Study findings could lead to new clinical laboratory diagnostics that give pathologists a more detailed understanding about certain types of cancer
New studies proving artificial intelligence (AI) can be used effectively in clinical laboratory diagnostics and personalized healthcare continue to emerge. Scientists in the UK recently trained an AI model using machine learning and deep learning to enable earlier, more accurate detection of 13 different types of cancer.
DNA stores genetic information in sequences of four nucleotide bases: A (adenine), T (thymine), G (guanine) and C (cytosine). These bases can be modified through DNA methylation. There are millions of DNA methylation markers in every single cell, and they change in the early stages of cancer development.
One common characteristic of many cancers is an epigenetic phenomenon called aberrant DNA methylation. Modifications in DNA can influence gene expression and are observable in cancer cells. A methylation profile can differentiate tumor types and subtypes and changes in the process often come before malignancy appears. This renders methylation very useful in catching cancers while in the early stages.
However, deciphering slight changes in methylation patterns can be extremely difficult. According to the scientists, “identifying the specific DNA methylation signatures indicative of different cancer types is akin to searching for a needle in a haystack.”
Nevertheless, the researchers believe identifying these changes could become a useful biomarker for early detection of cancers, which is why they built their AI models.
“Computational methods such as this model, through better training on more varied data and rigorous testing in the clinic, will eventually provide AI models that can help doctors with early detection and screening of cancers,” said Shamith Samarajiwa, PhD (above), Senior Lecturer and Group Leader, Computational Biology and Genomic Data Science, Imperial College London, in a news release. “This will provide better patient outcomes.” With additional research, clinical laboratories and pathologists may soon have new cancer diagnostics based on these AI models. (Photo copyright: University of Cambridge.)
The researchers then used a combination of machine learning and deep learning techniques to train an AI algorithm to examine DNA methylation patterns of the collected data. The algorithm identified and differentiated specific cancer types, including breast, liver, lung and prostate, from non-cancerous tissue with a 98.2% accuracy rate. The team evaluated their AI model by comparing the results to independent research.
In their Biology Methods and Protocols paper, the authors noted that their model does require further training and testing and stressed that “the important aspect of this study was the use of an explainable and interpretable core AI model.” They also claim their model could help medical professionals understand “the underlying mechanisms that contribute to the development of cancer.”
Using AI to Lower Cancer Rates Worldwide
According to the Centers for Disease Control and Prevention (CDC), cancer ranks as the second leading cause of death in the United States with 608,371 deaths reported in 2022. The leading cause of death in the US is heart disease with 702,880 deaths reported in the same year.
Globally cancer diagnoses and death rates are even more alarming. World Health Organization (WHO) data shows an estimated 20 million new cancer cases worldwide in 2022, with 9.7 million persons perishing from various cancers that year.
The UK researchers are hopeful their new AI model will help lower those numbers. They state in their paper that “most cancers are treatable and curable if detected early enough.”
More research and studies are needed to confirm the results of this study, but it appears to be a very promising line of exploration and development of using AI to detect, identify, and diagnose cancer earlier. This type of probing could provide pathologists with improved tools for determining the presence of cancer and lead to better patient outcomes.
Researchers have been exploring the role metabolites play in the development of disease for some time. Alzheimer’s is a progressive, degenerative brain disease typically linked to age, family history, and deposits of certain proteins in the brain that cause the brain to shrink and brain cells to eventually die. Alzheimer’s is the most common form of dementia, accounting for an estimated 60% to 80% of all dementia cases. It has no cure or proven method of prevention, according to the Alzheimer’s Association.
There are nearly seven million people living with Alzheimer’s in the US and 55 million people worldwide live with it or other forms of dementia. Patients are usually over the age of 65, but it can present in younger patients as well.
“Gut metabolites are the key to many physiological processes in our bodies, and for every key there is a lock for human health and disease,” said Feixiong Cheng, PhD (above), founding director of the Cleveland Clinic Genome Center, in a news release. “The problem is that we have tens of thousands of receptors and thousands of metabolites in our system, so manually figuring out which key goes into which lock has been slow and costly. That’s why we decided to use AI.” Findings from the study could lead to new clinical laboratory biomarkers for dementia screening tests. (Photo copyright: Cleveland Clinic Lerner Research Institute.)
Changes to Gut Bacteria
Metabolites are substances released by bacteria when the body breaks down food, drugs, chemicals, or its own tissue, such as fat or muscle. They fuel cellular processes within the body that may be either helpful or harmful to an individual’s health.
The Cleveland Clinic researchers believe that preventing detrimental interactions between metabolites and cells could aid in disease prevention. Previous studies have shown that Alzheimer’s patients do experience changes in their gut bacteria as the disease progresses.
To complete their study, the scientists used AI and machine learning (ML) to analyze more than 1.09 million potential metabolite-receptor pairs to determine the likelihood of developing Alzheimer’s.
They then examined genetic and proteomic data from Alzheimer’s disease studies and looked at different receptor protein structures and metabolite shapes to determine how different metabolites can affect brain cells. The researchers identified significant interactions between the gut and the brain.
They discovered that the metabolite agmatine was most likely to interact with a receptor known as CA3R in Alzheimer’s patients. Agmatine is believed to protect brain cells from inflammation and damage. They found that when Alzheimer’s-affected neurons are treated with agmatine, CA3R levels reduce. Levels of phosphorylated tau proteins, a biomarker for Alzheimer’s disease, lowered as well.
The researchers also studied a metabolite called phenethylamine. They found that it too could significantly alter the levels of phosphorylated tau proteins, a result they believe could be beneficial to Alzheimer’s patients.
New Therapies for Alzheimer’s, Other Diseases
One of the most compelling results observed in the study was the identification of specific G-protein-coupled receptors (GPCRs) that interact with metabolites present in the gut microbiome. By focusing on orphan GPCRs, the researchers determined that certain metabolites could activate those receptors, which could help generate new therapies for Alzheimer’s.
“We specifically focused on Alzheimer’s disease, but metabolite-receptor interactions play a role in almost every disease that involves gut microbes,” said Feixiong Cheng, PhD, founding director of the Cleveland Clinic Genome Center in the news release. “We hope that our methods can provide a framework to progress the entire field of metabolite-associated diseases and human health.”
The team plans to use AI technology to further develop and study the interactions between genetic and environmental factors on human health and disease progression. More research and studies are needed, but results of the Cleveland Clinic study suggest new biomarkers for targeted therapies and clinical laboratory tests for dementia diseases may soon follow.
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