1- Cancer progression can be thought as a within-patient evolution.
2- Interaction between stochastic, where different outcomes arise (even if initial state is identical), and deterministic, where the outcomes are determined by the initial state, limits cancer predictability.
3- Fitness landscapes, which are simplified graphic representation of fitness as a function of genotype, can be used to predict cancer development.
Introduction: Precision cancer medicine is the personalisation of cancer treatment relating to aberrations in the patient’s genome. The oncogenes (a gene that can transform a cell into a tumour cell under specific conditions) varies between patients and so, to achieve efficacy in treatment, targeted treatment plans are proposed to the patient, such as immunotherapy and cancer vaccines. These treatments are tailored to specific tissues, gene mutations, and personal factors relevant to each unique case and are intended to increase survival rates and decrease treatment-related toxicity . However the efficacy of such treatments is limited, due to the stochastic and deterministic processes that govern cancer evolution, as shown in Fig.1 .
Cancer evolution: Cancer progression within an individual can be viewed an evolutionary process because it can readily adapt within the lifetime of the patient. Cancer is produced by competition among clonal lineages generated by somatic mutations, resulting in intratumor heterogeneity. Intratumor heterogeneity provides genetic and epigenetic material for selection and Darwinian evolution to act. This can lead to ambiguities about cancer’s trajectory on the patient; hence it is vital to understand the factors that contribute to this.
Stochastic Factors: Stochastic factors include mutation rates and genetic drift, which is a random process in which allele frequencies within a population change. Mutation rates, the site of mutagenesis and the timing of this can vary between an individual; therefore it is difficult to pinpoint the mutational mechanisms in a cancer cell. For example APOBEC, which are enzymes thatprotect from viral infections, canbe a major source ofmutations in cancer. However, the random nature of such mutations can cause difficulties within accurately predicting the course of a patient’s cancer. Inherited risk alleles are associated with certain cancers and have been driven by genetic drift . Drift has a more pronounced impact in smaller populations, hence after population bottlenecks. For example, after chemotherapy the cell population size is reduced and so few cells can colonise a new metastatic niche, leading to the prevalence of cancer in the patient. It is easy to see how (although chemotherapy is a standard and effective part of treatment for cancer), due to the nature of cancer to continuously evolve, it can be difficult to forecast the beneficial or detrimental effect to a patient .
Deterministic Factors: A new mutation that increases the likelihood of cell survival and reproductionwill have a higher frequency within a population, especially after genetic drift. This is an example of clonal selection. Clonal competition is present in tumours where subclonal populations, even those that contain beneficial mutations for a patient, may be outcompeted. This can be described as intratumor heterogeneity and causes the increased growth of certain cell populations. Since it is hard to detect which subclonal population can proliferate and which one gets outcompeted, the cancer progression of the patient is uncertain. Examples of this include mutations in the p16 gene and p53 gene, which have key roles in the cell cycle progression. Clonal selection drives clonal evolution as selection pressures, due to changes in the micro environment or genomic instability (caused by mutations), promote competition. This exhibits yet another limitation for precision cancer medicine .
Next Steps: The relative fitness of genetic alterations can be depicted by a fitness landscape, where the topology predicts the probability of which path evolution takes amongst the competing potentials. For example, a higher fitness increment for a mutation will correlate to a lower likelihood of being eradicated by genetic drift, hence that mutation will proliferate and become abundant. Although the accuracy of personalised medicine for cancer can be limited, it can be overcome by producing a coherent framework through computational modelling. This cancer evolutionary framework should take many parameters into consideration, including metastasis, empirical fitness landscapes of cell populations and exact measurements of the clonal expansion of cancer cells .
Conclusion: The most advanced tumours remain untreatable because of resistance to targeted therapies. This is due to the stochastic and deterministic factors outlined above, resulting in intratumor heterogeneity. However modelling the therapeutic outcome, by using an evolutionary framework, seems to be a probable resolution.
COPYRIGHT: This article is the property of We Speak Science, a non-profit institution co-founded by Dr. Detina Zalli and Dr. Argita Zalli. The article is written by Shanthavi Wijayakumar, King’s College London, UK.
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