Spatial profiling technologies and applications for brain cancers

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?

A Review of the Application of Spatial Transcriptomics in Neuroscience

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

Improvements and challenges of tissue preparation for spatial transcriptome analysis of skull base tumors

Background

Spatial transcriptome (ST) provides molecular profiles of tumor cells at the spatial level, which brings new progress to the research of tumors and the tumor microenvironment. This study summarizes the experiences and lessons learned in the spatial section preparation of two different pathological types of nose and skull base tumors at our institution, with the aim of offering guidelines to researchers to avoid wasting precious samples and provide a basis for the application of ST in clinical practice.

Methods

Frozen tissue blocks from patients with squamous cell carcinoma and adenocarcinoma of the nose and skull base diagnosed at our institution were prepared. The effects of different procedures and pathological tissue types on slide quality were explored and evaluated using RNA integrity number (RIN) and HE scores as criteria. The effects of different RIN values on ST sequencing data were explored.

Results

A total of 43 samples were obtained from 26 patients, including 22 with squamous carcinomas and 21 with adenocarcinomas. Thirteen samples with satisfactory RNA quality control and good histological morphology were sequenced for ST. Sample isolation time <15 min and abandonment of snap-frozen isopentane significantly improved RNA quality (p = 0.004, p < 0.0001) and histomorphological integrity (p = 0.02, p = 0.02). Selection of a suitable tissue RNA extraction kit was critical for RNA quality (p < 0.0001). No difference between 6 ≤ RIN <7 and RIN >7 in ST sequencing results was found, indicating that RIN ≥6 can be used as a criterion for qualified RNA quality control. Therefore, fresh tissues washed as soon as possible with cold PBS and then dried using OCT for snap freezing are currently the best method for preparing spatial sections of nose and skull base tumor tissues of different pathological types.

Conclusion

This study is the first to investigate the feasibility of applying ST to different pathological types of nose and skull base tumors and to demonstrate the widespread application of ST in tumors. Rational optimization of spatial slide preparation procedures and exploration of individualized pre-sequencing protocols are used as the first stage to ensure the quality of spatial sequencing and lay the foundation for subsequent spatial analysis.

SPASCER: spatial transcriptomics annotation at single-cell resolution

In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell-cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues.

Understanding the tumor microenvironment in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) represents a heterogeneous group of tumors. While significant progress has been made using multimodal treatment, the 5‐year survival remains at 50%. Developing effective therapies, such as immunotherapy, will likely lead to better treatment of primary and metastatic disease. However, not all HNSCC tumors respond to immune checkpoint blockade therapy. Understanding the complex cellular composition and interactions of the tumor microenvironment is likely to lead to new knowledge for effective therapies and treatment resistance. In this review, we discuss HNSCC characteristics, predictive biomarkers, factors influencing immunotherapy response, with a focus on the tumor microenvironment.

Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients

BACKGROUND

Pancreatic cancer (PC) is a malignancy with a poor prognosis and high mortality. Surgical resection is the only "curative" treatment. However, only a minority of patients with PC can obtain surgery. Improving the overall survival (OS) rate of patients with PC is still a major challenge. Molecular biomarkers are a significant approach for diagnostic and predictive use in PCs. Several prediction models have been developed for patients newly diagnosed with PC that is operable or patients with advanced and metastatic PC; however, these models require further validation. Therefore, precise biomarkers are urgently required to increase the efficiency of predicting a disease-free survival (DFS), OS, and sensitivity to immunotherapy in PC patients and to improve the prognosis of PC.

METHODS

In the present study, we first evaluated the highly and selectively expressed targets in PC, using the GeoMxTM Digital Spatial Profiler (DSP) and then, we analyzed the roles of these targets in PCs using TCGA database.

RESULTS

LAMB3, FN1, KRT17, KRT19, and ANXA1 were defined as the top five upregulated targets in PC compared with paracancer. The TCGA database results confirmed the expression pattern of LAMB3, FN1, KRT17, KRT19, and ANXA1 in PCs. Significantly, LAMB3, FN1, KRT19, and ANXA1 but not KRT17 can be considered as biomarkers for survival analysis, univariate and multivariate Cox proportional hazards model, and risk model analysis. Furthermore, in combination, LAMB3, FN1, KRT19, and ANXA1 predict the DFS and, in combination, LAMB3, KRT19, and ANXA1 predict the OS. Immunotherapy is significant for PCs that are inoperable. The immune checkpoint blockade (ICB) analysis indicated that higher expressions of FN1 or ANXA1 are correlated with lower ICB response. In contrast, there are no significant differences in the ICB response between high and low expression of LAMB3 and KRT19.

CONCLUSIONS

In conclusion, LAMB3, FN1, KRT19, and ANXA1 are good predictors of PC prognosis. Furthermore, FN1 and ANXA1 can be predictors of immunotherapy in PCs.

Emerging Biomarkers for Diagnosis, Prevention and Treatment of Brain Metastases-From Biology to Clinical Utility

Primary malignancies of the lung, skin (melanoma), and breast have higher propensity for metastatic spread to the brain. Advances in molecular tumour profiling have aided the development of targeted therapies, stereotactic radiotherapy, and immunotherapy, which have led to some improvement in patient outcomes; however, the overall prognosis remains poor. Continued research to identify new prognostic and predictive biomarkers is necessary to further impact patient outcomes, as this will enable better risk stratification at the point of primary cancer diagnosis, earlier detection of metastatic deposits (for example, through surveillance), and more effective systemic treatments. Brain metastases exhibit considerable inter- and intratumoural heterogeneity-apart from distinct histology, treatment history and other clinical factors, the metastatic brain tumour microenvironment is incredibly variable both in terms of subclonal diversity and cellular composition. This review discusses emerging biomarkers; specifically, the biological context and potential clinical utility of tumour tissue biomarkers, circulating tumour cells, extracellular vesicles, and circulating tumour DNA.

