Dataset Information

An mRNA Expression Signature of Gleason Grade Predicts Lethal Prostate Cancer

ABSTRACT: PSA screening has led to enormous overtreatment of prostate cancer, due to the inability to distinguish potentially lethal disease at diagnosis. We reasoned that by identifying an mRNA signature of Gleason grade, the best predictor of prognosis, we could improve prediction of lethal disease among men with moderate Gleason 7 tumors, the most common grade, and most indeterminate in terms of prognosis. Using the complementary DNA (cDNA)–mediated annealing, selection, extension, and ligation assay, we measured the mRNA expression of 6,100 genes in prostate tumor tissue in the Swedish Watchful Waiting cohort (N=358) and Physicians’ Health Study (PHS; N=109). We developed an mRNA signature of Gleason comparing individuals with Gleason ≤6 to those with Gleason ≥8 tumors, and applied the model among Gleason 7 cases to discriminate lethal cases. We built a157-gene signature using the Swedish data that predicted Gleason with low misclassification (AUC=0.91); when this signature was tested in the PHS validation set, the discriminatory ability remained high (AUC=0.94). In men with Gleason 7 tumors, who were excluded from the model building, the signature significantly improved the prediction of lethal disease beyond knowing whether the Gleason score was 4+3 or 3+4 (p=0.006). Our expression signature and the genes identified may improve our understanding of the de-differentiation process of prostate tumors. Additionally, the signature may have clinical applications among men with Gleason 7, by further estimating their risk of lethal prostate cancer and thereby guiding therapy decisions to improve outcomes and reduce overtreatment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE37663 | GEO | 2012/05/01

SECONDARY ACCESSION(S): PRJNA162649

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Current prostate cancer prognostic models are based on pre-treatment prostate-specific antigen (PSA) levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict clinical disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. We analyzed a Swedish Watchful Waiting cohort (1977â1999) with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL) method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP) samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer or developed metastases and men who survived more than 10 years without metastases (lethals and indolents, respectively). Several models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. We reasoned that tumor sampling might preclude the identification of the dominant metastatic nodule. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Thus the determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses preventing the development of molecular biomarkers for prostate cancer progression. 281 cases from the population-based Swedish-Watchful Waiting cohort. The cohort consists of men with localized prostate cancer (clinical stage T1-T2, Mx, N0); Training set: first 186 samples; Validation cohort: remaining 95 cases from the same population.

Project description:Common epithelial cancers are believed to become more aggressive through the accumulation of multiple independent molecular events that lead to the deregulation of cell signaling. However, the discovery that the majority of prostate cancers harbor gene fusions of the 5'-untranslated region of androgen regulated TMPRSS2 promoter with ETS transcription factor family members has brought this paradigm into question1,2. TMPRSS2-ERG gene fusion is the most common molecular sub-type of prostate cancer. Recent work suggests that the TMPRSS2-ERG fusion is associated with a more aggressive clinical phenotype3. In the most advanced castration resistant prostate cancers where the androgen receptor has been inactivated, the TMPRSS2-ERG fusion remains functionally active. Here we show compelling clinical and gene expression data supporting the existence of a TMPRSS2-ERG fusion prostate cancer subclass. Using expression array profiling on 455 primary prostate tumors, we identified an 87 gene expression signature, distinguishing TMPRSS2-ERG fusion prostate cancer as a discrete molecular entity. Computational analysis suggested that this fusion signature was associated with estrogen receptor signaling. Functional studies demonstrated regulation of the TMPRSS2-ERG fusion transcript by estrogenic compounds. These data identify a previously unrecognized mechanism for regulation of the TMPRSS2-ERG, even in the absence of a functional androgen receptor, and thus may have broader implications in the treatment of prostate cancer. Keywords: Prostate cancer, Expression array, Illumina, gene fusion, TMPRSS2, ERG, Signatures, Estrogen Test Cohort: 388 cases from the population based Swedish-Watchful Waiting cohort. The cohort consists of men with localized prostate cancer (clinical stage T1-T2, Mx, N0); Validation cohort: The PhysiciansM-bM-^@M-^Y Health Study (PHS) cohort included 116 US men diagnosed with incidental prostate cancer between 1983 and 2003; 455 cases were annotated for TMPRSS2-ERG fusion. Test Set: GSM208029 ... GSM208392 Validation Set: GSM208404 ... GSM208512

Project description:Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer. 545 formalin-fixed paraffin-embedded (FFPE) tissue samples from primary prostate cancer obtained from Radical Prostatectomy.

Dataset Information

An mRNA Expression Signature of Gleason Grade Predicts Lethal Prostate Cancer

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets