Raman micro-spectroscopy monitors acquired resistance to targeted cancer therapy at the cellular level

Mohamad K Hammoud1, Hesham K Yosef1, Tatjana Lechtonen1

  • 1Department of Biophysics, Ruhr-University Bochum, 44780, Bochum, Germany.

Scientific Reports
|October 17, 2018
PubMed

Insights

Raman spectroscopy monitors integral cell response to tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC). This method accurately predicts drug resistance, offering potential for personalized cancer therapy diagnostics.

Area of Science:

  • Biophysics
  • Spectroscopy
  • Oncology

Background:

  • Traditional in vitro drug efficacy monitoring focuses on single proteins, potentially misaligning with integral cell response and patient outcomes.
  • Non-small cell lung cancer (NSCLC) patients with EGFR mutations often develop acquired resistance to tyrosine kinase inhibitors (TKIs).

Purpose of the Study:

  • To present a Raman spectroscopic method for detecting integral cell response to TKIs in NSCLC.
  • To evaluate the method's ability to identify drug resistance associated with specific EGFR mutations.

Main Methods:

  • Utilized Raman micro-spectroscopy to analyze the integral cellular response of NSCLC cells to TKIs.
  • Investigated differences in spectral responses related to EGFR mutations (T790M, T790M/C797S) and TKI treatments (erlotinib, neratinib, osimertinib).

Main Results:

  • Raman spectroscopy detected significant differences in NSCLC cells without the T790M EGFR mutation when treated with erlotinib.
  • The method identified resistance to erlotinib in cells with T790M mutation and to osimertinib in cells with T790M/C797S mutation, aligning with clinical observations.
  • Raman spectra also reflected cellular responses to neratinib and osimertinib in cells harboring specific EGFR mutations.

Conclusions:

  • Raman micro-spectroscopy can effectively monitor drug resistance in NSCLC cells with specific EGFR mutations.
  • This technique shows promise for in vitro companion diagnostics to guide personalized TKI therapies for cancer patients.

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