A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines

2019 ◽  
Vol 15 (6) ◽  
pp. 399-405 ◽  
Author(s):  
Julia L. Fleck ◽  
Ana B. Pavel ◽  
Christos G. Cassandras
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Late Stage ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Resistance Mechanisms ◽  
General Effect ◽  
Tumor Types ◽  
Pan Cancer ◽  
Genetic Events

Sequences of genetic events were identified that may help explain common patterns of oncogenesis across 22 tumor types. The general effect of late-stage mutations on drug sensitivity and resistance mechanisms in cancer cell lines was evaluated.

scholarly journals Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

scholarly journals Thiourea and Guanidine Compounds and their Iridium Complexes in Drug-Resistant Cancer Cell Lines: Structure-Activity Relationships and Direct Luminescent Imaging

2019 ◽  
Author(s):  
Samuel J. Thomas ◽  
Barbora Balonova ◽  
Jindrich Cinatl ◽  
Mark Wass ◽  
Christopher Serpell ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Nature Medicine ◽  
Cancer Cell Lines ◽  
Resistance Mechanisms ◽  
New Drugs ◽  
Iridium Complexes ◽  
Safety Issues ◽  
Structure Activity Relationships ◽  
Structure Activity

<p>Thiourea and guanidine units are found in nature, medicine, and materials. Their continued exploration in applications as diverse as cancer therapy, sensors, and electronics means that their toxicity is an important consideration. We have systematically synthesised a set of thiourea compounds and their guanidine analogues, and elucidated structure-activity relationships in terms of cellular toxicity in three ovarian cancer cell lines and their cisplatin-resistant sub-lines. We have been able to use the intrinsic luminescence of iridium complexes to visualise the effect of both structure alteration and cellular resistance mechanisms. These findings provide starting points for the development of new drugs and consideration of safety issues for novel thiourea- and guanidine-based materials.</p>

scholarly journals Intra-Tumoral Expression of SLC7A11 Is Associated with Immune Microenvironment, Drug Resistance, and Prognosis in Cancers: A Pan-Cancer Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiajun He ◽  
Hongjian Ding ◽  
Huaqing Li ◽  
Zhiyu Pan ◽  
Qian Chen
Keyword(s):  
Pancreatic Cancer ◽  
Drug Resistance ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Pancreatic Cancer Cell ◽  
Mononuclear Cells ◽  
Cancer Cell Lines ◽  
Clinical Practices ◽  
Pan Cancer

While many anti-cancer modalities have shown potent efficacy in clinical practices, cancer prevention, timely detection, and effective treatment are still challenging. As a newly recognized iron-dependent cell death mechanism characterized by excessive generation of lipid peroxidation, ferroptosis is regarded as a potent weapon in clearing cancer cells. The cystine/glutamate antiporter solute carrier family 7 member 11 (SLC7A11) is the core target for ferroptosis regulation, the overexpression of which dictates downregulated sensitivity to ferroptosis in cancer cells. Hence, we elaborated the pan-cancer level bioinformatic study and systematically elucidated the role of intra-tumoral expression of SLC7A11 in the survival of cancer patients and potential immunotherapeutic response. Specifically, 25/27 (92.6%) cancers were featured with upregulated SLC7A11 expression, where SLC7A11 overexpression is a risk factor for worse overall survival in 8 cancers. We also validated SLC7A11 expression in multiple pancreatic cancer cell lines in vitro and found that it was upregulated in most pancreatic cancer cell lines (p &lt; 0.05). Single-cell sequencing method revealed the SLC7A11 was majorly expressed in cancer cells and mononuclear cells. To further explore the function of SLC7A11 in cancer progression, we analyzed the influence on cell proliferation after the knockdown or knockout of SLC7A11 by either CRISPR or RNAi methods. Besides, the association between SLC7A11 and drug resistance was characterized using bioinformatic approaches as well. We also analyzed the association between the expression of SLC7A11 in multi-omics level and the intra-tumoral infiltration of immune cells based on cell annotation algorithms. Moreover, the relationship between SLC7A11 and the expression of MHC, immune stimulators, immune inhibitors as well as the response to immunotherapy was investigated. In addition, the SLC7A11 expression in colon adenocarcinoma, uterine corpus endometrial carcinoma, and stomach adenocarcinoma (STAD) is also positively associated with microsatellite instability and that in head and neck squamous cell carcinoma, STAD, and prostate adenocarcinoma is positively associated with neoantigen level, which further revealed the potential relationship between SLC7A11 and immunotherapeutic response.

