Chemo-response biomarker discovery via expression profiling using soft-tissue sarcoma xenografts

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 9569-9569
Author(s):  
S. Bruheim ◽  
Y. Xi ◽  
J. Ju ◽  
O. Fodstad
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Expression Profiling ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Clinical Samples ◽  
Marker Genes ◽  
Candidate Marker ◽  
General Response

9569 Background: Soft-tissue sarcoma (STS) constitute a heterogeneous group of tumours of mesenchymal origin. Whereas the mainstay of treatment has been surgery and radiation, these tumours are generally considered as quite chemoresistant. However, it is well known that subgroups of patients benefit from chemotherapy. Markers that could predict drug response would therefore be beneficial for the management of this malignancy. We have previously established panel of 17 unique human soft tissue xenografts, representing 7 different histological subgroups and assessed their responsiveness to doxorubicin, ifosfamide, etoposide, and cisplatin. We wanted to utilize these xenografts as a model system to discover for novel candidate marker genes for STS chemo-response. Methods: GE Uniset Human 20K microarrays were used to obtain gene expression profiles from the each xenografts. One-way ANOVA test with a Benjamini-Hochberg multiple test correction allowing a false discovery rate of 5% was used to identify genes with significantly differential expression. Results: Doxorubicin, ifosfamide, etoposide and cisplatin were efficient in 6/17, 10/17, 1/17 and 7/17 xenografts respectively. However, in the expression profiles obtained none of the genes showed significantly correlation with chemo-responsiveness to any of the drugs. Two of the xenografts, TAX 1 and TAX 2, both originate from a malignant fibrous histiocytoma (MFH) in the same patient, but show strikingly different sensitivity to ifosfamide (TAX1 resistant, TAX2 sensitive). When triplicate hybridizations of TAX1 and 2 were compared, 294 genes met the above criteria. In addition we identified a subset of 122 genes that were flagged absent in one of the specimens, present in the other. Among genes with an already described role in mediating drug resistance are GST-pi and glutathione peroxidase. Taken together, these results indicate that discovery of general response markers in STSs may be difficult due to the heterogeneity of the different subgroups constituting this malignancy. Conclusions: Gene expression profiling of the TAX 1 and TAX 2 xenografts revealed a number of interesting candidate marker genes for ifosfamide sensitivity of MFH. This list of genes will be further refined by validation in clinical samples. No significant financial relationships to disclose.

Proteomic Profiling of Multiple Myeloma: Correlation of Protein and Gene Expression Data.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1705-1705
Author(s):  
Ricky D Edmondson ◽  
Sheeno P Thyparambil ◽  
Veronica MacLeod ◽  
Bart Barlogie ◽  
John D. Shaughnessy
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Expression Profiles ◽  
Single Agent ◽  
Clinical Samples ◽  
Proteomics Data

Abstract Although melphalan-based autologous stem cell transplantation has improved prognosis for patients diagnosed with Multiple Myeloma, survival varies from a few months to more than 15 years with an individual’s risk not accurately predicted with standard prognostic variables. Correlating genome-wide mRNA expression profiles in purified myeloma cells with outcome, we recently showed that that the differential expression of 70 genes could identify patients at high risk for early disease related death [1]. The utility of a high throughput proteomics platform in the analysis of clinical samples has great potential but as of yet none have been firmly established. Herein, we describe the use of such a platform and its utility in stratifying patients with Multiple Myeloma in terms of high and low risk disease. Preliminary analysis indicates that the proteomics data can separate the patients into risk groups, although the proteins responsible for the assignment are not identical to the 70 genes identified in the gene expression profiling experiments. In addition to the proteomic analysis of plasma cells enriched using anti-CD138 immunomagnetic beads from mononuclear cell fractions of bone marrow aspirates from newly diagnosed myeloma patients; we have performed (in triplicate) LCMS profiling on plasma cells from 30 patients isolated prior to and 48 hours after a single test-dose application of bortezomib at 1.0mg/m2. An aliquot of 100,000 plasma cells was enzymatically digested with trypsin and a fraction (~5,000 cells) analyzed using our proteomics platform (an Eksigent nanoHPLC coupled to a ThermoElectron LTQ-Orbitrap with data analyzed using the Elucidator software package from Rosetta Biosoftware). The correlation of the proteomic profiles to gene expression profiles and clinical parameters will be presented. The analysis of proteins that were observed to change (p<0.01) in abundance after the single agent dose of the proteasome inhibitor bortezomib yielded an unanticipated finding; the abundance of 30 proteins associated with the proteasome were observed to increase in a subset of patients. The majority of the patients with the increased levels of proteasome related proteins are predicted by GEP to have high risk disease. The proteomic data will be discussed in terms of its utility in the identification of activated pathways as well as in the development of a prognostic indicator as was achieved using gene expression profiling.

