scholarly journals Simulation of cancer cell line pharmacogenomics data to optimise experimental design and analysis strategy

2018 ◽  
Author(s):  
Hitesh Mistry ◽  
Phil Chapman
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Dose Response ◽  
Statistical Power ◽  
Drug Sensitivity ◽  
Mixed Effects ◽  
Conventional Approach ◽  
Cancer Drug ◽  
Simulation Studies ◽  
Non Linear

Explaining the variability in drug sensitivity across a panel of cell lines using genomic information is a key aspect of cancer drug discovery. The results of such analyses may ultimately determine which patients are likely to benefit from a new treatment. There are numerous experimental factors that can influence the outcomes of cell line screening panels such as the number of replicates, number of doses explored etc. Simulation studies can aid in understanding how variability in these experimental factors can affect the statistical power of a given analysis method. In this study dose response data was simulated for a variety of experimental designs and the ability of different methods to retrieve the original simulation parameters was compared. The analysis methods under consideration were a combination of non-linear least squares and ANOVA, conventional approach, versus non-linear mixed effects. Across the simulation studies explored the mixed-effects approach gave similar and in some situations greater statistical power than the conventional approach. In particular the mixed-effects approach gave significantly greater power when there was less information per dose response curve, and when more cell lines screened.More generally the best way to improve statistical power was to screen more cell lines. This study demonstrates the value of simulating data to understand design and analysis choices in the context of cancer drug sensitivity screening. By illustrating the performance of different methods in different situations these results will help researchers in the field generate and analyse data on future preclinical compounds. Ultimately this will benefit patients by ensuring that biomarkers of drug sensitivity have an increased chance of being identified at the preclinical stage.

scholarly journals Simulation of cancer cell line pharmacogenomics data to optimise experimental design and analysis strategy

2018 ◽  
Author(s):  
Hitesh Mistry ◽  
Phil Chapman
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Dose Response ◽  
Statistical Power ◽  
Drug Sensitivity ◽  
Mixed Effects ◽  
Conventional Approach ◽  
Cancer Drug ◽  
Simulation Studies ◽  
Non Linear

Explaining the variability in drug sensitivity across a panel of cell lines using genomic information is a key aspect of cancer drug discovery. The results of such analyses may ultimately determine which patients are likely to benefit from a new treatment. There are numerous experimental factors that can influence the outcomes of cell line screening panels such as the number of replicates, number of doses explored etc. Simulation studies can aid in understanding how variability in these experimental factors can affect the statistical power of a given analysis method. In this study dose response data was simulated for a variety of experimental designs and the ability of different methods to retrieve the original simulation parameters was compared. The analysis methods under consideration were a combination of non-linear least squares and ANOVA, conventional approach, versus non-linear mixed effects. Across the simulation studies explored the mixed-effects approach gave similar and in some situations greater statistical power than the conventional approach. In particular the mixed-effects approach gave significantly greater power when there was less information per dose response curve, and when more cell lines screened.More generally the best way to improve statistical power was to screen more cell lines. This study demonstrates the value of simulating data to understand design and analysis choices in the context of cancer drug sensitivity screening. By illustrating the performance of different methods in different situations these results will help researchers in the field generate and analyse data on future preclinical compounds. Ultimately this will benefit patients by ensuring that biomarkers of drug sensitivity have an increased chance of being identified at the preclinical stage.

scholarly journals Simulation of cancer cell line pharmacogenomics data to optimise experimental design and analysis strategy

2017 ◽  
Author(s):  
Hitesh Mistry ◽  
Phil Chapman
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Dose Response ◽  
Statistical Power ◽  
Drug Sensitivity ◽  
Mixed Effects ◽  
Conventional Approach ◽  
Cancer Drug ◽  
Simulation Studies ◽  
Non Linear

