Advanced Cell Mapping Visualizations for Single Cell Functional Proteomics Enabling Patient Stratification

Applications of single-cell omics for chimeric antigen receptor T cell therapy

Chimeric antigen receptor (CAR) T cell therapy is a promising cancer treatment modality. The breakthroughs in CAR T cell therapy were, in part, possible with the help of cell analysis methods, such as single-cell analysis. Bulk analyses have provided invaluable information regarding the complex molecular dynamics of CAR T cells, but their results are an average of thousands of signals in CAR T or tumour cells. Since cancer is a heterogeneous disease where each minute detail of a subclone could change the outcome of the treatment, single-cell analysis could prove to be a powerful instrument in deciphering the secrets of tumour microenvironment for cancer immunotherapy. With the recent studies in all aspects of adoptive cell therapy making use of single-cell analysis, a comprehensive review of the recent preclinical and clinical findings in CAR T cell therapy was needed. Here, we categorized and summarized the key points of the studies in which single-cell analysis provided insights into the genomics, epigenomics, transcriptomics and proteomics as well as their respective multi-omics of CAR T cell therapy.

Host metabolome predicts the severity and onset of acute toxicities induced by CAR T-cell therapy

Anti-CD19 chimeric antigen receptor (CAR) T-cell therapy is a highly effective treatment option for patients with relapsed/refractory large B-cell lymphoma. However, widespread use is deterred by the development of clinically significant acute inflammatory toxicities, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), that induce significant morbidity and require close monitoring. Identification of host biochemical signatures that predict the severity and time-to-onset of CRS and ICANS may assist patient stratification to enable timely mitigation strategies. Here, we report pretreatment host metabolites that are associated with CRS and ICANS induced by axicabtagene ciloleucel or tisagenlecleucel therapy. Both untargeted metabolomics analysis and validation using targeted assays revealed a significant association between the abundance of specific pretreatment biochemical entities and an increased risk and/or onset of clinically significant CRS (q < .1) and ICANS (q < .25). Higher pretreatment levels of plasma glucose and lower levels of cholesterol and glutamate were associated with a faster onset of CRS. In contrast, low baseline levels of the amino acids proline and glycine and the secondary bile acid isoursodeoxycholate were significantly correlated with clinically significant CRS. Lower concentration of the amino acid hydroxyproline was associated with higher grade and faster onset of ICANS, whereas low glutamine was negatively correlated with faster development of ICANS. Overall, our data indicate that the pretreatment host metabolome has biomarker potential in determining the risk of clinically significant CRS and ICANS, and may be useful in risk stratification of patients before anti-CD19 CAR T-cell therapy.

Release Assays and Potency Assays for CAR T-Cell Interventions

Chimeric antigen receptor (CAR) T-cells are considered "living drugs" and offer a compelling alternative to conventional anticancer therapies. Briefly, T-cells are redirected, using gene engineering technology, toward a specific cancer cell surface target antigen via a synthetic chimeric antigen receptor (CAR) protein. CARs have a modular design comprising four main structures: an antigen-binding domain, a hinge region, a transmembrane domain, and one or more intracellular signaling domains for T-cell activation. A major challenge in the CAR T-cell manufacturing field is balancing product quality with scalability and cost-effectiveness, especially when transitioning from an academic clinical trial into a marketed product, to be implemented across many collection, manufacturing, and treatment sites. Achieving product consistency while circumnavigating the intrinsic variability associated with autologous products is an additional barrier. To overcome these limitations, a robust understanding of the product and its biological actions is crucial to establish a target product profile with a defined list of critical quality attributes to be assessed for each batch prior to product certification. Additional challenges arise as the field progresses, such as new safety considerations associated with the use of allogenic T-cells and genome editing tools. In this chapter, we will discuss the release and potency assays required for CAR T-cell manufacturing, covering their relevance, current challenges, and future perspectives.

Establishment and Systematic Evaluation of Gastric Cancer Classification Model Based on Pyroptosis

Background: Gastric cancer (GC) is considered the fifth most prevalent type of cancer and the third leading cause of cancer deaths worldwide. This in-depth investigation was performed to generate fresh concepts for the clinical classification, diagnosis, and prognostic evaluation of GC. Methods: The data were retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Unsupervised cluster analysis was used to divide up the GC patients using pyroptosis-related differentially expressed genes (DEGs), which were discovered to be significantly linked with GC prognosis. The therapeutic importance of pyroptosis in GC patients was discovered using PCA analysis of genes associated with pyroptosis. The models were then carefully scrutinized. Results: Three hub genes, ELANE, IL6, and TIRAP, exhibit significant predictive importance among the 15 pyroptosis-related genes. Unsupervised clustering analysis revealed that the DEGs were enriched in the pathway of cytokine-cytokine receptor interactions, and Clusters 1 and 2 had statistically distinct prognoses. PCA analysis revealed significant differences in the area under the curve, immunological checkpoints, immunogenic cell death, and prognostic value between the high- and low-risk groups. Conclusions: These two GC classification models, based on pyroptosis, have significant clinical value for patients with GC.

