Colonoscopic screening for colorectal cancer improves quality of life measures: a population-based screening study

BACKGROUND: Screening asymptomatic individuals for neoplasia can have adverse consequences on quality of life. Colon cancer screening is widespread but the quality of life (QOL) consequences are unknown. This study determined the impact of screening colonoscopy on QOL measures in asymptomatic average-risk participants. METHODS: Asymptomatic male and female participants aged 55–74 years were randomly selected from the Australian Electoral Roll or six primary care physicians' databases. Participants completed the Short-Form (SF-36) Quality of Life Assessment at baseline and at a mean of 39 days after colonoscopy. Outcome measures were (i) significant changes in raw scores in any of the eight SF-36 domains assessed following colonoscopic screening and (ii) improvements or declines in previously validated categories, representing clinically significant changes, within any of the eight SF-36 domains. RESULTS: Baseline QOL measures were similar to those of a matched general population sample. Role Limitations due to Emotions, Mental Health and Vitality raw scores significantly improved following colonoscopy (P < 0.05, 2-tailed t-test). Health ratings according to Category were similar (same clinical status) in the majority of participants. However, 30% participants recorded clinically significant improvement in the Mental Health and Vitality domains (P < 0.05, Wilcoxon Signed-Ranks test). This improvement was not offset by declines in other domains or in other participants. Improvement in QOL was not related to colonoscopy results. CONCLUSION: Average-risk persons benefit significantly from colon cancer screening with colonoscopy, improving in Mental Health and Vitality domains of Quality of Life. This improvement is not offset by declines in other domains

Revisiting the technical validation of tumour biomarker assays: how to open a Pandora's box

A tumour biomarker is a characteristic that is objectively measured and evaluated in tumour samples as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The development of a biomarker contemplates distinct phases, including discovery by hypothesis-generating preclinical or exploratory studies, development and qualification of the assay for the identification of the biomarker in clinical samples, and validation of its clinical significance. Although guidelines for the development and validation of biomarkers are available, their implementation is challenging, owing to the diversity of biomarkers being developed. The term 'validation' undoubtedly has several meanings; however, in the context of biomarker research, a test may be considered valid if it is 'fit for purpose'. In the process of validation of a biomarker assay, a key point is the validation of the methodology. Here we discuss the challenges for the technical validation of immunohistochemical and gene expression assays to detect tumour biomarkers and provide suggestions of pragmatic solutions to address these challenges

Haptoglobin phenotype is not a predictor of recurrence free survival in high-risk primary breast cancer patients

Contains fulltext : 70104tjan-heijnen.pdf (publisher's version ) (Open Access)BACKGROUND: Better breast cancer prognostication may improve selection of patients for adjuvant therapy. We conducted a retrospective follow-up study in which we investigated sera of high-risk primary breast cancer patients, to search for proteins predictive of recurrence free survival. METHODS: Two sample sets of high-risk primary breast cancer patients participating in a randomised national trial investigating the effectiveness of high-dose chemotherapy were analysed. Sera in set I (n = 63) were analysed by surface enhanced laser desorption ionisation time-of-flight mass spectrometry (SELDI-TOF MS) for biomarker finding. Initial results were validated by analysis of sample set II (n = 371), using one-dimensional gel-electrophoresis. RESULTS: In sample set I, the expression of a peak at mass-to-charge ratio 9198 (relative intensity 20), identified as haptoglobin (Hp) alpha-1 chain, was strongly associated with recurrence free survival (global Log-rank test; p = 0.0014). Haptoglobin is present in three distinct phenotypes (Hp 1-1, Hp 2-1, and Hp 2-2), of which only individuals with phenotype Hp 1-1 or Hp 2-1 express the haptoglobin alpha-1 chain. As the expression of the haptoglobin alpha-1 chain, determined by SELDI-TOF MS, corresponds to the phenotype, initial results were validated by haptoglobin phenotyping of the independent sample set II by native one-dimensional gel-electrophoresis. With the Hp 1-1 phenotype as the reference category, the univariate hazard ratio for recurrence was 0.87 (95% CI: 0.56 - 1.34, p = 0.5221) and 1.03 (95% CI: 0.65 - 1.64, p = 0.8966) for the Hp 2-1 and Hp 2-2 phenotypes, respectively, in sample set II. CONCLUSION: In contrast to our initial results, the haptoglobin phenotype was not identified as a predictor of recurrence free survival in high-risk primary breast cancer in our validation set. Our initial observation in the discovery set was probably the result of a type I error (i.e. false positive). This study illustrates the importance of validation in obtaining the true clinical applicability of a potential biomarker

Comparing the old and new generation SELDI-TOF MS: implications for serum protein profiling

<p>Abstract</p> <p>Background</p> <p>Although the PBS-IIc SELDI-TOF MS apparatus has been extensively used in the search for better biomarkers, issues have been raised concerning the semi-quantitative nature of the technique and its reproducibility. To overcome these limitations, a new SELDI-TOF MS instrument has been introduced: the PCS 4000 series. Changes in this apparatus compared to the older one are a.o. an increased dynamic range of the detector, an adjusted configuration of the detector sensitivity, a raster scan that ensures more complete desorption coverage and an improved detector attenuation mechanism. In the current study, we evaluated the performance of the old PBS-IIc and new PCS 4000 series generation SELDI-TOF MS apparatus.</p> <p>Methods</p> <p>To this end, two different sample sets were profiled after which the same ProteinChip arrays were analysed successively by both instruments. Generated spectra were analysed by the associated software packages. The performance of both instruments was evaluated by assessment of the number of peaks detected in the two sample sets, the biomarker potential and reproducibility of generated peak clusters, and the number of peaks detected following serum fractionation.</p> <p>Results</p> <p>We could not confirm the claimed improved performance of the new PCS 4000 instrument, as assessed by the number of peaks detected, the biomarker potential and the reproducibility. However, the PCS 4000 instrument did prove to be of superior performance in peak detection following profiling of serum fractions.</p> <p>Conclusion</p> <p>As serum fractionation facilitates detection of low abundant proteins through reduction of the dynamic range of serum proteins, it is now increasingly applied in the search for new potential biomarkers. Hence, although the new PCS 4000 instrument did not differ from the old PBS-IIc apparatus in the analysis of crude serum, its superior performance after serum fractionation does hold promise for improved biomarker detection and identification.</p

Does It Matter Who Writes Medical News Stories?

David Henry and colleagues review Australian news stories over a five-year period to assess whether quality is associated with who wrote the story: a specialist health journalist or a non-specialist

Approaches to working in high-dimensional data spaces: gene expression microarrays

This review provides a focused summary of the implications of high-dimensional data spaces produced by gene expression microarrays for building better models of cancer diagnosis, prognosis, and therapeutics. We identify the unique challenges posed by high dimensionality to highlight methodological problems and discuss recent methods in predictive classification, unsupervised subclass discovery, and marker identification