scholarly journals Predicting breast cancer drug response using a multiple-layer cell line drug response network model

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shujun Huang ◽  
Pingzhao Hu ◽  
Ted M. Lakowski
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Drug Response ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Layer Cell ◽  
Response Network ◽  
Line Drug

Abstract Background Predicting patient drug response based on a patient’s molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs. Methods We first collected the baseline gene expression profiles of 49 BC cell lines along with IC50 values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC50 profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples. Results ML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC50 values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA). Conclusions The ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.

scholarly journals ADRML: anticancer drug response prediction using manifold learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Fatemeh Ahmadi Moughari ◽  
Changiz Eslahchi
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Anticancer Drug ◽  
Manifold Learning ◽  
Drug Response ◽  
Drug Information ◽  
Response Prediction ◽  
Drug Mechanism ◽  
Line Drug ◽  
Anticancer Drug Response

AbstractOne of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. The proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response.

scholarly journals Genome-Wide Role of HSF1 in Transcriptional Regulation of Desiccation Tolerance in the Anhydrobiotic Cell Line, Pv11

2021 ◽  
Vol 22 (11) ◽  
pp. 5798
Author(s):  
Shoko Tokumoto ◽  
Yugo Miyata ◽  
Ruslan Deviatiiarov ◽  
Takahiro G. Yamada ◽  
Yusuke Hiki ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Desiccation Tolerance ◽  
Expression Profiles ◽  
Insect Cell Line ◽  
Gene Expression Profiles ◽  
Late Embryogenesis Abundant ◽  
Heat Shock Factor 1 ◽  
Genome Wide ◽  
A Genome

The Pv11, an insect cell line established from the midge Polypedilum vanderplanki, is capable of extreme hypometabolic desiccation tolerance, so-called anhydrobiosis. We previously discovered that heat shock factor 1 (HSF1) contributes to the acquisition of desiccation tolerance by Pv11 cells, but the mechanistic details have yet to be elucidated. Here, by analyzing the gene expression profiles of newly established HSF1-knockout and -rescue cell lines, we show that HSF1 has a genome-wide effect on gene regulation in Pv11. The HSF1-knockout cells exhibit a reduced desiccation survival rate, but this is completely restored in HSF1-rescue cells. By comparing mRNA profiles of the two cell lines, we reveal that HSF1 induces anhydrobiosis-related genes, especially genes encoding late embryogenesis abundant proteins and thioredoxins, but represses a group of genes involved in basal cellular processes, thus promoting an extreme hypometabolism state in the cell. In addition, HSF1 binding motifs are enriched in the promoters of anhydrobiosis-related genes and we demonstrate binding of HSF1 to these promoters by ChIP-qPCR. Thus, HSF1 directly regulates the transcription of anhydrobiosis-related genes and consequently plays a pivotal role in the induction of anhydrobiotic ability in Pv11 cells.

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 Improving Drug Response Prediction Using Dual Similarity Regularization

2021 ◽  
Author(s):  
Ali Reza Ebadi ◽  
Ali Soleimani ◽  
Abdulbaghi Ghaderzadeh
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Matrix Factorization ◽  
Drug Response ◽  
Pearson Correlation ◽  
Heterogeneous Data ◽  
Latent Factors ◽  
Proposed Model ◽  
Similar Cell ◽  
Drug Similarity

Abstract Anti-cancer medicine for a particular patient has been a personal medical goal. Many computational models have been proposed by researchers to predict drug response. But predictive accuracy still remains a challenge. Base on this concept which “Similar cells have similar responses to drugs”, we developed the basic method of matrix factorization method by adding fines to similarity. So that the distance of latent factors to two cell lines or (drug) should be inversely related to similarity. This means that two similar drugs or similar cell lines should have a short distance, whereas two similar cell lines or non-similar drugs should have a large gap with their latent factors. We proposed a Dual similarity-regularized matrix factorization (DSRMF) model, then generated new data for drug similarity from the two-dimensional three-dimensional chemical structure, which were obtained from the CCLE and GDSC databases. In this research, by using the proposed model, and generating new drug similarity data we achieved the average Pearson correlation coefficient (PCC) about 0.96, and average mean square error (RMSE) Root about 0.30, between the observed value and the predicted value for the cell line response to the drug. Our analysis in this research showed, using heterogeneous data, has better results, and can be obtained with the proposed model, using other panels’ cancer cell lines, to calculate similarity between cells. Also, by imposing more restrictions on the similarity between cells, we were able to achieve more accurate prediction for the response of the cell line to the anticancer drug.

