Optimizing molecular signatures for prostate cancer recurrence
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE25136 | GEO | 2010/11/05
SECONDARY ACCESSION(S): PRJNA134591
REPOSITORIES: GEO
Similar Datasets
Project description:The derivation of molecular signatures indicative of disease status and predictive of subsequent behavior could facilitate the optimal choice of treatment for prostate cancer patients. In this study, we conducted a computational analysis of gene expression profile data obtained from 79 cases, 39 of which were classified as having disease recurrence, to investigate whether advanced computational algorithms can derive more accurate prognostic signatures for prostate cancer. At the 90% sensitivity level, a newly derived prognostic genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid prognostic signature derived by combination of the nomogram and gene expression data significantly outperformed both genetic and clinical signatures, and achieved a specificity of 95%. Our study demonstrates the feasibility of utilizing gene expression information for highly accurate prostate cancer prognosis beyond the current clinical systems, and shows that more advanced computational modeling of tissue-derived microarray data is warranted before clinical application of molecular signatures is considered. mRNA profiling was performed using 79 cases of prostate cancer of known disease recurrence status
2010-11-05 | E-GEOD-25136 | biostudies-arrayexpress
Project description:Background. To develop a radiomics signature for predicting overall survival (OS) and progression-free survival (PFS) in patients with medulloblastoma (MB), and to investigate the incremental prognostic value and underlying biological pathways of the radiomics signature.Methods. A radiomics signature was constructed based on five magnetic resonance (MR) sequences (T1, T1c, T2, FLAIR and ADC) from a training cohort (n = 83) using LASSO-Cox model. Association between the signature and survival was evaluated on a testing cohort (n = 83). Incremental prognostic value of the signature beyond clinicomolecular factors was assessed with respect to calibration, discrimination, reclassification and clinical usefulness by a radiomics-clinicomolecular nomogram. Key pathways associated with the signature were identified. Prognostic value of pathway genes was assessed in a public TCGA cohort.Results. The radiomics signature was significantly associated with OS/PFS, independent from clinicomolecular factors. The radiomics-clinicomolecular nomogram predicted OS (C-index 0.762) and PFS (C-index 0.697) better than either the radiomics signature (C-index: OS: 0.649; PFS: 0.593) or the clinicomolecular nomogram (C-index: OS: 0.725; PFS: 0.691) only, with a better calibration and classification accuracy (net reclassification improvement: OS: 0.298, P = 0.022; PFS: 0.252, P = 0.026). Nine pathways were significantly correlated with the radiomics signature. Average expression value of pathway genes achieved significant risk stratification in public cohort (log-rank P = 0.016). Conclusion. This study demonstrated radiomics signature was an independent prognostic marker and offered incremental value over clinicomolecular factors in OS/PFS predictions for MB patients. The signature was associated with dysregulated pathways affecting patient prognosis in MB.
2020-06-01 | GSE151519 | GEO
Project description:Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV) were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason’s grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.
2017-02-09 | GSE70650 | GEO
Project description:Prostate cancer is the most common malignancy in men. Yet, the modest benefit of treatment highlights the unmet need for prognostic biomarkers in prostate cancer (1). Few large prostate oncogenome resources currently exist that combine the molecular and clinical outcome data necessary for prognostic discovery. To determine the extent to which genomic aberrations reflect the risk of prostate cancer-specific outcomes, we profiled more than 100 primary prostate cancers with long-term follow-up for genome-wide copy number alterations (CNA). We also updated the long-term clinical outcome (median 8 years) of an additional independent cohort of 181 primary prostate cancers that we previously profiled for CNA and expression changes (2). Together, we found that CNA burden across the genome, defined as the percent of the tumor genome affected by CNA, is prognostic for recurrence and metastasis in these two cohorts. This prognostic significance of CNA is independent of Gleason grade, a major existing histopathological prognostic variable in prostate cancer. Moreover, in intermediate-risk Gleason 7 prostate cancers that show a wide range of outcomes, CNA burden is also prognostic for biochemical recurrence, independent of prostate-specific antigen or nomogram score. CNA burden therefore has the potential to stratify patients by their risk of recurrence in an otherwise intermediate risk subpopulation. We further demonstrate that CNA burden can be established in diagnostic FFPE needle biopsies using low-input whole genome sequencing. Together, this work highlights the potential of oncogenomics to identify useful and clinically amenable prognostic factors that may inform prostate cancer outcome and treatment. Human prostate samples were profiled on Agilent 1M aCGH arrays per manufacturer's instructions. A pooled reference normal DNA was used as the reference.
