Organ-specific isogenic metastatic breast cancer cell lines exhibit distinct Raman spectral signatures and metabolomes

Winnard, Paul T.; Zhang, Chi; Vesuna, Farhad; Kang, Jeon Woong; Garry, Jonah; Dasari, Ramachandra R.; Barman, Ishan; Raman, Venu

doi:10.18632/oncotarget.14865

Cited by 44 publications

(41 citation statements)

References 62 publications

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“…The findings of the present study have implications for future research, including in‐depth mechanistic research into changes metastatic cells acquire to adapt into new environments such as organs. Previous studies conducted by Hedegaard et al and Winnard Jr et al using breast cancer isogenic cell lines have shown that this phenomenon is common, however, there are no studies in prostate cancer. Furthermore, with novel chemotherapeutic agents being introduced for the treatment of hormone‐resistant prostate cancer, it is important to develop and validate new technologies (such as Raman spectroscopy) to monitor changes in the phenotype of cells as a marker of response to these treatments.…”

Section: Resultsmentioning

confidence: 95%

Raman spectroscopy for accurately characterizing biomolecular changes in androgen‐independent prostate cancer cells

Corsetti

Rabl

McGloin

et al. 2017

Journal of Biophotonics

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Metastatic prostate cancer resistant to hormonal manipulation is considered the advanced stage of the disease and leads to most cancer-related mortality. With new research focusing on modulating cancer growth, it is essential to understand the biochemical changes in cells that can then be exploited for drug discovery and for improving responsiveness to treatment. Raman spectroscopy has a high chemical specificity and can be used to detect and quantify molecular changes at the cellular level. Collection of large data sets generated from biological samples can be employed to form discriminatory algorithms for detection of subtle and early changes in cancer cells. The present study describes Raman finger printing of normal and metastatic hormone-resistant prostate cancer cells including analyses with principal component analysis and linear discrimination. Amino acid-specific signals were identified, especially loss of arginine band. Androgen-resistant prostate cancer cells presented a higher content of phenylalanine, tyrosine, DNA and Amide III in comparison to PNT2 cells, which possessed greater amounts of L-arginine and had a B conformation of DNA. The analysis utilized in this study could reliably differentiate the 2 cell lines (sensitivity 95%; specificity 88%).

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Section: Resultsmentioning

confidence: 95%

Raman spectroscopy for accurately characterizing biomolecular changes in androgen‐independent prostate cancer cells

Corsetti

Rabl

McGloin

et al. 2017

Journal of Biophotonics

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“…Therefore, only a few studies have attempted label-free characterization of cells for studying biological processes associated with physiological changes, disease progression, and drug response [48][49][50]. Our recent label-free Raman investigation in pellets of isogenic breast cancer cell lines that exhibit organotropism to brain, liver, lung, and spine revealed distinct metastatic organ-specific spectral signatures that were confirmed by metabolomics analysis [23].…”

Section: Introductionmentioning

confidence: 81%

Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

Paidi

Shah

Raj

et al. 2020

Preprint

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Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive underscoring the need to marry emerging imaging techniques with tumor biology. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging the molecular specificity of Raman spectroscopy, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also leverage multivariate curve resolution – alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

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“…PLS-DA was selected to accomplish discrimination between the healthy and diseased classes. PLS-DA algorithms have been shown to be effective in various disease diagnostic applications including for investigating inflammatory bowel diseases 50 , coronary heart diseases 51 , and various forms of cancer [52][53][54][55][56][57][58][59][60][61][62][63][64] , among many others. Specifically, PLS-DA is a supervised technique which is used to predict categorical variables.…”

Section: Partial Least Squares Discriminant Analysis (Pls-da)mentioning

confidence: 99%

Diagnosis of Duchenne Muscular Dystrophy using Raman Hyperspectroscopy

Ralbovsky

Dey

Galfanoa

et al. 2020

Preprint

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Duchenne muscular dystrophy (DMD) is the most common and severe form of muscular dystrophy and affects boys in infancy or early childhood. DMD is known to trigger progressive muscle weakness due to skeletal muscle degeneration and ultimately causes death. There are limited treatment regimens available that can either slow or stop the progression of DMD. An accurate and specific method for diagnosing DMD in its earliest stages is needed to prevent progressive muscle degeneration and death. Current methods for diagnosing DMD are often laborious, expensive, invasive, and typically diagnose the disease later on it is progression. In an effort to improve the accuracy and ease of diagnosis, this study focused on developing a novel method for diagnosing DMD which combines Raman hyperspectroscopic analysis of blood serum with advanced statistical analysis. Partial Least Squares Discriminant Analysis (PLS-DA), was applied to the spectral dataset acquired from control and mdx blood serum of 3-and 12-month old mice to build a diagnostic algorithm. Internal cross-validation showed 95.2% sensitivity and 94.6% specificity for identifying diseased spectra. These results were verified using external validation, which achieved 100% successful classification efficiency at the level of individual donor. This proof-of-concept study presents Raman hyperspectroscopic analysis of blood serum as a fast, nonexpensive, minimally invasive and early detection method for the diagnosis of Duchenne muscular dystrophy.

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Organ-specific isogenic metastatic breast cancer cell lines exhibit distinct Raman spectral signatures and metabolomes

Cited by 44 publications

References 62 publications

Raman spectroscopy for accurately characterizing biomolecular changes in androgen‐independent prostate cancer cells

Raman spectroscopy for accurately characterizing biomolecular changes in androgen‐independent prostate cancer cells

Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

Diagnosis of Duchenne Muscular Dystrophy using Raman Hyperspectroscopy

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