Abstract
Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.
| Original language | English |
|---|---|
| Title of host publication | Cutting Edge Artificial Intelligence, Spatial Transcriptomics and Proteomics Approaches to Analyze Cancer |
| Editors | Esha Madan, Paul B. Fisher, Rajan Gogna |
| Publisher | Elsevier |
| Publication date | 2024 |
| Pages | 187-222 |
| Chapter | 6 |
| ISBN (Electronic) | 978-0-443-29650-5 |
| DOIs | |
| Publication status | Published - 2024 |
| Series | Advances in Cancer Research |
|---|---|
| Volume | 163 |
| ISSN | 0065-230X |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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