Fifteen Eighty Four

Academic perspectives from Cambridge University Press


The Cancer Stem Cell Theory – A Physics Approach

Caterina A. M. La Porta, Stefano Zapperi

By Destroyer of furries (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons

Complexity of cancer stem cells evolution and precision medicine

Understanding how tumours grow and develop is a key objective in cancer research. It is, however, an extremely complex problem that needs an interdisciplinary approach where biomedical researchers work side by side with scientists from hard disciplines such as physics (see our latest book: The Physics of Cancer for a discussion of this approach). Recent advances on the plasticity of cancer stem cells has enormous relevance for the understanding of the metastatic process, which, in turns, is crucial to guide therapeutic interventions.


Two main models have been used to describe tumour growth: according to the stochastic clonal evolution model, most neoplasms arise from a single cell of origin, and cancer progression results from acquired genetic variability within the original clone allowing sequential selection of more aggressive sublines. On the other hand, the cancer stem cell (CSC) theory states that cancer cells are not all the same but are organised in a hierarchical structure, with a few of them acting as stem cells that reproduce themselves and sustain the cancer. Which means that a good way to get rid of a tumour would be targeting these CSCs with specific drugs and prevent them from nurturing the neoplasm.

The idea that only two possible scenarios are possible for cancer development appears too simple


The question of how tumours evolve, either stochastically or hierarchically, is at the core of an intense scientific debate. The idea that only two possible scenarios are possible for cancer development appears too simple, as proved by contrasting results that appeared regularly in scientific literature. Results that could be explained with the discovery the depletion of cancer stem cells leads the other cancer cells to switch back into the cancer stem cell phenotype. This phenotypic plasticity could be either driven by genetic mutations or regulated by epigenetic factors.


Moreover, this plasticity has important implications for metastasis since migrating cells do not need to be cancer stem cells in order to seed a metastasis. In fact, the migrating cell could be a cancer cell that would automatically switch into a CSC once it has spread far enough from the primary tumour, thus triggering a new metastatic cycle. The phenotypic switch may be triggered by a depletion in the number of CSCs, but also by the surrounding microenvironment, as proved by the exposure of cancer cells to transforming growth factor beta (TGF-b), or repeated hypoxia/reoxygenation cycles.


A relationship that might be interesting to investigate, as it could provide information and ideas for new immune therapies aimed to influence the cancer microenvironment, is the interplay between the CSCs niche and the immune system, in the light of the phenotypic switching mechanisms. It is clear that the phenotypic switch in CSCs pose a challenge to existing therapeutic strategies. However, it also opens possible new avenues. Instead of directly targeting CSCs, a more successful strategy might be to prevent cancer cells from switching back into a pluripotent state. To this end, a possible future scenario might be to understand by microarray analysis the complex network of miRNAs produced by each tumour and study, with the aid of computational analysis, its possible impact on the plasticity of the cells.


About The Authors

Caterina A. M. La Porta

Caterina A. M. La Porta author of The Physics of Cancer 2017, is Associate Professor of General Pathology at Università degli Studi di Milano and group leader of the Molecular Onc...

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Stefano Zapperi

Stefano Zapperi author of The Physics of Cancer 2017, is Professor of Theoretical Physics at Università degli Studi di Milano, where he coordinates the Center for Complexity and B...

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