Gene expression signature predicts chemoresponse of microdissected papillary serous ovarian tumors

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 5064-5064
Author(s):  
L. Ozbun ◽  
T. Bonome ◽  
M. E. Johnson ◽  
M. Radonovich ◽  
C. Pise-Masison ◽  
...  
Keyword(s):  
Gene Expression ◽  
Real Time ◽  
Real Time Pcr ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene List ◽  
Response To Therapy ◽  
Advanced Stage ◽  
Protein Ubiquitination ◽  
Ovarian Cancers

5064 Background: The purpose of this study was to identify a predictive gene signature for chemoresponse in patients with advanced stage papillary serous ovarian cancer. Methods: Expression profiling was performed on 50 chemonaive, microdissected advanced stage papillary serous ovarian cancers using Affymetrix Human Genome U133 Plus 2.0 microarrays. Chemoresistance was defined as disease progression while the patients remained on primary chemotherapy. Nine normal human ovarian surface epithelial (HOSE) brushings were also assessed to quantify normal gene expression levels. Validation was performed by quantitative real time PCR using the HOSE isolates and microdissected ovarian tumor samples. Results: A supervised learning algorithm applied to genes differentially expressed between chemosensitive/resistance tumors (p < 0.001) using leave-one-out cross-validation (LOOCV), identified over 2000 genes associated with tumor chemosensitivity. The chemoresponsive gene list was further refined to 576 genes by including only genes used for all LOOCV iterations. An independent gene list was generated comparing expression profiles of chemoresistant tumors to HOSE. The two lists were compared to identify common genes, generating final classifier list of 75 genes that included genes involved in apoptosis, RNA processing, protein ubiquitination, transcription regulation, and other novel genes. We hypothesized genes identified in both data sets would be predictive and biologically relevant. Of these 75 genes, 20 were validated by real-time PCR. Validated genes were ranked by a univariate t-stat value to further resolve the predictor. 4 multivariate predictor algorithms demonstrated the 10 top ranked validated genes maximixed prediction accuracy (compound covariate, 91%; diagonal linear discriminant analysis, 91%; 3-nearest neighbor, 86%; nearest centroid, 95%). The predictive value of these genes will be evaluated on an independent sample set. Conclusions: Gene expression profiling can distinguish between chemosensitive and chemoresistant ovarian cancers. This signature can predict response to therapy and has identified novel biologically and clinically relevant targets. No significant financial relationships to disclose.

scholarly journals Effectiveness of Gene Expression Profiling for Response Prediction of Rectal Adenocarcinomas to Preoperative Chemoradiotherapy

2005 ◽  
Vol 23 (9) ◽  
pp. 1826-1838 ◽  
Author(s):  
B. Michael Ghadimi ◽  
Marian Grade ◽  
Michael J. Difilippantonio ◽  
Sudhir Varma ◽  
Richard Simon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Preoperative Chemoradiotherapy ◽  
Response To Therapy ◽  
Phase Iii ◽  
Correct Prediction

Purpose There is a wide spectrum of tumor responsiveness of rectal adenocarcinomas to preoperative chemoradiotherapy ranging from complete response to complete resistance. This study aimed to investigate whether parallel gene expression profiling of the primary tumor can contribute to stratification of patients into groups of responders or nonresponders. Patients and Methods Pretherapeutic biopsies from 30 locally advanced rectal carcinomas were analyzed for gene expression signatures using microarrays. All patients were participants of a phase III clinical trial (CAO/ARO/AIO-94, German Rectal Cancer Trial) and were randomized to receive a preoperative combined-modality therapy including fluorouracil and radiation. Class comparison was used to identify a set of genes that were differentially expressed between responders and nonresponders as measured by T level downsizing and histopathologic tumor regression grading. Results In an initial set of 23 patients, responders and nonresponders showed significantly different expression levels for 54 genes (P < .001). The ability to predict response to therapy using gene expression profiles was rigorously evaluated using leave-one-out cross-validation. Tumor behavior was correctly predicted in 83% of patients (P = .02). Sensitivity (correct prediction of response) was 78%, and specificity (correct prediction of nonresponse) was 86%, with a positive and negative predictive value of 78% and 86%, respectively. Conclusion Our results suggest that pretherapeutic gene expression profiling may assist in response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. The implementation of gene expression profiles for treatment stratification and clinical management of cancer patients requires validation in large, independent studies, which are now warranted.

scholarly journals Gene expression profiling of advanced-stage serous ovarian cancers distinguishes novel subclasses and implicatesZEB2in tumor progression and prognosis

2009 ◽  
Vol 100 (8) ◽  
pp. 1421-1428 ◽  
Author(s):  
Kosuke Yoshihara ◽  
Atsushi Tajima ◽  
Dai Komata ◽  
Tadashi Yamamoto ◽  
Shoji Kodama ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Progression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Advanced Stage ◽  
Ovarian Cancers

