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.
Image Credit: The Scientist
News This Week
Shigella bacteria, which causes Shigellosis, is the primary cause of bacterial diarrhea and diarrheal death among juveniles under five years of age. Because of the antibiotic resistance of Shigella strains, no commercial vaccines are available to date. [...]
Scientists have built microscopic robots equipped with electronic “brains” that are capable of walking autonomously. A team from Cornell University in the US developed the solar-powered bots as part of research into a new generation of [...]
Blood samples from patients with long COVID who are still suffering from fatigue and shortness of breath after a year show signs of autoimmune disease, according to a study published today (Thursday) [...]
High-grade serous ovarian cancer (HGSOC) is among the deadliest human cancers and its prognosis remains extremely poor. An article published in Advanced Science explored the self-therapeutic properties of gold nanoparticles to identify a molecular axis that [...]
Antimicrobial peptides (AMPs) have a broad spectrum of antimicrobial activity and lyse microbial cells by interaction with biomembranes, offering great potential in designing new therapeutics. The antimicrobial resistance (AMR) caused due to overuse of [...]
Tumor cells are notoriously good at evading the human immune system; they put up physical walls, wear disguises and handcuff the immune system with molecular tricks. Now, UC San Francisco researchers have developed a [...]
Hyperspectral microscopy is an advanced visualization technique that combines hyperspectral imaging with state-of-the-art optics and computer software to enable rapid identification of nanomaterials. Since hyperspectral datacubes are large, their acquisition is complicated and time-consuming. [...]
Malignant brain tumors are cancerous growth in the brain with the possibility of spreading to other parts of the central nervous system (CNS). Brain tumors are highly invasive and have devastating consequences, poor prognosis, [...]