Migration of cells from one part of the body to another can lead to metastatic disease, which causes about 90 per cent of cancer deaths from solid tumours. Researchers at Princeton University in US have developed a new computational method that increases the ability to track the spread of cancer cells from one part of the body to another.
Understanding the drivers of metastasis could lead to new treatments aimed at blocking the process of cancer spreading through the body.
Princeton professor Ben Paphael said algorithm or MACHINA enables researchers to infer the past process of metastasis from DNA sequence data obtained at the present time.
The technique yields a clearer picture of cancer migration histories than previous studies that relied on methods based on DNA sequences alone.
“The data sets we get these days are very complex, but complex data sets do not always require complex explanations,” Raphael said.
MACHINA found that metastatic disease in some patients could result from fewer cellular migrations than previously thought.
In one breast cancer patient, a previously published analysis proposed that metastatic disease resulted from 14 separate migration events, while MACHINA suggested that a single secondary tumour in the lung seeded the remaining metastases through just five cell migrations.
The researchers applied their algorithm to analyse metastasis patterns from patients with melanoma, ovarian and prostate cancers, besides a breast cancer data set.
Several additional features helped improve MACHINA’s accuracy. The algorithm includes a model for the comigration of genetically different cells, based on experimental evidence that tumour cells can travel in clusters to new sites in the body.
It also accounts for the uncertainty in DNA data that comes from sequencing mixtures of genetically distinct tumour cells and healthy cells.
The findings were published in the journal Nature Genetics.
(With inputs from agencies)