A gene expression signature to predict high-grade prostate cancer response to oxaliplatin.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 150-150
Author(s):  
Philippe Pourquier ◽  
Stephane Puyo ◽  
Pierre Richaud ◽  
Jacques Robert ◽  
Nadine Houede
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Clinical Trials ◽  
Cell Lines ◽  
Gene Expression Signature ◽  
Chemotherapeutic Agents ◽  
Low Grade ◽  
Gleason Grade ◽  
High Grade ◽  
Expression Signature

150 Background: Prostate cancer (PCa) is one of the leading causes of death from cancer in men. High Gleason grade prostate cancers are characterized by aggressive tumors with poorly differentiated cells and a high metastatic potential. They are often refractory to chemical castration but still treated with hormone therapy to which docetaxel or cabazitaxel are added when they become resistant to the anti-androgen. Despite many clinical trials with other chemotherapeutic agents, response rates remain low. Moreover, none of these trials took into account the tumor grade. Methods: We used an in silico approach to screen for drug candidates that could be used as an alternative to taxanes, based on a 86 genes signature which could distinguish between low-grade and high-grade tumors. We extracted from the NCI60 panel databases the expression profiles of the 86 genes across 60 human tumor cell lines and the corresponding in vitro cytotoxicity data of 152 drugs and looked for correlation between their expression level and cell sensitivity to each of these drugs. Results: Among the 86 genes, we identified 9 genes (PCCB, SHMT2, DPM1, RHOT2, RPL13, CD59, EIF4AI, CDKN2C, JUN) for which expression levels across the 60 cell lines was significantly correlated (p< 0.05) to oxaliplatin but not to cisplatin sensitivity. This signature was validated at the functional level since repression of each of these genes conferred a significant change in the sensitivity of PCa cell lines to oxaliplatin but not cisplatin. Conclusions: Our results provide a proof of concept that gene expression signature specific from high grade PCa could be used for the identification of alternative therapies to taxanes. They could also be used to select patients for further clinical trials and as predictive markers of response to these drugs, which represents a further step forward towards personalized therapy of PCa.

Gene Expression Signature Predicting High-Grade Prostate Cancer Responses to Oxaliplatin

2012 ◽  
Vol 82 (6) ◽  
pp. 1205-1216 ◽  
Author(s):  
Stéphane Puyo ◽  
Nadine Houédé ◽  
Audrey Kauffmann ◽  
Pierre Richaud ◽  
Jacques Robert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Signature ◽  
High Grade ◽  
Expression Signature

Abstract 5064: A gene expression signature which could predict high grade prostate cancer response to oxaliplatin

2011 ◽  
Author(s):  
Stéphane Puyo ◽  
Jacques Robert ◽  
Pierre Richaud ◽  
Philippe Pourquier ◽  
Nadine Houédé
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Signature ◽  
High Grade ◽  
Expression Signature

scholarly journals A DNA Repair and Cell Cycle Gene Expression Signature in Pediatric High-Grade Gliomas: Prognostic and Therapeutic Value

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2252
Author(s):  
Natacha Entz-Werlé ◽  
Laetitia Poidevin ◽  
Petr V. Nazarov ◽  
Olivier Poch ◽  
Benoit Lhermitte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Dna Repair ◽  
Cell Lines ◽  
Gene Expression Signature ◽  
Cell Cycle Gene ◽  
High Grade ◽  
Expression Signature ◽  
Top2a Amplification ◽  
Cell Cycle Gene Expression

Background: Pediatric high-grade gliomas (pHGGs) are the leading cause of mortality in pediatric neuro-oncology, displaying frequent resistance to standard therapies. Profiling DNA repair and cell cycle gene expression has recently been proposed as a strategy to classify adult glioblastomas. To improve our understanding of the DNA damage response pathways that operate in pHGGs and the vulnerabilities that these pathways might expose, we sought to identify and characterize a specific DNA repair and cell-cycle gene expression signature of pHGGs. Methods: Transcriptomic analyses were performed to identify a DNA repair and cell-cycle gene expression signature able to discriminate pHGGs (n = 6) from low-grade gliomas (n = 10). This signature was compared to related signatures already established. We used the pHGG signature to explore already transcriptomic datasets of DIPGs and sus-tentorial pHGGs. Finally, we examined the expression of key proteins of the pHGG signature in 21 pHGG diagnostic samples and nine paired relapses. Functional inhibition of one DNA repair factor was carried out in four patients who derived H3.3 K27M mutant cell lines. Results: We identified a 28-gene expression signature of DNA repair and cell cycle that clustered pHGGs cohorts, in particular sus-tentorial locations, in two groups. Differential protein expression levels of PARP1 and XRCC1 were associated to TP53 mutations and TOP2A amplification and linked significantly to the more radioresistant pHGGs displaying the worst outcome. Using patient-derived cell lines, we showed that the PARP-1/XRCC1 expression balance might be correlated with resistance to PARP1 inhibition. Conclusion: We provide evidence that PARP1 overexpression, associated to XRCC1 expression, TP53 mutations, and TOP2A amplification, is a new theranostic and potential therapeutic target.

