RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines.
Ontology highlight
ABSTRACT:
SUBMITTER: Basu A
PROVIDER: S-EPMC6157095 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Different types of genomic aberration may simultaneously contribute to tumorigenesis. To obtain a more accurate prognostic assessment to guide therapeutic regimen choice for cancer patients, the heterogeneous multi-omics data should be integrated harmoniously, which can often be difficult. For this purpose, we propose a Gene Interaction Regularized Elastic Net (GIREN) model that predicts clinical outcome by integrating multiple data types. GIREN conveniently embraces both gene measurements and gene-gene interaction information under an elastic net formulation, enforcing structure sparsity, and the "grouping effect" in solution to select the discriminate features with prognostic value. An iterative gradient descent algorithm is also developed to solve the model with regularized optimization. GIREN was applied to human ovarian cancer and breast cancer datasets obtained from The Cancer Genome Atlas, respectively. Result shows that, the proposed GIREN algorithm obtained more accurate and robust performance over competing algorithms (LASSO, Elastic Net, and Semi-supervised PCA, with or without average pathway expression features) in predicting cancer progression on both two datasets in terms of median area under curve (AUC) and interquartile range (IQR), suggesting a promising direction for more effective integration of gene measurement and gene interaction information.
| S-EPMC5374042 | biostudies-literature
Project description:MOTIVATION:Predictive models are a powerful tool for solving complex problems in computational biology. They are typically designed to predict or classify data coming from the same unknown distribution as the training data. In many real-world settings, however, uncontrolled biological or technical factors can lead to a distribution mismatch between datasets acquired at different times, causing model performance to deteriorate on new data. A common additional obstacle in computational biology is scarce data with many more features than samples. To address these problems, we propose a method for unsupervised domain adaptation that is based on a weighted elastic net. The key idea of our approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term. In doing so, we encourage the model to assign a higher importance to features that are robust and behave similarly across domains. RESULTS:We evaluate our method both on simulated data with varying degrees of distribution mismatch and on real data, considering the problem of age prediction based on DNA methylation data across multiple tissues. Compared with a non-adaptive standard model, our approach substantially reduces errors on samples with a mismatched distribution. On real data, we achieve far lower errors on cerebellum samples, a tissue which is not part of the training data and poorly predicted by standard models. Our results demonstrate that unsupervised domain adaptation is possible for applications in computational biology, even with many more features than samples. AVAILABILITY AND IMPLEMENTATION:Source code is available at https://github.com/PfeiferLabTue/wenda. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6612879 | biostudies-other
Project description:Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the "omic" diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines.
| S-EPMC6610642 | biostudies-literature
Project description:We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.
| S-EPMC8455625 | biostudies-literature
Project description:We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.
| S-EPMC4784899 | biostudies-literature
Project description:BackgroundMany kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, thus providing a map of the strength of action of each compound on a large number of its targets. These profiled libraries define drug-kinase networks that can predict the effectiveness of untested drugs and elucidate the roles of specific kinases in different cellular systems. Predictions of drug effectiveness based on a comprehensive network model of cellular signalling are difficult, due to our partial knowledge of the complex biological processes downstream of the targeted kinases.ResultsWe have developed the Kinase Inhibitors Elastic Net (KIEN) method, which integrates information contained in drug-kinase networks with in vitro screening. The method uses the in vitro cell response of single drugs and drug pair combinations as a training set to build linear and nonlinear regression models. Besides predicting the effectiveness of untested drugs, the KIEN method identifies sets of kinases that are statistically associated to drug sensitivity in a given cell line. We compared different versions of the method, which is based on a regression technique known as elastic net. Data from two-drug combinations led to predictive models, and we found that predictivity can be improved by applying logarithmic transformation to the data. The method was applied to the A549 lung cancer cell line, and we identified specific kinases known to have an important role in this type of cancer (TGFBR2, EGFR, PHKG1 and CDK4). A pathway enrichment analysis of the set of kinases identified by the method showed that axon guidance, activation of Rac, and semaphorin interactions pathways are associated to a selective response to therapeutic intervention in this cell line.ConclusionsWe have proposed an integrated experimental and computational methodology, called KIEN, that identifies the role of specific kinases in the drug response of a given cell line. The method will facilitate the design of new kinase inhibitors and the development of therapeutic interventions with combinations of many inhibitors.
| S-EPMC4094402 | biostudies-literature
Project description:Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. The new method for predicting metabolites using microbiome data (ENVIM) is based on the elastic net model (ENM). ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 289 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health. We select gene family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan when trained with metatranscriptomics data only, metagenomics data only, or both. Better metabolite prediction is achieved in the gut microbiome compared with the oral microbiome setting. We report the best-predictable compounds in all these three datasets from two different body sites. For example, the metabolites trehalose, maltose, stachyose, and ribose are all well predicted by the supragingival microbiome.
