Cell-cell and cell-matrix interactions in the tumor microenvironment (TME) determine cancer progression as well as treatment response. Deciphering and understanding such interactions has been key for the design of effective therapeutic modalities and has led to the development of sophisticated tools and models. To avoid oversimplification, such models have been tissue-specific, multi-cellular, spatially organized, dynamic and multi-directional. However, these requirements pose major technical challenges, and render a “one model fits all” approach futile. Instead, researchers have increasingly adopted combinations of in vivo, in vitro, and in silico models together with advanced transdisciplinary analytic tools and methods.
This special issue will focus on addressing clinically relevant cell-cell and cell-matrix interactions through TME modeling. Particular emphasis will be placed on solutions that accelerate and inspire personalized cancer therapy approaches, unravel the mechanisms of action of cancer therapeutics, optimize novel combination strategies, decipher new TME targets, or define new players of cancer development and treatment resistance.
The aim of this Research Topic is to showcase state-of-the-art innovative tools and models and their application in translational research, as well as to propose novel solutions for addressing specific unsolved questions at the level of the TME.
In this Research Topic we welcome Original Research, Reviews, Methods, Mini-Reviews, Opinion Articles, Perspective focusing on (but not limited to) the following themes:
1. Engineering of the TME and its complexity using organoids or organotypic models
2. Mathematical and computational modeling of the TME spatial niche
3. Biophysics of the TME
4. Application of multi-omics data integration for a more detailed analysis of the TME
5. Using multiplex immunohistochemistry and imaging mass cytometry for deeper insight into the TME
6. Use of in vivo and ex vivo imaging approaches for cancer therapy assessment
7. Tumor tissue proteomic by quantitative mass spectrometry
Cell-cell and cell-matrix interactions in the tumor microenvironment (TME) determine cancer progression as well as treatment response. Deciphering and understanding such interactions has been key for the design of effective therapeutic modalities and has led to the development of sophisticated tools and models. To avoid oversimplification, such models have been tissue-specific, multi-cellular, spatially organized, dynamic and multi-directional. However, these requirements pose major technical challenges, and render a “one model fits all” approach futile. Instead, researchers have increasingly adopted combinations of in vivo, in vitro, and in silico models together with advanced transdisciplinary analytic tools and methods.
This special issue will focus on addressing clinically relevant cell-cell and cell-matrix interactions through TME modeling. Particular emphasis will be placed on solutions that accelerate and inspire personalized cancer therapy approaches, unravel the mechanisms of action of cancer therapeutics, optimize novel combination strategies, decipher new TME targets, or define new players of cancer development and treatment resistance.
The aim of this Research Topic is to showcase state-of-the-art innovative tools and models and their application in translational research, as well as to propose novel solutions for addressing specific unsolved questions at the level of the TME.
In this Research Topic we welcome Original Research, Reviews, Methods, Mini-Reviews, Opinion Articles, Perspective focusing on (but not limited to) the following themes:
1. Engineering of the TME and its complexity using organoids or organotypic models
2. Mathematical and computational modeling of the TME spatial niche
3. Biophysics of the TME
4. Application of multi-omics data integration for a more detailed analysis of the TME
5. Using multiplex immunohistochemistry and imaging mass cytometry for deeper insight into the TME
6. Use of in vivo and ex vivo imaging approaches for cancer therapy assessment
7. Tumor tissue proteomic by quantitative mass spectrometry