The Role of Circulating Biomarkers in Lung Cancer

Lung cancer is the leading cause of cancer morbidity and mortality worldwide and early diagnosis is crucial for the management and treatment of this disease. Non-invasive means of determining tumour information is an appealing diagnostic approach for lung cancers as often accessing and removing tumour tissue can be a limiting factor. In recent years, liquid biopsies have been developed to explore potential circulating tumour biomarkers which are considered reliable surrogates for understanding tumour biology in a non-invasive manner. Most common components assessed in liquid biopsy include circulating tumour cells (CTCs), cell-free DNA (cfDNA), circulating tumour DNA (ctDNA), microRNA and exosomes. This review explores the clinical use of circulating tumour biomarkers found in liquid biopsy for screening, early diagnosis and prognostication of lung cancer patients.

Tumor-Associated Microglia and Macrophages in the Glioblastoma Microenvironment and Their Implications for Therapy

Glioblastoma is the most frequent and malignant primary brain tumor. Standard of care includes surgery followed by radiation and temozolomide chemotherapy. Despite treatment, patients have a poor prognosis with a median survival of less than 15 months. The poor prognosis is associated with an increased abundance of tumor-associated microglia and macrophages (TAMs), which are known to play a role in creating a pro-tumorigenic environment and aiding tumor progression. Most treatment strategies are directed against glioblastoma cells; however, accumulating evidence suggests targeting of TAMs as a promising therapeutic strategy. While TAMs are typically dichotomously classified as M1 and M2 phenotypes, recent studies utilizing single cell technologies have identified expression pattern differences, which is beginning to give a deeper understanding of the heterogeneous subpopulations of TAMs in glioblastomas. In this review, we evaluate the role of TAMs in the glioblastoma microenvironment and discuss how their interactions with cancer cells have an extensive impact on glioblastoma progression and treatment resistance. Finally, we summarize the effects and challenges of therapeutic strategies, which specifically aim to target TAMs.

The Pandora's box of novel technologies that may revolutionize lung cancer

Non-small cell lung cancer (NSCLC) is one of the most common cancers globally and has a 5-year survival rate ~20%. Immunotherapies have demonstrated long-term and durable responses in NSCLC patients, although they appear to be effective in only a subset of patients. A more comprehensive understanding of the underlying tumour biology may contribute to identifying those patients likely to achieve optimal outcomes. Profiling the tumour microenvironment (TME) has shown to be beneficial in addressing fundamental tumour-immune cell interactions. Advances in multiplexing immunohistochemistry and molecular barcoding has led to recent advances in profiling genes and proteins in NSCLC. Here, we review the recent advancements in spatial profiling technologies for the analysis of NSCLC tissue samples to gain new insights and therapeutic options for NSCLC. The combination of spatial transcriptomics combined with advanced imaging is likely to lead to deep insights into NSCLC tissue biology, which can be a powerful tool to predict likelihood of response to therapy.

Characterization of Immune Cell Subsets of Tumor Infiltrating Lymphocytes in Brain Metastases

Simple Summary

Brain metastases arising from breast cancers, occur in about 20% of women with a poor-survival outcome. Unfortunately, most patients survive only up to eighteen months from diagnosis. Therefore, there is an urgent need to understand how these cancers survive in the brain. It is thought that the immune cells in the brain, together with brain resident cells, may provide a favorable environment for cancer growth. However, this is not very well understood at this point. We aimed to profile the cells found in these tumors, focusing on five different cell types based on the marker expressed by them. Our results indicate that certain molecules contained within the cancer and the surrounding environment are associated with poor survival. This suggests that these molecules might be important in brain metastasis. This finding is a step towards our understanding of how some patients with brain metastasis survive longer than others.

Abstract

The heterogeneity of tumor infiltrating lymphocytes (TILs) is not well characterized in brain metastasis. To address this, we performed a targeted analysis of immune-cell subsets in brain metastasis tissues to test immunosuppressive routes involved in brain metastasis. We performed multiplex immunofluorescence (mIF), using commercially available validated antibodies on formalin-fixed paraffin embedded whole sections. We quantitated the subsets of immune-cells utilizing a targeted panel of proteins including PanCK, CD8, CD4, VISTA and IBA-1, and analyzed an average of 15,000 cells per sample. Classifying tumors as either high (>30%) or low (<30%) TILs, we found that increased TILs density correlated with survival. Phenotyping these TILs we found tumors with low TILs had significantly higher expression of the immune-checkpoint molecule VISTA in tumor cells (p < 0.01) as well as in their microenvironment (p < 0.001). Contrastingly, the tumors with high TILs displayed higher levels of microglia, as measured by IBA-1 expression. Low TILs-tumors displayed CD8+ T-cells that co-express VISTA (p < 0.01) significantly more compared to high TILs group, where CD8+cells significantly co-express IBA-11 (p < 0.05). These results were supported by RNA analysis of a publicly available, independent cohort. Our work contributes to a growing understanding of the immune surveillance escape routes active in brain metastasis.