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 Predicting chemosensitivity using drug perturbed gene dynamics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Joshua D. Mannheimer ◽  
Ashok Prasad ◽  
Daniel L. Gustafson
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Computational Methods ◽  
Predictive Models ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Signatures

Abstract Background One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel. Results This work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24 h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes. Conclusion Our models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.

Deregulated Apoptotic Pathways Point to Effectiveness of IAP Inhibitor Therapy in Acute Myeloid Leukemia.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1275-1275
Author(s):  
Sonja C Lück ◽  
Annika C Russ ◽  
Konstanze Döhner ◽  
Ursula Botzenhardt ◽  
Domagoj Vucic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Cell Line ◽  
Cell Lines ◽  
Myeloid Leukemia ◽  
Gene Set Enrichment Analysis ◽  
Core Binding Factor ◽  
Binding Factor ◽  
Acute Myeloid

Abstract Abstract 1275 Poster Board I-297 Core binding factor (CBF) leukemias, characterized by translocations t(8;21) or inv(16)/t(16;16) targeting the core binding factor, constitute acute myeloid leukemia (AML) subgroups with favorable prognosis. However, 40-50% of patients relapse, and the current classification system does not fully reflect the heterogeneity existing within the cytogenetic subgroups. Therefore, illuminating the biological mechanisms underlying these differences is important for an optimization of therapy. Previously, gene expression profiling (GEP) revealed two distinct CBF leukemia subgroups displaying significant outcome differences (Bullinger et al., Blood 2007). In order to further characterize these GEP defined CBF subgroups, we again used gene expression profiles to identify cell line models similar to the respective CBF cohorts. Treatment of these cell lines with cytarabine (araC) revealed a differential response to the drug as expected based on the expression patterns reflecting the CBF subgroups. In accordance, the cell lines resembling the inferior outcome CBF cohort (ME-1, MONO-MAC-1, OCI-AML2) were less sensitive to araC than those modeling the good prognostic subgroup (Kasumi-1, HEL, MV4-11). A previous gene set enrichment analysis had identified the pathways Caspase cascade in apoptosis and Role of mitochondria in apoptotic signaling among the most significant differentially regulated BioCarta pathways distinguishing the two CBF leukemia subgroups. Thus, we concluded that those pathways might be interesting targets for specific intervention, as deregulated apoptosis underlying the distinct subgroups should also result in a subgroup specific sensitivity to apoptotic stimuli. Therefore, we treated our model cell lines with the Smac mimetic BV6, which antagonizes inhibitor of apoptosis (IAP) proteins that are differentially expressed among our CBF cohorts. In general, sensitivity to BV6 treatment was higher in the cell lines corresponding to the subgroup with good outcome. Time-course experiments with the CBF leukemia cell line Kasumi-1 suggested a role for caspases in this response. Interestingly, combination treatment of araC and BV6 in Kasumi-1 showed a synergistic effect of these drugs, with the underlying mechanisms being currently further investigated. Based on the promising sensitivity to BV6 treatment in some cell lines, we next treated mononuclear cells (mostly leukemic blasts) derived from newly diagnosed AML patients with BV6 in vitro to evaluate BV6 potency in primary leukemia samples. Interestingly, in vitro BV6 treatment also discriminated AML cases into two distinct populations. Most patient samples were sensitive to BV6 monotherapy, but about one-third of cases were resistant even at higher BV6 dosage. GEP of BV6 sensitive patients (at 24h following either BV6 or DMSO treatment) provided insights into BV6-induced pathway alterations in the primary AML patient samples, which included apoptosis-related pathways. In contrast to the BV6 sensitive patients, GEP analyses of BV6 resistant cases revealed no differential regulation of apoptosis-related pathways in this cohort. These results provide evidence that targeting deregulated apoptosis pathways by Smac mimetics might represent a promising new therapeutic approach in AML and that GEP might be used to predict response to therapy, thereby enabling novel individual risk-adapted therapeutic approaches. Disclosures Vucic: Genentech, Inc.: Employment. Deshayes:Genentech, Inc.: Employment.

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 Molecular phenotyping of breast cancer cell lines at the single-cell level for automated cancer diagnosis and prediction of drug sensitivity

2021 ◽  
Author(s):  
G Gambardella ◽  
G Viscido ◽  
B Tumaini ◽  
A Isacchi ◽  
R Bosotti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Anticancer Agents ◽  
Drug Sensitivity ◽  
Expression Profiles ◽  
Breast Cancer Cell Lines ◽  
Isogenic Cell Line