2014-06-30 | E-GEOD-54691 | biostudies-arrayexpress
Project description:An integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), was carried out based on transcriptomics data from a series of nine matched, histologically confirmed PCa and benign samples using the Affymetrix Clariom D Human array. The analysis identified a set of potential gene markers highly associated with tumor status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using the least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in the TCGA-PRAD dataset, with an area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. These findings may improve current prognostic tools for PFS and contribute to clinical decision-making in PCa treatment.
2023-10-31 | GSE246282 | GEO
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
2008-05-27 | E-GEOD-8402 | biostudies-arrayexpress
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.
2013-07-01 | E-GEOD-46691 | biostudies-arrayexpress
Project description:Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign samples from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.
2023-11-30 | PXD041005 | Pride
Project description:Gene signatures have been shown to predict the response/resistance to immunotherapies but with only modest accuracy. Reduction in this precision might be due to the lack of spatial information which prevents the ability from distinguish tumor from tumor-microenvironment (TME) genes. Here we collected gene expression data spatially from three compartments (CD68+macrophages, CD45+leukocytes and S100+tumor cells) of 59-immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. We achieved AUC≥0.90 for CD45, AUC≥0.94 for CD68, and AUC≥0.86 for S100B signatures, whereas AUC≥0.70 for pseudo-bulk () signature. Cross-testing in different compartments (e.g., CD45 signature in CD68 and S100B compartments) showed poor performance indicating compartment-specificity. Our novel spatial S100B signature showed the best performance with AUC≥0.80 in the validation cohort (N=46). Testing our signatures in computationally deconvolved pseudo-compartments revealed lower AUCs. We conclude that the spatially defined compartment signatures utilize tumor and TME-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical
2023-05-24 | GSE233305 | GEO
Project description:Prostate cancer is the most common malignancy in men. Yet, the modest benefit of treatment highlights the unmet need for prognostic biomarkers in prostate cancer (1). Few large prostate oncogenome resources currently exist that combine the molecular and clinical outcome data necessary for prognostic discovery. To determine the extent to which genomic aberrations reflect the risk of prostate cancer-specific outcomes, we profiled more than 100 primary prostate cancers with long-term follow-up for genome-wide copy number alterations (CNA). We also updated the long-term clinical outcome (median 8 years) of an additional independent cohort of 181 primary prostate cancers that we previously profiled for CNA and expression changes (2). Together, we found that CNA burden across the genome, defined as the percent of the tumor genome affected by CNA, is prognostic for recurrence and metastasis in these two cohorts. This prognostic significance of CNA is independent of Gleason grade, a major existing histopathological prognostic variable in prostate cancer. Moreover, in intermediate-risk Gleason 7 prostate cancers that show a wide range of outcomes, CNA burden is also prognostic for biochemical recurrence, independent of prostate-specific antigen or nomogram score. CNA burden therefore has the potential to stratify patients by their risk of recurrence in an otherwise intermediate risk subpopulation. We further demonstrate that CNA burden can be established in diagnostic FFPE needle biopsies using low-input whole genome sequencing. Together, this work highlights the potential of oncogenomics to identify useful and clinically amenable prognostic factors that may inform prostate cancer outcome and treatment.
2014-06-30 | GSE54691 | GEO