First Ever Technique Identifies Single Cancer Cells in Blood for Targeted Treatments

A team of academics including researchers from Keele University (Keele, UK) employed Fourier Transform Infrared (FTIR) microspectroscopy, a technique for separating cells based on their biochemical composition using infrared light. For the first time, combining FTIR microspectroscopy with a machine learning algorithm led to the successful identification of a single lung cancer cell in a blood sample. This breakthrough supports the move towards personalized medicine, which significantly enhances patient treatment by customizing therapies to match individual profiles and cancer types.

By leveraging this technique to detect individual tumor cells in the bloodstream, it becomes possible to more accurately evaluate patients at various stages of cancer care, from initial diagnosis and staging to monitoring treatment responses and ongoing surveillance. This advancement could refine the personalized medicine strategy, offering a more precise alternative to current cancer cell detection methods. Following this initial success, the research team has received approval to extend their study to include blood samples from patients with a variety of cancers, beyond lung cancer, aiming to validate the effectiveness of this technique across different cancer types.

“Identifying cancer cells in blood using this technique could be a game-changer in the management of patients with cancer,” said Josep Sulé-Suso, Professor of Oncology at Keele University.

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