Gene Expression Profiling of Paired Pre- and Post-Prednisolone (PRED) BM Samples from Childhood ALL Identifies Robust Signatures for PRED Response and Eventual Outcome.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 222-222 ◽  
Author(s):  
Yi Lu ◽  
Huiqing Liu ◽  
Ying Xu ◽  
Pei Lin Koh ◽  
Ariffin Hany ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Response To Therapy ◽  
Test Set ◽  
Childhood All ◽  
Blast Count ◽  
Highly Correlated ◽  
Eventual Outcome

Abstract Early response to therapy is the most important prognostic factor for childhood ALL. CCG investigators have shown that Day-7 and Day-14 BM blast counts were prognostically important although there is great inter-observer variability. BFM group have shown that day 8 prednisolone (PRED) response is highly predictive of the treatment outcome. While gene expression profiling (GEP) of diagnostic marrow can discern a pattern of PRED sensitivity as determined by in vitro MTT assay, the accuracy was low at only 70%. We hypothesized that changes in global GEP after therapy have a higher likelihood to predict response as the signatures of sensitivity and resistance may be unmasked during the therapy. We prospectively studied the changes in GEP using Affymetrix HG-U133A or Plus 2 chips on paired BM samples before and after 7-day course of PRED and one dose IT MTX in 58 patients with newly diagnosed or relapsed ALL. Unsupervised hierarchical clustering revealed that pre- and post- PRED samples in the patients still tended to cluster together, indicating that expression profiles of molecular subgroups were still most important. To remove intrinsic influence of molecular subtypes and identify potential signatures independent of genetic abnormalities, we subtracted Day-0 GEP from its paired Day-8 profile and retained probe sets with significant changes (≥ 10-fold). To avoid the ambiguity of variation in BM blast counting at Day-8, we divided the samples into a stringently reproducible group where “Good” PRED response was defined as that Day-8 blast count in PBL < 109/L and BM lymphoblasts ≤ 30% (n=16). “Poor” response was when Day 8 PBL ≥ 109/L (n=11). This stringently reproducible group (n=27) formed the training group to help define a distinct signature while the rest (n=31 pairs) were used as a blinded test set. 54 and 19 discriminating genes were identified by 2 independent statistical methods respectively, and an integrated predictor model was constructed based on shortlisted entries. This model predicted the PRED response with 100% accuracy for the training set using the leave-one-out cross validation but was less accurate in predicting the BM blast count in blinded test set. But intriguingly, in the blinded test set, this model predicted correctly 19 out of 21 reliable “Good” PRED responses are in CCR (91%), while among 8 predicted as “Poor” responses, only 2 are in CCR (25%). This suggests that as gene expression profiling as early as day 8 of PRED could discern the beginning of leukaemia cell death even before morphological changes are discernable and is highly correlated to eventual outcome. In conclusion, we have shown that analyses on the relative changes of gene expression profile can identify real genetic signatures indicating the sensitivity to PRED administration which is highly correlated with outcome.

Early T-Cell Precursor-ALL in Adult T-ALL.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 911-911 ◽  
Author(s):  
Martin Neumann ◽  
Sandra Heesch ◽  
Stefan Schwartz ◽  
Nicola Gökbuget ◽  
Dieter Hoelzer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Real Time ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Real Time Pcr ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Study Group

Abstract Abstract 911 Introduction: Recently, a small subgroup of pediatric acute T-lymphoblastic leukemia (T-ALL) was described, which is closely associated with the gene expression profile of early T-cell precursors (ETPs). This subtype, termed ETP-ALL, showed a highly unfavorable outcome compared to non-ETP(='typical')-ALL. Based on the results of Coustan-Smith et al. (Lancet Oncology, 2009), the Italian national study Associazione Italiana Ematologia Oncologia Pediatrica (AIEOP) and St-Jude Children's hospital modified their treatment in children with ETP-ALL to a more intensive regime including stem cell transplantation. ETP-ALL is characterized by a specific immunophenotype (CD1a-, CD8-, CD5weak with expression of stem cell or myeloid markers). Here we explored the existence of ETP-ALL in adults and further studied the molecular characteristics of this specific T-ALL subtype. Patients and methods: We examined the gene expression profiles of 86 adult T-ALL patients obtained from the Microarray Innovations in LEukemia (MILE) multicenter study (HG-U133 Plus 2.0, Affymetrix, Haferlach et al., JCO in press). In addition, bone marrow of 296 patients from the German Acute Lymphoblastic Leukemia Multicenter Study Group (GMALL) were analyzed by flow cytometry and expression levels of BAALC, IGFBP7, MN1, and WT1 were determined by real-time-PCR. Results: Using the published list of differentially expressed genes in ETPs (Coustan-Smith et al. 2009) we performed unsupervised clustering analyses of the 86 T-ALL samples. A cluster of 17 samples (19.8%) displayed an ETP-associated gene expression profile and were defined as ETP-ALL. Comparing the gene expression profiles of ETP-ALL and typical T-ALL, 2065 probe sets were differentially expressed in ETP-ALL (FDR 0.05). In addition to genes used for classification, we also identified genes known to be involved in the pathogenesis of T-ALL (e.g. PROM1, BCL2, LMO2, LYL1). In particular, stem cell associated genes such as, BAALC (2.52-fold, p=0.003), IGFBP7 (2.76-fold, p=0.002) or MN1 (3.41-fold, p<0.001) were upregulated in ETP-ALL, whereas HOX11 (45-fold, p=0.004), a marker for thymic T-ALL, was downregulated. An independent cohort of 297 patient samples from the GMALL study group was examined by flow cytometry and real-time PCR. 19 (6.4%) samples revealed the ETP-ALL immunophenotype. As expected, all patient samples were found in the group of early T-ALL, representing 23.5% of all early T-ALLs. There was a significant correlation between a lower leukocyte count at first diagnosis and the classification of ETP-ALL (p=0.001). Gene expression measured by real-time-PCR was performed for genes associated with poor outcome in T-ALL: BAALC (2.11-fold, p<0.001) and IGFBP7 (3.59-fold, p=0.003) were significantly upregulated in the group of ETP-ALL. Similarly, the genes MN1 (4.52-fold, p<0.001) and WT1 (2.76-fold, p=0.036), described as poor prognostic markers in cytogenetically normal AML, were also upregulated in ETP-ALL. Conclusion: In adult T-ALL, a subset of patients shares the gene expression profil and immunophenotype of ETP-ALL, which is in line with recent findings in pediatric patients. The gene expression profile of this subset is significantly correlated to stem cell associated markers predictive for inferior outcome in T-ALL. Interestingly, adverse factors in CN-AML are also aberrantly expressed in ETP-ALL suggesting a myeloid origin of ETPs and indicating a closer relationship between ETP-ALL and AML. The prognostic impact and the determination of the most appropiate set of markers needs to be further investigated. These results support the GMALL strategy to regard early T-ALL patients as high risk with assignment to stem cell transplantation. Disclosures: Haferlach: MLL Munich Leukemia Laboratory: Equity Ownership.

