A study published in Cell introduces a multiparameter imaging method that maps how cancer cells interact with surrounding tissue in head and neck squamous cell carcinoma (HNSCC).
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Researchers studied tissue samples from 212 HNSCC patients. Using advanced single-cell imaging, they found that a specific type of cancer cell, known as the partial epithelial-mesenchymal transition (pEMT) cell, was tied to aggressive cancer growth. When these pEMT cells were combined with cancer-associated fibroblasts (CAFs), the cancer cells became more invasive. This interaction was driven by specific signaling pathways, including the amphiregulin and epidermal growth factor (AREG/EGF) pathway.
The study also categorized the surrounding tissue (stroma) into three types: immune-enriched, CAF-enriched, and myeloid-rich. Findings showed that patients with CAF-enriched tissue had lower survival rates than those with immune-enriched tissue. Indeed, the combination of pEMT cancer cells and CAF-rich stroma was identified as a strong predictor of poor outcomes.
“Our research utilizes the latest analysis methods in machine learning and spatial biology. We analyze hundreds of patient samples and millions of cells, which is only possible with the help of high-performance computers and artificial intelligence,” research director Sara Wickström explained in a press release. “This study is part of a new revolution in cancer diagnostics. We believe that the technology will significantly improve cancer diagnostics and the accuracy of treatment strategies.”
This research offers a new prognostic tool for identifying HNSCC patients with higher risks of aggressive disease. By examining the stromal composition alongside cancer cell phenotypes, clinicians could better predict patient outcomes and tailor treatments. Moreover, further studies could confirm whether targeting CAF and pEMT interactions can improve therapeutic outcomes for patients with these aggressive tumor profiles.
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