Project description:A comprehensive expression analysis of Wnt signaling genes was performed in a panel of prostate cancer cell lines and tissue specimens using TaqMan low density arrays. The effect of DNA methylation on gene expression was investigated using DNMT inhibitor 5-Aza-CdR. Tissue specimens from a range of disease states were used to represent the stepwise progression of prostate cancer, including benign prostatic hyperplasia (BPH), histologically benign epithelium adjacent to tumor (HB), pre-invasive high-grade prostatic intraepithelial neoplasia (HGPIN) and primary localized tumors categorized into low- and high-grade disease. Fifteen Wnt signaling related genes were idenified with significantly altered expression in prostate cancer; the majority of which were upregulated in tumors. Notably, histologically benign tissue from men with prostate cancer appeared more similar to tumour (r=0.76) than to BPH (r=0.57) (P<0.001). Overall, the expression profile was highly similar between tumors of high (M-bM-^IM-%7) and low (M-bM-^IM-$6) Gleason scores. Pharmacological demethylation of PC-3 cells reactivated 38 genes (M-bM-^IM-%2-fold); 40% of which inhibit Wnt signaling. qPCR gene expression profiling using TLDA Human Wnt Gene set v1.0 microfluidic card (Applied Biosystems, Foster City, CA).The TLDA consisted of 4 identical 96-gene sets preconfigured in a 384-well format. Three different malignant cell lines were profiled LNCaP, 22Rv1 and PC3 (+/- 5-Aza-2M-bM-^@M-^YDeoxycytidine). One benign cell line was included for comparative puposes: PWR1E. Patient samples were obtained from FFPE tissue sections from men who underwent radical prostatectomy or transrectal resection of the prostate. To overcome the limited amount of RNA obtained from FFPE tissues, RNA samples were pooled. Five different pools were generated: high grade prostate cancer (Gleason score M-bM-^IM-%7), low grade prostate cancer (Gleason score M-bM-^IM-$6), HGPIN, HB and BPH. Each pool consisted of DNase-treated total RNA (100ng), isolated from microdissected tissue from 4 individual cases, selected on the basis of similar histological and clinical features and previous epigenetic characterization in our laboratory. Two Biological replicates for each pool were prepared. The high capacity cDNA Archive Kit (Applied Biosystems) was used to reverse transcribe pooled RNA (400ng). TaqManM-BM-. reactions were performed in duplicate on a 7900HT Sequence Detection System. RQ data from TLDAs were analyzed using Real Time StatMinerM-BM-. v3.1 (Integromics, Granada, Spain).

Project description:Background: We have previously shown that the Gene expression Grade Index (GGI) was able to identify two subtypes of estrogen receptor (ER)-positive tumors that were associated with statistically distinct clinical outcomes in both untreated and tamoxifen-treated patients. Here, we aim to investigate the ability of the GGI to predict relapses in postmenopausal women who were treated with tamoxifen (T) or letrozole (L) within the BIG 1-98 trial. Methods: We generated gene expression profiles (Affymetrix) and computed the GGI for a matched, case-control sample of patients enrolled in the BIG 1-98 trial from the two hospitals where frozen samples were available. All relapses (cases) were identified from patients randomized to receive monotherapy or from the switching treatment arms for whom relapse occurred before the switch. Each case was randomly matched with four controls based upon nodal status and treatment (T or L). The prognostic value of GGI was assessed as a continuous predictor and divided at the median. Predictive accuracy of GGI was estimated using time-dependent area under the curve (AUC) of the ROC curves. Results: Frozen samples were analyzable for 48 patients (10 cases and 38 controls). Seven of the 10 cases had been assigned to receive L. Cases and controls were comparable with respect to menopausal and nodal status, local and chemotherapy, and HER2 positivity. Cases were slightly older than controls and had a larger proportion of large, poorly differentiated ER+/PgR- tumors. The GGI was significantly and linearly related to risk of relapse: each 10-unit increase in GGI resulted in an increase of approximately 11% in the hazard rate (p=0.02). Within the subgroups of patients with node-positive disease or who were treated with L, the hazard of relapse was significantly greater for patients with GGI at or above the median. AUC reached a maximum of 78% at 27 months. Conclusions: This analysis supports the GGI as a good predictor of relapse for ER-positive patients, even among patients who receive L. Validation of these results, in a larger series from BIG 1-98, is planned using the simplified GGI represented by a smaller set of genes and tested by qRT-PCR on paraffin-embedded tissues. Test whether the Gene expression Grade Index (GGI) is a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1-98 trial. 55 microarray experiments from primary breast tumors of endocrine-treated patients. No replicate, no reference sample.

Project description:Background: We have previously shown that the Gene expression Grade Index (GGI) was able to identify two subtypes of estrogen receptor (ER)-positive tumors that were associated with statistically distinct clinical outcomes in both untreated and tamoxifen-treated patients. Here, we aim to investigate the ability of the GGI to predict relapses in postmenopausal women who were treated with tamoxifen (T) or letrozole (L) within the BIG 1-98 trial. Methods: We generated gene expression profiles (Affymetrix) and computed the GGI for a matched, case-control sample of patients enrolled in the BIG 1-98 trial from the two hospitals where frozen samples were available. All relapses (cases) were identified from patients randomized to receive monotherapy or from the switching treatment arms for whom relapse occurred before the switch. Each case was randomly matched with four controls based upon nodal status and treatment (T or L). The prognostic value of GGI was assessed as a continuous predictor and divided at the median. Predictive accuracy of GGI was estimated using time-dependent area under the curve (AUC) of the ROC curves. Results: Frozen samples were analyzable for 48 patients (10 cases and 38 controls). Seven of the 10 cases had been assigned to receive L. Cases and controls were comparable with respect to menopausal and nodal status, local and chemotherapy, and HER2 positivity. Cases were slightly older than controls and had a larger proportion of large, poorly differentiated ER+/PgR- tumors. The GGI was significantly and linearly related to risk of relapse: each 10-unit increase in GGI resulted in an increase of approximately 11% in the hazard rate (p=0.02). Within the subgroups of patients with node-positive disease or who were treated with L, the hazard of relapse was significantly greater for patients with GGI at or above the median. AUC reached a maximum of 78% at 27 months. Conclusions: This analysis supports the GGI as a good predictor of relapse for ER-positive patients, even among patients who receive L. Validation of these results, in a larger series from BIG 1-98, is planned using the simplified GGI represented by a smaller set of genes and tested by qRT-PCR on paraffin-embedded tissues.

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Genomic Grade Index (GGI): Feasibility in routine practice and impact on treatment decisions in early breast cancer

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