scholarly journals A biochemical comparison of the lung, colonic, brain, renal, and ovarian cancer cell lines using 1H-NMR spectroscopy

2020 ◽  
Vol 40 (4) ◽  
Author(s):  
Cong Hu ◽  
Zhigang Liu ◽  
Hailin Zhao ◽  
Lingzhi Wu ◽  
Qingquan Lian ◽  
...  
Keyword(s):  
Cancer Research ◽  
Nmr Spectroscopy ◽  
Cell Lines ◽  
Cancer Cell ◽  
1H Nmr ◽  
1H Nmr Spectroscopy ◽  
Cancer Cell Lines ◽  
Media Analysis ◽  
Metabolic Phenotyping ◽  
Biochemical Comparison

Abstract Cancer cell lines are often used for cancer research. However, continuous genetic instability-induced heterogeneity of cell lines can hinder the reproducibility of cancer research. Molecular profiling approaches including transcriptomics, chromatin modification profiling, and proteomics are used to evaluate the phenotypic characteristics of cell lines. However, these do not reflect the metabolic function at the molecular level. Metabolic phenotyping is a powerful tool to profile the biochemical composition of cell lines. In the present study, 1H-NMR spectroscopy-based metabolic phenotyping was used to detect metabolic differences among five cancer cell lines, namely, lung (A549), colonic (Caco2), brain (H4), renal (RCC), and ovarian (SKOV3) cancer cells. The concentrations of choline, creatine, lactate, alanine, fumarate and succinate varied remarkably among different cell types. The significantly higher intracellular concentrations of glutathione, myo-inositol, and phosphocholine were found in the SKOV3 cell line relative to other cell lines. The concentration of glutamate was higher in both SKOV3 and RCC cells compared with other cell lines. For cell culture media analysis, isopropanol was found to be the highest in RCC media, followed by A549 and SKOV3 media, while acetone was the highest in A549, followed by RCC and SKOV3. These results demonstrated that 1H-NMR-based metabolic phenotyping approach allows us to characterize specific metabolic signatures of cancer cell lines and provides phenotypical information of cellular metabolism.

scholarly journals Comprehensive transcriptomic analysis of cell lines as models of primary tumor samples across 22 tumor types

2018 ◽  
Author(s):  
K. Yu ◽  
B. Chen ◽  
D. Aran ◽  
J. Charalel ◽  
A. Butte ◽  
...  
Keyword(s):  
Cancer Research ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Primary Tumor ◽  
Cancer Biology ◽  
Cancer Cell Lines ◽  
Primary Tumors ◽  
Tumor Types

AbstractCancer cell lines are commonly used as models for cancer biology. While they are limited in their ability to capture complex interactions between tumors and their surrounding environment, they are a cornerstone of cancer research and many important findings have been discovered utilizing cell line models. Not all cell lines are appropriate models of primary tumors, however, which may contribute to the difficulty in translating in vitro findings to patients. Previous studies have leveraged public datasets to evaluate cell lines as models of primary tumors, but they have been limited in scope to specific tumor types and typically ignore the presence of tumor infiltrating cells in the primary tumor samples. We present here a comprehensive pan-cancer analysis utilizing approximately 9,000 transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 different tumor types. After adjusting for tumor purity in the primary tumor samples, we performed correlation analysis and differential gene expression analysis between the primary tumor samples and cell lines. We found that cell-cycle pathways are consistently upregulated in cell lines, while no pathways are consistently upregulated across the primary tumor samples. In a case study, we compared colorectal cancer cell lines with primary tumor samples across the colorectal subtypes and identified three colorectal cell lines that were derived from fibroblasts rather than tumor epithelial cells. Lastly, we propose a new set of cell lines panel, the TCGA-110, which contains the most representative cell lines from 22 different tumor types as a more comprehensive and informative alternative to the NCI-60 panel. Our analysis of the other tumor types are available in our web app (http://comphealth.ucsf.edu/TCGA110) as a resource to the cancer research community, and we hope it will allow researchers to select more appropriate cell line models and increase the translatability of in vitro findings.