ABSTRACTBrest Cancer (BC) patient stratification is mainly driven by receptor status and histological grading and subtyping, with about twenty percent of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention to apply. Here, we evaluated the potentiality of single-cell transcriptomics for automated diagnosis and drug treatment of breast cancer. We transcriptionally profiled 35,276 individual cells from 32 BC cell-lines covering all main BC subtypes to yield a Breast Cancer Single Cell Atlas. We show that single cell transcriptomics can successfully detect clinically relevant BC biomarkers and that atlas can be used to automatically predict cancer subtype and composition from a patient’s tumour biopsy. We found that BC cell lines arbour a high degree of heterogeneity in the expression of clinically relevant BC biomarkers and that such heterogeneity enables cells with differential drug sensitivity to co-exist even within a genomically stable isogenic cell line. Finally, we developed a novel bioinformatics approach named DREEP (DRug Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from single-cell transcriptional profiles. We validated DREEP both in-silico and in-vitro by selectively inhibiting the growth of the HER2-deficient subpopulation in the MDAMB361 cell line. Our work shows transcriptional heterogeneity is common, dynamic and plays a relevant role in determining drug sensitivity. Moreover, our Breast Cancer Single Cell Atlas and DREEP approach are a unique resource for the BC research community and to advance the use of single-cell sequencing in the clinics.

Robust Metabolic Transcriptional Components in 34,494 Patient-Derived Samples and Cell Lines

2021 ◽  
Author(s):  
Vincent Christiaan Leeuwenburgh ◽  
Carlos G. Urzúa-Traslaviña ◽  
Arkajyoti Bhattacharya ◽  
Marthe T.C. Walvoort ◽  
Mathilde Jalving ◽  
...  
Keyword(s):  
Cell Lines ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Metabolic Processes ◽  
Gene Sets ◽  
Transcriptional Changes ◽  
Cancer Tissues ◽  
Tumor Biopsies ◽  
Transcriptional Landscape

Abstract Background: Patient-derived bulk expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus Independent Component Analyses (c-ICA) can capture statistically independent transcriptional footprints, of both subtle and more pronounced metabolic processes. Methods: We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs were determined in all samples to create a metabolic transcriptional landscape. Results: A set of 555 mTCs were identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. Conclusions: To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal ( www.themetaboliclandscapeofcancer.com ). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

The Role of PAX2 in Breast Cancer: A Study Based on Bioinformatics Analysis and in Vitro Validation

2021 ◽  
Author(s):  
Shan Yang ◽  
Wei Gao ◽  
Haoqi Wang ◽  
Xi Zhang ◽  
Yunzhe Mi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Growth ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Migration And Invasion ◽  
Clinical Samples

Abstract Background: Breast cancer (BC) is the most frequently diagnosed cancer in women and is the second most common cancer among newly diagnosed cancers worldwide. Studies have shown that paired box 2 (PAX2) participates in the tumorigenesis of some cancer cells. However, the functions of PAX2 in the BC context are still unclear.Methods: Transcriptome expression profiles and clinicopathological information of BC were download from the TCGA database. Then the expression level and prognostic value in TCGA database were explored. Gene Set Enrichment Analysis (GSEA) and functional enrichment analysis were performed to investigate the functions and pathways of PAX2. Moreover, RT-qPCR was used to determine the expression of PAX2 in BC tissues, and the predictive value of PAX2 in clinical samples was assessed. CCK-8 assay was used to evaluate cell growth. The migration and invasion capacities of cells were assessed by wound healing assay and Transwell assay.Results: PAX2 was up-regulated in the TCGA-BC datasets. GSEA analysis suggested that PAX2 might be involved in the regulation of MAPK signaling pathways and so on. Moreover, PAX2 was overexpressed in BC tissues, and PAX2 expression was associated with menopause. PAX2 deficiency could inhibit the growth, migration, and invasion of BC cells.Conclusion: This study suggested that PAX2 was up-regulated in BC, which inhibited BC cell growth, migration, and invasion. Thus, PAX2 could be a potential therapeutic target for BC.

24 GLOBAL TRANSCRIPT PROFILING OF CLONED BOVINE BLASTOCYSTS USING AFFYMETRIX GENECHIP TECHNOLOGY

2006 ◽  
Vol 18 (2) ◽  
pp. 120
Author(s):  
Z. Beyhan ◽  
P. Ross ◽  
A. Iager ◽  
A. Kocabas ◽  
K. Cunniff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Expression Profiles ◽  
Cell Structure ◽  
Donor Cell ◽  
Lipid Binding ◽  
Differentially Expressed ◽  
Gtp Binding ◽  
Cloned Bovine

