New method to track spread of cancer cells

Washington, July 1: Scientists 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.

This migration of cells can lead to metastatic disease, which causes about 90 per cent of cancer deaths from solid tumours — masses of cells that grow in organs such as the breast, prostate or colon.

Understanding the drivers of metastasis could lead to new treatments aimed at blocking the process of cancer spreading.

In a study published in the May issue of Nature Genetics, researchers from Princeton University in the US presented an algorithm that can track cancer metastasis by integrating DNA sequence data with information on where cells are located in the body.

They call it MACHINA, which stands for

“metastatic and clonal history integrative analysis.”

“Our algorithm enables researchers to infer the past process of metastasis from DNA sequence data obtained at the present time,” said Ben Raphael, a professor at Princeton.

The technique yields a clearer picture of cancer migration histories than previous studies that relied on methods based on DNA sequences alone.

Some of these studies inferred complex migration patterns that did not reflect current knowledge of cancer biology.

“The data sets we get these days are very complex, but complex data sets do not always require complex explanations,” said Raphael.

By simultaneously tracing cells’ mutations and movements, MACHINA found that metastatic disease in some patients could result from fewer cellular migrations than previously thought.

For example, 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.

In addition to a breast cancer data set, researchers applied their algorithm to analyse metastasis patterns from patients with melanoma, ovarian and prostate cancers.

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.

It also accounts for the uncertainty in DNA data that comes from sequencing mixtures of genetically distinct tumour cells and healthy cells.