103 GENE EXPRESSION PROFILES OF VITRIFIED MOUSE EMBRYOS DETECTED BY MICROARRAYS

2006 ◽  
Vol 18 (2) ◽  
pp. 160
Author(s):  
S. Mamo ◽  
Sz. Bodo ◽  
Z. Polgar ◽  
A. Dinnyes
Keyword(s):  
Gene Expression ◽  
Real Time ◽  
Real Time Pcr ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Analysis Tool ◽  
Cell Stage ◽  
Base Medium ◽  
Independent Analysis

Very little is known about the effect of vitrification on gene functions after warming. The goals of our study were to examine the transcript variations and identify genes most affected by the treatment. For this, 8-cell-stage embryos were collected from female ICR mice mated with ICR males. The embryos were washed with CZB-HEPES base medium and suspended briefly in equilibrium medium consisting of 4% ethylene glycol (EG) in base medium at room temperature. Following equilibration, the embryos were vitrified in a 35% EG, 0.4 M trehalose, 5% polyvinylpyrrolidone (PVP) solution by means of a solid-surface vitrification (SSV) technique as described earlier (Dinnyes 2000 Biol. Reprod. 63, 513-518). Then 40 embryos each from the control and the vitrified/warmed groups were cultured in CZB medium for 3 h. Total RNAs were extracted from cultured embryos in each group using TRIzol (Invitrogen, Bio-Science, Ltd., Budapest, Hungary), following the manufacturer's instructions. Two rounds of amplification were employed to produce labeled RNA, using low input RNA amplification kit (Agilent Technologies, Kromat, Ltd., Budapest, Hungary) procedures with modifications. Three micrograms of contrasting RNA samples were hybridized on the Agilent Mouse 22K oligonucleotide slides with subsequent analysis of the results. Moreover, as an independent analysis tool, real time PCR was used with eight designed primers. All of the vitrified embryos were recovered after warming with no morphological signs of cryodamage and used for analysis. The two rounds of amplification yielded 15-16 �g of cRNA. The analysis of repeated hybridizations by Rosetta luminator software (Agilent) showed 20 183 genes and expressed sequence tags (ESTs) that passed the selection criteria and were identified as common signatures in all of the slides. Unsupervised analysis of the gene expression data identified a total of 631 differentially expressed (P < 0.01) genes. However, to support the reliability of the results, only those variations above 1.5 fold differences were considered as significant in the final analysis. Therefore, with this stringent criterion 183 genes were differentially expressed (P < 0.01), of which 109 were up-regulated and the remainder down-regulated. Although genes have multiple and overlapping functions, most of the differentially expressed genes were functionally classified into various physiological categories. These include stress response (8), apoptosis related (6), metabolism (51), temperature response (4), and transcription regulation (15). Moreover, the independent analysis with real time PCR and unamplified samples verified the results of microarray. Thus, based on confirmation of the results by an independent analysis and support by the previous studies for some of the genes, it is possible to conclude that the expression patterns reflect the true biological image of embryos after vitrification, with most effects on stress- and cell metabolism-related genes. This work was supported by EU FP6 (MEXT-CT-2003-59582), Wellcome Trust Foundation (Grant No. 070246), and National Office of Research and Technology (NKTH) (#BIO-00017/2002, #BIO-00086/2002).

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