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Publications

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Epublish 23 citations

PubMed: 35910801 DOI: 10.7150/thno.74002

Raman spectroscopy reveals phenotype switches in breast cancer metastasis.

Theranostics 12(12):5351-5363

Authors: Santosh Kumar Paidi, Joel Rodriguez Troncoso, Mason G Harper, Zhenhui Liu, Khue G Nguyen, Sruthi Ravindranathan, Lisa Rebello, David E Lee, Jesse D Ivers, David A Zaharoff, Narasimhan Rajaram, Ishan Barman

Affiliations

  • Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218.
  • Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR 72701.
  • University of Arkansas for Medical Sciences, Little Rock, AR, 72205.
  • Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, NC, 27695.
  • Department of Hematology and Oncology, Emory University, Atlanta, GA, 30322.
  • Cell and Molecular Biology Program, University of Arkansas, Fayetteville, AR 72701.
  • Department of Health, Human Performance, and Recreation, University of Arkansas, Fayetteville, AR, 72701.
  • Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, 72205.
  • The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205.
  • Department of Oncology, Johns Hopkins University, Baltimore, MD, 21287.

Abstract

The accurate analytical characterization of metastatic phenotype at primary tumor diagnosis and its evolution with time are critical for controlling metastatic progression of cancer. Here, we report a label-free optical strategy using Raman spectroscopy and machine learning to identify distinct metastatic phenotypes observed in tumors formed by isogenic murine breast cancer cell lines of progressively increasing metastatic propensities. We employed the 4T1 isogenic panel of murine breast cancer cells to grow tumors of varying metastatic potential and acquired label-free spectra using a fiber probe-based portable Raman spectroscopy system. We used MCR-ALS and random forests classifiers to identify putative spectral markers and predict metastatic phenotype of tumors based on their optical spectra. We also used tumors derived from 4T1 cells silenced for the expression of TWIST, FOXC2 and CXCR3 genes to assess their metastatic phenotype based on their Raman spectra. The MCR-ALS spectral decomposition showed consistent differences in the contribution of components that resembled collagen and lipids between the non-metastatic 67NR tumors and the metastatic tumors formed by FARN, 4T07, and 4T1 cells. Our Raman spectra-based random forest analysis provided evidence that machine learning models built on spectral data can allow the accurate identification of metastatic phenotype of independent test tumors. By silencing genes critical for metastasis in highly metastatic cell lines, we showed that the random forest classifiers provided predictions consistent with the observed phenotypic switch of the resultant tumors towards lower metastatic potential. Furthermore, the spectral assessment of lipid and collagen content of these tumors was consistent with the observed phenotypic switch. Overall, our findings indicate that Raman spectroscopy may offer a novel strategy to evaluate metastatic risk during primary tumor biopsies in clinical patients.

MeSH

Cells, Genes, Nanomedicine, Breast Neoplasms, Neoplasms, Phenotype, Collagen, Machine Learning, Neoplasm Metastasis, Cell Line, Breast, Learning, Forests

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