AbstractBackgroundExplaining the variability in drug sensitivity across a panel of cell lines using genomic information is a key aspect of cancer drug discovery. The results of such analyses may ultimately determine which patients are likely to benefit from a new treatment. There are numerous experimental factors that can influence the outcomes of cell line screening panels such as the number of replicates, number of doses explored etc. Simulation studies can aid in understanding how variability in these experimental factors can affect the statistical power of a given analysis method. In this study dose response data was simulated for a variety of experimental designs and the ability of different methods to retrieve the original simulation parameters was compared. The analysis methods under consideration were a combination of non-linear least squares and ANOVA, conventional approach, versus non-linear mixed effects.ResultsAcross the simulation studies explored the mixed-effects approach gave similar and in some situations greater statistical power than the conventional approach. In particular the mixed-effects approach gave significantly greater power when there was less information per dose response curve, and when more cell lines screened. More generally the best way to improve statistical power was to screen more cell lines.ConclusionsThis study demonstrates the value of simulating data to understand design and analysis choices in the context of cancer drug sensitivity screening. By illustrating the performance of different methods in different situations these results will help researchers in the field generate and analyse data on future preclinical compounds. Ultimately this will benefit patients by ensuring that biomarkers of drug sensitivity have an increased chance of being identified at the preclinical stage.

scholarly journals A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response

2020 ◽  
Author(s):  
Evanthia Koukouli ◽  
Dennis Wang ◽  
Frank Dondelinger ◽  
Juhyun Park
Keyword(s):  
Gene Expression ◽  
Regression Model ◽  
Cell Line ◽  
Cell Lines ◽  
Dose Response ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Drug ◽  
Functional Regression ◽  
Response Curves

AbstractCancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalised regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumourgenesis and DNA damage response.Author SummaryTumour cell lines allow scientists to test anticancer drugs in a laboratory environment. Cells are exposed to the drug in increasing concentrations, and the drug response, or amount of surviving cells, is measured. Generally, drug response is summarized via a single number such as the concentration at which 50% of the cells have died (IC50). To avoid relying on such summary measures, we adopted a functional regression approach that takes the dose-response curves as inputs, and uses them to find biomarkers of drug response. One major advantage of our approach is that it describes how the effect of a biomarker on the drug response changes with the drug dosage. This is useful for determining optimal treatment dosages and predicting drug response curves for unseen drug-cell line combinations. Our method scales to large numbers of biomarkers by using regularisation and, in contrast with existing literature, selects the most informative genes by accounting for drug response at untested dosages. We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response. We show that the selected genes recapitulate prior biological knowledge, and belong to known cancer pathways.

scholarly journals The power of modelling pulsatile profiles

2021 ◽  
Author(s):  
Michiel J. van Esdonk ◽  
Jasper Stevens
Keyword(s):  
Growth Hormone ◽  
Statistical Power ◽  
Quantitative Description ◽  
Mixed Effects ◽  
Post Dose ◽  
Deconvolution Analysis ◽  
Linear Mixed Effects ◽  
Non Linear ◽  
Over Time ◽  
Modelling Approach

AbstractThe quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration–time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.

Quantitative cell fusion: the fusion sensitivity (FS) potential

1981 ◽  
Vol 49 (1) ◽  
pp. 87-97
Author(s):  
D. Rohme
Keyword(s):  
Cell Line ◽  
Cell Fusion ◽  
Cell Lines ◽  
Dose Response ◽  
Sendai Virus ◽  
Biological Significance ◽  
Diploid Cell ◽  
Mammalian Cell Lines ◽  
Virus Dose ◽  
A Cell

