scholarly journals A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Li Wang ◽  
Robert P. Sebra ◽  
John P. Sfakianos ◽  
Kimaada Allette ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Tumor Type ◽  
Deconvolution Method ◽  
Intrinsic Subtypes ◽  
Reference Profile

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 Quantification of microenvironmental metabolites in murine cancer models reveals determinants of tumor nutrient availability

2018 ◽  
Author(s):  
Mark R. Sullivan ◽  
Laura V. Danai ◽  
Caroline A. Lewis ◽  
Sze Ham Chan ◽  
Dan Y. Gui ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Nutrient Availability ◽  
Cell Biology ◽  
Interstitial Fluid ◽  
Cell Metabolism ◽  
Extracellular Fluid ◽  
Anatomical Location ◽  
Tumor Type ◽  
Nutrient Levels ◽  
Tumor Interstitial Fluid

AbstractCancer cell metabolism is heavily influenced by microenvironmental factors, including nutrient availability. Therefore, knowledge of microenvironmental nutrient levels is essential to understand tumor metabolism. To measure the extracellular nutrient levels available to tumors, we developed a quantitative metabolomics method to measure the absolute concentrations of >118 metabolites in plasma and tumor interstitial fluid, the extracellular fluid that perfuses tumors. Comparison of nutrient levels in tumor interstitial fluid and plasma revealed that the nutrients available to tumors differ from those present in circulation. Further, by comparing interstitial fluid nutrient levels between autochthonous and transplant models of murine pancreatic and lung adenocarcinoma, we found that tumor type, anatomical location and animal diet affect local nutrient availability. These data provide a comprehensive characterization of the nutrients present in the tumor microenvironment of widely used models of lung and pancreatic cancer and identify factors that influence metabolite levels in tumors.Impact StatementNutrient availability is an important tumor microenvironmental factor that impacts cancer cell biology; we developed methods to measure nutrients available to tumor cells and characterized factors that influence tumor nutrient availability.

scholarly journals Transcriptional fidelity enhances cancer cell line selection in pediatric cancers

2021 ◽  
Author(s):  
Cuyler Luck ◽  
Katharine Yu ◽  
Ross A Okimoto ◽  
Marina Sirota
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Molecular Subtype ◽  
Unmet Need ◽  
Cancer Cell Lines ◽  
Tumor Type ◽  
Pediatric Cancers ◽  
Public Data ◽  
Tumor Types

Multi-omic technologies have allowed for comprehensive profiling of patient-derived tumor samples and the cell lines that are intended to model them. Yet, our understanding of how cancer cell lines reflect native pediatric cancers in the age of molecular subclassification remains unclear and represents a clinical unmet need. Here we use Treehouse public data to provide an RNA-seq driven analysis of 799 cancer cell lines, focusing on how well they correlate to 1,655 pediatric tumor samples spanning 12 tumor types. For each tumor type we present a ranked list of the most representative cell lines based on correlation of their transcriptomic profiles to those of the tumor. We found that most (8/12) tumor types best correlated to a cell line of the closest matched disease type. We furthermore showed that inferred molecular subtype differences in medulloblastoma significantly impacted correlation between medulloblastoma tumor samples and cell lines. Our results are available as an interactive web application to help researchers select cancer cell lines that more faithfully recapitulate pediatric cancer.

scholarly journals Cancer microcell initiation and determination

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zane Simsone ◽  
Tālivaldis Freivalds ◽  
Dina Bēma ◽  
Indra Miķelsone ◽  
Liene Patetko ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Anticancer Agents ◽  
Fluorescent Protein ◽  
Transfection Efficiency ◽  
Cell Subpopulation ◽  
Cancer Type ◽  
Tumor Type ◽  
Cancer Resistance ◽  
Enzyme Activity Assay ◽  
Anticancer Treatment

