Clinical staging of prostate cancer: Reproducibility and clarification of issues

Campbell, Timothy; Blasko, John C.; Crawford, E. David; Forman, Jeffrey D.; Hanks, Gerald E.; Kuban, Deborah A.; Montie, James E.; Moul, Judd W.; Pollack, Alan; Ray, Debra; Roach, Mack; Steinberg, Gary D.; Stone, Nelson N.; Thompson, Ian M.; Vogelzang, Nicholas J.; Vijayakumar, Srinivasan

doi:10.1002/ijc.1017

Cited by 23 publications

(8 citation statements)

References 39 publications

(40 reference statements)

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“…The mean age of all participants was 63.2 years (± 8.5), which is within ± 5 years from the matched controls (58.5 ± 10.8). Five of the 16 participants had T2a clinical T-stage of their prostate cancer, two had a T1c stage, and the rest had a T2b to T3c stage (N = 9/16) (Campbell et al, 2001). Six patients (38%) had a Gleason score of 9 and 10 patients had Gleason scores between 6 and 8.…”

Section: Resultsmentioning

confidence: 99%

Upregulation of α-synuclein during localized radiation therapy signals the association of cancer-related fatigue with the activation of inflammatory and neuroprotective pathways

Saligan¹,

Hsiao²,

Wang³

et al. 2013

Brain, Behavior, and Immunity

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Purpose Neuroinflammatory mechanisms are associated with fatigue in neurodegenerative conditions such as Parkinson’s. The symptoms in Parkinson’s including fatigue are thought to be related to α-synuclein overexpression. This study investigated genomic correlates of fatigue experienced by men with prostate cancer receiving external beam radiation therapy (EBRT). Patients and Methods Sixteen men with non-metastatic prostate cancer who were scheduled to receive EBRT were enrolled. Fatigue scores and blood were obtained at baseline (prior to EBRT, D0); one hour following initiation of EBRT (D1), day 7 (D7), day 14 (D14), midpoint (days 19–21, D21), completion (days 38–42, D42), and four weeks post EBRT (days 68–72, D72). Gene expression profiling using microarray analysis was performed from whole blood and confirmatory qPCR and protein (ELISA) analyses verified the microarray results. Correlations between fatigue and gene/protein expressions were determined using a mixed model approach. Results Microarray data showed significant, differential expression of 463 probesets following EBRT. SNCA had a 2.95 fold change at D21 from baseline. SNCA expression was confirmed by qPCR (p < 0.001) and ELISA (p < 0.001) over time during EBRT. Fatigue scores were significantly correlated with SNCA gene expression on D14 (r = 0.55, p < 0.05) and plasma α-synuclein concentrations on D42 of EBRT (r = 0.54, p = 0.04). Conclusion Fatigue experienced during EBRT may be mediated by α-synuclein overexpression. Alpha-synuclein may serve as a useful biomarker to understand the mechanisms and pathways related to the development of fatigue in this population.

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Section: Resultsmentioning

confidence: 99%

Upregulation of α-synuclein during localized radiation therapy signals the association of cancer-related fatigue with the activation of inflammatory and neuroprotective pathways

Saligan¹,

Hsiao²,

Wang³

et al. 2013

Brain, Behavior, and Immunity

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“…An important piece of meta-data is the method used to identify the classes of the sample, including the expected accuracy of that method. Many diagnosis methods (e.g., grade of cancer Campbell et al 2001) are far from 100% accurate, and this is obviously going to affect the later data analysis. In addition a particularly important issue that can cause confounding or bias (Broadhurst and Kell 2006;Ioannidis 2005;Ransohoff 2005) is the likelihood that 'cases' will be often be taking considerably more pharmaceutical drugs than are the 'matched' 'controls.'…”

Section: Design Of Experiments (Doe)mentioning

confidence: 99%

Proposed minimum reporting standards for data analysis in metabolomics

et al. 2007

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The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent

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“…Furthermore, over 80% of PCas are multifocal, which makes accurate clinical staging even more difficult and unreliable 7 . Clinical tumour understaging has consistently been shown to range from 40% to 60% 8–11 . This is clearly attributable to the multifocal, histologically heterogeneous nature of prostatic carcinoma.…”

Section: Clinical Staging Versus Pathological Stagingmentioning

confidence: 99%

“…7 Clinical tumour understaging has consistently been shown to range from 40% to 60%. [8][9][10][11] This is clearly attributable to the multifocal, histologically heterogeneous nature of prostatic carcinoma. Bostwick et al found that 59% of clinical stage T2, T3 and T4 PCas in men undergoing radical prostatectomy were understaged when compared with final pathological stage.…”

Section: Clinical Staging Versus Pathological Stagingmentioning

confidence: 99%

Staging of prostate cancer

et al. 2011

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Prostatic carcinoma (PCa) is a significant cause of cancer morbidity and mortality worldwide. Accurate staging is critical for prognosis assessment and treatment planning for PCa. Despite the large volume of clinical activity and research, the challenge to define the most appropriate and clinically relevant staging system remains. The pathologically complex and uncertain clinical course of prostate cancer further complicates the design of staging classification and a substaging system suitable for individualized care. This review will focus on recent progress and controversial issues related to prostate cancer staging. The 2010 revision of the American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) tumour, node and metastasis (TNM) system is the most widely used staging system at this time. Despite general acceptance of the system as a whole, there is controversy and uncertainty about its application, particularly for T2 subclassification. The three-tiered T2 classification system for organ-confined prostate cancer is superfluous, considering the biology and anatomy of PCa. A tumour size-based substaging system may be considered in the future TNM subclassification of pT2 cancer. Lymph node status is one of the most important prognostic factors for prostate cancer. Nevertheless, clinical outcomes in patients with positive lymph nodes are variable. Identification of patients at the greatest risk of systemic progression helps in the selection of appropriate therapy. The data suggest that the inherent aggressiveness of metastatic prostate cancer is closely linked to the tumour volume of lymph node metastasis. We recommend that a future TNM staging system should consider subclassification of node-positive cancer on the basis of nodal cancer volume, using the diameter of the largest nodal metastasis and/or the number of positive nodes.

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Clinical staging of prostate cancer: Reproducibility and clarification of issues

Cited by 23 publications

References 39 publications

Upregulation of α-synuclein during localized radiation therapy signals the association of cancer-related fatigue with the activation of inflammatory and neuroprotective pathways

Upregulation of α-synuclein during localized radiation therapy signals the association of cancer-related fatigue with the activation of inflammatory and neuroprotective pathways

Proposed minimum reporting standards for data analysis in metabolomics

Staging of prostate cancer

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