Machine-learning algorithms tuned to detecting cancer DNA in the blood could pave the way for personalized cancer care.
copyright by www.the-scientist.com
Modern cancer medicine is hampered by two big challenges—detecting cancers when they are small and offering cancer patients personalized, dynamic cancer care. To find solutions, several academic labs and biotech firms are turning to artificial intelligence, working to develop machine-learning algorithms that could help decipher weak signals in the blood that can identify cancers at an early stage and indicate whether a cancer is responding to treatment in real time.
“You have to find this needle in a haystack . . . this very weak signal amongst all of the cacophony of everything else happening in the body,” says Dave Issadore , a bioengineer at Pennsylvania State University and founder of Chip Diagnostics, which is developing a machine-learning method to diagnose disease by sequencing extracellular vesicles’ cargo.
So far, machine-learning algorithms designed to detect minute quantities of tumor DNA in a blood sample—the goal of so-called liquid biopsies—have performed well in clinical validation studies, but no self-learning algorithm has yet been approved for clinical use. These have the potential to outperform imaging and tissue biopsies in detecting and monitoring cancers by looking for mutations in DNA, RNA, and proteins directly from the blood.
“I think the dream of liquid biopsy is to, A), detect cancer when there is very little of it, and, B) detect cancers during treatment,” says Dan Landau, a clinical oncologist and researcher at Weill Cornell Medical School in New York. However, in the both contexts, liquid biopsy techniques struggle to accurately detect cancer among the infinitesimally small quantities of tumor nucleic acids in the blood. Although the technique’s performance varies between cancer types, liquid biopsies so far have been able to detect cancer in around half of early-stage patients diagnosed through imaging, giving it a sensitivity of just 50 percent.
The problem is that “we do not have the luxury to run all the different kinds of assays with the limited cell-free nucleic acid,” writes Siew-Kee Low, a researcher at the Japanese Foundation for Cancer Research in Tokyo, in an email. Because of this limitation, only targeted assays of known cancer mutations have so far been possible. But many cancers lack a simple set of known mutations that can identify a tumor and, subsequently, determine the best treatment option. That’s where machine-learning comes in.
Artificial neural networks, which use thousands of connected nodes to interpret data much like neurons in the brain and form the basis of machine learning, can process vast amounts of data and identify patterns that would likely elude a human doctor. Not only that, machine learning is self-improving; as more data are put into the system, it fine-tunes its own algorithm to improve its diagnostic acumen. “It’s an evolving diagnostic,” says Gabe Otte, CEO of artificial intelligence (AI) genomics company Freenome. The company announced its first clinical validation study in May on a machine-learning algorithm that screens at-risk patients for DNA, RNA, and protein sequences in the blood that indicate colorectal cancer. […]
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Machine-learning algorithms tuned to detecting cancer DNA in the blood could pave the way for personalized cancer care.
copyright by www.the-scientist.com
“You have to find this needle in a haystack . . . this very weak signal amongst all of the cacophony of everything else happening in the body,” says Dave Issadore , a bioengineer at Pennsylvania State University and founder of Chip Diagnostics, which is developing a machine-learning method to diagnose disease by sequencing extracellular vesicles’ cargo.
So far, machine-learning algorithms designed to detect minute quantities of tumor DNA in a blood sample—the goal of so-called liquid biopsies—have performed well in clinical validation studies, but no self-learning algorithm has yet been approved for clinical use. These have the potential to outperform imaging and tissue biopsies in detecting and monitoring cancers by looking for mutations in DNA, RNA, and proteins directly from the blood.
“I think the dream of liquid biopsy is to, A), detect cancer when there is very little of it, and, B) detect cancers during treatment,” says Dan Landau, a clinical oncologist and researcher at Weill Cornell Medical School in New York. However, in the both contexts, liquid biopsy techniques struggle to accurately detect cancer among the infinitesimally small quantities of tumor nucleic acids in the blood. Although the technique’s performance varies between cancer types, liquid biopsies so far have been able to detect cancer in around half of early-stage patients diagnosed through imaging, giving it a sensitivity of just 50 percent.
The problem is that “we do not have the luxury to run all the different kinds of assays with the limited cell-free nucleic acid,” writes Siew-Kee Low, a researcher at the Japanese Foundation for Cancer Research in Tokyo, in an email. Because of this limitation, only targeted assays of known cancer mutations have so far been possible. But many cancers lack a simple set of known mutations that can identify a tumor and, subsequently, determine the best treatment option. That’s where machine-learning comes in.
Artificial neural networks, which use thousands of connected nodes to interpret data much like neurons in the brain and form the basis of machine learning, can process vast amounts of data and identify patterns that would likely elude a human doctor. Not only that, machine learning is self-improving; as more data are put into the system, it fine-tunes its own algorithm to improve its diagnostic acumen. “It’s an evolving diagnostic,” says Gabe Otte, CEO of artificial intelligence (AI) genomics company Freenome. The company announced its first clinical validation study in May on a machine-learning algorithm that screens at-risk patients for DNA, RNA, and protein sequences in the blood that indicate colorectal cancer. […]
read more – copyright by www.the-scientist.com
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