COXEN prediction of antineoplastic drug sensitivity in bladder cancer patients.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 365-365
Author(s):  
Shalin Kothari ◽  
Daniel Gustafson ◽  
Keith Killian ◽  
James Costello ◽  
Daniel C. Edelman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Data ◽  
Drug Sensitivity ◽  
Model Building ◽  
Response To Therapy ◽  
Antineoplastic Drug ◽  
In Vitro Assays ◽  
Expression Data

365 Background: COXEN (Co-eXpression ExtrapolatioN) uses molecular profiles as a “rosetta stone” for translating drug sensitivities of one set of cancers into predictions for another completely independent set of cell lines or human tumors. The ability of COXEN to predict drug effectiveness in pts using tumor samples from in vitro assays is unique. Methods: We tested the predictive value of COXEN for standard chemotherapies in a cohort of bladder cancer pts. Total RNA was extracted from formalin fixed paraffin embedded (FFPE) tissue and converted to cDNA, amplified with Ovation FFPE WTA, and hybridized to a GeneChip Human Genome U133 Plus 2.0 array. Using gene expression data from 278 independent bladder tumors, COXEN scores were generated using bioinformatics models originally built using the NCI-60 cell line panel and a model building algorithm (MiPP). Gene expression data was processed to score 76 FDA approved antineoplastic drugs. Results: A total of 24 samples were tested (15 tumors with 1 sample and 9 tumors with 2 biological replicas (2 samples from the same tumor)) from 15 pts who received chemotherapy (median age 64 (41-74); 73% male; with muscle invasive bladder cancer (MIBC) (12/15, 80%) or metastatic bladder cancer (mBC) (3/15, 20%)). Response to therapy was confirmed by pathologic response in MIBC pts and radiologic response in mBC pts. Chemotherapies evaluated included: methotrexate/vinblastine/doxorubicin/cisplatin; gemcitabine/cisplatin; gemcitabine/carboplatin; and cisplatin/etoposide. COXEN accurately predicted antineoplastic drug sensitivity in 11/15 (73%) pts (75% MIBC and 67% mBC), of which 7/11 pts had 2 biological samples. However, only 3/7 (43%) biological replicas confirmed COXEN prediction. COXEN accurately predicted drug sensitivity in 9/10 (90%) pts with response and 2/5 (40%) pts with resistance to therapy. Conclusions: COXEN did well in predicting antineoplastic drug response for the majority of bladder cancer pts in this cohort. However, predictions from 2 samples within the same tumor were not always consistent, likely due to the expected tumor heterogeneity found in bladder cancer tumors. A prospective clinical trial in patients with mBC using COXEN to select next best therapy is in development.

In vitro gene expression data supporting a DNA non-reactive genotoxic mechanism for ochratoxin A

2007 ◽  
Vol 220 (2) ◽  
pp. 216-224 ◽  
Author(s):  
Leire Arbillaga ◽  
Amaia Azqueta ◽  
Joost H.M. van Delft ◽  
Adela López de Cerain
Keyword(s):  
Gene Expression ◽  
Ochratoxin A ◽  
Gene Expression Data ◽  
Expression Data

scholarly journals Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Julien Racle ◽  
Kaat de Jonge ◽  
Petra Baumgaertner ◽  
Daniel E Speiser ◽  
David Gfeller
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Response To Therapy ◽  
Expression Data ◽  
Cell Type ◽  
Tumor Gene Expression ◽  
Tumor Gene

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

scholarly journals Discovery and Validation of Novel Methylation Markers in Helicobacter pylori-Associated Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huan Wang ◽  
Nian-Shuang Li ◽  
Cong He ◽  
Chuan Xie ◽  
Yin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Helicobacter Pylori ◽  
Gene Expression Data ◽  
Functional Enrichment ◽  
Expression Data ◽  
H Pylori

Previous studies have shown that abnormal methylation is an early key event in the pathogenesis of most human cancers, contributing to the development of tumors. However, little attention has been given to the potential of DNA methylation patterns as markers for Helicobacter pylori- (H. pylori-) associated gastric cancer (GC). In this study, an integrated analysis of DNA methylation and gene expression was conducted to identify some potential key epigenetic markers in H. pylori-associated GC. DNA methylation data of 28 H. pylori-positive and 168 H. pylori-negative GC samples were compared and analyzed. We also analyzed the gene expression data of 18 H. pylori-positive and 145 H. pylori-negative GC cases. Finally, the results were verified by in vitro and in vivo experiments. A total of 5609 differentially methylated regions associated with 2454 differentially methylated genes were identified. A total of 228 differentially expressed genes were identified from the gene expression data of H. pylori-positive and H. pylori-negative GC cases. The screened genes were analyzed for functional enrichment. Subsequently, we obtained 28 genes regulated by methylation through a Venn diagram, and we identified five genes (GSTO2, HUS1, INTS1, TMEM184A, and TMEM190) downregulated by hypermethylation. HUS1, GSTO2, and TMEM190 were expressed at lower levels in GC than in adjacent samples ( P < 0.05 ). Moreover, H. pylori infection decreased HUS1, GSTO2, and TMEM190 expression in vitro and in vivo. Our study identified HUS1, GSTO2, and TMEM190 as novel methylation markers for H. pylori-associated GC.

