1. Detection of acquired radioresistance in breast cancer cell lines using Raman spectroscopy and machine learning.
- Author
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Tipatet, Kevin Saruni, Davison-Gates, Liam, Tewes, Thomas Johann, Fiagbedzi, Emmanuel Kwasi, Elfick, Alistair, Neu, Björn, and Downes, Andrew
- Subjects
BREAST cancer ,MACHINE learning ,CANCER cells ,CELL lines ,RAMAN spectroscopy ,RADIATION ,HORMONE receptors - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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