Quantitative two-dimensional HRMAS 1H-NMR spectroscopy-based metabolite profiling of human cancer cell lines and response to chemotherapy

2010 ◽  
Vol 63 (5) ◽  
pp. 1172-1183 ◽  
Author(s):  
Mathilde Bayet-Robert ◽  
Dominique Loiseau ◽  
Pascale Rio ◽  
Aicha Demidem ◽  
Chantal Barthomeuf ◽  
...  
Keyword(s):  
Nmr Spectroscopy ◽  
Cell Lines ◽  
Cancer Cell ◽  
Metabolite Profiling ◽  
1H Nmr Spectroscopy ◽  
Human Cancer ◽  
Two Dimensional ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines ◽  
Response To Chemotherapy

scholarly journals A New Tocopherol Derivative and Cytotoxicity from the Leaves of Dalbergia velutina

2018 ◽  
Vol 13 (12) ◽  
pp. 1934578X1801301
Author(s):  
Sutin Kaennakam ◽  
Thammarat Aree ◽  
Kitiya Rassamee ◽  
Pongpun Siripong ◽  
Santi Tip-pyang
Keyword(s):  
Nmr Spectroscopy ◽  
Cell Lines ◽  
Cancer Cell ◽  
Spectroscopic Analysis ◽  
Cancer Cell Lines ◽  
The Absolute ◽  
Ht 29 ◽  
Mcf 7

A new tocopherol derivative, named (+)-α-tocuspirone (1), along with eleven known compounds, including six tocopherol derivatives (2–7) and five triterpenes (8–12) were isolated from the leaves of Dalbergia velutina. Their structures were determined by spectroscopic analysis especially NMR spectroscopy. The absolute configurations of 1 and 2 were assigned by NOESY experiments and ECD calculations. All isolated compounds were evaluated for their cytotoxicity against five cancer cell lines (KB, HeLa S-3, HT-29, MCF-7 and HepG-2). Dioslupecin A (10) showed potent cytotoxicity against all the five cancer cell lines with IC50 values in the range of 0.28–2.05 μM. In addition, caffeoxylupeol (12) showed potent cytotoxicity against KB cell with an IC50 value of 2.28 μM.

1H NMR Spectroscopy-Based Interventional Metabolic Phenotyping: A Cohort Study of Rheumatoid Arthritis Patients

2010 ◽  
Vol 9 (9) ◽  
pp. 4545-4553 ◽  
Author(s):  
Michael B. Lauridsen ◽  
Henning Bliddal ◽  
Robin Christensen ◽  
Bente Danneskiold-Samsøe ◽  
Robert Bennett ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Cohort Study ◽  
Nmr Spectroscopy ◽  
1H Nmr ◽  
1H Nmr Spectroscopy ◽  
Metabolic Phenotyping

scholarly journals Acanthomine A, a new Pyrimidine-β-Carboline Alkaloid from the Sponge Acanthostrongylophora Ingens

2008 ◽  
Vol 3 (2) ◽  
pp. 1934578X0800300 ◽  
Author(s):  
Sabrin R. M. Ibrahim ◽  
RuAngelie Ebel ◽  
Rainer Ebel ◽  
Peter Proksch
Keyword(s):  
Mass Spectrometry ◽  
Nmr Spectroscopy ◽  
Ethyl Acetate ◽  
Cytotoxic Activity ◽  
Cell Lines ◽  
Cancer Cell ◽  
Brine Shrimp ◽  
Ethyl Acetate Extract ◽  
Cancer Cell Lines ◽  
Chemical Investigation

Chemical investigation of the ethyl acetate extract of the sponge Acanthostrongylophora ingens afford one new pyrimidine-β-carboline alkaloid named acanthomine A (2), together with two known compounds annomontine (1) and 1,2,3,4-tetrahydronorharman-1-one (3). Their structures were unambiguously established on the basis of NMR spectroscopy (1H, 13C, 1H-1H COSY, HMQC and HMBC) and mass spectrometry. The isolated compounds were tested for cytotoxic activity using brine shrimp bioassay and different cancer cell lines.

scholarly journals Therapeutic applications of three-dimensional organoid models in lung cancer

