Abstract 676: Leveraging TCGA gene expression data to build predictive models for cancer drug response

2019 ◽  
Author(s):  
Evan Clayton
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Predictive Models ◽  
Drug Response ◽  
Cancer Drug ◽  
Expression Data

scholarly journals Leveraging TCGA gene expression data to build predictive models for cancer drug response

2020 ◽  
Vol 21 (S14) ◽  
Author(s):  
Evan A. Clayton ◽  
Toyya A. Pujol ◽  
John F. McDonald ◽  
Peng Qiu
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Predictive Models ◽  
Drug Response ◽  
Cancer Drug ◽  
Expression Data ◽  
Classification Methods ◽  
Clustering And Classification ◽  
Machine Learning Models

Abstract Background Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients’ primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study’s limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

Predictive models of hepatotoxicity using gene expression data from primary rat hepatocytes

Xenobiotica ◽  
2006 ◽  
Vol 36 (10-11) ◽  
pp. 1122-1139 ◽  
Author(s):  
L. Hultin-Rosenberg ◽  
S. Jagannathan ◽  
K. C. Nilsson ◽  
S. A. Matis ◽  
N. Sjögren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Predictive Models ◽  
Rat Hepatocytes ◽  
Expression Data ◽  
Primary Rat Hepatocytes

Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction

2021 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Parsa Alamzadeh Harjandi ◽  
Olga Zolotareva ◽  
Colin C. Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Drug Response ◽  
Response Prediction ◽  
Expression Data

scholarly journals Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance

2016 ◽  
Author(s):  
Amin Emad ◽  
Carl R. Woese ◽  
Junmei Cairns ◽  
Krishna R. Kalari ◽  
Liewei Wang, M.D. ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Resistance ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Drug Response ◽  
Global Analysis ◽  
Extensive Literature ◽  
Expression Data ◽  
Chemotherapy Drugs ◽  
New Genes

ABSTRACTBackgroundIdentification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance.ResultsWe developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group.ConclusionsOur results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

scholarly journals A recursive framework for predicting the time-course of drug sensitivity

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Cheng Qian ◽  
Amin Emad ◽  
Nicholas D. Sidiropoulos
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Drug Response ◽  
Time Course ◽  
Drug Sensitivity ◽  
Expression Data ◽  
Expression Levels ◽  
Time Points ◽  
Multiple Sclerosis Patients

Abstract The biological processes involved in a drug’s mechanisms of action are oftentimes dynamic, complex and difficult to discern. Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug. However, the majority of previous work has not fully utilized this temporal dimension. In these studies, the gene expression data is either considered at one time-point (before the administration of the drug) or two time-points (before and after the administration of the drug). This is clearly inadequate in modeling dynamic gene–drug interactions, especially for applications such as long-term drug therapy. In this work, we present a novel REcursive Prediction (REP) framework for drug response prediction by taking advantage of time-course gene expression data. Our goal is to predict drug response values at every stage of a long-term treatment, given the expression levels of genes collected in the previous time-points. To this end, REP employs a built-in recursive structure that exploits the intrinsic time-course nature of the data and integrates past values of drug responses for subsequent predictions. It also incorporates tensor completion that can not only alleviate the impact of noise and missing data, but also predict unseen gene expression levels (GEXs). These advantages enable REP to estimate drug response at any stage of a given treatment from some GEXs measured in the beginning of the treatment. Extensive experiments on two datasets corresponding to multiple sclerosis patients treated with interferon are included to showcase the effectiveness of REP.

scholarly journals Velodrome: Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

2021 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Parsa Alamzadeh Harjandi ◽  
Olga Zolotareva ◽  
Colin C Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Transfer Learning ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Drug Response ◽  
Response Prediction ◽  
Fine Tuning ◽  
Expression Data ◽  
Target Domain ◽  
Data Discrepancy

