Identification of a Common Gene Expression Signature Associated with Immature Clonal Mesenchymal Cell Populations Derived from Bone Marrow and Dental Tissues

2010 ◽  
Vol 19 (10) ◽  
pp. 1501-1510 ◽  
Author(s):  
Danijela Menicanin ◽  
P. Mark Bartold ◽  
Andrew C.W. Zannettino ◽  
Stan Gronthos
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Gene Expression Signature ◽  
Mesenchymal Cell ◽  
Cell Populations ◽  
Common Gene ◽  
Expression Signature ◽  
Dental Tissues

scholarly journals Aneuploid IMR90 cells induced by depletion of pRB, DNMT1 and MAD2 show a common gene expression signature

Genomics ◽  
2020 ◽  
Vol 112 (3) ◽  
pp. 2541-2549
Author(s):  
Danilo Cilluffo ◽  
Viviana Barra ◽  
Sergio Spatafora ◽  
Claudia Coronnello ◽  
Flavia Contino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Common Gene ◽  
Expression Signature

scholarly journals Identification of a Common Gene Expression Signature in Dilated Cardiomyopathy Across Independent Microarray Studies

2006 ◽  
Vol 48 (8) ◽  
pp. 1610-1617 ◽  
Author(s):  
Andreas S. Barth ◽  
Ruprecht Kuner ◽  
Andreas Buness ◽  
Markus Ruschhaupt ◽  
Sylvia Merk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dilated Cardiomyopathy ◽  
Gene Expression Signature ◽  
Common Gene ◽  
Expression Signature ◽  
Microarray Studies

Data Mining of Hematopoietic Stem Cell Microarray Studies Reveals a Novel Stem Cell Gene Expression Signature and Demonstrates Feasibility of Integrating Data from Different Labs and Different Microarray Platforms.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 4073-4073
Author(s):  
D. Sohal ◽  
A. Yeatts ◽  
K. Ye ◽  
A. Pellagatti ◽  
L. Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Stem Cell ◽  
Hematopoietic Stem Cell ◽  
Missing Values ◽  
Gene Expression Signature ◽  
Unsupervised Clustering ◽  
Biological Information ◽  
Hematopoietic Stem ◽  
Expression Signature

Abstract Microarray based studies of global Gene Expression (GE) have led to dramatic advances in our understanding of various biological processes and have resulted in a large amount of data in public repositories, like the Gene Expression Omnibus (GEO). Metaanalysis of this data has the potential to yield important biological information, but is hampered by technical issues due to different platforms and gene annotations used in various studies. In an attempt to conduct a metaanalysis, a total of 69 individual normal hematopoietic stem cell (HSC) GE datasets (9 whole bone marrow, 57 CD34+ cell studies) were identified in GEO. These had been done on 3 microarray platforms (Affymetrix U95, U133 A/B and U133 Plus 2.0). Since the probe identifiers and complementary cDNAs were different on these platforms, we integrated the data using both Unigene and RefSeq protein IDs and obtained a total of 8598 common Unigene and 8345 RefSeq probes after removing missing values. Unsupervised clustering of normalized GE values demonstrated that experimental conditions, lab where the experiments were performed and different microarray platforms can result in variability in GE patterns from similar sources of cells. To determine the degree of dissimilarity of these datasets from those obtained from biologically distinct tissues, GE profiles from various human tissues (brain, heart, kidney, etc.) were obtained from GEO and compared with hematopoietic stem cells. Unsupervised clustering showed that samples from the same tissue of origin clustered together despite different platforms/labs, demonstrating that our approach can group biologically distinct tissues together in spite of experimental and platform variability. To further test the discriminatory ability of the metaanalysis, we took datasets from hematologic malignancies and normal hematopoietic and non-hematopoietic tissues analyzed with the same platform (U133). We observed greater similarity between leukemias, myelodysplasia (MDS) and normal HSCs when compared to non-hematopoietic tissues, again validating the discriminatory power of this metaanalysis. In fact, some datasets from bone marrow samples from MDS were very similar to normal CD34+ cells and clustered within their groups. We believe this was a strong validation of our analysis as MDS is a preleukemic disorder with varying levels of pathology and can have cases that are genetically very similar to normal hematopoietic stems. We next attempted to search for a gene expression signature characteristic of HSCs by finding genes that were uniformly enriched in HSC datasets and at the same time differentially expressed when compared to normal non-hematopoietic tissues. We found 46 such “stemness” genes in our dataset. Functional pathway analysis by Ingenuity revealed that these genes were part of cell cycle and hematopoiesis pathways, thus decreasing the likelihood of our findings to be due to chance. In addition to known genes such as Gata2, Myb, Lyn kinase and Stat5A; several novel functional genes like SWI/SNF family member SMARCE1, Bone marrow stromal antigen 2, Septin 6, Topoisomerase II and H2A histone proteins were found to be enriched in HSCs by our analysis. Thus, we demonstrate a feasible and valid approach for metaanalysis of publicly available gene expression data that can yield further insights into human physiology and disease.