scholarly journals Clinical Utility of Microarray-Based Gene Expression Profiling in the Diagnosis and Subclassification of Leukemia: Report From the International Microarray Innovations in Leukemia Study Group

2010 ◽  
Vol 28 (15) ◽  
pp. 2529-2537 ◽  
Author(s):  
Torsten Haferlach ◽  
Alexander Kohlmann ◽  
Lothar Wieczorek ◽  
Giuseppe Basso ◽  
Geertruy Te Kronnie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Clinical Utility ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Diagnostic Methods ◽  
Stage I ◽  
Data Set

Purpose The Microarray Innovations in Leukemia study assessed the clinical utility of gene expression profiling as a single test to subtype leukemias into conventional categories of myeloid and lymphoid malignancies. Methods The investigation was performed in 11 laboratories across three continents and included 3,334 patients. An exploratory retrospective stage I study was designed for biomarker discovery and generated whole-genome expression profiles from 2,143 patients with leukemias and myelodysplastic syndromes. The gene expression profiling–based diagnostic accuracy was further validated in a prospective second study stage of an independent cohort of 1,191 patients. Results On the basis of 2,096 samples, the stage I study achieved 92.2% classification accuracy for all 18 distinct classes investigated (median specificity of 99.7%). In a second cohort of 1,152 prospectively collected patients, a classification scheme reached 95.6% median sensitivity and 99.8% median specificity for 14 standard subtypes of acute leukemia (eight acute lymphoblastic leukemia and six acute myeloid leukemia classes, n = 693). In 29 (57%) of 51 discrepant cases, the microarray results had outperformed routine diagnostic methods. Conclusion Gene expression profiling is a robust technology for the diagnosis of hematologic malignancies with high accuracy. It may complement current diagnostic algorithms and could offer a reliable platform for patients who lack access to today's state-of-the-art diagnostic work-up. Our comprehensive gene expression data set will be submitted to the public domain to foster research focusing on the molecular understanding of leukemias.

scholarly journals Analysis of Gene Expression Profiles of Soft Tissue Sarcoma Using a Combination of Knowledge-Based Filtering with Integration of Multiple Statistics

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106801 ◽  
Author(s):  
Anna Takahashi ◽  
Robert Nakayama ◽  
Nanako Ishibashi ◽  
Ayano Doi ◽  
Risa Ichinohe ◽  
...  
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Knowledge Based

scholarly journals Toxicogenomics in Drug Development

2003 ◽  
Vol 31 (5) ◽  
pp. 471-479 ◽  
Author(s):  
Nelson Guerreiro ◽  
Frank Staedtler ◽  
Olivier Grenet ◽  
Jeanne Kehren ◽  
Salah-Dine Chibout
Keyword(s):  
Gene Expression ◽  
Drug Development ◽  
Expression Profiling ◽  
Development Process ◽  
Molecular Mechanisms ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Dna Polymorphisms ◽  
Drug Development Process ◽  
Predictive Toxicology

