Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning

Kevin Tipatet, Liam Davison-Gates, Thomas Tewes , Emmanuel Fiagbedzi, Alistair P D Elfick, Björn Neu, Andy Downes

Research output: Contribution to journalArticlepeer-review

Abstract

Radioresistance—a living cell’s response to, and development of resistance to ionising radiation—can lead to radiotherapy failure and/or tumour recurrence. We used Raman spectroscopy and machine learning to characterise biochemical changes that occur in acquired radioresistance for breast cancer cells. We were able to distinguish between wild-type and acquired radioresistant cells by changes in chemical composition using Raman spectroscopy and machine learning with 100% accuracy. In studying both hormone receptor positive and negative cells, we found similar changes in chemical composition that occur with the development of acquired radioresistance; these radioresistant cells contained less lipids and proteins compared to their parental counterparts. As well as characterising acquired radioresistance in vitro, this approach has the potential to be translated into a clinical setting, to look for Raman signals of radioresistance in tumours or biopsies; that would lead to tailored clinical treatments.
Original languageEnglish
Number of pages12
JournalAnalyst
Early online date22 Apr 2021
DOIs
Publication statusE-pub ahead of print - 22 Apr 2021

Keywords

  • Radioresistance
  • Raman spectroscopy
  • breast cancer
  • hormone receptors

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