{"id":396202,"date":"2026-04-24T23:51:10","date_gmt":"2026-04-24T23:51:10","guid":{"rendered":"https:\/\/www.newsbeep.com\/nz\/396202\/"},"modified":"2026-04-24T23:51:10","modified_gmt":"2026-04-24T23:51:10","slug":"ai-learns-to-predict-breast-cancer-risk-from-how-single-cells-respond-to-pressure","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/nz\/396202\/","title":{"rendered":"AI Learns to Predict Breast Cancer Risk from How Single Cells Respond to Pressure"},"content":{"rendered":"

\"Researchers<\/a>Researchers at UC Berkeley and City of Hope have developed a machine-learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells. This photo depicts the MechanoAge platform. [Adam Lau\/Berkeley Engineering] <\/p>\n

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 \u201cfunctional readout\u201d of biological age and breast cancer susceptibility.<\/p>\n

The team created a novel, high-throughput microfluidic platform that can assess women\u2019s 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.<\/p>\n

Using the platform the researchers uncovered\u00a0an unexpected\u00a0insight, which is that breast cells appear to have a\u00a0\u201cmechanical age\u201d\u00a0separate from a person\u2019s chronological age, demonstrated\u00a0by\u00a0how the cells physically respond to stress.\u00a0For 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.<\/p>\n

\u201cWe learned that the\u00a0older the mechanical age,\u00a0as determined by how cells respond to being squeezed through\u00a0our microfluidic device,\u00a0the higher the risk for breast cancer,\u201d\u00a0explained\u00a0Lydia Sohn, PhD, the Almy C.\u00a0Maynard\u00a0and Agnes Offield Maynard Chair in Mechanical Engineering at\u00a0UC\u00a0Berkeley. The researchers suggest that, as more than 90% of women lack a known genetic predisposition to or\u00a0a\u00a0family history of breast cancer, their innovative\u00a0approach could fill a critical gap in risk assessment\u00a0and\u00a0save countless lives.<\/p>\n

Sohn is co-senior author of the team\u2019s published paper in eBioMedicine, titled \u201cMechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells<\/a>,\u201d in which they concluded, \u201cAge-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.\u201d<\/p>\n

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 \u201c\u2026 has long been the subject of efforts to improve risk stratification and early-detection strategies.\u201d<\/p>\n

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\u2019s 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\u2014particularly among those considered average risk\u2014are most likely to develop breast cancer remains what the team calls \u201cone of the most persistent challenges in oncology and public health.\u201d<\/p>\n

For these \u201costensibly average-risk individuals,\u201d the team added, \u201cit remains difficult to identify those with latent risk that stems from cellular, molecular, and biophysical alterations that current models are not designed to capture.\u201d<\/p>\n

\"ResearchersResearchers Mark LaBarge of City of Hope (right) and Lydia Sohn (left) UC Berkeley [City of Hope and UC Berkeley]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 \u201cFor 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\u2019re left wondering, \u2018Am I at high risk?\u2019\u201d<\/p>\n

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.<\/p>\n

In\u00a0this type of\u00a0mechano-node-pore sensing,\u00a0an electrical current\u00a0is measured across a liquid-filled channel,\u00a0much like how\u00a0current is measured across a\u00a0wire.\u00a0As cells pass through, they disrupt the current, generating measurements about the cells\u2019 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.<\/p>\n

Machine-learning algorithms\u00a0developed by the researchers were then\u00a0used to\u00a0detect differences in cells from older and younger women. The\u00a0researchers\u00a0found 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.<\/p>\n

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. \u201cNormal epithelial cells from women with germline mutations, strong family history of cancer, or contralateral breast cancer exhibit mechanically aged phenotypes despite normal histology,\u201d the investigators stated. \u201cTogether 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.\u201d<\/p>\n

Using the MechanoAge platform, researchers shifted the scientific lens to the cellular level, calculating risk by looking for physical changes in individual cells. \u201cMechanical phenotyping captures an integrative cellular state that reflects underlying molecular networks rather than single biomarkers,\u201d the team noted. \u201cMechano-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.\u201d<\/p>\n

\u201cWith accuracy, we were able to figure out which women were at high risk of breast cancer and which women didn\u2019t seem to be,\u201d LaBarge said. \u201cBy translating physical changes in cells into quantifiable data, this tool gives women something tangible to discuss with their doctors\u2014not just risk estimates, but evidence drawn directly from their own cells.\u201d In their paper the scientists further stated, \u201cThis approach could enable earlier, individualized risk stratification, particularly for women who lack identifiable high-risk mutations yet harbor susceptible tissue states.\u201d<\/p>\n

Importantly, the AI platform uses simple electronics that would be easy and affordable to replicate on a large scale. \u201cOur team isn\u2019t the first to measure the mechanical properties of cells; however, other approaches require advanced imaging technology that\u2019s expensive, cumbersome and has limited availability,\u201d said Sohn. \u201cIn contrast, MechanoAge uses computer chips that are simpler than an Apple Watch and \u2018Radio Shack parts\u2019 that are cheap and easy to assemble, potentially making the device highly scalable.\u201d<\/p>\n

While\u00a0engineers\u00a0study the\u00a0aging of\u00a0materials such as\u00a0metals,\u00a0concrete\u00a0and polymers, this\u00a0is the first time\u00a0that\u00a0mechanical age\u00a0has been quantified in\u00a0biological cells. The finding that cells have a \u201cmechanical age\u201d\u00a0separate from the individual\u2019s chronological age would not have been possible without\u00a0MechanoAge.<\/p>\n

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.\u00a0 \u201cIt\u2019s a true collaboration. We\u2019ve learned a lot from each other,\u201d Sohn said. \u201cIn my view, this is what happens when you have a real collaboration that develops over a long time,\u201d LaBarge added. \u201cThis result is not what we imagined at the beginning.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"Researchers at UC Berkeley and City of Hope have developed a machine-learning platform to identify individuals susceptible to…\n","protected":false},"author":2,"featured_media":396203,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[204685,363,4677,2534,204686,204687,2489,204688,38738,111,43,139,69,147,2477],"class_list":{"0":"post-396202","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-science","8":"tag-aging-phenomenon","9":"tag-artificial-intelligence","10":"tag-breast-cancer","11":"tag-cancer","12":"tag-epithelial-cell","13":"tag-germline-mutations","14":"tag-machine-learning","15":"tag-mechanoage","16":"tag-microfluidics","17":"tag-new-zealand","18":"tag-news","19":"tag-newzealand","20":"tag-nz","21":"tag-science","22":"tag-topics"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/396202","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/comments?post=396202"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/396202\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media\/396203"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media?parent=396202"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/categories?post=396202"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/tags?post=396202"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}