Researchers teach AI to spot cancer risk by squeezing individual breast cells

An AI tool could help spot breast cancer risk by analysing how individual breast cells behave when squeezed under stress, research suggests.

Researchers at City of Hope and the University of California, Berkeley, created a microfluidic platform that assesses women’s breast cancer risk at the cellular level.

The platform squeezes individual breast epithelial cells, which line breast tissue, to measure how they deform, recover and behave under stress.

Because more than 90 per cent of women do not have a known genetic predisposition to breast cancer or a family history of the disease, the researchers said the approach could help fill a key gap in risk assessment.

Mark LaBarge, 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?’

“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.”

The researchers developed a machine learning algorithm that identifies and measures cells showing signs of accelerated ageing, generating an individual breast cancer risk score.

They said the platform uses simple electronics that could be easy and affordable to replicate on a large scale.

Lydia Sohn, chair in mechanical engineering at UC Berkeley, said: “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.

“In contrast, MechanoAge uses computer chips that are simpler than an Apple Watch and ‘RadioShack parts’ that are cheap and easy to assemble, potentially making the device highly scalable.”

About 6 per cent of women who develop breast cancer carry known genetic mutations.

For women outside this group, risk is usually estimated indirectly using population models or measures such as breast density, which can both overestimate and underestimate individual risk.

The researchers said there is currently no non-genetic test that can identify women at higher risk of breast cancer.

Screening mammograms can detect cancer only once it has started to grow, but the MechanoAge platform aims to assess risk earlier by looking for physical changes in individual cells.

Using the platform, the researchers found that breast cells appear to have a “mechanical age” separate from a person’s chronological age, based on how the cells respond to stress.

They said this is the first time mechanical age has been quantified in biological cells.

Sohn said: “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.”

In this type of mechano-node-pore sensing, an electrical current is measured across a liquid-filled channel.

As cells pass through, they disrupt the current, generating measurements about their size and shape. By narrowing parts of the channel, researchers squeeze the cells and then measure how long each one takes to return to its normal shape.

The team found that cells from older women were stiffer and took longer to bounce back after being squeezed.

They also identified a subset of younger women whose cells behaved more like those from older women. These cells came from women with genetic mutations linked to a higher breast cancer risk.

The researchers then refined the algorithm to assign a risk score based on the cells’ measured mechanical and physical properties. They said it successfully identified women with known genetic risks.

The team then used it to compare cells from healthy women, women with a family history of breast cancer, and cells taken from the healthy breast of women with breast cancer in the other breast.

LaBarge said: “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.”

The work grew out of more than 12 years of collaboration between the two labs, combining engineering with cancer and ageing biology.

Sohn said: “It’s a true collaboration. We’ve learned a lot from each other.

LaBarge added: “In my view, this is what happens when you have a real collaboration that develops over a long time. This result is not what we imagined at the beginning.”