Identification of genes implicated in the biological processes of somatic cell nuclear transfer will improve our understanding of reprogramming events, i.e. the transformation of a lineage-committed cell into a pluripotent one. In addition, the gene expression profile of cloned embryos can help explain the widely reported developmental failures in cloned animals. In this study, we investigated global gene expression profiles of bovine in vitro-fertilized and cloned embryos using Gene Chip Bovine Genome Arrays (Affymetrix, Inc., Santa Clara, CA, USA). For the generation of cloned bovine blastocysts from two adult fibroblast lines (C and D), we employed methods previously proven to generate live offspring and compared these offspring to in vitro-produced blastocysts. Total RNA isolated from groups of 10 blastocysts was amplified by a template-switching PCR. Amplified cDNAs were used to synthesize biotin-labeled antisense RNAs (aRNAs) during and in vitro transcription reaction. Labeled aRNAs were hybridized to microarrays as described by the manufacturer. Experiments were performed in four replicates. Expression data were analyzed using the Significance Analysis of Microarrays (SAM; Tusher et al. 2001 Proc. Natl. Acad. Sci. 98, 5116-5121) procedure and software. Overall, 48.4% and 46% of 23 000 bovine transcripts spotted on the arrays were present in cloned and in in vitro-produced control blastocysts, respectively. The SAM procedure identified 43 genes that changed at least 1.5-fold, with an estimated false discovery rate (FDR) of 20%. Comparison of gene expression between NT embryos produced from two different cell lines and IVF controls with the same criteria revealed 6 (clones from cell line C vs. IVF) and 46 (clones from cell line D vs. IVF) differentially expressed genes. The number of transcripts expressed differentially between the cloned embryos with different donor cell origin was 437. Of the 43 differentially expressed transcripts in cloned blastocysts, 13 have unknown functions and the rest of the genes related to cell structure (tuftelin, desmoplakin), cell cycle/mitosis (Kinesin like 4, katanin, stathmin, PCNA), energy metabolism (lactate dehydrogenase, ATPsynthase, lipid-binding protein, keto acid dehydrogenase E1, metallothionein), and cell signaling (GTP-binding protein1, GTP binding stimulatory protein). Our results indicate that expression profiles of cloned blastocysts could be affected by somatic donor cell.

Gene Expression Profiles of Hydroxyurea Tolerant Cell Lines Identify Genetic Mechanisms That Influence Hydroxyurea Maximum Tolerated Dose

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1005-1005
Author(s):  
Rosa Diaz ◽  
Jonathan M Flanagan ◽  
Thad A Howard ◽  
Russell E. Ware
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Lines ◽  
Expression Profiles ◽  
K562 Cells ◽  
Maximum Tolerated Dose ◽  
False Discovery ◽  
Microarray Expression ◽  
Tolerant Cell

Abstract Abstract 1005 Hydroxyurea has emerged over the past decade as an effective therapeutic agent for patients with sickle cell anemia (SCA). However, drug dosing and hematological responses can be highly variable; both %HbF response and maximum tolerated dose (MTD) vary widely among patients with SCA who receive hydroxyurea treatment. To obtain further insight into the cellular and molecular pathways, as well as genetic factors that might influence the hydroxyurea MTD, K562 erythroleukemia cells were exposed to hydroxyurea in vitro, to create cell lines that were highly drug tolerant to doses ranging from 250μM to 1500μM. Cell lines had dose-response curves that exhibited clear drug tolerance; naïve K562 showed 50% proliferation in the presence of 250μM hydroxyurea, while tolerant cell lines showed >90% proliferation at the same dose as measured by the BrdU Cell Proliferation Assay. In addition, the tolerant lines showed normal and equivalent progression through cell cycle by flow cytometry cell cycle analysis. After 15 weeks of continuous exposure, cells were harvested and mRNA microarray expression profiles were analyzed for naïve K562 (no hydroxyurea exposure) and cell lines tolerant to 500, 1000, or 1500μM hydroxyurea. Gene expression was measured on Affymetrix U133 Plus 2.0 chips. Differential expression between sample groups was determined using ANOVA, and p-values were corrected for multiple testing using the Benjamin-Hochberg false discovery rate (FDR) method to identify genetic profiles and genes consistently increased or decreased compared to naïve K562 cells. Using a threshold of 2-fold change compared to untreated cells and a false discovery rate <5%, a total of 864 genes were significantly altered in hydroxyurea tolerant cells, including 337 genes whose expression consistently correlated with increasing hydroxyurea dose (Pearson correlation p<.001). The PANTHER classification system was used to group genes into categories based on molecular functions. Of the genes that correlated significantly with increasing hydroxyurea dosing (n=337), there were 181 up-regulated genes and 156 down-regulated genes that had molecular functions including catalytic activity, binding, transcription regulator activity and transporter activity. Genes with transporter activity included SLC6A19, ATP6VOD1, ABCG2, ATP6V1B2 and KCNN4. Other genes of interest based on function included RRM2, PLS3, KCNAB2, UBE2A and SRI. Real-time quantitative reverse transcription (RT)-PCR then quantified the expression of 20 candidate genes to verify the accuracy of the microarray expression data. The next steps will include correlation of these findings with clinical data, specifically early reticulocyte mRNA expression and hydroxyurea MTD values obtained from children with SCA enrolled in the prospective Hydroxyurea Study of Long-term Effects (HUSTLE, NCT00305175). These data document that continuous in vitro exposure of K562 cells to hydroxyurea leads to tolerant cell lines that feature substantial changes in gene expression. Altered expression of certain genes present in erythroid cells including RRM2 and membrane transporters represent compensatory changes in response to hydroxyurea exposure, and may help explain the variability in hydroxyurea MTD observed among patients with SCA. Disclosures: Off Label Use: Hydroxyurea is not FDA approved for pediatric sickle cell patients. Howard:Baylor College of Medicine: Employment.

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