Genome sequencing accelerates cancer detection

Esophageal cancer is the eighth most common cancer in the world. It often develops in a condition called Barrett’s esophagus. Existing monitoring and treatment methods are very cumbersome, and many patients have to go through tedious steps to keep up with cancer.

The researchers are now developing a statistical model that uses genomic data to accurately predict whether patients with Barrett’s esophagus are at high or low risk of developing cancer.

Researchers at the University of Cambridge, EMBL’s European Bioinformatics Institute (EMBL-EBI), and collaborators have sequenced the genome from biopsies routinely collected from patients with Barrett’s esophagus. These patients will be monitored for early signs of esophageal cancer. The researchers used the data to look at the difference between patients who were eventually diagnosed with cancer and those who weren’t. The data was used to develop a statistical model that measures the individual risk of each patient. The research was published in Natural medicine..

Other recent cancer studies have shown that genomic mutations that lead to cancer can occur years before a patient is diagnosed with the disease. The ability to identify these mutations could provide a new route to early diagnosis and treatment.

Using genomic data from 88 patients with Barrett’s esophagus, the researchers identified half of the patients diagnosed with esophageal cancer as “high risk” more than 8 years before diagnosis. The number reached 70% two years before diagnosis. Equally important, the model accurately predicts patients who have a very low risk of developing cancer.

Moritz Gerstung, Group Leader of EMBL-EBI, explains: “One of the unique things about this study is the wealth of data provided by colleagues at the Cambridge Addenbrooke’s Hospital.” “These patients have been monitored for over 15 years and, overall, over time. There were over 800 samples taken from different areas of the esophagus, which allowed us to make a detailed measurement of what kind of genomic alterations occurred and how these trajectories differed. Between one patient and the other. Without such a thorough surveillance program, this study would have been impossible.”

People with Barrett’s esophagus are at a much higher risk of developing esophageal cancer than the general population, but only 1 in 300 patients is diagnosed with cancer each year. Nevertheless, they all have to go through intrusion monitoring procedures every two years. This monitoring can be uncomfortable, stressful, time-consuming for the patient, and places an additional burden on the healthcare system.

“There are two advantages of our method,” explains Sarah Killcoyne, a visiting postdoctoral fellow at EMBL-EBI. “Patients with a high-risk Barrett who are more likely to be cancerous can get treatment sooner, appear genetically stable, and are less likely to develop disease Individuals who have ones do not have to undergo such intense monitoring, because our method helps improve early detection and early treatment without compromising patient safety and eliminates the need for low-risk patients. I hope we can reduce treatment.”

These results mean that the highest-risk patients can be treated immediately, rather than having to repeat a biopsy until early signs of cancer are found. Conversely, patients with low risk and stable disease will be monitored less frequently. Overall, the authors estimate that monitoring can be reduced in 50% of patients with Barrett’s esophagus.

“This is an exciting example of how collaboration between computational biologists and clinical scientists can bring new insights to important clinical issues,” says Rebecca Fitzgerald, Professor of Cancer Prevention and MRC Program Lead at the University of Cambridge. Says. “Esophageal cancer is devastating when diagnosed late, but early intervention can be done endoscopically, avoiding unnecessary chemotherapy and esophagectomy for patients. Similar approaches will be available in the future for other types. It can also spread to cancer.”

According to the authors, the next step is to improve the method, ideally by analyzing data from more patients. It is also important to incorporate clinical information and improve the accuracy of the model. Ultimately, this leads to clinical trials and shows that this model helps in clinical practice for patients currently under surveillance.

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Killcoin, S. , Others (2020). Genome copy number predicts esophageal cancer before transformation. Natural medicine.. Published online 07 09. DOI: 10.1038 / s41591-020-1033-y

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