scholarly journals Prognostic gene expression signature for high-grade serous ovarian cancer

2020 ◽  
Vol 31 (9) ◽  
pp. 1240-1250 ◽  
Author(s):  
J. Millstein ◽  
T. Budden ◽  
E.L. Goode ◽  
M.S. Anglesio ◽  
A. Talhouk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Gene Expression Signature ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Expression Signature ◽  
Prognostic Gene

scholarly journals Gene expression signature of Gleason score is associated with prostate cancer outcomes in a radical prostatectomy cohort

Oncotarget ◽  
2017 ◽  
Vol 8 (26) ◽  
pp. 43035-43047 ◽  
Author(s):  
Min A. Jhun ◽  
Milan S. Geybels ◽  
Jonathan L. Wright ◽  
Suzanne Kolb ◽  
Craig April ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Gleason Score ◽  
Gene Expression Signature ◽  
Cancer Outcomes ◽  
Expression Signature

730 THE ATORVASTATIN TARGET SCD1 IDENTIFIED WITHIN THE GENE EXPRESSION SIGNATURE OF HIGH BMI PROSTATE CANCER PATIENTS

2011 ◽  
Vol 185 (4S) ◽  
Author(s):  
Patrick Parker ◽  
Shashwat Sharad ◽  
Anjali Srivastava ◽  
Suma Ravulapalli ◽  
Yongmei Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Patients ◽  
Gene Expression Signature ◽  
Expression Signature ◽  
High Bmi

The Expression Signature of M3 AML Suggests Direct and Indirect Effects of PML-RARA on Target Genes.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 719-719 ◽  
Author(s):  
Jacqueline E. Payton ◽  
Nicole R. Grieselhuber ◽  
Li-Wei Chang ◽  
Mark A. Murakami ◽  
Wenlin Yuan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Cell Lines ◽  
Binding Sites ◽  
Expression Profiles ◽  
Gene Expression Signature ◽  
Myeloid Cell ◽  
Expression Signature ◽  
Signature Genes ◽  
Cell Fractions