The dose response of Sendai virus-induced cell fusion was studied in 10 mammalian cell lines, comprising 5 continuous and 5 diploid cell lines originating from 5 species. The extent of fusion was calculated using a parameter directly proportional to the number of fusion events (t-parameter). At lower levels of fusion the dose response was found to be based on the same simple kinetic rules in all cell lines and was defined by the formula: t = FS. FAU/(I + FS. FAU), where FS (fusion sensitivity) is a cell-specific constant of the fusion rate and FAU (fusion activity units) is the virus dose. The FS potential of a cell line was determined as the linear regression coefficient of the fusion index (t/(I - t)) on the virus dose. At higher levels of fusion, when the fusion extent reached cell-line-specific maximal levels, the dose response was not as uniform. In general, and particularly in the cases of the diploid cell lines, these maximal levels were directly proportional to the FS potentials. Thus, it was concluded that the FS potential is the basic quantitative feature, which expresses the cellular fusion efficiency. The fact that FS varied extensively between cell lines, but at the same time apparently followed certain patterns (being higher in continuous compared to diploid cell lines and being related to the species of origin of the cells), emphasizes it biological significance as well as its possible usefulness in studies of the efficiency of various molecular interactions in the cell membrane/cytoskeleton system.

scholarly journals Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate a Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database

2018 ◽  
Vol 24 (3) ◽  
pp. 242-263 ◽  
Author(s):  
David A. Close ◽  
Allen Xinwei Wang ◽  
Stanton J. Kochanek ◽  
Tongying Shun ◽  
Julie L. Eiseman ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Growth Inhibition ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Cancer Drug ◽  
Large Matrix ◽  
Data Points

Animal and clinical studies demonstrate that cancer drug combinations (DCs) are more effective than single agents. However, it is difficult to predict which DCs will be more efficacious than individual drugs. Systematic DC high-throughput screening (HTS) of 100 approved drugs in the National Cancer Institute’s panel of 60 cancer cell lines (NCI-60) produced data to help select DCs for further consideration. We miniaturized growth inhibition assays into 384-well format, increased the fetal bovine serum amount to 10%, lengthened compound exposure to 72 h, and used a homogeneous detection reagent. We determined the growth inhibition 50% values of individual drugs across 60 cell lines, selected drug concentrations for 4 × 4 DC matrices (DCMs), created DCM master and replica daughter plate sets, implemented the HTS, quality control reviewed the data, and analyzed the results. A total of 2620 DCMs were screened in 60 cancer cell lines to generate 3.04 million data points for the NCI ALMANAC (A Large Matrix of Anti-Neoplastic Agent Combinations) database. We confirmed in vitro a synergistic drug interaction flagged in the DC HTS between the vinca-alkaloid microtubule assembly inhibitor vinorelbine (Navelbine) tartrate and the epidermal growth factor-receptor tyrosine kinase inhibitor gefitinib (Iressa) in the SK-MEL-5 melanoma cell line. Seventy-five percent of the DCs examined in the screen are not currently in the clinical trials database. Selected synergistic drug interactions flagged in the DC HTS described herein were subsequently confirmed by the NCI in vitro, evaluated mechanistically, and were shown to have greater than single-agent efficacy in mouse xenograft human cancer models. Enrollment is open for two clinical trials for DCs that were identified in the DC HTS. The NCI ALMANAC database therefore constitutes a valuable resource for selecting promising DCs for confirmation, mechanistic studies, and clinical translation.

Eltrombopag, but Not Romiplostim or Thrombopoietin, Inhibits Growth of Thrombopoietin Receptor Positive and Negative Human Leukemia Cell Lines

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4726-4726
Author(s):  
David W. Rusnak ◽  
Sharon K. Rudolph ◽  
Afshin Safavi ◽  
Connie L. Erickson-Miller
Keyword(s):  
Clinical Trials ◽  
Cell Line ◽  
Cell Lines ◽  
Dose Response ◽  
Leukemia Cell ◽  
Cell Counts ◽  
Thrombopoietin Receptor ◽  
Ic50 Values ◽  
Equity Ownership ◽  
The Impact

