Raman micro‐spectroscopy applied to treatment resistant and sensitive human ovarian cancer cells

Despite the many advances intended to enhance the response to treatment, the survival rate of patients with ovarian cancer has only marginally improved in the past few decades. One major cause for this, is the lack of diagnostics for platinum‐resistant disease. The goal of this study was to determin...

Full description

Saved in:
Bibliographic Details
Published in:Journal of biophotonics 2017-10, Vol.10 (10), p.1327-1334
Main Authors: Moradi, Hamid, Ahmad, Abrar, Shepherdson, Dean, Vuong, Nhung H., Niedbala, Gosia, Eapen, Libni, Vanderhyden, Barbara, Nyiri, Balazs, Murugkar, Sangeeta
Format: Article
Language:English
Subjects:
Cancer
Cell Line, Tumor
Cisplatin
Diagnostic systems
Discriminant analysis
Drug Resistance, Neoplasm
Female
Glutathione
Humans
Multivariate statistical analysis
Ovarian cancer
Ovarian carcinoma
Ovarian Neoplasms - drug therapy
Ovarian Neoplasms - pathology
PCA‐LDA
Platinum
Principal components analysis
Proteins
Raman micro‐spectroscopy
Raman spectra
Raman spectroscopy
ROC Curve
Spectroscopy
Spectrum analysis
Spectrum Analysis, Raman
Statistical analysis
treatment resistance
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Despite the many advances intended to enhance the response to treatment, the survival rate of patients with ovarian cancer has only marginally improved in the past few decades. One major cause for this, is the lack of diagnostics for platinum‐resistant disease. The goal of this study was to determine whether Raman micro‐spectroscopy in conjunction with multivariate statistical analysis could discriminate between chemically fixed cisplatin‐resistant (A2780cp) and cisplatin‐sensitive (A2780s) human ovarian carcinoma cells. Raman spectra collected from individual cells were pre‐processed and subsequently analyzed with Principal Component Analysis – Linear Discriminant Analysis (PCA‐LDA). Statistically significant differences (P 
ISSN:1864-063X
1864-0648
DOI:10.1002/jbio.201600211