Data discrepancy between preclinical and clinical datasets poses a major challenge for accurate drug response prediction based on gene expression data. Different methods of transfer learning have been proposed to address this data discrepancy. These methods generally use cell lines as source domains and patients, patient-derived xenografts, or other cell lines as target domains. However, they assume that they have access to the target domain during training or fine-tuning and they can only take labeled source domains as input. The former is a strong assumption that is not satisfied during deployment of these models in the clinic. The latter means these methods rely on labeled source domains which are of limited size. To avoid this assumption, we formulate drug response prediction as an out-of-distribution generalization problem which does not assume that the target domain is accessible during training. Moreover, to exploit unlabeled source domain data, which tends to be much more plentiful than labeled data, we adopt a semi-supervised approach. We propose Velodrome, a semi-supervised method of out-of-distribution generalization that takes labeled and unlabeled data from different resources as input and makes generalizable predictions. Velodrome achieves this goal by introducing an objective function that combines a supervised loss for accurate prediction, an alignment loss for generalization, and a consistency loss to incorporate unlabeled samples. Our experimental results demonstrate that Velodrome outperforms state-of-the-art pharmacogenomics and transfer learning baselines on cell lines, patient-derived xenografts, and patients and therefore, may guide precision oncology more accurately.

scholarly journals Clinical Omics Analysis of Colorectal Cancer Incorporating Copy Number Aberrations and Gene Expression Data

2010 ◽  
Vol 9 ◽  
pp. CIN.S3851 ◽  
Author(s):  
Tsuyoshi Yoshida ◽  
Takumi Kobayashi ◽  
Masaya Itoda ◽  
Taika Muto ◽  
Ken Miyaguchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Copy Number ◽  
Gene Expression Analysis ◽  
Cancer Drug ◽  
Expression Data ◽  
New Genes ◽  
Wide Range

Background Colorectal cancer (CRC) is one of the most frequently occurring cancers in Japan, and thus a wide range of methods have been deployed to study the molecular mechanisms of CRC. In this study, we performed a comprehensive analysis of CRC, incorporating copy number aberration (CRC) and gene expression data. For the last four years, we have been collecting data from CRC cases and organizing the information as an “omics” study by integrating many kinds of analysis into a single comprehensive investigation. In our previous studies, we had experienced difficulty in finding genes related to CRC, as we observed higher noise levels in the expression data than in the data for other cancers. Because chromosomal aberrations are often observed in CRC, here, we have performed a combination of CNA analysis and expression analysis in order to identify some new genes responsible for CRC. This study was performed as part of the Clinical Omics Database Project at Tokyo Medical and Dental University. The purpose of this study was to investigate the mechanism of genetic instability in CRC by this combination of expression analysis and CNA, and to establish a new method for the diagnosis and treatment of CRC. Materials and methods Comprehensive gene expression analysis was performed on 79 CRC cases using an Affymetrix Gene Chip, and comprehensive CNA analysis was performed using an Affymetrix DNA Sty array. To avoid the contamination of cancer tissue with normal cells, laser micro-dissection was performed before DNA/RNA extraction. Data analysis was performed using original software written in the R language. Result We observed a high percentage of CNA in colorectal cancer, including copy number gains at 7, 8q, 13 and 20q, and copy number losses at 8p, 17p and 18. Gene expression analysis provided many candidates for CRC-related genes, but their association with CRC did not reach the level of statistical significance. The combination of CNA and gene expression analysis, together with the clinical information, suggested UGT2B28, LOC440995, CXCL6, SULT1B1, RALBP1, TYMS, RAB12, RNMT, ARHGDIB, S1000A2, ABHD2, OIT3 and ABHD12 as genes that are possibly associated with CRC. Some of these genes have already been reported as being related to CRC. TYMS has been reported as being associated with resistance to the anti-cancer drug 5-fluorouracil, and we observed a copy number increase for this gene. RALBP1, ARHGDIB and S100A2 have been reported as oncogenes, and we observed copy number increases in each. ARHGDIB has been reported as a metastasis-related gene, and our data also showed copy number increases of this gene in cases with metastasis. Conclusion The combination of CNA analysis and gene expression analysis was a more effective method for finding genes associated with the clinicopathological classification of CRC than either analysis alone. Using this combination of methods, we were able to detect genes that have already been associated with CRC. We also identified additional candidate genes that may be new markers or targets for this form of cancer.

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