Abstract Background Cancer remains one of the leading causes of death worldwide, despite the possibilities to detect early onset of the most common cancer types. The search for the optimal therapy is complicated by the cancer diversity within tumors and the unsynchronized development of cancerous cells. Therefore, it is necessary to characterize cancer cell populations after treatment has been applied, because cancer recurrence is not rare. In our research, we concentrated on small cancer cell subpopulation (microcells) that has a potential to be cancer resistance source. Previously made experiments has shown that these cells in small numbers form in specific circumstances after anticancer treatment. Methods In experiments described in this research, the anticancer agents’ paclitaxel and doxorubicin were used to stimulate the induction of microcells in fibroblast, cervix adenocarcinoma, and melanoma cell lines. Mainly for the formation of microcells in melanoma cells. The drug-stimulated cells were then characterized in terms of their formation efficiency, morphology, and metabolic activity. Results We observed the development of cancer microcells and green fluorescent protein (GFP) transfection efficiency after stress. In the time-lapse experiment, we observed microcell formation through a renewal process and GFP expression in the microcells. Additionally, the microcells were viable after anticancer treatment, as indicated by the nicotinamide adenine dinucleotide hydrogen phosphate (NADPH) enzyme activity assay results. Taken together, these findings indicate that cancer microcells are viable and capable of resisting the stress induced by anticancer drugs, and these cells are prone to chemical substance uptake from the environment. Conclusion Microcells are not only common to a specific cancer type, but can be found in any tumor type. This study could help to understand cancer emergence and recurrence. The appearance of microcells in the studied cancer cell population could be an indicator of the individual anticancer therapy effectiveness and patient survival.

scholarly journals Prospective patient stratification into robust cancer-cell intrinsic subtypes from colorectal cancer biopsies

2018 ◽  
Vol 245 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Matthew Alderdice ◽  
Susan D Richman ◽  
Simon Gollins ◽  
James P Stewart ◽  
Chris Hurt ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Cell ◽  
Patient Stratification ◽  
Intrinsic Subtypes

Cytotoxic effect of euphol from Euphorbia tirucalli on a large panel of human cancer cell lines.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e13557-e13557 ◽  
Author(s):  
Rui Manuel Reis ◽  
Viviane Aline Oliveira Silva ◽  
Marcela Nunes Rosa ◽  
Aline Tansini ◽  
Joao Paulo Da S.N. Lima ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Tumor Type ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines ◽  
Euphorbia Tirucalli ◽  
Large Panel

e13557 Background: The tetracyclic triterpene alcohol euphol is the main constituent found in the sap of Euphorbia tirucalli. In Brazil its latex is used as anticancer and other diseaseas folk treatment, yet, little is known about its anticancer proprieties. We aimed to study the antitumor effect of euphol on a large panel of human cancer cell lines. Methods: Anti-tumor effects of euphol in vitro were assessed using MTS assays on 77 human cancer lines from13 solid tumor models, such as breast, colon, bladder, prostate, lung, pancreas, esophagus, glioblastoma, melanoma, head and neck and cervical cancer. Additionally, we evaluate the its potential combinatorial value with temozolomide in gliomas. Ongoing experiments will identify potential drug target(s) by assessing changes in global protein expression. Results: Euphol exhibited dose and time-dependent cytotoxic effects on all cancer cell lines analyzed. Among each tumor type, the distinct cell line exhibited a heterogeneous profile of response to euphol. Esophageal squamous cell carcinoma and pancreatic carcinomas showed the most sensitive profile. In comparison with temozolomide, euphol showed a median of 30 fold higher efficacy, range 5-167 fold, in the glioma cell lines analyzed (Table). Conclusions: Euphol demonstrated potent anti-tumor activity on the majority of cancer cell lines evaluated. Our findings may provide insight into the tailoring designing of euphol-based therapies for cancer patients. [Table: see text]

Patient stratification into robust cancer-cell intrinsic subtypes from colorectal cancer biopsies may inform prospective clinical trials

2018 ◽  
Vol 44 ◽  
pp. S49
Author(s):  
Matt Alderdice ◽  
Amy McCorry ◽  
Claudio Isella ◽  
Darragh McArt ◽  
Susan Richman ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Clinical Trials ◽  
Cancer Cell ◽  
Patient Stratification ◽  
Intrinsic Subtypes

scholarly journals Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

2013 ◽  
Vol 14 (4) ◽  
pp. R37 ◽  
Author(s):  
Pedro Casado ◽  
Maria P Alcolea ◽  
Francesco Iorio ◽  
Juan-Carlos Rodríguez-Prados ◽  
Bart Vanhaesebroeck ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Kinase Inhibitors ◽  
Cancer Cell Lines ◽  
Tumor Type ◽  
Hematological Cancer

scholarly journals Predicting Tumor Response to Drugs Based on Gene-expression Biomarkers of Sensitivity Learned From Cancer Cell Lines

2020 ◽  
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://edelgene.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 predictions could have clinical relevance for patients’ care.

scholarly journals Predicting Tumor Response to Drugs based on Gene-Expression Biomarkers of Sensitivity Learned from Cancer Cell Lines

2020 ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno 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

SUMMARYHuman 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. 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://edelgene.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. We further 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 predictions could have clinical relevance for patients’ care.

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