AI Learns to Predict Breast Cancer Risk from How Single Cells Respond to Pressure

A study headed by researchers at City of Hope and the University of California, Berkeley has found that physical and mechanical properties of normal human mammary epithelial cells can offer a “functional readout” of biological age and breast cancer susceptibility.

The team created a novel, high-throughput microfluidic platform that can assess women’s breast cancer risk at the cellular level. The mechano-node-pore sensing (mechano-NPS) platform, which the researchers claim is the first of its kind, squeezes individual breast epithelial cells, creating a taxing environment to measure how they deform, recover, and behave under stress.

Using the platform the researchers uncovered an unexpected insight, which is that breast cells appear to have a “mechanical age” separate from a person’s chronological age, demonstrated by how the cells physically respond to stress. For their study the team developed a machine learning classifier, MechanoAge, to estimate chronological age based on the mechanical phenotypes, and a biological age-based risk index, Mechano-RISQ.

“We learned that the older the mechanical age, as determined by how cells respond to being squeezed through our microfluidic device, the higher the risk for breast cancer,” explained Lydia Sohn, PhD, the Almy C. Maynard and Agnes Offield Maynard Chair in Mechanical Engineering at UC Berkeley. The researchers suggest that, as more than 90% of women lack a known genetic predisposition to or a family history of breast cancer, their innovative approach could fill a critical gap in risk assessment and save countless lives.

Sohn is co-senior author of the team’s published paper in eBioMedicine, titled “MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells,” in which they concluded, “Age-related biomechanical changes may represent a fundamental hallmark of cellular function, with distinct mechanical phenotypes underlying critical processes in aging, cancer, and potentially other diseases. Recognizing and utilizing these biomechanical markers could greatly enhance early detection, refine risk stratification, and improve targeted intervention strategies.”

Breast cancer is one of the most frequently diagnosed cancers worldwide and a leading cause of cancer-related mortality among women, the authors noted, and “… has long been the subject of efforts to improve risk stratification and early-detection strategies.”

About 6% of women who develop breast cancer carry known genetic mutations. But for women outside this group, risk is estimated indirectly based on population models or measurements like breast density. These approaches can both overestimate and underestimate women’s individual breast cancer risk, leading to over-screening, under-screening, unnecessary worry or missed warning signs. And despite significant progress in screening technologies and therapeutic interventions, accurately determining which individuals—particularly among those considered average risk—are most likely to develop breast cancer remains what the team calls “one of the most persistent challenges in oncology and public health.”

For these “ostensibly average-risk individuals,” the team added, “it remains difficult to identify those with latent risk that stems from cellular, molecular, and biophysical alterations that current models are not designed to capture.”

Currently, there is no non-genetic test available that can identify women at higher risk for breast cancer. A downside to screening mammograms is that they can catch cancer only once it has begun to grow. Co-senior author, Mark LaBarge, PhD, a professor in the Department of Population Sciences at City of Hope, said “For women with a known genetic risk factor for breast cancer, there are things you can do like follow a higher-risk screening protocol. For everybody else, you’re left wondering, ‘Am I at high risk?’”

Emerging evidence links cellular aging and biophysical alterations with cancer susceptibility. For their reported study the researchers used the mechano-NPS platform to profile primary human mammary epithelial cells (HMECs) from women of different ages and risk backgrounds. They also developed a machine learning algorithm that identifies and measures cells that show signs of accelerated aging, quantifying an individual breast cancer risk score.

In this type of mechano-node-pore sensing, an electrical current is measured across a liquid-filled channel, much like how current is measured across a wire. As cells pass through, they disrupt the current, generating measurements about the cells’ size and shape. By making parts of the channel very narrow, researchers squeeze cells, then measure how long it takes each cell to recover its normal shape.

Machine-learning algorithms developed by the researchers were then used to detect differences in cells from older and younger women. The researchers found that the physical properties of breast cells changed with age; cells from older women were stiffer and took longer to bounce back after being squeezed.

Then came a surprising finding: a subset of younger women had cells that behaved like they came from older women. These cells came from women with genetic mutations that put them at high risk of breast cancer. Researchers then refined the algorithm to assign a risk score based on all the mechanical and physical properties measured in the cells. This algorithm successfully identified women with known genetic risks. Next the team used it to compare cells from healthy women, women who had family history of breast cancer and cells taken from the healthy breast of women with breast cancer in the other breast. “Normal epithelial cells from women with germline mutations, strong family history of cancer, or contralateral breast cancer exhibit mechanically aged phenotypes despite normal histology,” the investigators stated. “Together with prior molecular and epigenetic studies, these findings support a model in which accelerated biological aging of mammary epithelia may underpin breast cancer susceptibility across genetic and non-genetic risk groups.”

Using the MechanoAge platform, researchers shifted the scientific lens to the cellular level, calculating risk by looking for physical changes in individual cells. “Mechanical phenotyping captures an integrative cellular state that reflects underlying molecular networks rather than single biomarkers,” the team noted. “Mechano-RISQ offers a proof of principle approach for identifying individuals at elevated risk of breast cancer, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of mammary epithelial cell aging.”

“With accuracy, we were able to figure out which women were at high risk of breast cancer and which women didn’t seem to be,” LaBarge said. “By translating physical changes in cells into quantifiable data, this tool gives women something tangible to discuss with their doctors—not just risk estimates, but evidence drawn directly from their own cells.” In their paper the scientists further stated, “This approach could enable earlier, individualized risk stratification, particularly for women who lack identifiable high-risk mutations yet harbor susceptible tissue states.”

Importantly, the AI platform uses simple electronics that would be easy and affordable to replicate on a large scale. “Our team isn’t the first to measure the mechanical properties of cells; however, other approaches require advanced imaging technology that’s expensive, cumbersome and has limited availability,” said Sohn. “In contrast, MechanoAge uses computer chips that are simpler than an Apple Watch and ‘Radio Shack parts’ that are cheap and easy to assemble, potentially making the device highly scalable.”

While engineers study the aging of materials such as metals, concrete and polymers, this is the first time that mechanical age has been quantified in biological cells. The finding that cells have a “mechanical age” separate from the individual’s chronological age would not have been possible without MechanoAge.

This work grew out of more than 12 years of collaboration between the two labs, combining engineering innovation with cancer and aging biology. The long-term partnership enabled discoveries that neither group could have reached alone.  “It’s a true collaboration. We’ve learned a lot from each other,” Sohn said. “In my view, this is what happens when you have a real collaboration that develops over a long time,” LaBarge added. “This result is not what we imagined at the beginning.”

The post AI Learns to Predict Breast Cancer Risk from How Single Cells Respond to Pressure appeared first on GEN – Genetic Engineering and Biotechnology News.

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