Expression Signature of E2F1 and Its Associated Genes Predict Superficial to Invasive Progression of Bladder Tumors

2010 ◽  
Vol 28 (16) ◽  
pp. 2660-2667 ◽  
Author(s):  
Ju-Seog Lee ◽  
Sun-Hee Leem ◽  
Sang-Yeop Lee ◽  
Sang-Cheol Kim ◽  
Eun-Sung Park ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Cancer Progression ◽  
Gene Expression Data ◽  
Gene Expression Signature ◽  
Molecular Signature ◽  
Expression Data ◽  
Bladder Tumors ◽  
Expression Signature ◽  
Leave One Out

Purpose In approximately 20% of patients with superficial bladder tumors, the tumors progress to invasive tumors after treatment. Current methods of predicting the clinical behavior of these tumors prospectively are unreliable. We aim to identify a molecular signature that can reliably identify patients with high-risk superficial tumors that are likely to progress to invasive tumors. Patients and Methods Gene expression data were collected from tumor specimens from 165 patients with bladder cancer. Various statistical methods, including leave-one-out cross-validation methods, were applied to identify a gene expression signature that could predict the likelihood of progression to invasive tumors and to test the robustness of the expression signature in an independent cohort. The robustness of the gene expression signature was validated in an independent (n = 353) cohort. Results Supervised analysis of gene expression data revealed a gene expression signature that is strongly associated with invasive bladder tumors. A molecular classifier based on this gene expression signature correctly predicted the likelihood of progression of superficial tumor to invasive tumor. Conclusion We present a molecular signature that can predict, at diagnosis, the likelihood of bladder cancer progression and, possibly, lead to improvements in patient therapy.

scholarly journals Double-stage discretization approaches for biomarker-based bladder cancer survival modeling

2021 ◽  
Vol 12 (1) ◽  
pp. 29-47
Author(s):  
Mauro Nascimben ◽  
Manolo Venturin ◽  
Lia Rimondini
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Data ◽  
Cancer Survival ◽  
Survival Rates ◽  
Sample Population ◽  
Expression Data ◽  
Discrete Interval ◽  
Classic Approach ◽  
Genetic Profiles

Abstract Bioinformatic techniques targeting gene expression data require specific analysis pipelines with the aim of studying properties, adaptation, and disease outcomes in a sample population. Present investigation compared together results of four numerical experiments modeling survival rates from bladder cancer genetic profiles. Research showed that a sequence of two discretization phases produced remarkable results compared to a classic approach employing one discretization of gene expression data. Analysis involving two discretization phases consisted of a primary discretizer followed by refinement or pre-binning input values before the main discretization scheme. Among all tests, the best model encloses a sequence of data transformation to compensate skewness, data discretization phase with class-attribute interdependence maximization algorithm, and final classification by voting feature intervals, a classifier that also provides discrete interval optimization.

Selection of Reference Genes for Transcription Studies on Adventitious Root Induction in Olive (Olea Europaea L.) Microshoots Considering Co-expression and Average Transcriptional Stability

2020 ◽  
Author(s):  
Carlos Noceda ◽  
Augusto Peixe ◽  
Birgit Arnholdt-Schmitt
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Reference Genes ◽  
Ad Hoc ◽  
Root Induction ◽  
Expression Data ◽  
Internal Reference ◽  
Experimental Conditions ◽  
Selection Of

Abstract BackgroungSelection of reference genes (RGs) for normalization of PCR-gene expression data includes two crucial steps: determination of the among-sample transcriptionally more stable genes and subsequent choosing of the most suitable genes as internal controls. Both steps can be carried-out through generally accepted strategies each having different strengths and weaknesses. The present study proposes to reinforce normalization of gene expression data by integrating and adding analytical revision at critical steps of those accepted procedures. Especially crucial is to counterbalance a higher representative number of RGs with a correspondent increase in their average transcriptional instability or a generalised co-expression trend among the samples. This methodological study used in vitro olive adventitious rooting as an experimental system, since the underlying morphogenetic process -wich is common to diverse species- is still not completely understood.ResultsFirstly, RG candidates were ranked according to transcriptional stability following a simple statistical method that reduces biasing effects of concomitant, systematic biological variations associated to experimental conditions, such as the variations caused by gene co-regulation. Those types of systematic co-variation are unconsidered by several popular ad hoc informatics programmes. To select the adequate genes among those already ranked, an algorithm of one of the ad hoc informatics programmes (GeNorm) was adapted to allow partial automatization of RG selection for any strategy of transcriptional-gene stability ordering. In order to delve into the resulting possible RG sets suitability for inter-assay comparisons and technical-error compensation, separate statistics were formulated. The achieved results were compared with those obtained by standard stability ranking methods. Finally, a double evaluation was performed to accurately contrast two choice RG sets. The whole strategy was applied to a panel considering several independent factors, but the suitability of the obtained putative RG sets was tested for cases restricted to fewer variables. H2B, OUB and ACT are valid for normalization in transcriptional studies on olive microshoot rooting when comparing treatments, time points and assays.ConclusionsThe set of genes identified as internal reference is now available for wider expression studies on any target gene in similar biological systems. The overall methodology aims to constitute a guide for general application.

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 Gene expression data from acetaminophen-induced toxicity in human hepatic in vitro systems and clinical liver samples

Data in Brief ◽  
2016 ◽  
Vol 7 ◽  
pp. 1052-1057 ◽  
Author(s):  
Robim M. Rodrigues ◽  
Olivier Govaere ◽  
Tania Roskams ◽  
Tamara Vanhaecke ◽  
Vera Rogiers ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
In Vitro Systems
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