Cancer develops due to the acquisition of somatic mutations – those are the mutations you acquire throughout your life, they are not inherited nor passed to the offspring. These are the opposite of germline mutations, which are inherited from ancestors or gained very early during embryogenesis, in the uterus. For many years it has been clear that cancer is a genetic disease, or more specifically that cancer is a disease of our genome; however, it took many years to conclude that an individual patient’s prognosis and treatment strategy cannot be based only on a single lab test due to the complexity of the disease. Even pathomorphological assay might not be enough, especially if we want to administer targeted therapy. Biopsy and detailed genetic analysis are necessary. 

Cancer development risk increases with age. However, some cancer types differ in their prognosis and symptoms between adults and children. For example, childhood acute myeloid leukaemia, AML, is more often connected with complicated infections and increased mortality. Similarly, breast cancer at an early age is usually more aggressive and difficult to treat. 

Cancer heterogeneity revealed by sequencing

Recent studies shed new light on the molecular landscape of cancer as a highly heterogeneous disease, composed of many clones with distinct mutational patterns and mutational signatures, therefore presenting differential susceptibilities to treatment. This phenomenon explains why some cancers progress rapidly after chemotherapy, leading to patients’ relapse, while other cancers respond well and the effects of treatment are long-lasting. 

Cancer initiation and further progression occur due to the positive selection of some “driver” mutations. Such mutations inactivate tumour suppressor genes or activate oncogenes. Average cancer contains 2-8 drivers and the total number of drivers seem to be definite since all of those genes affect such pathways as apoptosis, proliferation, chromatin regulation and genome stability. Other mutations, which were not positively selected during cancer evolution, are called “passengers”.  

To facilitate the hunt for drivers, new treatment strategies and prevention methods, as the foundation of personalised medicine, a catalogue of genetic changes in cancer was created – The Cancer Genome Atlas. Information from this project is used in online genomic databases, e.g. Cosmic. Next Generation Sequencing techniques have revolutionized cancer research, enabling fast and precise detection of tumour-specific mutations, as well as cancer biomarkers. In most cases, such mutations are located in the coding sequence of a gene, leading to aberrations in that particular signalling pathway. Despite this,  cancer biomarkers are often much more complicated, consisting of entire chromosomal fragments, small structural variants, or even combinations thereof.

Cancer changes over time

Globally-expressed genes have more important functions, such as housekeeping genes. Since most positively-selected cancer mutations are located within globally-expressed genes, the development of therapeutic agents targeting them without causing harm to healthy cells could be even more challenging. The significance of mutation order remains unclear. The initial mutation can possibly change the cellular composition, facilitating the next mutation to occur, creating a more “friendly” environment for another change, genetic or epigenetic. One of the mutations will provide a growth advantage and pass positive selection, leading to tumour growth. 

In fact, tyrosine kinase inhibitors, such as imatinib have revolutionized cancer treatment, starting from leukaemia. However, dynamically evolving cancer can respond to selective pressure and gain new mutations, even during clinical trials and the treatment process itself. Intratumoural and Intertumoural heterogeneity lead to the evolution of resistance. New approaches to cancer evolution appeared with the advent of NGS methods. Cancer is no longer taken as a whole, but rather as a sum of subclones, related to each other, arising from a single ancestral cell that collected different somatic mutations over time. Selection acts on individual dividing cells to confer a survival advantage; it operates on cell’s phenotypes, which are unstable and prone to modifications by both internal and external factors. Some of these changes are very stable, such as oncogenic mutations. 

Personalised cancer therapy should take into account real-time tracking of a patient’s genomic landscape or at least any changes (CT scans), also microenvironmental changes, such as different cytokine levels, for example, IL-2, IL-8, IL-15. This inevitably increases the price of already expensive personalised therapy but brings confidence that the therapy really works.

Every cancer cell might be different

In very rare cases, mutation can improve cell functioning and give a survival advantage to the subclone population. A tumour is in fact highly heterogeneous. Methods of cancer stratification are usually based on cancer panel tests, which reflect mostly those mutations that are present in the majority of cancer cells, neglecting minor subclones. These subclones could be equally clinically relevant, providing resistance to the treatment used. Most modern chemotherapeutics are capable of eradicating the majority of cancer cells; despite this, failing to remove even a small fraction of cancer subclones can result in cancer recurrence. This is one of the reasons why we often administer a combination of drugs rather than a single agent, trying to maximise the positive treatment outcome. Combining more than one drug and treatment advancement is often crucial for final success. Fast identification and prognostic evaluation of all clones present in particular cancer might be necessary for proper molecular diagnosis and choosing the best treatment scenario for an auspicious outcome. According to the bottleneck effect, after treatment, such resistant clones will expand soon after the treatment and become dominant, causing the disease’s relapse. In such cases, organoid structures can be established in vitro using a patient’s cancer cells of individual tumour clonal lineages. Every subclone should be carefully sequenced and treated according to mutations present – firstly in the culture, but after successful laboratory trials, the most effective treatment could be given to the patient. It is also relevant to establish a suitable treatment plan, including whether to treat cancer clones simultaneously, or one by one. If the cancer is polyclonal and in an advanced stage, combination therapies could be implemented.

Cancer recurrence theories

Two theories have been formulated to explain the emergence of intratumoural heterogeneity: the model of cancer stem cells (CSC) and the model of clonal evolution. Historically, both models were presented as separate and exclusive processes. Some cancers were believed to follow CSCs model, whereas other types of cancer – as following the clonal evolution model. Now it becomes clear that in most cancer types both models are relevant, contributing to cancer heterogeneity and complexity. 

The first model states that only a small subset of CSCs exist and possess the capacity to sustain cancer development through self-renewal, proliferation, differentiation and all characteristics typical to normal stem cells. It is believed that thanks to the differentiation process, those daughter cells can also form cancer heterogeneity and hierarchy. In contrast, the clonal evolution model establishes competition between genetically diverse cancer subclones, which might be similar to the “survival of the fittest” rule in the theory of evolution. Every cell has equal potential for growth and development, depending on factors such as microenvironment, external influences, or simply by chance. Cancer cells within the same clone will share similar genotypes, but they can behave differently depending on the environment (context), for example, the presence of particular chemicals or oxygen. Such exposure leads to the waxing and waning of subclonal populations of cancer cells within the same tumour.

Purifying selection could be typical of germline mutations, likely due to the fact that organisms are already well-adapted. In contrast, cancer mutations are affected mostly by positive selection, which reflects the selection of those driver mutations, which give adaptive properties to the cancer cells. Recurrent cancer might have some mutations present at diagnosis, but not necessarily – some cells could have escaped from therapy and gained new mutations, allowing them to survive and progress. 

Personalised medicine in action

Personalised medicine uses genomic profiles to identify a patient’s tumour characteristics, based on molecular cancer signatures. The aim is to target the most dangerous mutation and all of the subclones, preventing further progression or recurrence. Modern genomics, with the aid of AI-based algorithms, may also provide information about the best treatment for each tumour, ranking cancer therapies from the one with the highest probability of being effective, to the one that possibly won’t work at all. This is a major breakthrough, allowing clinicians to pick the right treatment for the right patient without wasting their precious time. This approach is also cost-effective and prevents unnecessary suffering – since every treatment is accompanied by side effects, patients do not undergo ineffective treatment regimens at all. The ultimate goal for all clinical practices should be the complete eradication of the disease, with all its subclonal populations, to prevent self-restoring and relapse.


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