Heterogeneity of the Cancer Cell Line Metabolic Landscape

The unravelling of the complexity of cellular metabolism is in its infancy. Cancer-associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer-associated metabolic transformation we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1099 different species using liquid chromatography-mass spectrometry (LC-MS). We correlate known cancer-associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control.

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Comprehensive Analysis of SWI/SNF Inactivation in Lung Adenocarcinoma Cell Models

Cancers ◽

10.3390/cancers12123712 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3712

Author(s):

Paola Peinado ◽

Alvaro Andrades ◽

Marta Cuadros ◽

Maria Isabel Rodriguez ◽

Isabel F. Coira ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Cell Line ◽

Cell Lines ◽

Cancer Cell ◽

Cancer Cell Line ◽

Genetic Alterations ◽

Cell Models ◽

Functional Studies ◽

Cellular Models ◽

Mutational Status

Mammalian SWI/SNF (SWitch/Sucrose Non-Fermentable) complexes are ATP-dependent chromatin remodelers whose subunits have emerged among the most frequently mutated genes in cancer. Studying SWI/SNF function in cancer cell line models has unveiled vulnerabilities in SWI/SNF-mutant tumors that can lead to the discovery of new therapeutic drugs. However, choosing an appropriate cancer cell line model for SWI/SNF functional studies can be challenging because SWI/SNF subunits are frequently altered in cancer by various mechanisms, including genetic alterations and post-transcriptional mechanisms. In this work, we combined genomic, transcriptomic, and proteomic approaches to study the mutational status and the expression levels of the SWI/SNF subunits in a panel of 38 lung adenocarcinoma (LUAD) cell lines. We found that the SWI/SNF complex was mutated in more than 76% of our LUAD cell lines and there was a high variability in the expression of the different SWI/SNF subunits. These results underline the importance of the SWI/SNF complex as a tumor suppressor in LUAD and the difficulties in defining altered and unaltered cell models for the SWI/SNF complex. These findings will assist researchers in choosing the most suitable cellular models for their studies of SWI/SNF to bring all of its potential to the development of novel therapeutic applications.

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Biological network topology features predict gene dependencies in cancer cell lines

10.1101/751776 ◽

2019 ◽

Cited By ~ 2

Author(s):

Graeme Benstead-Hume ◽

Sarah K. Wooller ◽

Samantha Dias ◽

Lisa Woodbine ◽

Anthony M. Carr ◽

...

Keyword(s):

Cell Line ◽

Cell Lines ◽

Cancer Cell ◽

Individual Cell ◽

Cancer Cell Line ◽

Cancer Cell Lines ◽

Genetic Alterations ◽

Essential Genes ◽

Loss Of Function ◽

Ppi Network

AbstractIn this paper we explore computational approaches that enable us to identify genes that have become essential in individual cancer cell lines. Using recently published experimental cancer cell line gene essentiality data, human protein-protein interaction (PPI) network data and individual cell-line genomic alteration data we have built a range of machine learning classification models to predict cell line specific acquired essential genes. Genetic alterations found in each individual cell line were modelled by removing protein nodes to reflect loss of function mutations and changing the weights of edges in each PPI to reflect gain of function mutations and gene expression changes.We found that PPI networks can be used to successfully classify human cell line specific acquired essential genes within individual cell lines and between cell lines, even across tissue types with AUC ROC scores of between 0.75 and 0.85. Our novel perturbed PPI network models further improved prediction power compared to the base PPI model and are shown to be more sensitive to genes on which the cell becomes dependent as a result of other changes. These improvements offer opportunities for personalised therapy with each individual’s cancer cell dependencies presenting a potential tailored drug target.The overriding motivation for predicting cancer cell line specific acquired essential genes is to provide a low-cost approach to identifying personalised cancer drug targets without the cost of exhaustive loss of function screening.

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Genetic load makes cancer cells more sensitive to common drugs: evidence from Cancer Cell Line Encyclopedia

10.1101/139311 ◽

2017 ◽

Author(s):

Ana B. Pavel ◽

Kirill S. Korolev

Keyword(s):

Cell Line ◽

Anticancer Drugs ◽

Cancer Cell ◽

Genetic Load ◽

Cancer Cell Line ◽

Genetic Alterations ◽

Cell Cycle Checkpoints ◽

Tissue Type ◽

Cancer Cell Line Encyclopedia ◽

Negative Effect

AbstractGenetic alterations initiate tumors and enable the evolution of drug resistance. The pro-cancer view of mutations is however incomplete, and several studies show that mutational load can reduce tumor fitness. Given its negative effect, genetic load should make tumors more sensitive to anticancer drugs. Here, we test this hypothesis across all major types of cancer from the Cancer Cell Line Encyclopedia, that provides genetic and expression data of 496 cell lines together with their response to 24 common anticancer drugs. We found that the efficacy of 9 out of 24 drugs showed significant association with genetic load in a pan-cancer analysis. The associations for some tissue-drug combinations were remarkably strong with genetic load explaining up to 83% of the variance in the drug response. Overall, the role of genetic load depended on both the drug and the tissue type with 10 tissues being particularly vulnerable to genetic load. We also identified changes in gene expression associated with increased genetic load, which included cell-cycle checkpoints, DNA damage and apoptosis. Our results show that genetic load is an important component of tumor fitness and can predict drug sensitivity. Beyond being a biomarker, genetic load might be a new, unexplored vulnerability of cancer.

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In Vitro Anticancer Drug Sensitivity Sensing through Single-Cell Raman Spectroscopy

Biosensors ◽

10.3390/bios11080286 ◽

2021 ◽

Vol 11 (8) ◽

pp. 286

Author(s):

Jingkai Wang ◽

Kaicheng Lin ◽

Huijie Hu ◽

Xingwang Qie ◽

Wei E. Huang ◽

...

Keyword(s):

Single Cell ◽

Cell Line ◽

Anticancer Drugs ◽

Cancer Cell ◽

Anticancer Drug ◽

Metabolic Activity ◽

Drug Sensitivity ◽

Cancer Cell Line ◽

Sensitivity Testing

Traditional in vitro anticancer drug sensitivity testing at the population level suffers from lengthy procedures and high false positive rates. To overcome these defects, we built a confocal Raman microscopy sensing system and proposed a single-cell approach via Raman-deuterium isotope probing (Raman-DIP) as a rapid and reliable in vitro drug efficacy evaluation method. Raman-DIP detected the incorporation of deuterium into the cell, which correlated with the metabolic activity of the cell. The human non-small cell lung cancer cell line HCC827 and human breast cancer cell line MCF-7 were tested against eight different anticancer drugs. The metabolic activity of cancer cells could be detected as early as 12 h, independent of cell growth. Incubation of cells in 30% heavy water (D2O) did not show any negative effect on cell viability. Compared with traditional methods, Raman-DIP could accurately determine the drug effect, meanwhile, it could reduce the testing period from 72–144 h to 48 h. Moreover, the heterogeneity of cells responding to anticancer drugs was observed at the single-cell level. This proof-of-concept study demonstrated the potential of Raman-DIP to be a reliable tool for cancer drug discovery and drug susceptibility testing.

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