Toxicogenomics represents the merging of toxicology with technologies that have been developed, together with bioinformatics, to identify and quantify global gene expression changes. It represents a new paradigm in drug development and risk assessment, which promises to generate a wealth of information towards an increased understanding of the molecular mechanisms that lead to drug toxicity and efficacy, and of DNA polymorphisms responsible for individual susceptibility to toxicity. Gene expression profiling, through the use of DNA microarray and proteomic technologies will aid in establishing links between expression profiles, mode of action and traditional toxic endpoints. Such patterns of gene expression, or `molecular fingerprints' could be used as diagnostic or predictive markers of exposure, that is characteristic of a specific mechanism of induction of that toxic or efficacious effect. It is anticipated that toxicogenomics will be increasingly integrated into all phases of the drug development process particularly in mechanistic and predictive toxicology, and biomarker discovery. This review provides an overview of the expression profiling technologies applied in toxicogenomics, and discusses the promises as well as the future challenges of applying this discipline to the drug development process.

scholarly journals Gene expression profiles for an immunoscore model in bone and soft tissue sarcoma

Aging ◽  
2021 ◽  
Author(s):  
Jingyuan Fan ◽  
Xinyi Qin ◽  
Rongquan He ◽  
Jie Ma ◽  
Qingjun Wei
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Expression Profiles ◽  
Gene Expression Profiles

scholarly journals LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Ren ◽  
Ting-You Wang ◽  
Leah C. Anderton ◽  
Qi Cao ◽  
Rendong Yang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Regulatory Pathways ◽  
Cancer Transcriptome ◽  
Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

scholarly journals 256CONSTRUCTION OF A BOVINE CDNA ARRAY TO MONITOR GENE EXPRESSION PROFILES IN BOVINE PREIMPLANTATION EMBRYOS

2004 ◽  
Vol 16 (2) ◽  
pp. 248
Author(s):  
C. Wrenzycki ◽  
T. Brambrink ◽  
D. Herrmann ◽  
J.W. Carnwath ◽  
H. Niemann
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Cdna Array ◽  
Preimplantation Embryos ◽  
Average Insert Size ◽  
Rt Pcr ◽  
Public Data

Array technology is a widely used tool for gene expression profiling, providing the possibility to monitor expression levels of an unlimited number of genes in various biological systems including preimplantation embryos. The objective of the present study was to develop and validate a bovine cDNA array and to compare expression profiles of embryos derived from different origins. A bovine blastocyst cDNA library was generated. Poly(A+)RNA was extracted from in vitro-produced embryos using a Dynabead mRNA purification kit. First-strand synthesis was performed with SacIT21 primer followed by randomly primed second-strand synthesis with a DOP primer mix (Roche) and a global PCR with 35 cycles using SacIT21 and DOP primers. Complementary DNA fragments from 300 to 1500bp were extracted from the gel and normalized via reassoziation and hydroxyapatite chromatography. Resulting cDNAs were digested with SacI and XhoI, ligated into a pBKs vector, and transfected into competent bacteria (Stratagene). After blue/white colony selection, plasmids were extracted and the inserts were subjected to PCR using vector specific primers. Average insert size was determined by size idenfication on agarose gels stained with ethidium bromide. After purification via precipitation and denaturation, 192 cDNA probes were double-spotted onto a nylon membrane and bound to the membrane by UV cross linking. Amplified RNA (aRNA) probes from pools of three or single blastocysts were generated as described recently (Brambrink et al., 2002 BioTechniques, 33, 3–9) and hybridized to the membranes. Expression profiles of in vitro-produced blastocysts cultured in either SOF plus BSA or TCM plus serum were compared with those of diploid parthenogenetic ones generated by chemical activation. Thirty-three probes have been sequenced and, after comparison with public data bases, 26 were identified as cDNAs or genes. Twelve out of 192 (6%) seem to be differentially expressed within the three groups;; 7/12 (58%) were down-regulated, 3/12 (25%) were up-regulated in SOF-derived embryos, and 2/12 (20%) were up-regulated in parthenogenetic blastocysts compared to their in vitro-generated counterparts. Three of these genes involved in calcium signaling (calmodulin, calreticulin) and regulation of actin cytoskeleton (destrin) were validated by semi-quantitative RT-PCR (Wrenzycki et al., 2001 Biol. Reprod. 65, 309–317) employing poly(A+) RNA from a single blastocyst as starting material. No differences were detected in the relative abundance of the analysed gene transcripts within the different groups. These findings were confirmed employing the aRNA used for hybridization in RT-PCR and showed a good representativity of the selected transcripts. Results indicate that it is possible to construct a homologous cDNA array which could be used for gene expression profiling of bovine preimplantation embryos. Supported by the Deutsche Forschungsgemeinschaft (DFG Ni 256/18-1).

