Genetic and Non-Genetic Mechanisms Underlying Cancer Evolution

Abstract

Cancer development can be defined as a process of cellular and tissular microevolution ultimately leading to malignancy. Strikingly, though this concept has prevailed in the field for more than a century, the precise mechanisms underlying evolutionary processes occurring within tumours remain largely uncharacterized and rather cryptic. Nevertheless, although our current knowledge is fragmentary, data collected to date suggest that most tumours display features compatible with a diverse array of evolutionary paths, suggesting that most of the existing macro-evolutionary models find their avatar in cancer biology. Herein, we discuss an up-to-date view of the fundamental genetic and non-genetic mechanisms underlying tumour evolution with the aim of concurring into an integrated view of the evolutionary forces at play throughout the emergence and progression of the disease and into the acquisition of resistance to diverse therapeutic paradigms. Our ultimate goal is to delve into the intricacies of genetic and non-genetic networks underlying tumour evolution to build a framework where both core concepts are considered non-negligible and equally fundamental.

Keywords: cancer evolution; genetic heterogeneity; non-genetic heterogeneity; plasticity; tumour.

Figure 1

Models of tumour evolution. Scheme summarizing our current knowledge on evolutionary processes taking place in different types of human cancers. Phylogenetic trees depict the relationship among sub-clonal populations over time. Black circles represent nodes of diversification. Red circles depict functional/phenotypic convergence in two genetically distinct sub-clones.

Figure 2

Non-genetic heterogeneity within clonal populations of cells. (a) Flow cytometry analysis reveals that cells within a clonal population display divergence in protein expression levels (normal distribution), which results in the establishment of distinct phenotypes. (b) Scheme representing a model [96] that suggests that cells within a population may occupy distinct positions in a “gene expression space”. Depending on the position within the “gene expression space” and the characteristics of the microenvironment, some of those cells will represent specialized phenotypes—archetypes (in the figure: Archetype 1, Archetype 2, Archetype 3, Archetype 4) that display a “functional optimum” at performing defined tasks (proliferation, drug resistance, hypoxia resistance, etc.) in the present environment. The model also proposes that a number of intermediate phenotypes may exist and reside in the phenotypic space between specialized archetypes. Those phenotypes are called generalists. It is suggested that generalists are less efficient at performing specialized tasks; however, they may have a survival advantage in rapidly changing environments.

Figure 3

Models of cancer progression upon drug treatment. (a) A population of cancer cells may contain a pre-existing genetic mutation that confers resistance to an anti-cancer drug. This genetically encoded phenotype is further selected upon drug treatment, leading to outgrowth of a drug-resistant tumour. (b) A population of cancer cells may display extensive non-genetic heterogeneity resulting in multiple phenotypic (epi-)states co-existing within a population. Some of those (epi-)states may be resistant to drug treatment. Non-genetically encoded phenotypes can be further selected, leading to an outgrowth of a drug-resistant tumour. However, contrary to genetically encoded resistance, when drug treatment is discontinued, the population of cancer cells may re-establish its initial heterogeneity that displays drug sensitive phenotypes. (c) A population of cancer cells may contain drug resistant phenotypes driven by both genetic and non-genetic mechanisms. Such resistant phenotypes are selected upon drug treatment, leading to the generation of drug-resistant tumours. Each genotype may produce a variety of phenotypic (epi-)states. The colour of the nuclei depicts a defined genotype; the colour of the cytoplasm shows distinct phenotypes that may result from genetic and non-genetic mechanisms.

Figure 4

Genetic and non-genetic heterogeneity and their functional relevance in cancer evolution. (a) Cancer cell populations may be characterized by extensive genetic and non-genetic heterogeneity, which leads to a variety of phenotypic states with distinct functional characteristics. This may have a crucial impact on cancer progression, growth rates, metastatic potential, resistance to immune surveillance, response to drugs, etc. (b) A single genotype may give rise to cells that display functionally distinct phenotypes that will respond differently to various biological cues (for example, anti-cancer drugs). Understanding the molecular basis orchestrating the establishment and maintenance of such phenotypic states may provide a tool to selectively direct the development of cancer cell populations towards less aggressive drug sensitive phenotypes. The colour of the nuclei depicts a defined genotype; the colour of the cytoplasm shows distinct phenotypes that may result from genetic and non-genetic mechanisms.

Figure 5

Novel systematic experimental approaches—powerful tools to diagnose cancer and monitor its progression. Tumours are rapidly-changing constantly-evolving open ecosystems. This feature of cancer development significantly affects the efficacy of anti-cancer therapy and patient outcomes. Therefore, it is crucial to monitor the dynamic changes taking place within a tumour along its development and upon drug treatment. Emerging experimental approaches that allow extraction and enrichment of circulating tumour cells and circulating tumour DNA may facilitate systematic analysis of tumour development at different stages of its progression. Furthermore, novel molecular biology technologies such as single-cell RNA and single-cell DNA sequencing as well as spatial transcriptomics provide an integral and more complete view on complex intra-tumour architecture and the dynamic changes taking place along cancer development. Overall, these may allow the design of more effective personalized therapeutic paradigms. The colour of the nuclei depicts a defined genotype; the colour of the cytoplasm shows distinct phenotypes that may result from genetic and non-genetic mechanisms.