Health and Wellness News: Hebrew U. discovery offers new, predictive tool for cancer that uses nano informatics and AI

 

Hebrew U. discovery offers new, predictive tool for cancer that uses nano informatics and AI

Hebrew University discovery to ‘revolutionize’ cancer treatment with a new, predictive-tool ‘breakthrough’ in predicting cancer using nano informatics and AI.

By JUDY SIEGEL-ITZKOVICH, Jerusalem Post, MAY 29, 2024 

https://www.jpost.com/health-and-wellness/article-804220

           Cell illustration. (photo credit: Made by Prof. Ofra Benny by OpenAI software DALL-E)

Another step in the battle against cancer has been accomplished by researchers from the Hebrew University of Jerusalem (HU). They have introduced a novel method that combines nano informatics and machine learning to precisely predict cancer-cell behaviors.

According to the researchers, this will make it possible to identify subpopulations of cells with distinct characteristics, such as drug sensitivity and the danger of spreading (metastasizing) to other parts of the body.

Doctoral student Yoel Goldstein and Prof. Ofra Benny from the School of Pharmacy in the HU Faculty of Medicine collaborated with Prof. Tommy Kaplan, head of the computational biology department at the School of Engineering and Computer Science.

They said their research could transform cancer diagnosis and treatment, enhancing personalized medicine by making possible speedy and accurate testing of cancer-cell behaviors from patient biopsies. This could lead to the development of new clinical tests to monitor disease progression and the effectiveness of treatment.

The researchers recently published their study in the Nature Group’s prestigious journal Science Advances under the title “Magnetism and photo dual-controlled supramolecular assembly for suppression of tumor invasion and metastasis.”

                    Yoel Goldstein and Prof. Ofra Benny in the Lab. (credit: Yoram Aschheim)

The first part of the research involved exposing cancer cells to particles of various sizes, each identified by a unique color. The precise number of particles consumed by each cell was then measured. Machine-learning algorithms then analyzed these uptake patterns to predict the critical behaviors of cells, such as drug sensitivity and the potential to spread.

“Our method is unique in its ability to distinguish between cancer cells that appear identical but behave differently at a biological level,” Goldstein said. “This precision is achieved through algorithmic analysis of how micro and nanoparticles are absorbed by cells. Being able to collect and analyze new types of data raises new possibilities for the field, with the potential to revolutionize clinical treatment and diagnosis through the development of new tools.”The research has paved the way for new types of clinical tests that could significantly impact patient care, the researchers said in their study.

“This discovery allows us to potentially use cells from patient biopsies to quickly predict disease progression or chemotherapy resistance,” Benny said. “It could also lead to the development of innovative blood tests that assess the efficacy of targeted immunotherapy treatments as an example.”

Lacking accuracy and efficiency

Current tools for predicting and detecting cancer often lack accuracy and efficiency. Traditional methods such as imaging scans and tissue biopsies can be invasive, costly, and time-consuming, leading to delays in treatment and potential misdiagnoses.

These approaches may not capture the dynamic nature of cancer progression and can result in limited insights into the disease’s behavior at a cellular level. As a result, patients may face delays in diagnosis, suboptimal treatment outcomes, and increased psychological distress.

The study highlighted the urgent need for more effective and noninvasive diagnostic tools, such as the HU discovery, which presents a significant advancement in personalized medicine, providing hope for more effective and customized treatment strategies for cancer patients.

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