MicroRNAs related to intrinsic resistance to oxaliplatin and the irinotecan metabolite SN-38 in 10 colorectal cancer cell lines.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 524-524 ◽  
Author(s):  
Niels Frank Jensen ◽  
Rolf Soekilde ◽  
Jan Stenvang ◽  
Birgitte Sander Nielsen ◽  
Thomas Litman ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Predictive Biomarkers ◽  
Cancer Cell Lines ◽  
Colorectal Cancer Cell ◽  
Intrinsic Resistance ◽  
Short Term ◽  
Colorectal Cancer Cell Lines

524 Background: Chemotherapy of metastatic colorectal cancer is based on 5-flourouracil combined with either oxaliplatin or irinotecan (active metabolite: SN-38). Identification of predictive biomarkers of drug response is needed to provide a better personalized treatment. In this study we aimed to identify microRNAs related to intrinsic resistance to oxaliplatin or irinotecan in a panel of ten colorectal cancer cell lines. Methods: Drug sensitivity towards oxaliplatin and SN-38 was determined for ten colorectal cancer cell lines (Colo-205, DLD-1, HCC-2998, HCT-15, HCT-116, HT-29, KM12, LoVo, LS-174T, and SW620), using the cell viability MTT assay and the cell death LDH assay. In addition, two cell lines (DLD-1 and LoVo) were exposed to the drugs for 6, 24 or 48 hours. MicroRNA expression profiles were generated using the Exiqon miRCURY LNA microarray platform (including 840 microRNAs), and four differentially expressed microRNAs were validated by independent qRT-PCR measurements. Results: The drug sensitivities of the ten colorectal cancer cell lines varied about 50 times between the least and most sensitive cell lines. Correlation of drug sensitivity data to microRNA expression data across the ten cell lines yielded about 25 microRNA biomarker candidates, for each of the drug/assay combinations. Following short-term drug treatment 10-20 microRNAs were altered for each drug/cell line combination. Validation by qRT-PCR showed a very good correlation to the microarray data. MicroRNAs identified by correlation to drug sensitivity and by short-term treatment were compared, and less than 10% were identified by both approaches, perhaps representing the most promising candidates. These candidates are for SN-38 miR-15a, miR-22, miR-24, miR-98, miR-142-3p, miR-1290, and let-7b, and for oxaliplatin miR-23b, miR-27a, miR-192, miR-200a, miR-222, miR-886-5p, and miR-1308. Conclusions: In the present study we identified a number of microRNAs that are potentially involved in intrinsic resistance and/or could be predictive biomarkers for either irinotecan or oxaliplatin.

scholarly journals Identification of candidate predictive markers of anticancer drug sensitivity using a panel of human cancer cell lines

2003 ◽  
Vol 94 (12) ◽  
pp. 1074-1082 ◽  
Author(s):  
Shingo Dan ◽  
Mieko Shirakawa ◽  
Yumiko Mukai ◽  
Yoko Yoshida ◽  
Kanami Yamazaki ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Predictive Markers ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines

scholarly journals The germline genetic component of drug sensitivity in cancer cell lines

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Michael P. Menden ◽  
Francesco Paolo Casale ◽  
Johannes Stephan ◽  
Graham R. Bignell ◽  
Francesco Iorio ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Genetic Component

scholarly journals Network-Based Matching of Patients and Targeted Therapies for Precision Oncology*

2019 ◽  
Author(s):  
Qingzhi Liu ◽  
Min Jin Ha ◽  
Rupam Bhattacharyya ◽  
Lana Garmire ◽  
Veerabhadran Baladandayuthapani
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Precision Oncology ◽  
Large Set ◽  
Sensitivity Data ◽  
The Right

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible – allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients’ responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the “missing link” between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.Data and code availabilityhttps://github.com/bayesrx/bayesrx.github.io/tree/master/authors/liu-q./ Shiny app (data and results visualization tool): https://qingzliu.shinyapps.io/psb-app/

Sign in / Sign up

Export Citation Format

Share Document