Spatial omics technologies for understanding molecular status associated with cancer progression.
Satoi NagasawaJunko ZenkohYutaka SuzukiAyako SuzukiPublished in: Cancer science (2024)
Cancer cells are generally exposed to numerous extrinsic stimulations in the tumor microenvironment. In this environment, cancer cells change their expression profiles to fight against circumstantial stresses, allowing their progression in the challenging tissue space. Technological advancements of spatial omics have had substantial influence on cancer genomics. This technical progress, especially that occurring in the spatial transcriptome, has been drastic and rapid. Here, we describe the latest spatial analytical technologies that have allowed omics feature characterization to retain their spatial and histopathological information in cancer tissues. Several spatial omics platforms have been launched, and the latest platforms finally attained single-cell level or even higher subcellular level resolution. We discuss several key papers elucidating the initial utility of the spatial analysis. In fact, spatial transcriptome analyses reveal comprehensive omics characteristics not only in cancer cells but also their surrounding cells, such as tumor infiltrating immune cells and cancer-associated fibroblasts. We also introduce several spatial omics platforms. We describe our own attempts to investigate molecular events associated with cancer progression. Furthermore, we discuss the next challenges in analyzing the multiomics status of cells, including their morphology and location. These novel technologies, in conjunction with spatial transcriptome analysis and, more importantly, with histopathology, will elucidate even novel key aspects of the intratumor heterogeneity of cancers. Such enhanced knowledge is expected to open a new path for overcoming therapeutic resistance and eventually to precisely stratify patients.
Keyphrases
- single cell
- rna seq
- papillary thyroid
- gene expression
- high throughput
- healthcare
- induced apoptosis
- genome wide
- squamous cell
- squamous cell carcinoma
- machine learning
- newly diagnosed
- ejection fraction
- deep learning
- cell proliferation
- young adults
- chronic kidney disease
- peritoneal dialysis
- signaling pathway
- dna methylation
- pi k akt