Phase 2 Trial of the Farnesyltransferase Inhibitor Tipifarnib in Previously Untreated Older Adults with AML and Baseline Presence of a Specific 2-Gene Expression Signature Ratio

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1508-1508
Author(s):  
Jeffrey E Lancet ◽  
Eric Feldman ◽  
Matthew C Foster ◽  
Ivana Gojo ◽  
Olatoyosi Odenike ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Pilot Trial ◽  
Gene Expression Signature ◽  
Phase 2 ◽  
Antileukemic Activity ◽  
Farnesyltransferase Inhibitor ◽  
Open Label ◽  
Expression Signature ◽  
Bone Marrow Blasts

Abstract Abstract 1508 Background: Tipifarnib is an orally bioavailable farnesyltransferase inhibitor with well-established antileukemic activity in a minority of unselected patients with AML. Seeking to identify patients most likely to have tipifarnib-responsive AML, we applied a PCR-based quantitative 2 gene expression signature ratio (RASGRP1:APTX), which had been tested and validated retrospectively as a predictor of response and survival in previous large studies of tipifarnib, as a molecular screening tool for selection of patients to this pilot trial. Objectives: 1) primary: to determine the complete response (CR +CRi) rate in AML patients prospectively selected for tipifarnib treatment on the basis of a 2-gene signature (RASGRP1:APTX ratio) in bone marrow aspirates. 2) secondary: to assess safety, overall survival, and immunophenotypic expression of RASGRP1 on baseline bone marrow blasts for correlation with PCR-based detection. Methods: This was a multicenter, open-label, phase 2 study. Key eligibility criteria included 1) patients with previously untreated AML, age ≥65 years and deemed unsuitable for or refusing traditional induction chemotherapy, and 2) RASGRP1: APTX ratio of ≥5.0 (measured in bone marrow aspirate) based upon qRT-PCR. Other inclusion criteria included: ECOG PS 0–2, adequate end-organ function, and WBC < 30,000/microliter. Treatment consisted of Tipifarnib 300 mg BID × 21 days, with 7 days off. Dose escalation to 600 mg BID following cycle 1 was permitted for patients who had stable disease (SD) after cycle 1. Patients who achieved CR/CRi or PR after 2 cycles were eligible to continue treatment for up to 6 cycles maximum. Results: A total of 62 patients were consented to the trial. Of these, 21 (34%) were eligible based upon the required 2-gene ratio. Assay results were reported within 72 hours for all patients. Median age was 75 years. Fourteen (67%) patients had adverse karyotype and 14 (67%) had secondary AML (including 7 patients who received prior therapy for MDS). Three patients were replaced during cycle 1, due to withdrawal of consent or early unrelated death, such that 18 patients were considered evaluable for response. Median RASGRP1:APTX was 14.8. Two patients (11%) achieved CR and 6 (33%) additional patients had both SD and achievement of ≥50% reduction in bone marrow blasts by completion of cycle 2. Two of these 6 patients also had ≥50% increase in peripheral blood neutrophils or platelets. All 8 patients with CR or SD received at least 2 cycles of therapy. There was no correlation between baseline RASGRP1:APTX ratio and response. Overall, tipifarnib was well tolerated. Early death (≤ 30 days) was observed in only 1 patient (5%). The most common therapy-related non-hematologic toxicities (mainly grade 1–2) included: anorexia (33%), nausea (33%), fatigue (28%), febrile neutropenia (23%) and diarrhea (23%). Due to the trial not meeting its primary endpoint of at least 3 CR/CRi after 2 cycles, accrual to the trial was suspended. Conclusion: Monotherapy with tipifarnib in preselected elderly patients with AML and a RASGRP1:APTX ratio of ≥5 was associated with significant antileukemic activity and good tolerance. Although the primary endpoints during the first stage of the trial were not met, this study demonstrated the ability to rapidly and efficiently execute a biomarker-driven trial in an aggressive malignancy. Given the observed antileukemic activity in this selected population, along with a favorable toxicity and bioavailability profile for chronic dosing, further study of tipifarnib with less rigid endpoints than CR/CRi is warranted. Updated survival and RASGRP1 protein expression data will also be presented. Disclosures: Off Label Use: Tipifarnib in AML. Knoblauch:Johnson & Johnson: Employment. Karkera:Johnson & Johnson: Employment.

Investigation of a common gene expression signature in gastrointestinal cancers using systems biology approaches

2017 ◽  
Vol 13 (11) ◽  
pp. 2277-2288 ◽  
Author(s):  
Kaveh Baghaei ◽  
Nazanin Hosseinkhan ◽  
Hamid Asadzadeh Aghdaei ◽  
M. R. Zali
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Mortality Rate ◽  
Gene Expression Signature ◽  
Gastrointestinal Cancers ◽  
Common Gene ◽  
Expression Signature ◽  
Liver Cancers ◽  
Gi Cancers

According to GLOBOCAN 2012, the incidence and the mortality rate of colorectal, stomach and liver cancers are the highest among the total gastrointestinal (GI) cancers.

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