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Applying Computing Power to Track the Spread of Cancer

Summary: A new computational model increases the ability to track the spread of cancer throughout the body, researchers report.

Source: Princeton.

Princeton researchers 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 percent of cancer deaths from solid tumors — 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 through the body.

“Are there specific changes, or mutations, within these cells that allow them to migrate?” asked Ben Raphael, a professor of computer science at Princeton and the senior author of the new research. “This has been one of the big mysteries.”

In a study published in the May issue of Nature Genetics, Raphael and his colleagues 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 Raphael.

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 didn’t reflect current knowledge of cancer biology.

“The data sets we get these days are very complex, but complex data sets don’t 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 tumor in the lung seeded the remaining metastases through just five cell migrations. In addition to a breast cancer data set, Raphael and his team applied their algorithm to analyze 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 tumor 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 tumor cells and healthy cells.

This approach overcomes a number of challenges to draw meaningful conclusions from the “difficult to analyze, noisy” data that result from tumor DNA sequencing, said Andrea Sottoriva, the Chris Rokos Fellow in Evolution and Cancer at The Institute of Cancer Research, London. “I predict this new method will be of widespread use to the genomic community and will shed new light on the most deadly phase of cancer evolution,” he said.

ai image

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 didn’t reflect current knowledge of cancer biology. NeuroscienceNews.com image is in the public domain.

MACHINA’s development paves the way for a broader examination of metastasis patterns in large cohorts of cancer patients, which could reveal key mutations that cause different types of cancer to spread.

Raphael also plans to make the method more powerful by incorporating data from tumor DNA and tumor cells that circulate in the bloodstream, as well as epigenetic changes — reversible chemical modifications of DNA.

“A better algorithm is like a better microscope,” said Raphael. “When you look at nature with a magnifying glass, you may miss important details. If you look with a microscope you can see much more.”

About this neuroscience research article

Other study authors were Mohammed El-Kebir, a former post-doctoral research associate in Raphael’s group who is now an assistant professor at the University of Illinois at Urbana-Champaign; and Ph.D. student Gryte Satas.

Funding: The work was supported by the National Institutes of Health and the National Science Foundation.

Source: Molly Sharlach – Princeton
Publisher: Organized by NeuroscienceNews.com.
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Abstract for “Inferring parsimonious migration histories for metastatic cancers” by Mohammed El-Kebir, Gryte Satas & Benjamin J. Raphael in Nature Genetics. Published April 26 2018.
doi:10.1038/s41588-018-0106-z

Cite This NeuroscienceNews.com Article
Princeton “Insights Into Amyloids Associated with Alzheimer’s and Type 2 Diabetes.” NeuroscienceNews. NeuroscienceNews, 29 June 2018.
<http://neurosciencenews.com/ai-cancer-tracking-9484/>.
Princeton (2018, June 29). Insights Into Amyloids Associated with Alzheimer’s and Type 2 Diabetes. NeuroscienceNews. Retrieved June 29, 2018 from http://neurosciencenews.com/ai-cancer-tracking-9484/
Princeton “Insights Into Amyloids Associated with Alzheimer’s and Type 2 Diabetes.” http://neurosciencenews.com/ai-cancer-tracking-9484/ (accessed June 29, 2018).

Abstract

Inferring parsimonious migration histories for metastatic cancers

Metastasis is the migration of cancerous cells from a primary tumor to other anatomical sites. Although metastasis was long thought to result from monoclonal seeding, or single cellular migrations, recent phylogenetic analyses of metastatic cancers have reported complex patterns of cellular migrations between sites, including polyclonal migrations and reseeding. However, accurate determination of migration patterns from somatic mutation data is complicated by intratumor heterogeneity and discordance between clonal lineage and cellular migration. We introduce MACHINA, a multi-objective optimization algorithm that jointly infers clonal lineages and parsimonious migration histories of metastatic cancers from DNA sequencing data. MACHINA analysis of data from multiple cancers shows that migration patterns are often not uniquely determined from sequencing data alone and that complicated migration patterns among primary tumors and metastases may be less prevalent than previously reported. MACHINA’s rigorous analysis of migration histories will aid in studies of the drivers of metastasis.

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