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Automatic subtyping of Diffuse Large B-cell Lymphomas (DLBCL): Raman-based genetic and metabolic classification.

Patrycja Dawiec1, Patrycja Leszczenko1, Anna M Nowakowska2

  • 1Department of Chemical Physics, Faculty of Chemistry, Jagiellonian University, Krakow, Poland; Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|January 7, 2024
PubMed
Summary

Raman imaging offers a new way to classify Diffuse Large B-cell Lymphoma (DLBCL) subtypes. This technique analyzes spectral profiles to identify DLBCL cells and their specific metabolic or cell-of-origin subtypes.

Keywords:
ChemometricsDiffuse Large B-cell Lymphoma (DLBCL)MetabolismOxPhosRaman spectroscopy

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Area of Science:

  • Biomedical Optics
  • Molecular Spectroscopy
  • Oncology

Background:

  • Diffuse Large B-cell Lymphoma (DLBCL) is a common, aggressive blood cancer with significant heterogeneity.
  • Accurate classification of DLBCL subtypes is crucial for effective treatment selection but remains challenging.
  • Existing diagnostic methods struggle with the molecular complexity and metabolic variability of DLBCL.

Purpose of the Study:

  • To evaluate Raman imaging as a diagnostic tool for classifying DLBCL cells.
  • To develop and present models for automated DLBCL cell identification and subtype assignment.
  • To correlate spectral profiles with biochemical, genetic, and metabolic features of DLBCL subtypes.

Main Methods:

  • Utilized Raman imaging to acquire spectral data from DLBCL cells.
  • Developed classification models based on Raman spectra for automated identification.
  • Assigned DLBCL cells to cell-of-origin (COO) subtypes (ABC/GCB) and comprehensive cluster classification (CCC) subtypes (OxPhos/non-OxPhos).

Main Results:

  • Raman imaging provides unique biochemical and metabolic information from DLBCL cells.
  • Developed models successfully identified DLBCL cells and assigned them to specific subtypes.
  • Distinct spectral profiles were identified for each lymphoma subtype, linked to underlying biological characteristics.

Conclusions:

  • Raman imaging demonstrates high diagnostic potential for DLBCL classification.
  • Automated spectral analysis enables efficient and accurate identification of DLBCL subtypes.
  • This technique offers a novel approach to understanding DLBCL heterogeneity and guiding treatment.