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A Curious World of Tumour Complexity

The sophisticated mechanisms of tumours

Immune defence

In the human body, macrophages play crucial roles in both innate and adaptive immunity. However, a population of macrophages can become associated with tumours (so-called, TAM – tumour-associated macrophages) and start playing a completely opposite set of roles: promote tumour growth, invasion, metastasis, and drug resistance.

Plasticity

A tumour contains cancer stem cells that can morph into multiple types of cancer cells, and some of them can morph back to stem-like state. The intensity of this back and forth process depends on the cell type, the type of stressors, and the duration of stress, making a tumour highly adaptable to changes in its environment. Since CSCs are highly resistant to treatment and are crucial in metastatic spreading to other organs, they form a sort of a reservoir that can change its size when needed. This enables active adaptation of tumour’s resistance and invasion.

Metastasis

In metastasis, cancer cells detach from the tumour, enter circulation through blood vessels and invade distant organs. However, this process is far from unregulated shedding of cells that travel to random distant places in the patient’s body. First, to reach blood vessels, cancer stem cells form a group that is led by Cancer-Associated Fibroblasts (CAFs). CAFs lead the way by actively remodelling the tumour matrix, thus generating a track through which the entire group can squeeze. Once the group reaches a blood vessel, cancer cells attach to a vessel’s wall, making tiny ruptures across which they enter circulation. Finally, secondary sites do not receive invading cancer cells passively. Instead, the primary tumour selectively prepares them, by the release of significant amounts of invasion-promoting factors, long before the metastatic spreading begins..

“Blob of cells” or a complex autonomous system?

“It seems more fitting to consider a tumour as a partially autonomous, self-organizing, adaptable structure with a wide spectrum of stress response mechanisms, rather than as a blob of dangerous cells that could be eradicated in a full-frontal attack”.

With these examples in mind (and numerous others not listed here but described in, for example, 1 and 2), it seems more fitting to consider a tumour as a partially autonomous, self-organizing, adaptable structure with a wide spectrum of stress response mechanisms, rather than as a blob of dangerous cells that could be eradicated in a full-frontal attack (which is, unfortunately,still the dominant approach in developing novel therapies). Such emergent collective behaviour stems from a network of cell-cell interactions and numerous (direct or indirect) feedbacks between tumour cells and the extracellular matrix. As a result, we are facing a tumour collective that can fight back by developing drug resistance and initiating metastasis.

Redefining the way we asses tumours

“We need to have a much broader discussion on the conceptual basis of tumour physiology

To better understand the processes that lead to such emergence of a highly integrated and adaptable system and eventually learn how to more efficiently fight tumours (in addition to the accumulation of precise experimental data) we need to have a much broader discussion on the

conceptual basis of tumour physiology. It will lead to the utilization of new mathematical and modelling tools that can be either developed from scratch and/or adapted from other disciplines. 

For example: 

  • Using Complex Network Analysis tools, we can use single-cell level interactions to analyse large-scale cell interaction patterns (both spatial and temporal). 
  • With the Ensemble Modelling Approach (a common practice in climate science) we can develop an integrated and systematic analysis of a wide range of model assumptions. 
  • By using category theory, we can view a tumour as an abstract algebraic structure and investigate its structure-preserving maps to assess the boundaries of possible tumour transformations under which a tumour’s structure remains stable. 
  • Finally, with machine learning, we can speed up the harmonization of experimental data, their metadata enrichment, and the comparison of different models and their dynamics. 

All these efforts could be coordinated via an authoritative body that analyses and validates a set of well-established models and coordinates their improvements. This is a huge effort but potential benefits are of immense importance.

References

  1. https://doi.org/10.1016/j.cell.2011.02.013
  2. https://doi.org/10.1016/S0092-8674(00)81683-9

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