Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data

Liu, Ke; Newbury, Patrick; Glicksberg, Benjamin S.; Zeng, William Z. D.; Paithankar, Shreya; Andrechek, Eran R.; Chen, Bin

doi:10.1038/s41467-019-10148-6

Cited by 73 publications

(73 citation statements)

References 48 publications

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“…The conventional 2D cultures are a further limitation. Cell shape and microenvironment influence cellular lipid content and composition [64] and 2D cultures do not bring out site-specific transcriptomic profiles of metastases [65]. The added serum is the main source of lipids on the culture medium, and this was the same for all cell lines.…”

Section: Limitations Of the Study Designmentioning

confidence: 99%

Lipidomic study of cell lines reveals differences between breast cancer subtypes

et al. 2020

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Breast cancer (BC) is the most prevalent type of cancer in women in western countries. BC mortality has not declined despite early detection by screening, indicating the need for better informed treatment decisions. Therefore, a novel noninvasive diagnostic tool for BC would give the opportunity of subtype-specific treatment and improved prospects for the patients. Heterogeneity of BC tumor subtypes is reflected in the expression levels of enzymes in lipid metabolism. The aim of the study was to investigate whether the subtype defined by the transcriptome is reflected in the lipidome of BC cell lines. A liquid chromatography mass spectrometry (LC-MS) platform was applied to analyze the lipidome of six cell lines derived from human BC cell lines representing different BC subtypes. We identified an increased abundance of triacylglycerols (TG) � C-48 with moderate or multiple unsaturation in fatty acyl chains and down-regulated ether-phosphatidylethanolamines (PE) (C-34 to C-38) in cell lines representing estrogen receptor and progesterone receptor positive tumor subtypes. In a cell line representing HER2-overexpressing tumor subtype an elevated expression of TG (� C-46), phosphatidylcholines (PC) and PE containing short-chained (� C-16) saturated or monounsaturated fatty acids were observed. Increased abundance of PC � C-40 was found in cell lines of triple negative BC subtype. In addition, differences were detected in lipidomes within these previously defined subtypes. We conclude that subtypes defined by the transcriptome are indeed reflected in differences in the lipidome and, furthermore, potentially biologically relevant differences may exist within these defined subtypes.

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Section: Limitations Of the Study Designmentioning

confidence: 99%

Lipidomic study of cell lines reveals differences between breast cancer subtypes

et al. 2020

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“…Based on these results, we proposed that the cell lines used in the standard "NCI-60" preclinical panel should be replaced by a "TCGA-110-CL," employing a cohort of lines with the most similarity to patient tumors. In parallel, others have also used transcriptional correlation profiling to suggest the best cell line models of metastatic breast cancer [14] and hepatocellular carcinoma [15], for example, demonstrating the widespread utility of this approach.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the efficacy of multiple myeloma cell lines as models for patient tumors via transcriptomic correlation analysis

Sarin

Ferguson

et al. 2020

Leukemia

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Multiple myeloma (MM) cell lines are routinely used to model the disease. However, a long-standing question is how well these cell lines truly represent tumor cells in patients. Here, we employ a recently described method of transcriptional correlation profiling to compare similarity of 66 MM cell lines to 779 newly diagnosed MM patient tumors. We found that individual MM lines differ significantly with respect to patient tumor representation, with median R ranging from 0.35 to 0.54. ANBL-6 was the "best" line, markedly exceeding all others (p < 2.2e−16). Notably, some widely used cell lines (RPMI-8226, U-266) scored poorly in our patient similarity ranking (48 and 52 of 66, respectively). Lines cultured with interleukin-6 showed significantly improved correlations with patient tumor (p = 9.5e−4). When common MM genomic features were matched between cell lines and patients, only t(4;14) and t(14;16) led to increased transcriptional correlation. To demonstrate the utility of our top-ranked line for preclinical studies, we showed that intravenously implanted ANBL-6 proliferates in hematopoietic organs in immunocompromised mice. Overall, our large-scale quantitative correlation analysis, utilizing emerging datasets, provides a resource informing the MM community of cell lines that may be most reliable for modeling patient disease while also elucidating biological differences between cell lines and tumors.

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“…1c-e). There are 20177 genes have alteration in 403 bladder patients and 662 of them were frequently variation (mutation frequency > 5%) [25]. The top 30 of the individual mutation frequencies are illustrated in Fig.…”

Section: Somatic Mutation Pro Le Of Bladder Patientsmentioning

confidence: 99%

Analysis of genome-wide mutation profile and establishment of risk signature for prognosis of bladder cancer

Zhao¹,

Ma²,

Liu³

et al. 2020

Preprint

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Background: Emerging studies have shown that a variety of gene mutations occur in development and progression of cancer and highly mutation genes could play oncogenic or tumor suppressive roles in cancer. Therefore, our aim is to explore mutation genes which affect the prognosis of bladder.Methods: Mutation profile was obtained and analyzed from TCGA data set. A mutation-based signature was established by multivariable Cox regression analysis. Kaplan-Meier was performed to assess the prognostic power of signature. Time-dependent ROC was conducted to evaluate predictive accuracy of signature for bladder cancer patients.Results: There are 20177 genes have alteration in 403 bladder patients and 662 of them were frequently variation (mutation frequency > 5%). In this study, we assessed the prognostic predictive ability of 662 highly mutated genes and identified a mutation signature as an independent indicator for predicting the prognosis of bladder. The time-dependent ROC showed that AUC were 0.893, 0.896, 0.916 and 0.965 at 1, 3, 5 and 10 year, respectively. Stratified analysis and Multivariate Cox analysis showed that this mutation signature was reliable and independent biomarker. Furthermore, the nomogram predictive model can be used to effectively predict clinical prognosis of bladder patients. The decision analysis curve showed patients with risk threshold of 0.03-0.92 potentially yielded clinical net benefit. Finally, we identified several signaling pathways that associated with risk score by GSEA and KEGG analysis including PI3K-Akt signaling pathway and so on.Conclusions: In general, this study provide an optimal mutation signature as potential prognosis biomarker for bladder patients.

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Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data

Cited by 73 publications

References 48 publications

Lipidomic study of cell lines reveals differences between breast cancer subtypes

Lipidomic study of cell lines reveals differences between breast cancer subtypes

Evaluating the efficacy of multiple myeloma cell lines as models for patient tumors via transcriptomic correlation analysis

Analysis of genome-wide mutation profile and establishment of risk signature for prognosis of bladder cancer

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