scholarly journals A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction

2019 ◽  
Author(s):  
Jinyu Chen ◽  
Louxin Zhang
Keyword(s):  
Drug Response ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Future Research ◽  
Learning Approaches ◽  
Systematic Assessment ◽  
The Past ◽  
Public Datasets ◽  
Prediction Problems

AbstractDrug response prediction arises from both basic and clinical research of personalized therapy, as well as drug discovery for cancer and other diseases. With gene expression profiles and other omics data being available for over 1000 cancer cell lines and tissues, different machine learning approaches have been applied to solve drug response prediction problems. These methods appear in a body of literature and have been evaluated on different datasets with only one or two accuracy metrics. We systematically assessed 17 representative methods for drug response prediction, which have been developed in the past five years, on four large public datasets in nine metrics. This study provides insights and lessons for future research into drug response prediction.

scholarly journals Improving Drug Response Prediction Using Dual Similarity Regularization

2020 ◽  
Author(s):  
ALI REZA EBADI ◽  
Ali Soleimani ◽  
ABDULBAGHI GHADERZADEH
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Matrix Factorization ◽  
Drug Response ◽  
Pearson Correlation ◽  
Heterogeneous Data ◽  
Latent Factors ◽  
Proposed Model ◽  
Similar Cell ◽  
Drug Similarity

Abstract Background:Anti-cancer medicine for a particular patient has been a personal medical goal. Many computational models have been proposed by researchers to predict drug response. But predictive accuracy still remains a challenge. Base on this concept which “Similar cells have similar responses to drugs”, we developed the basic method of matrix factorization method by adding fines to similarity. So that the distance of latent factors to two cell lines or (drug) should be inversely related to similarity. This means that two similar drugs or similar cell lines should have a short distance, whereas two similar cell lines or non-similar drugs should have a large gap with their latent factors.Results:We proposed a Dual similarity-regularized matrix factorization (DSRMF) model, then generated new data for drug similarity from the two-dimensional three-dimensional chemical structure, which were obtained from the CCLE and GDSC databases. In this research, by using the proposed model, and generating new drug similarity data we achieved the average Pearson correlation coefficient (PCC) about 0.96, and average mean square error (RMSE) Root about 0.30, between the observed value and the predicted value for the cell line response to the drug, Conclusions:Our analysis in this research showed, using heterogeneous data, has better results, and can be obtained with the proposed model, using other panels’ cancer cell lines, to calculate similarity between cells. Also, by imposing more restrictions on the similarity between cells, we were able to achieve more accurate prediction for the response of the cell line to the anticancer drug.

Melphalan Resistance in Multiple Myeloma Can Be Overcome by Proteasome Inhibition or Histone Deacetylation: Lack of Syngergism of the Combination.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3393-3393
Author(s):  
Pieter Sonneveld ◽  
Eric Kamst ◽  
Yvonne de Knegt ◽  
Naomi Klarenbeek ◽  
Martijn Schoester
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Line ◽  
Cell Lines ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Proteasome Inhibition ◽  
Histone Deacetylation ◽  
Interaction Factor ◽  
Resistant Cells

Abstract Multiple Myeloma (MM) is a disease of monoclonal plasma cells in the bone marrow which has a transient response to classic chemotherapy. At diagnosis, induction chemotherapy followed by high-dose melphalan (HDM) with stem cell support is used in most patients to achieve a clinical response. Because all patients will ultimately relapse, the treatment of melphalan-refractory disease represents a major clinical challenge and new agents are needed to overcome melphalan resistance. We have investigated the anti-myeloma efficacy of two new classes of targeted agents, i.e. proteasome inhibition and histone deacetylation inhibition alone or in combination in the melphalan sensitive MM1S and the Melphalan refractory MM1MEL2000 cell lines. The IC50 values of Bortezomib (B), Melphalan (M) and LAQ824 (L) in MM1S were 2.1 nM, 1.9 uM and 1.7 nM, respectively and in MM1MEL2000 3.9 nM, 50 uM and 4.0 nM. Using isobologram analysis a synergysm between B and L was observed in the sensitive, however not in the melphalan refractory cell line. These data indicate that B proteasome inhibition and histone deacetylation inhibition may be effective ways to overcome melphalan resistance. However, the previously reported synergism between these drugs does not seem to occur in melphalan resistant cells. The gene expression profiles of these cell lines were analysed using the Affymetrix U133plus 2.0 gene chip before and after treatment with melfaphalan or the proteasome inhibitor B or the histone deacetylation inhibitor L or the combination of B and L. Genes that were highly expressed in the melphalan refractory derivate cell line MM1MEL2000 as compared with wild-type MM1S included GP M6B, ADAM23 and HTPAP. Following melphalan exposure, TMF1, a CEBp glucocorticoid interaction factor, WHSC1L1, a MMSET homologue with EGF like domain and several transcription factors had highly increased expression as compared to MM1S. With exposure to B combined with L, increased expression in MM1MEL2000 over MM1S was observed for GTP exchange factor TIAM1 which interacts with RAS and JNK, and the lymphoid enhancer factor, a notch transcription factor. It is concluded that Bortezomib and the histone deacetylase inhibitor LAQ824 are effective agents to overcome melphalan resistance in multiple myeloma. However, the combination fails to show the synergism observed in melphalan sensitive cells. Gene analysis sofar does not provide a clear explanation for this lack of synergism. A comprehensive summary of the observed shifts of gene expression profiles in melphalan resistant cells following exposure to these agents, will be presented.

