Pharmacometrics Ltd. provides consulting and proprietary software tools that can aid in interpreting biomarker data and increasing its predictive power. These tools can also be applied to enzyme inhibition data, pharmacokinetics, and analysis of drug combinations.
NEW - Biomathematics
Modelling the Process of Malignant Progression with a Genetic Algorithm – The Two Checkpoint Theory of Cancer
Malignant progression is the series of events by which a normal cell is progressively transformed into an increasingly abnormal, ultimately life-threatening, cancer cell. It is widely accepted that mutations (which may be random events, or caused by chemical carcinogens or radiation) progressively accumulate in dividing cells, and that a potentially cancerous cell population may become established through a process of Darwinian selection. However, the dynamics of this process are not well understood.
Pharmacometrics has used a genetic algorithm to model the process of malignant progression in mice, and shown that selection of randomly occurring mutations in control of the cell cycle cannot explain the observed incidence of tumours in mice. However, if the mutations occur in a specific order, the genetic algorithm predicts lifetime incidence of tumours in mice that is in good agreement with the observed data.
The first event to occur is loss or dysfunction of the G1 checkpoint. This results in premalignant cells (e.g. a benign tumour or intestinal polyp) that have a selective growth advantage but remain localised at the site of origin. If at this point a second mutation occurs that results in loss or dysfunction of the mitotic spindle assembly checkpoint (SAC), the cell now loses the ability to replicate its genetic material accurately, and chromosomal aberrations (aneuploidy) are observed in subsequent daughter cells. This has two consequences: many of the daughter cells will lack a full complement of genes required for survival, so they will die. However, a fraction of the surviving aneuploid cells will lose negative growth regulatory mechanisms. They will now have chromosomal instability, and effective mutation rates show a large increase. This process of loss of control of accurate chromosomal segregation at cell division is iterative, autocatalytic, and irreversible. The order of loss of the two checkpoints required for malignant transformation to occur is obligatory: a population of premalignant cells with a dysfunctional G1 checkpoint must become established before cells that also have a dysfunctional SAC can accumulate. Cells that have both a dysfunctional G1 checkpoint and a dysfunctional SAC are termed tumour stem cells. They are transformed, aneuploid, and genetically unstable, but remain localised at the site of origin.
If tumour stem cells now acquire a subsequent mutation that causes loss of anchorage to extracellular matrix (e.g. a mutation in integrin signalling) the cell becomes invasive. This often results in cells that were previously growing as a flat sheet on basement membrane growing into a 3-dimensional mass. Because of the genetic instability of tumour cells, this process can be very rapid. At this point the tumour starts to penetrate surrounding normal tissues – it has become invasive.
These invasive tumour cells may now acquire defects in a second anchorage - dependence process, responsible for cell-cell adhesion. This may occur through mutations in cadherin signalling. The resulting metastatic tumour cells are able to detach from the primary tumour mass, migrate to other parts of the body, and establish metastatic tumour growths.
According to the two checkpoint theory of malignant progression, normal cells are converted into full-blown tumour cells in four stages, the first two of which are slow (which is why malignant transformation is a relatively rare process), and which produce, successively, premalignant cells and tumour stem cells. These two events are then followed by two rapid, essentially inevitable events resulting successively in invasive and finally metastatic tumour cells. The two checkpoint theory is able to explain the observed incidence of spontaneous tumours in mice. Future work will examine the ability of the theory to predict tumour incidence in humans.
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