Xiao Fu, PhD, Cancer Research UK Scotland Institute, UK
Weini Huang, PhD, Queen Mary University of London, UK
Robert Noble, DPhil, City St George’s, University of London, UK
Dmitrii Shek, MD, MRes, PhD, Western Sydney University, Australia
Sister Collection: Step-by-step protocols describing experimental techniques can be submitted to the BMC Methods Collection "Spatial Biology". More details here.
Xiao Fu, PhD, Cancer Research UK Scotland Institute, UK
Weini Huang, PhD, Queen Mary University of London, UK
Robert Noble, DPhil, City St George’s, University of London, UK
Dmitrii Shek, MD, MRes, PhD, Western Sydney University, Australia
Spatial tumor heterogeneity refers to the genetic variations in cellular composition, gene expression, and microenvironmental factors within different regions of a tumor. This phenomenon plays a critical role in tumor progression, treatment resistance, and patient outcomes. Recent advancements in technologies such as spatial transcriptomics and imaging have enabled researchers to investigate the intricate spatial organization of tumors, providing insights into how these variations influence tumor biology and therapeutic responses.
Enhancing our understanding of spatial tumor heterogeneity is crucial for several reasons. First, it can lead to the identification of novel biomarkers that predict treatment responses and disease progression. Recent studies have demonstrated the importance of the tumor microenvironment in shaping tumor behavior, revealing that spatially distinct regions can exhibit different responses to therapies. Furthermore, integrating spatial data with genomic and proteomic information has the potential to uncover new therapeutic targets and enhance our understanding of tumor evolution. Continued research in this area will ultimately contribute to more effective cancer management strategies.
Future advances in the field of spatial tumor heterogeneity may include the development of more sophisticated imaging techniques and computational models that can analyze complex spatial data in real-time. These innovations could facilitate the identification of dynamic changes in tumor architecture and microenvironment during treatment, allowing for adaptive therapeutic strategies. Additionally, the integration of multi-omics approaches with spatial analysis may provide deeper insights into the interplay between tumor cells and their microenvironment, paving the way for novel therapeutic interventions.
In recognition of this important area of research we invite contributions on topics such as:
This Collection supports and amplifies research related to SDG 3: Good Health & Well-being.
All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.
Image credit: © Callista Images / Image Source / picture alliance
To extract intratumoral, peritumoral, and integrated intratumoral-peritumoral CT radiomic features, develop multi-source radiomic models using various machine learning algorithms to identify the optimal model,...
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