| S-EPMC8573316 | biostudies-literature
Project description:A total of 20 KYSE human esophageal squamous cell carcinoma cell lines (KYSE-30, -140, -150, -170, -180, -200, -220, -350, -410, -450, -510, -520, -590, -770, -850, -890, -1170, -1190, -1250, and -2270) were kindly provided by Dr. Y. Shimada (Kyoto University, Kyoto, Japan). The KYSE cell lines were cultured in RPMI 1640 medium (Life Technologies, Inc., Grand Island, NY) containing 10% heat-inactivated fetal bovine serum (FBS; BioWhittaker, Verviers, Belgium) in a humidified atmosphere of 5% CO2 and maintained in continuous exponential growth by passage every 3 days. The exponentially growing cultured cells (2 x 10^6) were collected after two-washings with PBS. Total RNA was prepared from the frozen cell pellets using QIAGEN RNeasy mini kit (QIAGEN, Inc., Valencia, CA). The quality of the RNA was checked using Agilent Technologies 2100 Bioanalyzer (Agilent, Palo Alto, CA). CodeLink Expression Bioarray System (Amersham Bioscience, Tokyo, Japan) was used according to the manufacturerâs protocol. Briefly, first-strand cDNA was generated from 1 µg of total RNA for each cell line using reverse transcriptase and a T7 primer, and then second-strand cDNA was produced using DNA polymerase mix and RNase H. cRNA was generated via an in vitro transcription reacrion using T7 RNA polymerase and biotin-11-UTP (Perkin Elmer, Boston, MA). After quantified by spectrometry and qualified using Agilent Technologies 2100 Bioanalyzer (Agilent), 10 µg of cRNA was fragmented and hybridized to a Uniset Human 20K I Bioarray containing 19,981 human probes with 108 positive and 300 negative bacterial control probes. After hybridization, the arrays were rinsed and labeled with Streptavidin-Cy5, scanned using Agilent DNA Microarray Scanner (Agilent), then analyzed with CodeLink Expression Analysis Software ver.2.3. Expression levels were normalized to the median expression value of the whole array spots except for the bacterial controls. To explore the genes responsible for chemosensitivity in cancer cells, expression profiles of various cancer cell lines were examined. Statistically comparing them with the chemosensitivity measured by in vitro MTT assay in each cell line, several candidate genes were selected and predicition models for anticancer efficacy were established using their expression levels.
2010-06-05 | E-GEOD-2447 | biostudies-arrayexpress
Project description:BackgroundUrban sound levels are a ubiquitous environmental stressor and have been shown to be associated with a wide variety of health outcomes. While much is known about the predictors of A-weighted sound pressure levels in the urban environment, far less is known about other frequencies.ObjectiveTo develop a series of spatial-temporal sound models to predict A-weighted sound pressure levels, low, mid, and high frequency sound for Boston, Massachusetts.MethodsShort-term sound levels were gathered at n = 400 sites from February 2015 - February 2016. Spatial and meteorological attributes at or near the sound monitoring site were obtained using publicly available data and a portable weather station. An elastic net variable selection technique was used to select predictors of A-weighted, low, mid, and high frequency sound.ResultsThe final models for low, mid, high, and A-weighted sound levels explained 59 - 69% of the variability in each measure. Similar to other A-weighted models, our sound models included transportation related variables such as length of roads and bus lines in the surrounding area; distance to road and rail lines; traffic volume, vehicle mix, residential and commercial land use. However, frequency specific models highlighted additional predictors not included in the A-weighted model including temperature, vegetation, impervious surfaces, vehicle mix, and density of entertainment establishments and restaurants.ConclusionsBuilding spatial temporal models to characterize sound levels across the frequency spectrum using an elastic net approach can be a promising tool for noise exposure assessments within the urban soundscape. Models of sound's character may give us additional important sound exposure metrics to be utilized in epidemiological studies.
| S-EPMC5903552 | biostudies-literature
Project description:A total of 20 KYSE human esophageal squamous cell carcinoma cell lines (KYSE-30, -140, -150, -170, -180, -200, -220, -350, -410, -450, -510, -520, -590, -770, -850, -890, -1170, -1190, -1250, and -2270) were kindly provided by Dr. Y. Shimada (Kyoto University, Kyoto, Japan). The KYSE cell lines were cultured in RPMI 1640 medium (Life Technologies, Inc., Grand Island, NY) containing 10% heat-inactivated fetal bovine serum (FBS; BioWhittaker, Verviers, Belgium) in a humidified atmosphere of 5% CO2 and maintained in continuous exponential growth by passage every 3 days. The exponentially growing cultured cells (2 x 10^6) were collected after two-washings with PBS. Total RNA was prepared from the frozen cell pellets using QIAGEN RNeasy mini kit (QIAGEN, Inc., Valencia, CA). The quality of the RNA was checked using Agilent Technologies 2100 Bioanalyzer (Agilent, Palo Alto, CA). CodeLink Expression Bioarray System (Amersham Bioscience, Tokyo, Japan) was used according to the manufacturer’s protocol. Briefly, first-strand cDNA was generated from 1 µg of total RNA for each cell line using reverse transcriptase and a T7 primer, and then second-strand cDNA was produced using DNA polymerase mix and RNase H. cRNA was generated via an in vitro transcription reacrion using T7 RNA polymerase and biotin-11-UTP (Perkin Elmer, Boston, MA). After quantified by spectrometry and qualified using Agilent Technologies 2100 Bioanalyzer (Agilent), 10 µg of cRNA was fragmented and hybridized to a Uniset Human 20K I Bioarray containing 19,981 human probes with 108 positive and 300 negative bacterial control probes. After hybridization, the arrays were rinsed and labeled with Streptavidin-Cy5, scanned using Agilent DNA Microarray Scanner (Agilent), then analyzed with CodeLink Expression Analysis Software ver.2.3. Expression levels were normalized to the median expression value of the whole array spots except for the bacterial controls. Keywords: parallel sample
2005-12-31 | GSE2447 | GEO