scholarly journals The RNA helicase DDX5 promotes alveolar rhabdomyosarcoma growth and survival

2020 ◽  
Author(s):  
Alberto Gualtieri ◽  
Valerio Licursi ◽  
Chiara Mozzetta
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Therapeutic Target ◽  
Rna Helicase ◽  
Potential Therapeutic Target ◽  
Growth And Survival ◽  
Dead Box ◽  
Pharmacological Targets ◽  
Xenograft Models

AbstractRhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood characterized by the inability to exit the proliferative myoblast-like stage. The alveolar fusion positive subtype (FP-ARMS) is the most aggressive and is mainly caused by the expression of PAX3/7-FOXO1 oncoproteins, which are challenging pharmacological targets. Thus, other therapeutic vulnerabilities resulting from gene expression changes are progressively being recognized. Here, we identified the DEAD box RNA helicase 5 (DDX5) as a potential therapeutic target to inhibit FP-ARMS growth. We show that DDX5 is overexpressed in alveolar RMS cells, demonstrating that its depletion drastically decreases FP-ARMS viability and slows tumor growth in xenograft models. Mechanistically, we provide evidence that DDX5 functions upstream the G9a/AKT survival signalling pathway, by modulating G9a protein stability. Finally, we show that G9a interacts with PAX3-FOXO1 and regulates its activity, thus sustaining FP-ARMS myoblastic state. Together, our findings identify a novel survival-promoting loop in FP-ARMS and highlight DDX5 as potential therapeutic target to arrest rhabdomyosarcoma growth.

scholarly journals Three Years Follow-Up Study Showed Prenatal Methamphetamine Exposure May Cause Chronic Abnormalities In Transcriptomic Pattern

2021 ◽  
Author(s):  
Arvin Haghighatfard ◽  
Soha Seifollahi ◽  
Pegah Rajabi ◽  
Niloofar Rahmani ◽  
Rojin Ghannadzadeh
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Rna Extraction ◽  
Childbearing Age ◽  
Methamphetamine Use ◽  
Methamphetamine Use Disorder

Abstract Background: The high rate of methamphetamine use disorder among young adults and women of childbearing age makes it imperative to clarify the long-term effects of Methamphetamine exposure on the offspring. Behavioral and cognitive problems had been reported in children with parental Methamphetamine exposure (PME). The present study aimed to assess the acute and chronic effects of PME in molecular regulations and gene expression profiles of children during their first years of life.Methods: All subjects were recruited before birth, and sampling was conducted from the first ten days of birth, twelve months, twenty months, and thirty-six months of age. Finally, 2658 children with PME and 3573 normal children had been finished the follow-up. RNA extraction was operated from blood samples and gene expression profiling was conducted by using the Affymetrix GeneChip Human Genome U133 plus 2.0 Array Platform. Gene expression data were confirmed by Real-time PCR. Results: Gene expression profiling during thirty-six months showed several constant mRNA level alterations in children with PME compared with normal. These genes are involved in several gene ontologies and pathways involved with the immune system, neuronal functions, and bioenergetic metabolism. It seems that Methamphetamine use disorder before and during the pregnancy period may affect the expression profile of children, and these changes could remain years after birth. Affected genes have some similarities with the gene expression patterns of addiction, psychiatric disorders, neurodevelopmental disabilities, and immune deficiencies. Conclusion: Findings may shed light on the molecular effects of prenatal methamphetamine exposure and may lead to new psychological and somatic caring protocols for these children based on their potential abnormalities.

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