Abstract Abstract 4726 The thrombopoietin receptor agonists (TPO-RA), romiplostim and eltrombopag, are presently indicated for the treatment of certain patient groups with immune thrombocytopenia purpura. In a clinical study with romiplostim in patients with low-/intermediate-1 risk myelodysplastic syndromes (MDS), cases of transient increases in blast cell counts were observed and cases of MDS disease progression to acute myeloid leukemia (AML) were reported. In the present study, we evaluated the impact of romiplostim, eltrombopag, and recombinant human thrombopoietin (TPO) on the proliferation of 5 human AML and 1 TPO-dependent megakaryoblastic cell line. The cell lines evaluated include the TPO-dependent cell line, N2C TPO; the TPO-R positive AML lines, HEL92.1.7 and OCI-AML-3; and the TPO-R negative AML cell lines, HL60, THP-1, and NOMO-1. All cells were exposed to 11-point dose response curves of the 3 agents at concentrations sufficient to generate a full stimulatory response in the N2C TPO cell line. Cells were exposed to concentrations of romiplostim and eltrombopag that met or exceeded the reported Cmax achieved for each agent in high-dose clinical trials and were 3- (eltrombopag) to 30-fold (romiplostim) above trough levels from the same clinical trials. Neither romiplostim nor TPO treatment resulted in detectable stimulation or inhibition of leukemia cell growth at concentrations up to 10 μg/mL. Treatment with eltrombopag up to 40 μg/mL caused inhibition of all AML cell lines with mean IC50 values ranging from 6.4 to 13.5 μg/mL. These IC50 values reflect concentrations that are 3- to 6-fold below the Cmax of a 300 qd dose of eltrombopag (40.5 μg/mL) and at concentrations as low as 2-fold below Ctau levels (12.4 μg/mL). Cmax exceeded the IC90 for these AML cell lines, which ranged from 18.5 to 27.9 μg/mL. No stimulation of AML growth was evident through the range of the eltrombopag dose response curve on any of the cell lines evaluated. The results of this study confirm earlier in vitro studies (Will 2009, Erickson-Miller 2010) showing inhibitory effects of eltrombopag on leukemic cell lines and support clinical studies to evaluate a potential anti-leukemic effect of higher doses of eltrombopag in patients with AML. Disclosures: Rusnak: GlaxoSmithKline: Consultancy, Equity Ownership, Patents & Royalties. Off Label Use: Eltrombopag is an oral TPO agonist indicated for chronic ITP being studied in MDS/AML. Rudolph:GlaxoSmithKline: Consultancy, Equity Ownership. Erickson-Miller:GlaxoSmithKline: Employment, Equity Ownership, Patents & Royalties, Research Funding.

Connecting gene expression subtypes of colorectal cancer (CRC) with cell lines and drug resistance.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e14544-e14544
Author(s):  
Eva Budinska ◽  
Jenny Wilding ◽  
Vlad Calin Popovici ◽  
Edoardo Missiaglia ◽  
Arnaud Roth ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Clinical Significance ◽  
Cell Lines ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis

e14544 Background: We identified CRC gene expression subtypes (ASCO 2012, #3511), which associate with established parameters of outcome as well as relevant biological motifs. We now substantiate their biological and potentially clinical significance by linking them with cell line data and drug sensitivity, primarily attempting to identify models for the poor prognosis subtypes Mesenchymal and CIMP-H like (characterized by EMT/stroma and immune-associated gene modules, respectively). Methods: We analyzed gene expression profiles of 35 publicly available cell lines with sensitivity data for 82 drug compounds, and our 94 cell lines with data on sensitivity for 7 compounds and colony morphology. As in vitro, stromal and immune-associated genes loose their relevance, we trained a new classifier based on genes expressed in both systems, which identifies the subtypes in both tissue and cell cultures. Cell line subtypes were validated by comparing their enrichment for molecular markers with that of our CRC subtypes. Drug sensitivity was assessed by linking original subtypes with 92 drug response signatures (MsigDB) via gene set enrichment analysis, and by screening drug sensitivity of cell line panels against our subtypes (Kruskal-Wallis test). Results: Of the cell lines 70% could be assigned to a subtype with a probability as high as 0.95. The cell line subtypes were significantly associated with their KRAS, BRAF and MSI status and corresponded to our CRC subtypes. Interestingly, the cell lines which in matrigel created a network of undifferentiated cells were assigned to the Mesenchymal subtype. Drug response studies revealed potential sensitivity of subtypes to multiple compounds, in addition to what could be predicted based on their mutational profile (e.g. sensitivity of the CIMP-H subtype to Dasatinib, p<0.01). Conclusions: Our data support the biological and potentially clinical significance of the CRC subtypes in their association with cell line models, including results of drug sensitivity analysis. Our subtypes might not only have prognostic value but might also be predictive for response to drugs. Subtyping cell lines further substantiates their significance as relevant model for functional studies.

