Telomeres are repetitive DNA sequences located at the ends of each chromosome in the human genome. They act as protective caps that maintain the integrity of the genome. Significantly, telomeres ensure proper cell division and prevent the formation of genomic aberrations that may lead to disease.
During cell division, the cellular genome should be accurately replicated to produce an identical copy of the genome. Properly functioning telomeres contribute to this process by regulating chromosomal organization to prevent random interactions between the chromosomes or regions of chromosomes.
Progressive shortening of telomeres leads to growth arrest and cell death in normal cells. “Telomeres dysfunction” is commonly seen in cancer cells where critically short telomeres do not trigger cell death. Moreover, telomere dysfunction is characterized by an altered 3D organization of the telomeres, often associated with telomeric aggregates, altered numbers and lengths of individual telomeres.
Telomere dysfunction triggers the formation of a variety of genetic modifications including recombination, fusions, and translocations; leading to ongoing and dynamic genomic instability (“GI”).
GI is a process where the cellular genome undergoes uncontrolled modifications during each cell division, and may cause the cell to escape natural programed cell death leading to genetic diseases including cancer.
GI by nature is an uncontrolled process; it may vary from cell to cell, from cancer to cancer and from one patient to another. This variability leads to what is known as disease heterogeneity, a hallmark in cancer.
In order to tailor treatments for individual patients over the course of their disease, diagnostic and prognostic tools must account for cellular heterogeneity and the evolving nature of proliferative disease. This can be achieved with technologies that employ single cell analysis of circulating tumor cells that are typically isolated using liquid biopsies.
Telo Genomics’ platform technology, TeloView®, is conducted on sample single cells; it can account for disease heterogeneity and also reduces the sample size needed to reach highly predictive powers in clinical studies.