Dataset Information

Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy.

ABSTRACT: To test the hypothesis that a genomic classifier (GC) would predict biochemical failure (BF) and distant metastasis (DM) in men receiving radiation therapy (RT) after radical prostatectomy (RP).Among patients who underwent post-RP RT, 139 were identified for pT3 or positive margin, who did not receive neoadjuvant hormones and had paraffin-embedded specimens. Ribonucleic acid was extracted from the highest Gleason grade focus and applied to a high-density-oligonucleotide microarray. Receiver operating characteristic, calibration, cumulative incidence, and Cox regression analyses were performed to assess GC performance for predicting BF and DM after post-RP RT in comparison with clinical nomograms.The area under the receiver operating characteristic curve of the Stephenson model was 0.70 for both BF and DM, with addition of GC significantly improving area under the receiver operating characteristic curve to 0.78 and 0.80, respectively. Stratified by GC risk groups, 8-year cumulative incidence was 21%, 48%, and 81% for BF (P<.0001) and for DM was 0, 12%, and 17% (P=.032) for low, intermediate, and high GC, respectively. In multivariable analysis, patients with high GC had a hazard ratio of 8.1 and 14.3 for BF and DM. In patients with intermediate or high GC, those irradiated with undetectable prostate-specific antigen (PSA ?0.2 ng/mL) had median BF survival of >8 years, compared with <4 years for patients with detectable PSA (>0.2 ng/mL) before initiation of RT. At 8 years, the DM cumulative incidence for patients with high GC and RT with undetectable PSA was 3%, compared with 23% with detectable PSA (P=.03). No outcome differences were observed for low GC between the treatment groups.The GC predicted BF and metastasis after post-RP irradiation. Patients with lower GC risk may benefit from delayed RT, as opposed to those with higher GC; however, this needs prospective validation. Genomic-based models may be useful for improved decision-making for treatment of high-risk prostate cancer.

SUBMITTER: Den RB

PROVIDER: S-EPMC4432840 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy.

Den Robert B RB Feng Felix Y FY Showalter Timothy N TN Mishra Mark V MV Trabulsi Edouard J EJ Lallas Costas D CD Gomella Leonard G LG Kelly W Kevin WK Birbe Ruth C RC McCue Peter A PA Ghadessi Mercedeh M Yousefi Kasra K Davicioni Elai E Knudsen Karen E KE Dicker Adam P AP

International journal of radiation oncology, biology, physics 20140708 5

<h4>Purpose</h4>To test the hypothesis that a genomic classifier (GC) would predict biochemical failure (BF) and distant metastasis (DM) in men receiving radiation therapy (RT) after radical prostatectomy (RP).<h4>Methods and materials</h4>Among patients who underwent post-RP RT, 139 were identified for pT3 or positive margin, who did not receive neoadjuvant hormones and had paraffin-embedded specimens. Ribonucleic acid was extracted from the highest Gleason grade focus and applied to a high-den ...[more]

PMID: 25035207

Similar Datasets

Project description:PurposeClinicopathologic 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.MethodsA nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who 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 who 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.ResultsExpression 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.ConclusionA 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.