Abstract In order to better understand the pathogenesis of acute promyelocytic leukemia (APL, FAB M3), we sought to determine its gene expression signature by comparing the expression profiles of 14 APL samples to that of other AML subtypes (M0, M1, M2, M4, n=62) and to fractionated normal whole bone marrow cells (CD34 cells, promyelocytes, PMNs, n=5 each). We used ANOVA and SAM (Significance Analysis of Microarrays) to select genes that were highly expressed in APL cells and that displayed low to no expression in other AML subtypes. The APL signature was then further refined by filtering genes whose expression in APL was not significantly different from that of normal promyelocytes, yielding 1121 annotated genes that reliably distinguish APL from the other FAB subtypes using unsupervised hierarchical clustering, both in training and validation datasets. Fold change differences in expression between M3 and other AML FAB classes were striking, for example: GABRE 35.4, HGF 21.3, ANXA8 21.3, PTPRG 16.9, PTGDS 12.1, PPARG 11.1, STAB1 9.8. A large proportion of the APL versus other FAB dysregulome was recapitulated when we compared APL expression to that of the normal pattern of myeloid development. We identified 733 annotated genes with significantly different expression in APL versus normal myeloid cell fractions. These dysregulated genes were assigned to 4 classes: persistently expressed CD34 cell-specific genes, repressed promyelocyte-specific genes, prematurely expressed neutrophil-specific genes and genes with high expression in APL and low/no expression in normal myeloid cell fractions. Expression differences in several of the most dysregulated genes were validated by qRT-PCR. We then examined the expression of the APL signature genes in myeloid cell lines and tumors from a murine APL model. The bona fide M3 signature was not apparent in resting NB4 cells (which contain t(15;17), and which express PML-RARA), nor in PR-9 cells following Zn induction of PML-RARA expression, suggesting that neither cell line accurately models the gene expression signature of primary APL cells. Most of the nodal genes of the mCG-PML-RARA murine APL dysregulome (Yuan, et al, 2007) are similarly dysregulated in human M3 cells; however, the human and mouse dysregulomes do not completely coincide. Finally, we have begun investigating which APL signature genes are direct transcriptional targets of PML-RARA. The promoters of the APL signature genes were analyzed for the presence of known PML-RARA binding sites using multiple computational methods. The analyses demonstrated that several transcription factors (EBF3, TWIST1, SIX3, PPARG) have putative retinoic acid response elements (RAREs) in their upstream regulatory regions. Additionally, we examined the promoters of some of the most upregulated genes (HGF, PTGDS, STAB1) for known consensus sites of these transcription factors, and found that all have putative binding sites for at least one. These results suggest that PML-RARA may initiate a transcriptional cascade that relies not only on its own activity, but also on the actions of downstream transcription factors. In summary, our studies indicate that primary APL cells have a gene expression signature that is consistent and highly reproducible, but different from commonly used human APL cell lines and a mouse model of APL. The molecular mechanisms that govern this unique signature are currently under investigation.

Discovery and validation of a 30-gene expression signature to identify prostate cancer patients who are candidates for active surveillance.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 10-10
Author(s):  
Travis Gerke ◽  
Svitlana Tyekucheva ◽  
Kathryn Penney ◽  
Christopher Sweeney ◽  
Rosina Lis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Active Surveillance ◽  
Control Sample ◽  
Gene Expression Signature ◽  
Health Study ◽  
Validation Data ◽  
Expression Signature ◽  
Indolent Disease

10 Background: Considerable attention has been devoted to the search for biomarkers of aggressive prostate cancer. While many promising markers have been proposed, it is often unclear whether their ability to risk classify is adequate to reduce overtreatment of indolent cancers. We present and validate a gene expression signature that is highly specific for indolent disease and that, when combined with Gleason, improves upon the prognostic power of Gleason alone. Methods: A 30-gene signature of indolent disease was derived from a case-control sample of men (n=254) from the Health Professionals Follow-Up Study (HPFS) who were followed prospectively from cancer diagnosis for a median of 13.7 years. Cases were defined as men with prostate cancer who experienced a metastatic event or died of cancer during follow-up, while indolent controls survived at least 8 years without metastases. Whole-transcriptome gene expression was quantified from archival formalin-fixed, paraffin-embedded (FFPE) tumor tissue specimens acquired at prostatectomy. Genes were selected by a novel analytic strategy that maximizes a partial area under the curve (pAUC) to accurately identify indolent tumors. We validated the signature in two independent cohorts: the Physicians’ Health Study (PHS; n=150) and a Swedish Watchful Waiting cohort (WW; n=253) with respective median follow-up times of 14.4 and 9.0 years. Results: When compared to a model with Gleason alone, application of the signature to both validation data sets significantly improved prognostic accuracy as measured by pAUC (p=0.003 in PHS and p<0.001 in WW). Performance was particularly strong among men diagnosed with Gleason 7, where unit standard deviation increases in the signature score were associated with odds ratios of indolence of 5.24 (95% CI 2.21-15.75; p<0.001) and 2.37 (95% CI 1.45-4.19; p=0.001) in the PHS and WW cohorts, respectively. Conclusions: We present a signature of indolent prostate cancer that adds prognostic information beyond Gleason and has the potential to guide men to active surveillance and avoid overtreatment. Validation across both treated and untreated cohorts supports the discovery of a robust signature.

scholarly journals A 12-Gene Expression Signature Is Associated with Aggressive Histological in Prostate Cancer

2012 ◽  
Vol 181 (5) ◽  
pp. 1585-1594 ◽  
Author(s):  
Laia Agell ◽  
Silvia Hernández ◽  
Lara Nonell ◽  
Marta Lorenzo ◽  
Eulàlia Puigdecanet ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Signature ◽  
Expression Signature
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