Organoid ◽  
2021 ◽  
Vol 1 ◽  
pp. e6
Author(s):  
Chang Dong Yeo ◽  
Young-Pil Yun ◽  
Dong Hyuck Ahn ◽  
Yongki Hwang ◽  
Seung Hee Yang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Research ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Biology ◽  
Three Dimensional ◽  
Cancer Cell Lines ◽  
Genomic Diversity ◽  
Therapeutic Applications ◽  
Cancer Models

Lung cancer, which remains a major cause of mortality worldwide, is a histologically diverse condition and demonstrates substantial phenotypic and genomic diversity among individual patients, manifesting as both intertumoral and intratumoral heterogeneity. This heterogeneity has made it difficult to develop lung cancer models. Two-dimensional (2D) cancer cell lines have been used to study genetic and molecular alterations in lung cancer. However, cancer cell lines have several disadvantages, including random genetic drift caused by long-term culture, a lack of annotated clinical data, and most importantly, the fact that only a subset of tumors shows 2D growth on plastic. Three-dimensional models of cancer have the potential to improve cancer research and drug development because they are more representative of cancer biology and its diverse pathophysiology. Herein, we present an integrated review of current information on preclinical lung cancer models and their limitations, including cancer cell line models, patient-derived xenografts, and lung cancer organoids, and discuss their possible therapeutic applications for drug discovery and screening to guide precision medicine in lung cancer research. Altogether, the success rate of generating lung cancer organoids must be improved, and a lung cancer organoid culture system is necessary to achieve the goal of designing an individualized therapeutic strategy for each lung cancer patient.

scholarly journals Fibroblasts cell lines misclassified as cancer cell lines

2017 ◽  
Author(s):  
Antoine de Weck ◽  
Hans Bitter ◽  
Audrey Kauffmann
Keyword(s):  
Cancer Research ◽  
Drug Discovery ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Cancer Cell Lines ◽  
Model Systems ◽  
Genomic Characterization

Large collections of immortalized cancer cell lines have been widely used as model systems for cancer research and drug discovery. Some of these models however display more fibroblast-like than cancer-like characteristics based on their genetic and genomic characterization. The correct annotation of these cell lines remains a challenge. Here, we report the outcome of our analysis on a large cancer cell line collection, where we found a subset of cell lines misclassified.

scholarly journals Evaluating cell lines as models for metastatic cancer through integrative analysis of open genomic data

2018 ◽  
Author(s):  
Ke Liu ◽  
Patrick A. Newbury ◽  
Benjamin S. Glicksberg ◽  
William ZD Zeng ◽  
Eran R. Andrechek ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Research ◽  
Metastatic Breast Cancer ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Metastatic Cancer ◽  
Metastatic Breast ◽  
Genomic Data ◽  
Cancer Cell Lines

AbstractMetastasis is the most common cause of cancer-related death and, as such, there is an urgent need to discover new therapies to treat metastasized cancers. Cancer cell lines are widely-used models to study cancer biology and test drug candidates. However, it is still unknown to what extent they adequately resemble the disease in patients. The recent accumulation of large-scale genomic data in cell lines, mouse models, and patient tissue samples provides an unprecedented opportunity to evaluate the suitability of cell lines for metastatic cancer research. In this work, we used breast cancer as a case study. The comprehensive comparison of the genetic profiles of 57 breast cancer cell lines with those of metastatic breast cancer samples revealed substantial genetic differences. In addition, we identified cell lines that more closely resemble different subtypes of metastatic breast cancer. Surprisingly, a combined analysis of mutation, copy number variation and gene expression data suggested that MDA-MB-231, the most commonly used triple negative cell line for metastatic breast cancer research, had little genomic similarity with Basal-like metastatic breast cancer samples. We further compared cell lines with organoids, a new type of preclinical model which are becoming more popular in recent years. We found that organoids outperformed cell lines in resembling the transcriptome of metastatic breast cancer samples. However, additional differential expression analysis suggested that both types of models could not mimic the effects of tumor microenvironment and meanwhile had their own bias towards modeling specific biological processes. Our work provides a guide of cell line selection in metastasis-related study and sheds light on the potential of organoids in translational research.

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