ReviewExplaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems
Section snippets
Cancer stem cells, phenotypic switching and metastasis
Cancer stem cells (CSCs) have been defined as cells with the capability of self-renewing, generating an heterogeneous population within the tumor [1]. According to this view, tumors expand in a hierarchical way, with CSC at the apex of the tree. The other model of tumor growth is the stochastic one, postulating that cancer cells are heterogeneous but are all able to sustain tumor growth [2]. There was evidence in support of both hypotheses, but according to the recent literature the two models
Plasticity in cancer cells: the epithelial mesenchymal transition
The EMT, controlled by a numbers of transcription factors [19], is associated with the loss of cell-cell adhesion and the gain of invasive traits and is therefore a hallmark of plasticity within a stem cell population and appears particularly relevant for tumors. Almost 80% of human malignancies which origin from epithelial tissues express mesenchymal markers, being usually associated with a more aggressive phenotype [19], [20], [21], [22]. More recently, interesting evidence shows that the EMT
Cancer niche: new challenges
Tumor cells require nutrients and signals from the surrounding microenvironment which also regulates a dynamic balance between CSCs and CCs. The tumor niche comprises different types of cells in addition to a non-cellular component (cytokines, growth factors, etc.), mainly cancer-associated fibroblasts (CAF) and macrophages. Macrophages are present in the tumor microenvironment together with immune cells, endothelial cells, fibroblasts and mesenchymal stromal/stem cells and communicate with the
Artificial intelligence, personalized medicine and real world data
Two great revolutions happened in recent years: the possibility to store a great amount of data and the growing speed of computers which are able to perform more operations in a shorter time. These two simple technological improvements are already able to speed up many human daily functions and also have an impact on medicine, including cancer. Computer algorithms and data analysis tools are behind several consumer services from travel agencies to recommendation systems for books, movies and
Conclusions: precision medicine and tumor plasticity
The idea that tumors are highly heterogeneous is already mainstream in the field of cancer biology. The plasticity of tumor cells driven by the microenvironment, that under the right conditions induces cells to switch and seed new metastasis, is becoming an additional hallmark of cancer. Disentangling the evolution of single cells inside the tumor opens a new interesting area to understand better the complex relationship between tumor cells and the environment. Our new understanding of tumor
Transparency document
References (84)
- et al.
Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells
Cell
(2011) - et al.
A temporarily distinct subpopulation of slow-cycling melanoma cells is required for continuous tumor growth
Cell
(2010) - et al.
Poised chromatin at the ZEB1 promoter enables breast cancer cell plasticity and enhances tumorigenicity
Cell
(2013) - et al.
Melanoma contains CD133 and ABCG2 positive cells with enhanced tumourigenic potential
Eur. J. Cancer
(2007) - et al.
Cancer stem cells, cancer cell plasticity and radiation therapy
Semin. Cancer Biol.
(2015) - et al.
Complexity in cancer stem cells and tumor evolution: toward precision medicine
Semin. Cancer Biol.
(2017) - et al.
Epithelial–mesenchymal plasticity: a central regulator of cancer progression
Trends Cell Biol.
(2015) - et al.
Molecular requirements for epithelial–mesenchymal transition during tumor progression
Curr. Opin. Cell Biol.
(2005) - et al.
EMT and dissemination precede pancreatic tumor formation
Cell
(2012) - et al.
Primary cutaneous carcinosarcoma: insights into its clonal origin and mutational pattern expression analysis through next-generation sequencing
Hum. Pathol.
(2013)