scholarly journals Matching cell lines with cancer type and subtype of origin via mutational, epigenomic and transcriptomic patterns

2019 ◽  
Author(s):  
Marina Salvadores ◽  
Francisco Fuster-Tormo ◽  
Fran Supek
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Experimental Work ◽  
Drug Sensitivity ◽  
Tissue Cell ◽  
Cancer Type ◽  
Tumor Type ◽  
Cell Type ◽  
Cancer Subtypes ◽  
The One

AbstractCell lines are commonly used as cancer models. Because the tissue and/or cell type of origin provide important context for understanding mechanisms of cancer, we systematically examined whether cell lines exhibit features matching the cancer type that supposedly originated them. To this end, we aligned the mRNA expression and DNA methylation data between ∼9,000 solid tumors and ∼600 cell lines to remove the global differences stemming from growth in cell culture. Next, we created classification models for cancer type and subtype using tumor data, and applied them to cell line data. Overall, the transcriptomic and epigenomic classifiers consistently identified 35 cell lines which better matched a different tissue or cell type than the one the cell line was originally annotated with; we recommend caution in using these cell lines in experimental work. Six cell lines were identified as originating from the skin, of which five were further corroborated by the presence of a UV-like mutational signature in their genome, strongly suggesting mislabelling. Overall, genomic evidence additionally supports that 22 (3.6% of all considered) cell lines may be mislabelled because we predict they originate from a different tissue/cell type. Finally, we cataloged 366 cell lines in which both transcriptomic and epigenomic profiles strongly resemble the tumor type of origin, designating them as ‘golden set’ cell lines. We suggest these cell lines are better suited for experimental work that depends on tissue identity and propose tentative assignments to cancer subtypes. Finally, we show that accounting for the uncertain tissue-of-origin labels can change the interpretation of drug sensitivity and CRISPR genetic screening data. In particular, in brain, lung and pancreatic cancer cell lines, many novel determinants of drug sensitivity or resistance emerged by focussing on the cell lines that are best matched to the cancer type of interest.

scholarly journals A Non-Negative Matrix Tri-Factorization based Method for Predicting Antitumor Drug Sensitivity

2021 ◽  
Author(s):  
Sara Pidò ◽  
Carolina Testa ◽  
Pietro Pinoli
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Drug Response ◽  
Missing Values ◽  
Drug Sensitivity ◽  
Factorization Method ◽  
Data Types ◽  
Drug Profile ◽  
The Matrix ◽  
Ic50 Values

AbstractLarge annotated cell line collections have been proven to enable the prediction of drug response in the preclinical setting. We present an enhancement of Non-Negative Matrix Tri-Factorization method, which allows the integration of different data types for the prediction of missing associations. To test our method we retrieved a dataset from CCLE, containing the connections among cell lines and drugs by means of their IC50 values. We performed two different kind of experiments: a) prediction of missing values in the matrix, b) prediction of the complete drug profile of a new cell line, demonstrating the validity of the method in both scenarios.

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