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Tracing and predicting the course that metastatic cancers take through the body can shed light on the cellular changes that lead to metastasis. Metastatic disease 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. Now, researchers at Princeton University have developed a new calculation method that increases the ability to track the spread of cancer cells. The results of the new study were published recently in Nature Genetics through an article titled "Inventory of Parsimonious Migration Stories for Metastatic Cancers".
"Are there any specific changes? to emigrate? "asked lead investigator Ben Raphael, Ph.D., professor of computer science at Princeton." In the current study, Dr. Raphael and his colleagues have presented an algorithm that can track Cancer metastasis by integrating DNA sequence data with information on the location of cells in the body.They call it MACHINA, which means the integrative analysis of the disease. metastatic and clonal history
"Our algorithm allows researchers to infer the past process of metastasis from currently obtained DNA sequence data," noted Dr. Raphael. "Datasets that we get these days are very complex, but complex data sets do not always require complex explanations. "
The new technique gives a clearer picture of cancer migration histories than the previous ones. studies have deduced complex migration patterns that did not reflect current knowledge of cancer biology.
The Princeton team discovered that by simultaneously mapping mutations and cell motions, MACHINA discovered that metastatic disease in some patients could result from fewer cell migrations. than previously thought. For example, in a patient with breast cancer, a previously published analysis suggested that metastatic disease resulted from 14 distinct migration events, whereas MACHINA suggested that a single secondary tumor in the lung seeded the metastases remaining by only five cell migrations. In addition to a breast cancer data set, Dr. Raphael and his team have applied their algorithm to analyze the metastatic profiles of patients with melanoma, ovarian cancer, and prostate
. The algorithm includes a model for the co-migration of genetically different cells, based on experimental evidence that tumor cells can travel in clusters to new sites in the body. In addition, this also explains the uncertainty of DNA data from sequencing mixtures of genetically distinct tumor cells and healthy cells.
This approach overcomes a number of challenges in drawing meaningful conclusions from "hard to analyze, noisy" data According to Andrea Sottoriva, Ph.D., researcher on evolution and cancer at the Institute of Cancer Research of London. "I predict that this new method will be widely used by the genomic community and will shed new light on the most lethal phase of cancer evolution."
The development of MACHINA paves the way for a broader examination of metastases in large cohorts, which could reveal key mutations that cause the spread of different types of cancer. In addition, Dr. Raphael plans to make the method more powerful by incorporating data from tumor DNA and tumor cells circulating in the bloodstream, as well as epigenetic modifications – reversible chemical modifications of DNA .
microscope, "concluded Dr. Raphael. "When you look at nature with a magnifying glass, you can miss important details. If you look with a microscope, you can see a lot more."
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