Genome sequencing accelerates cancer detection

Date:
September 7, 2020
Source:
European Molecular Biology Laboratory - European Bioinformatics
Institute
Summary:
Recent cancer studies have shown that genomic mutations leading
to cancer can occur years, or even decades, before a patient
is diagnosed.

Researchers have developed a statistical model that analyses
genomic data to predict whether a patient has a high or low risk
of developing esophageal cancer. The results could enable early
detection and improve treatment of oesophageal cancer in future.



FULL STORY ========================================================================== Esophageal cancer is the eighth most common cancer worldwide. It often
develops from a condition called Barrett's esophagus. Existing monitoring
and treatment methods are very intrusive, and many patients have to
undergo burdensome procedures to ensure that no cancer is missed.


========================================================================== Researchers have now developed a statistical model that uses genomic
data to accurately predict whether a patient with Barrett's esophagus
has a high or low risk of developing cancer.

Genomics and statistics come together Researchers at the University
of Cambridge, EMBL's European Bioinformatics Institute (EMBL-EBI), and collaborators sequenced genomes from biopsies routinely collected from
patients with Barrett's esophagus. These patients are monitored for early
signs of esophageal cancer. The researchers used the data to look for differences between patients who were ultimately diagnosed with cancer
and those who were not. The data were used to develop a statistical model measuring each patient's individual risk. The research was published in
Nature Medicine.

Other recent cancer studies have shown that genomic mutations leading
to cancer may occur many years before a patient is diagnosed with the
disease. Being able 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 who were diagnosed
with esophageal cancer as 'high risk' more than eight years before
diagnosis. The numbers went up to 70% two years before diagnosis. Equally important, the model also accurately predicted patients who were at a
very low risk of developing cancer.



==========================================================================
"One of the unique things about this study was the richness of the data provided by colleagues at Addenbrooke's Hospital in Cambridge," explains
Moritz Gerstung, Group Leader at EMBL-EBI. "These patients have been in surveillance for over 15 years, so overall we had over 800 samples, taken
over time and from different areas of the esophagus. This allowed us to
measure in great detail what type of genomic changes occur and how these trajectories differ between patients with and without cancer. Without such thorough surveillance programmes, this study wouldn't have been possible."
The benefit of early detection Although people with Barrett's esophagus
are at considerably higher risk of developing esophageal cancer than the general population, only 1 patient in 300 will be diagnosed with cancer
per year. Nevertheless, they all have to go through intrusive monitoring procedures every two years. This surveillance can be uncomfortable,
stressful, and time-consuming for the patients, and it places an
additional burden on the healthcare system.

"The benefit of our method is twofold," explains Sarah Killcoyne,
Visiting Postdoctoral Fellow at EMBL-EBI. "The patients who have high-risk Barrett's, which is likely to become cancerous, can receive treatment
earlier. And individuals who have something that looks genetically stable,
and unlikely to develop into the disease, do not need to undergo such
intense surveillance. The hope is that our method can help improve
early detection and treatment, and decrease unnecessary treatment for
low-risk patients, without compromising patient safety." These results
mean that patients at greatest risk can be treated immediately, rather
than conducting repeated biopsies until early signs of cancer are found.

Conversely, patients with low risk and stable disease can be monitored
less frequently. Overall, the authors estimate that monitoring can be
reduced for 50% of patients with Barrett's esophagus.

"This is an exciting example of how a collaboration between computational biologists and clinician scientists can bring new insights into
an important clinical problem," says Rebecca Fitzgerald, Professor
of Cancer Prevention and MRC Programme Leader at the University of
Cambridge. "Esophageal cancer is devastating when it is diagnosed late,
but early intervention can be performed endoscopically and spare patients unnecessary chemotherapy and removal of their esophagus. Similar
approaches could be extended to other cancer types in the future."
According to the authors, the next steps are to refine the method, ideally
by analysing data from more patients. It is also important to bring
in clinical information and improve the model's accuracy. Eventually,
this will lead to clinical trials to show that this model is useful in
clinical practice for patients currently in surveillance.


========================================================================== Story Source: Materials provided by European_Molecular_Biology_Laboratory_-_European
Bioinformatics_Institute. Note: Content may be edited for style and
length.


========================================================================== Journal Reference:
1. Sarah Killcoyne, Eleanor Gregson, David C. Wedge, Dan J. Woodcock,
Matthew D. Eldridge, Rachel de la Rue, Ahmad Miremadi,
Sujath Abbas, Adrienn Blasko, Cassandra Kosmidou, Wladyslaw
Januszewicz, Aikaterini Varanou Jenkins, Moritz Gerstung,
Rebecca C. Fitzgerald. Genomic copy number predicts esophageal
cancer years before transformation. Nature Medicine, 2020; DOI:
10.1038/s41591-020-1033-y ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200907112339.htm

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