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Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification

Xinchen Deng1, Kirsty Milligan1, Ramie Ali-Adeeb1

  • 1Department of Physics, The University of British Columbia Kelowna, Canada.

Applied Spectroscopy
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A new Raman spectroscopy framework accurately monitors radiation response in breast cancer cells, identifying key biochemicals like glycogen and lipids for molecular histotype classification.

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Area of Science:

  • Biomedical Optics
  • Spectroscopy
  • Cancer Research

Background:

  • Raman spectroscopy offers non-invasive biochemical analysis of cells and tissues.
  • Monitoring metabolic radiation response is crucial for personalized cancer therapy.
  • Current Raman spectroscopy methods face challenges in tracking biochemical dynamics.

Purpose of the Study:

  • To develop a novel analytical framework for monitoring radiation response in breast cancer cells.
  • To enable classification of molecular histotypes and assess biochemical dynamics using Raman spectroscopy.
  • To improve the application of Raman spectroscopy in personalized radiation treatment design.

Main Methods:

  • Developed a combined group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) framework.
  • Applied the framework to irradiated human breast cancer cell lines (MCF-7, BT-474, MDA-MB-230, SK-BR-3) and normal cells (MCF10A).
  • Utilized reference Raman spectra of 20 biochemicals as constrained biomarkers within the GBR-NMF-RF framework.

Main Results:

  • The GBR-NMF-RF framework achieved high accuracy (>97%), sensitivity (>97%), and specificity (>97%) in classifications.
  • Successfully monitored radiation response profiles and biochemical dynamics in different breast cancer cell subtypes.
  • Identified significant contributions from glycogen and lipids (cholesterol, phosphatidylserine, stearic acid) in molecular histotype classifications.

Conclusions:

  • The GBR-NMF-RF framework provides a robust method for analyzing biochemical changes in response to radiation.
  • This approach can accurately classify molecular histotypes and non-cancerous cells based on metabolic profiles.
  • The findings support the potential integration of Raman spectroscopy into personalized radiation therapy for metabolic monitoring.