Single-cell polyfunctional proteomics of CD4 cells from patients with AML predicts responses to anti-PD-1-based therapy

Acute myeloid leukemia (AML) remains a difficult disease to treat disease. In a phase 2 clinical trial in patients with relapsed/refractory AML, combining the hypomethylating agent, azacitidine, with the PD-1 checkpoint inhibitor, nivolumab, demonstrated encouraging response rates (33%), median event-free, and overall survival, compared with a historical cohort of contemporary patients treated with azacitidine-based therapies, with an acceptable safety profile. Biomarkers of response are yet to be determined. In this study, we leveraged a multiplexed immune assay to assess the functional states of CD4+ and CD8+ cells at a single-cell level in pretherapy bone marrows in 16 patients with relapsed/refractory AML treated with azacitidine/nivolumab. Effector CD4+ but not CD8+ cells had distinct polyfunctional groups and were associated with responses and better outcomes. Further evaluation of the polyfunctional strength index composition across cell types revealed that interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) were the major drivers of enhanced polyfunctionality index of pretherapy CD4+ subset, whereas Granzyme B, IFN-γ, MIP-1b, and TNF-α drove the nonsignificantly enhanced pretreatment Polyfunctional Strength Index of CD8+ subset in the responders. Single-cell polyfunctional assays were predictive of response in AML and may have a potential role as a biomarker in the wider sphere of immunotherapy.

Tumor-derived NKG2D ligand sMIC reprograms NK cells to an inflammatory phenotype through CBM signalosome activation

Natural Killer (NK) cell dysfunction is associated with poorer clinical outcome in cancer patients. What regulates NK cell dysfunction in tumor microenvironment is not well understood. Here, we demonstrate that the human tumor-derived NKG2D ligand soluble MIC (sMIC) reprograms NK cell to secrete pro-tumorigenic cytokines with diminished cytotoxicity and polyfunctional potential. Antibody clearing sMIC restores NK cell to a normal cytotoxic effector functional state. We discovered that sMIC selectively activates the CBM-signalosome inflammatory pathways in NK cells. Conversely, tumor cell membrane-bound MIC (mMIC) stimulates NK cell cytotoxicity through activating PLC2γ2/SLP-76/Vav1 pathway. Ultimately, antibody targeting sMIC effectuated the in vivo anti-tumor effect of adoptively transferred NK cells. Our findings uncover an unrecognized mechanism that could instruct NK cell to a dysfunctional state in response to cues in the tumor microenvironment. Our findings provide a rationale for co-targeting sMIC to enhance the efficacy of the ongoing NK cell-based cancer immunotherapy.

Systematic Identification of Key Functional Modules and Genes in Gastric Cancer

Gastric cancer (GC) is associated with high incidence and mortality rates worldwide. Differentially expressed gene (DEG) analysis and weighted gene coexpression network analysis (WGCNA) are important bioinformatic methods for screening core genes. In our study, DEG analysis and WGCNA were combined to screen the hub genes, and pathway enrichment analyses were performed on the DEGs. SBNO2 was identified as the hub gene based on the intersection between the DEGs and the purple module in WGCNA. The expression and prognostic value of SBNO2 were verified in UALCAN, GEPIA2, Human Cancer Metastasis Database, Kaplan–Meier plotter, and TIMER. We identified 1974 DEGs, and 28 modules were uncovered via WGCNA. The purple module was identified as the hub module in WGCNA. SBNO2 was identified as the hub gene, which was upregulated in tumour tissues. Moreover, patients with GC and higher SBNO2 expression had worse prognoses. In addition, SBNO2 was suggested to play an important role in immune cell infiltration. In summary, based on DEGs and key modules related to GC, we identified SBNO2 as a hub gene, thereby offering novel insights into the development and treatment of GC.

Uniform Manifold Approximation and Projection for Clustering Taxa through Vocalizations in a Neotropical Passerine (Rough-Legged Tyrannulet, Phyllomyias burmeisteri)

Vocalizations from birds are a fruitful source of information for the classification of species. However, currently used analyses are ineffective to determine the taxonomic status of some groups. To provide a clearer grouping of taxa for such bird species from the analysis of vocalizations, more sensitive techniques are required. In this study, we have evaluated the sensitivity of the Uniform Manifold Approximation and Projection (UMAP) technique for grouping the vocalizations of individuals of the Rough-legged Tyrannulet Phyllomyias burmeisteri complex. Although the existence of two taxonomic groups has been suggested by some studies, the species has presented taxonomic difficulties in classification in previous studies. UMAP exhibited a clearer separation of groups than previously used dimensionality-reduction techniques (i.e., principal component analysis), as it was able to effectively identify the two taxa groups. The results achieved with UMAP in this study suggest that the technique can be useful in the analysis of species with complex in taxonomy through vocalizations data as a complementary tool including behavioral traits such as acoustic communication.