A blood test to diagnose and classify tumors could be revolutionary and practice-changing for patients and clinicians alike
TORONTO – A simple but highly sensitive blood test has been found to accurately diagnose and classify different types of brain tumours, resulting in more accurate diagnosis, less invasive methods and better treatment planning for patients, in the future.
The finding, published in Nature Medicine on June 22, 2020, describes a non-invasive and easy way to classify brain tumours. The study is also being presented virtually today at the prestigious Opening Plenary Session of the American Association for Cancer Research Annual Meeting 2020: Turning Science into Lifesaving Care.
A major challenge in treating brain cancers is the accurate diagnosis of different types of brain cancers, and tumours ranging from low grade – which can look almost normal under a microscope – to aggressive tumours. Cancer grades are used to determine prognosis, and assist in treatment planning.
Current methods to diagnose and establish the subtype of brain cancer based on molecular information rely upon invasive surgical techniques to obtain tissue samples, which is a high-risk procedure and anxiety-provoking for patients.
The ability to diagnose and classify the type of brain tumour without the need for a tissue sample is revolutionary and practice changing. In some cases, surgery may not even be necessary.
“If we had a better and more reliable way to diagnose and subtype tumours, we could transform patient care,” says Dr. Gelareh Zadeh, Medical Director of the Krembil Brain Institute, Head of Surgical Oncology at the Princess Margaret Cancer Centre, Senior Scientist at the Princess Margaret Cancer Research Institute, Professor of Surgery, University of Toronto, and a co-senior author in the study.
“It would have a tremendous impact on how we treat these cancers, and in how we plan our treatments.”
Dr. Zadeh worked with Senior Scientist Dr. Daniel De Carvalho at Princess Margaret Cancer Centre, who is a world leader in the field of cancer epigenetics applied to early detection, classification and novel therapeutic interventions.
Dr. De Carvalho’s lab specializes in a type of epigenetic modification called DNA methylation, which plays an important role in the regulation of gene expression (turning genes on or off) in cells. In cancer cells, DNA methylation patterns are disrupted, leading to unregulated cancer growth.
Dr. De Carvalho has previously developed a DNA methylation-based liquid biopsy approach to profile hundreds of thousands of these epigenetic alterations in DNA molecules circulating in the blood. These fragments are called circulating tumour DNA or ctDNA. Combining this new technology with machine learning, his team was able to develop a highly sensitive and accurate test to detect and classify multiple solid tumours.
Working together, Drs. Zadeh and De Carvalho decided to use this same approach in the challenging application of intracranial brain tumour classification. The clinicians and scientists tracked the cancer origin and type by comparing patient tumour samples of brain cancer pathology, with the analysis of cell-free DNA circulating in the blood plasma from 221 patients.
Using this approach, they were able to match the circulating plasma ctDNA to the tumour DNA, confirming their ability to identify brain tumour DNA circulating in the blood of these patients. Then, using a machine learning approach, they developed a computer program to classify the brain tumour type based solely on the circulating tumour DNA.
Prior to this, it was not thought possible to detect any brain cancers with a blood test because of the impermeable blood-brain barrier, says Dr. Zadeh. This barrier exists between the brain’s blood vessels and its tissue, protecting the brain from any toxins in the blood.
“But because this test is so sensitive in picking up even small amounts of highly specific tumour-derived signals in the blood, we now have a new, non-invasive way of detecting and discriminating between common brain tumours – something which was long thought impossible. This really is a tour de force,” explains Dr. Zadeh.
Dr. Daniel De Carvalho, a Canada Research Chair in Cancer Epigenetics and Associate Professor at University of Toronto, adds that the field of identifying tumour-specific alterations in ctDNA with new, more sensitive tests in various body fluids – such as blood and urine – is now at a turning point because advanced technologies can detect and analyze even the smallest traces of cancer-specific molecular signatures from the vast quantities of circulating non-tumour DNA fragments.
“The possibility to map epigenetic modifications genome-wide, combined with powerful computational approaches, has brought us to this tipping point,” says Dr. De Carvalho.
“Molecular characterization of tumours by profiling epigenetic alterations in addition to genetic mutations gives us a more comprehensive understanding of the altered features of a tumour, and opens the possibilities for more specific, sensitive, and tumour agnostic tests.”
In an accompanying paper, also published in Nature Medicine on June 22, 2020, Dr. Carvalho and his collaborators from the Dana-Farber Cancer Institute at Harvard University show the same blood test can accurately identify kidney cancer from circulating cell-free DNA obtained either from plasma or from urine.
Contributing to the study on diagnosing and classifying brain tumours are lead authors: Farshad Nassiri, Vanier Canada Graduate Scholar, Ankur Chakravarthy, Banting Postdoctoral Fellow, and Shengrui Feng. Additional authors include: Shu Yi Shen, Romina Nejad, Jeffrey Zuccato, Mathew R. Voisin, Vikas Patil, Craig Horbinski, Kenneth Aldape, and senior authors Drs. Gelareh Zadeh and Daniel De Carvalho.
Dr. Daniel De Carvalho, Shu Yi Shen and Dr. Ankur Chakravarthy are listed as inventors on patents filed that are related to this method.
Funding for the research includes: Canadian Institutes of Health Research, Brain Tumour Charity UK Quest for Cures, Princess Margaret Cancer Foundation, Toronto General & Western Hospital Foundation, Natural Sciences and Engineering Research Council of Canada and Ontario Institute for Cancer Research.
About Princess Margaret Cancer Centre
Princess Margaret Cancer Centre has achieved an international reputation as a global leader in the fight against cancer and delivering personalized cancer medicine. The Princess Margaret, one of the top five international cancer research centres, is a member of the University Health Network, which also includes Toronto General Hospital, Toronto Western Hospital, Toronto Rehabilitation Institute and the Michener Institute for Education at UHN. All are research hospitals affiliated with the University of Toronto. For more information: http://www.
About Krembil Brain Institute
Krembil Brain Institute at Toronto Western Hospital is home to one of the world’s largest and most comprehensive teams of physicians and scientists uniquely working hand-in-hand to prevent and confront problems of the brain and spine. One in three Canadians will experience a brain-related condition such as Parkinson’s, Alzheimer’s or stroke in their lifetime. Through state-of-the-art patient care and advanced research, we are working relentlessly to find new treatments and cures. For more information: http://www.
About University Health Network
University Health Network consists of Toronto General and Toronto Western Hospitals, the Princess Margaret Cancer Centre, Toronto Rehabilitation Institute, and The Michener Institute of Education at UHN. The scope of research and complexity of cases at University Health Network has made it a national and international source for discovery, education and patient care. It has the largest hospital-based research program in Canada, with major research in cardiology, transplantation, neurosciences, oncology, surgical innovation, infectious diseases, genomic medicine and rehabilitation medicine. University Health Network is a research hospital affiliated with the University of Toronto. For more information: http://www.
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Krembil Brain Institute, University Health Network
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