosis effect of Prismatomeris connate extract

Pulmonary fibrosis (PF) is a chronic progressive end-stage lung disease. However, the mechanisms underlying the progression of this disease remain elusive. Presently, clinically employed drugs are scarce for the treatment of PF. Hence, there is an urgent need for developing novel drugs to address such diseases. Our study found for the first time that a natural source of Prismatomeris connata Y. Z. Ruan (Huang Gen, HG) ethyl acetate extract (HG-2) had a significant anti-PF effect by inhibiting the expression of the transforming growth factor beta 1/suppressor of mothers against decapentaplegic (TGF-β1/Smad) pathway. Network pharmacological analysis suggested that HG-2 had effects on tyrosine kinase phosphorylation, cellular response to reactive oxygen species, and extracellular matrix (ECM) disassembly. Moreover, mass spectrometry imaging (MSI) was used to visualize the heterogeneous distribution of endogenous metabolites in lung tissue and reveal the anti-PF metabolic mechanism of HG-2, which was related to arginine biosynthesis and alanine, asparate and glutamate metabolism, the downregulation of arachidonic acid metabolism, and the upregulation of glycerophospholipid metabolism. In conclusion, we elaborated on the relationship between metabolite distribution and the progression of PF, constructed the regulatory metabolic network of HG-2, and discovered the multi-target therapeutic effect of HG-2, which might be conducive to the development of new drugs for PF.

Advancing cancer therapeutics: Integrating scalable 3D cancer models, extracellular vesicles, and omics for enhanced therapy efficacy

Cancer remains as one of the highest challenges to human health. However, anticancer drugs exhibit one of the highest attrition rates compared to other therapeutic interventions. In part, this can be attributed to a prevalent use of in vitro models with limited recapitulative potential of the in vivo settings. Three dimensional (3D) models, such as tumor spheroids and organoids, offer many research opportunities to address the urgent need in developing models capable to more accurately mimic cancer biology and drug resistance profiles. However, their wide adoption in high-throughput pre-clinical studies is dependent on scalable manufacturing to support large-scale therapeutic drug screenings and multi-omic approaches for their comprehensive cellular and molecular characterization. Extracellular vesicles (EVs), which have been emerging as promising drug delivery systems (DDS), stand to significantly benefit from such screenings conducted in realistic cancer models. Furthermore, the integration of these nanomedicines with 3D cancer models and omics profiling holds the potential to deepen our understanding of EV-mediated anticancer effects. In this chapter, we provide an overview of the existing 3D models used in cancer research, namely spheroids and organoids, the innovations in their scalable production and discuss how omics can facilitate the implementation of these models at different stages of drug testing. We also explore how EVs can advance drug delivery in cancer therapies and how the synergy between 3D cancer models and omics approaches can benefit in this process.

Tissue biomarkers of immune checkpoint inhibitor therapy

Cancer immunotherapy has been rejuvenated by the growing understanding of the immune system's role in tumor activity over the past two decades. During cancer initiation and progression, tumor cells employ various mechanisms that resemble peripheral immune tolerance to evade the antitumor responses of the immune system. Immune checkpoint molecules are the major mechanism of immune resistance that are exploited by tumor cells to inhibit T-cell activation and suppress immune responses. The targeting of immune checkpoint pathways has led to substantial improvements in survival rates in a number of solid cancers. However, a lack of understanding of the heterogeneity of the tumor microenvironment (TME) has resulted in inefficient therapy responses. A greater understanding of the TME is needed to identify patients likely to respond, and those that will have resistance to immune checkpoint inhibitors (ICIs). Advancement in spatial single-cell technologies has allowed deeper insight into the phenotypic and functional diversities of cells in the TME. In this review, we provide an overview of ICI biomarkers and highlight how high-dimensional spatially resolved, single-cell approaches provide deep molecular insights into the TME and allow for the discovery of biomarkers of clinical benefit.

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors - one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled - and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.