Cancer scientists led by principal
investigator Dr. Daniel De Carvalho at Princess Margaret Cancer Centre
have combined "liquid biopsy," epigenetic alterations and machine
learning to develop a blood test to detect and classify cancer at its
earliest stages.
The findings, published online today in Nature, describe not
only a way to detect cancer, but hold promise of being able to find it
earlier when it is more easily treated and long before symptoms ever
appear, says Dr. De Carvalho, Senior Scientist at the cancer centre,
University Health Network.
"We are very excited at this stage," says Dr. De Carvalho. "A major problem in cancer is how to detect it early. It has been a 'needle in the haystack' problem of how to find that one-in-a-billion cancer-specific mutation in the blood, especially at earlier stages, where the amount of tumour DNA in the blood is minimal."
By profiling epigenetic alterations instead of mutations, the team was able to identify thousands of modifications unique to each cancer type. Then, using a big data approach, they applied machine learning to create classifiers able to identify the presence of cancer-derived DNA within blood samples and to determine what cancer type. This basically turns the 'one needle in the haystack' problem into a more solvable 'thousands of needles in the haystack', where the computer just needs to find a few needles to define which haystack has needles.
The scientists tracked the cancer origin and type by comparing 300 patient tumour samples from seven disease sites (lung, pancreatic, colorectal, breast, leukemia, bladder and kidney) and samples from healthy donors with the analysis of cell-free DNA circulating in the blood plasma. In every sample, the "floating" plasma DNA matched the tumour DNA. The team has since expanded the research and has now profiled and successfully matched more than 700 tumour and blood samples from more cancer types.
Beyond the lab, next steps to further validate this approach include analysing data from large population health research studies already under way in several countries, where blood samples were collected months to years before cancer diagnosis. Then the approach will need to be ultimately validated in prospective studies for cancer screening.
Dr. De Carvalho is a trained immunologist (University of Sao Paulo, Brazil) with postdoctoral training in cancer epigenomics (University of Southern California, USA) whose research focuses on cancer epigenetics. He holds the Canada Research Chair in Cancer Epigenetics and Epigenetic Therapy and is an Associate Professor in Cancer Epigenetics, Department of Medical Biophysics, University of Toronto.
The research was supported by University of Toronto's McLaughlin Centre, Canadian Institutes of Health Research, Canadian Cancer Society, Ontario Institute for Cancer Research through the Province of Ontario, and The Princess Margaret Cancer Foundation.
"We are very excited at this stage," says Dr. De Carvalho. "A major problem in cancer is how to detect it early. It has been a 'needle in the haystack' problem of how to find that one-in-a-billion cancer-specific mutation in the blood, especially at earlier stages, where the amount of tumour DNA in the blood is minimal."
By profiling epigenetic alterations instead of mutations, the team was able to identify thousands of modifications unique to each cancer type. Then, using a big data approach, they applied machine learning to create classifiers able to identify the presence of cancer-derived DNA within blood samples and to determine what cancer type. This basically turns the 'one needle in the haystack' problem into a more solvable 'thousands of needles in the haystack', where the computer just needs to find a few needles to define which haystack has needles.
The scientists tracked the cancer origin and type by comparing 300 patient tumour samples from seven disease sites (lung, pancreatic, colorectal, breast, leukemia, bladder and kidney) and samples from healthy donors with the analysis of cell-free DNA circulating in the blood plasma. In every sample, the "floating" plasma DNA matched the tumour DNA. The team has since expanded the research and has now profiled and successfully matched more than 700 tumour and blood samples from more cancer types.
Beyond the lab, next steps to further validate this approach include analysing data from large population health research studies already under way in several countries, where blood samples were collected months to years before cancer diagnosis. Then the approach will need to be ultimately validated in prospective studies for cancer screening.
Dr. De Carvalho is a trained immunologist (University of Sao Paulo, Brazil) with postdoctoral training in cancer epigenomics (University of Southern California, USA) whose research focuses on cancer epigenetics. He holds the Canada Research Chair in Cancer Epigenetics and Epigenetic Therapy and is an Associate Professor in Cancer Epigenetics, Department of Medical Biophysics, University of Toronto.
The research was supported by University of Toronto's McLaughlin Centre, Canadian Institutes of Health Research, Canadian Cancer Society, Ontario Institute for Cancer Research through the Province of Ontario, and The Princess Margaret Cancer Foundation.
Journal Reference:
- Shu Yi Shen, Rajat Singhania, Gordon Fehringer, Ankur Chakravarthy, Michael H. A. Roehrl, Dianne Chadwick, Philip C. Zuzarte, Ayelet Borgida, Ting Ting Wang, Tiantian Li, Olena Kis, Zhen Zhao, Anna Spreafico, Tiago da Silva Medina, Yadon Wang, David Roulois, Ilias Ettayebi, Zhuo Chen, Signy Chow, Tracy Murphy, Andrea Arruda, Grainne M. O’Kane, Jessica Liu, Mark Mansour, John D. McPherson, Catherine O’Brien, Natasha Leighl, Philippe L. Bedard, Neil Fleshner, Geoffrey Liu, Mark D. Minden, Steven Gallinger, Anna Goldenberg, Trevor J. Pugh, Michael M. Hoffman, Scott V. Bratman, Rayjean J. Hung, Daniel D. De Carvalho. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature, 2018; DOI: 10.1038/s41586-018-0703-0
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