Although much progress has been made in recent years, cancer remains one of the most dreaded diagnosis. For many types of cancer, current treatment options only alleviate symptoms or stop working after a while, only delaying the inevitable for a few months. 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. . Machine learning is only ever as good as the data it has been trained on feeding algorithms extensive, representative samples of DNA, RNA, or other biomarkers from which to learn is crucial to creating a highly sensitive test. The very act of treating cancer makes it evolve faster.
So far, machine-learning algorithms designed to detect minute quantities of tumour DNA in a blood sample the goal of so-called liquid biopsies removing the need to make an invasive biopsy procedure. With the help of the AI it can then predict whether the current therapy is the most adequate for the patient or whether they should switch to another. 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, which 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. A start-up developing artificial intelligence to analyze mutations in cfDNA from multiple liquid biopsy tests to evaluate the effectiveness of a patient's current treatment as it progresses.Each tumour is unique, but the processes which drive this heterogeneity don't stop during treatment: tumours are dynamic and ever-evolving.
What can the algorithm do?
It is supposed that the algorithm compares whole-genome sequences from tumour biopsy samples with patterns of mutations in fragments of cfDNA extracted from the blood. Scientists normally identify real tumour mutations from sequencing errors by measuring how many of the millions of repeated fragments of DNA contain a given mutation the more fragments agree, the more certain you can be that the mutation is present in the tissue. But with so few fragments of cfDNA to work with, Landau's software instead looks for complex patterns of mutations across the entire sequence to estimate whether a fragment has been sequenced correctly. Using this method, the algorithm detected non-small cell lung cancer mutations with 90 percent sensitivity in two patients, considerably better than standard liquid biopsy techniques perform.
Techniques combining liquid biopsy with machine learning will not only be less invasive than traditional diagnostic methods such as imaging and tissue biopsy, they may also be less expensive. You can't PET-MRI a tumor every week, but you could take a liquid biopsy.