Raman spectroscopy reveals phenotype switches in breast cancer metastasis.

Autor:	Paidi SK; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218., Troncoso JR; Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR 72701., Harper MG; University of Arkansas for Medical Sciences, Little Rock, AR, 72205., Liu Z; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218., Nguyen KG; Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, NC, 27695., Ravindranathan S; Department of Hematology and Oncology, Emory University, Atlanta, GA, 30322., Rebello L; Cell and Molecular Biology Program, University of Arkansas, Fayetteville, AR 72701., Lee DE; Department of Health, Human Performance, and Recreation, University of Arkansas, Fayetteville, AR, 72701., Ivers JD; Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR 72701., Zaharoff DA; Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, NC, 27695., Rajaram N; Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR 72701.; Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, 72205., Barman I; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218.; 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.
Jazyk:	angličtina
Zdroj:	Theranostics [Theranostics] 2022 Jul 11; Vol. 12 (12), pp. 5351-5363. Date of Electronic Publication: 2022 Jul 11 (Print Publication: 2022).
DOI:	10.7150/thno.74002
Abstrakt:	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. Methods: 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. Results: 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. Conclusion: Overall, our findings indicate that Raman spectroscopy may offer a novel strategy to evaluate metastatic risk during primary tumor biopsies in clinical patients. Competing Interests: Competing Interests: The authors have declared that no competing interest exists. (© The author(s).)
Databáze:	MEDLINE
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