Spatial single cell analysis of tumor microenvironment remodeling pattern in primary central nervous system lymphoma

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.

Leveraging spatial omics for the development of precision sarcoma treatments

Sarcomas are rare and heterogeneous cancers that arise from bone or soft tissue, and are the second most prevalent solid cancer in children and adolescents. Owing to the complex nature of pediatric sarcomas, the development of therapeutics for pediatric sarcoma has seen little progress in the past decades. Existing treatments are largely limited to chemotherapy, radiation, and surgery. Limited knowledge of the sarcoma tumor microenvironment (TME) and of well-defined target antigens in the different subtypes necessitates an alternative investigative approach to improve treatments. Recent advances in spatial omics technologies have enabled a more comprehensive study of the TME in multiple cancers. In this opinion article we discuss advances in our understanding of the TME of some cancers enabled by spatial omics technologies, and we explore how these technologies might advance the development of precision treatments for sarcoma, especially pediatric sarcoma.

Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications

Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.

Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics

Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.