Metabolic evaluation of sporadic papillary kidney cancer.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 377-377
Author(s):  
Brian Shuch ◽  
Christopher Ricketts ◽  
Carole Sourbier ◽  
Shinji Tsutsumi ◽  
Xiu-ying Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Membrane Potential ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Kidney Cancer ◽  
Expression Profiles ◽  
Fumarate Hydratase ◽  
Gene Expression Profiles ◽  
Cancer Cell Lines

377 Background: Papillary kidney cancer, which occurs in 15% of patients with kidney cancer, can be aggressive and there is currently no effective form of therapy for this disease. To evaluate the metabolic characteristics of sporadic papillary kidney cancer, we have evaluated metabolic parameters of several papillary kidney cancer cell lines and available gene expression profiles. Methods: Established cell lines derived from patients with sporadic papillary kidney cancer (LABAZ, MDACC-55, HRC-86T2) and from a hereditary form of fumarate hydratase-deficient kidney cancer (UOK262) were evaluated. All sporadic lines were initially sequenced for fumarate hydratase (FH). All cell lines were metabolically profiled using the Seahorse Extracellular Flux Analyzer and further evaluated for reactive oxygen species (ROS), mitochondrial membrane potential, and glucose dependence. Finally gene expression profiles of publically available datasets of papillary and HLRCC tumors were downloaded, normalized, and analyzed. Results: Sporadic lines had no alterations in FH and metabolic analysis demonstrated normal oxygen consumption and minimal lactate production, in contrast to highly glycolytic UOK262. Also unlike UOK262, the sporadic papillary kidney cancer lines were not sensitive to glucose withdrawal, had low levels of ROS, and had normal mitochondria membrane potential. Principal component analysis (PCA) demonstrated that HLRCC tumor specimens are very different from sporadic papillary tumors at the molecular level. Conclusions: Our study of established sporadic papillary RCC and fumarate hydratase-deficient HLRCC cell line together with analysis of available gene expression profiles demonstrates that these sporadic papillary kidney cancer cell lines appear to have a distinct metabolic profile from those in the fumarate hydratase deficient kidney cancer cell line. Understanding the metabolic basis of papillary kidney cancer could provide the foundation for the development of targeted approaches to therapy for patients with this disease.

scholarly journals Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Yongsoo Kim ◽  
Tycho Bismeijer ◽  
Wilbert Zwart ◽  
Lodewyk F. A. Wessels ◽  
Daniel J. Vis
Keyword(s):  
Data Integration ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Biology ◽  
Drug Response ◽  
Genomic Data ◽  
Response Prediction ◽  
Molecular Data ◽  
Cancer Type

Abstract Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.

scholarly journals A survey and systematic assessment of computational methods for drug response prediction

2020 ◽  
Author(s):  
Jinyu Chen ◽  
Louxin Zhang
Keyword(s):  
Drug Response ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Future Research ◽  
Omics Data ◽  
Learning Approaches ◽  
Systematic Assessment ◽  
The Past ◽  
Public Datasets

Abstract Drug response prediction arises from both basic and clinical research of personalized therapy, as well as drug discovery for cancers. With gene expression profiles and other omics data being available for over 1000 cancer cell lines and tissues, different machine learning approaches have been applied to drug response prediction. These methods appear in a body of literature and have been evaluated on different datasets with only one or two accuracy metrics. We systematically assess 17 representative methods for drug response prediction, which have been developed in the past 5 years, on four large public datasets in nine metrics. This study provides insights and lessons for future research into drug response prediction.

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