Project description:To determine whether magnetic resonance (MR) imaging and MR spectroscopic imaging findings can improve predictions made with the Kattan nomogram for radiation therapy.The institutional review board approved this retrospective HIPAA-compliant study. Ninety-nine men who underwent endorectal MR and MR spectroscopy before external-beam radiation therapy for prostate cancer (January 1998 to June 2007) were included. Linear predictors were calculated with input variables from the study sample and the Kattan original coefficients. The linear predictor is a single weighted value that combines information of all predictor variables in a model, where the weight of each value is its association with the outcome. Two radiologists independently reviewed all MR images to determine extent of disease; a third independent reader resolved discrepancies. Biochemical failure was defined as a serum prostate-specific antigen level of 2 ng/mL (2 μg/L) or more above nadir. Cox proportional hazard models were used to determine the probabilities of treatment failure (biochemical failure) in 5 years. One model included only the Kattan nomogram data; the other also incorporated imaging findings. The discrimination performance of all models was determined with receiver operating characteristics (ROC) curve analyses. These analyses were followed by an assessment of net risk reclassification.The areas under the ROC curve for the Kattan nomogram and the model incorporating MR imaging findings were 61.1% (95% confidence interval: 58.1%, 64.0%) and 78.0% (95% confidence interval: 75.7%, 80.4%), respectively. Comparison of performance showed that the model with imaging findings performed significantly better than did the model with clinical variables alone (P < .001). Overall, the addition of imaging findings led to an improvement in risk classification of about 28%, ranging from approximately a minimum of 16% to a maximum of 39%, depending on the risk change considered important.MR imaging data improve the prediction of biochemical failure with the Kattan nomogram after external-beam radiation therapy for prostate cancer. The number needed to image to improve the prediction of biochemical failure in one patient ranged from three to six.

Project description:OBJECTIVE Although postoperative stereotactic body radiation therapy (SBRT) for spinal metastases is increasingly performed, few guidelines exist for this application. The purpose of this study is to develop consensus guidelines to promote safe and effective treatment for patients with spinal metastases. METHODS Fifteen radiation oncologists and 5 neurosurgeons, representing 19 centers in 4 countries and having a collective experience of more than 1300 postoperative spine SBRT cases, completed a 19-question survey about postoperative spine SBRT practice. Responses were defined as follows: 1) consensus: selected by ≥ 75% of respondents; 2) predominant: selected by 50% of respondents or more; and 3) controversial: no single response selected by a majority of respondents. RESULTS Consensus treatment indications included: radioresistant primary, 1-2 levels of adjacent disease, and previous radiation therapy. Contraindications included: involvement of more than 3 contiguous vertebral bodies, ASIA Grade A status (complete spinal cord injury without preservation of motor or sensory function), and postoperative Bilsky Grade 3 residual (cord compression without any CSF around the cord). For treatment planning, co-registration of the preoperative MRI and postoperative T1-weighted MRI (with or without gadolinium) and delineation of the cord on the T2-weighted MRI (and/or CT myelogram in cases of significant hardware artifact) were predominant. Consensus GTV (gross tumor volume) was the postoperative residual tumor based on MRI. Predominant CTV (clinical tumor volume) practice was to include the postoperative bed defined as the entire extent of preoperative tumor, the relevant anatomical compartment and any residual disease. Consensus was achieved with respect to not including the surgical hardware and incision in the CTV. PTV (planning tumor volume) expansion was controversial, ranging from 0 to 2 mm. The spinal cord avoidance structure was predominantly the true cord. Circumferential treatment of the epidural space and margin for paraspinal extension was controversial. Prescription doses and spinal cord tolerances based on clinical scenario, neurological compromise, and prior overlapping treatments were controversial, but reasonable ranges are presented. Fifty percent of those surveyed practiced an integrated boost to areas of residual tumor and density override for hardware within the beam path. Acceptable PTV coverage was controversial, but consensus was achieved with respect to compromising coverage to meet cord constraint and fractionation to improve coverage while meeting cord constraint. CONCLUSIONS The consensus by spinal radiosurgery experts suggests that postoperative SBRT is indicated for radioresistant primary lesions, disease confined to 1-2 vertebral levels, and/or prior overlapping radiotherapy. The GTV is the postoperative residual tumor, and the CTV is the postoperative bed defined as the entire extent of preoperative tumor and anatomical compartment plus residual disease. Hardware and scar do not need to be included in CTV. While predominant agreement was reached about treatment planning and definition of organs at risk, future investigation will be critical in better understanding areas of controversy, including whether circumferential treatment of the epidural space is necessary, management of paraspinal extension, and the optimal dose fractionation schedules.

Dataset Information

Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy.

Publications

Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets