Estimating the number needed to treat from continuous outcomes in randomised controlled trials: methodological challenges and worked example using data from the UK Back Pain Exercise and Manipulation (BEAM) trial

Background Reporting numbers needed to treat (NNT) improves interpretability of trial results. It is unusual that continuous outcomes are converted to numbers of individual responders to treatment (i.e., those who reach a particular threshold of change); and deteriorations prevented are only rarely considered. We consider how numbers needed to treat can be derived from continuous outcomes; illustrated with a worked example showing the methods and challenges. Methods We used data from the UK BEAM trial (n = 1, 334) of physical treatments for back pain; originally reported as showing, at best, small to moderate benefits. Participants were randomised to receive 'best care' in general practice, the comparator treatment, or one of three manual and/or exercise treatments: 'best care' plus manipulation, exercise, or manipulation followed by exercise. We used established consensus thresholds for improvement in Roland-Morris disability questionnaire scores at three and twelve months to derive NNTs for improvements and for benefits (improvements gained+deteriorations prevented). Results At three months, NNT estimates ranged from 5.1 (95% CI 3.4 to 10.7) to 9.0 (5.0 to 45.5) for exercise, 5.0 (3.4 to 9.8) to 5.4 (3.8 to 9.9) for manipulation, and 3.3 (2.5 to 4.9) to 4.8 (3.5 to 7.8) for manipulation followed by exercise. Corresponding between-group mean differences in the Roland-Morris disability questionnaire were 1.6 (0.8 to 2.3), 1.4 (0.6 to 2.1), and 1.9 (1.2 to 2.6) points. Conclusion In contrast to small mean differences originally reported, NNTs were small and could be attractive to clinicians, patients, and purchasers. NNTs can aid the interpretation of results of trials using continuous outcomes. Where possible, these should be reported alongside mean differences. Challenges remain in calculating NNTs for some continuous outcomes

The PTTG1-binding factor (PBF/PTTG1IP) regulates p53 activity in thyroid cells

The PTTG1-Binding Factor (PBF/PTTG1IP) has an emerging repertoire of roles, especially in thyroid biology, and functions as a proto-oncogene. High PBF expression is independently associated with poor prognosis and lower disease-specific survival in human thyroid cancer. However, the precise role of PBF in thyroid tumorigenesis is unclear. Here, we present extensive evidence demonstrating that PBF is a novel regulator of p53, a tumor suppressor protein with a key role in maintaining genetic stability, which is infrequently mutated in differentiated thyroid cancer. By coimmunoprecipitation and proximity ligation assays, we show that PBF binds specifically to p53 in thyroid cells, and significantly represses transactivation of responsive promoters. Further, we identify that PBF decreases p53 stability by enhancing ubiquitination, which appears dependent on the E3 ligase activity of Mdm2. Impaired p53 function was evident in a transgenic mouse model with thyroid-specific PBF over-expression (PBF-Tg), which had significantly increased genetic instability as indicated by FISSR-PCR analysis. Consistent with this, ~40% of all DNA repair genes examined were repressed in PBF-Tg primary cultures, including genes with critical roles in maintaining genomic integrity such as Mgmt, Rad51 and Xrcc3. Our data also revealed that PBF induction resulted in upregulation of the E2 enzyme Rad6 in murine thyrocytes, and was associated with Rad6 expression in human thyroid tumors. Overall, this work provides novel insights into the role of the proto-oncogene PBF as a negative regulator of p53 function in thyroid tumorigenesis, where PBF is generally over-expressed and p53 mutations are rare compared to other tumor types

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Displaced midshaft fractures of the clavicle: non-operative treatment versus plate fixation (Sleutel-TRIAL). A multicentre randomised controlled trial

Contains fulltext : 96826.pdf (publisher's version ) (Open Access)BACKGROUND: The traditional view that the vast majority of midshaft clavicular fractures heal with good functional outcomes following non-operative treatment may be no longer valid for all midshaft clavicular fractures. Recent studies have presented a relatively high incidence of non-union and identified speciic limitations of the shoulder function in subgroups of patients with these injuries. AIM: A prospective, multicentre randomised controlled trial (RCT) will be conducted in 21 hospitals in the Netherlands, comparing fracture consolidation and shoulder function after either non-operative treatment with a sling or a plate fixation. METHODS/DESIGN: A total of 350 patients will be included, between 18 and 60 years of age, with a dislocated midshaft clavicular fracture. The primary outcome is the incidence of non-union, which will be determined with standardised X-rays (Antero-Posterior and 30 degrees caudocephalad view). Secondary outcome will be the functional outcome, measured using the Constant Score. Strength of the shoulder muscles will be measured with a handheld dynamometer (MicroFET2). Furthermore, the health-related Quality of Life score (ShortForm-36) and the Disabilities of Arm, Shoulder and Hand (DASH) Outcome Measure will be monitored as subjective parameters. Data on complications, bone union, cosmetic aspects and use of painkillers will be collected with follow-up questionnaires. The follow-up time will be two years. All patients will be monitored at regular intervals over the subsequent twelve months (two and six weeks, three months and one year). After two years an interview by telephone and a written survey will be performed to evaluate the two-year functional and mechanical outcomes. All data will be analysed on an intention-to-treat basis, using univariate and multivariate analyses. DISCUSSION: This trial will provide level-1 evidence for the comparison of consolidation and functional outcome between two standardised treatment options for dislocated midshaft clavicular fractures. The gathered data may support the development of a clinical guideline for treatment of clavicular fractures. TRIAL REGISTRATION: Netherlands National Trial Register NTR2399

Effect of spinal manipulation on sensorimotor functions in back pain patients: study protocol for a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Low back pain (LBP) is a recognized public health problem, impacting up to 80% of US adults at some point in their lives. Patients with LBP are utilizing integrative health care such as spinal manipulation (SM). SM is the therapeutic application of a load to specific body tissues or structures and can be divided into two broad categories: SM with a high-velocity low-amplitude load, or an impulse "thrust", (HVLA-SM) and SM with a low-velocity variable-amplitude load (LVVA-SM). There is evidence that sensorimotor function in people with LBP is altered. This study evaluates the sensorimotor function in the lumbopelvic region, as measured by postural sway, response to sudden load and repositioning accuracy, following SM to the lumbar and pelvic region when compared to a sham treatment.</p> <p>Methods/Design</p> <p>A total of 219 participants with acute, subacute or chronic low back pain are being recruited from the Quad Cities area located in Iowa and Illinois. They are allocated through a minimization algorithm in a 1:1:1 ratio to receive either 13 HVLA-SM treatments over 6 weeks, 13 LVVA-SM treatments over 6 weeks or 2 weeks of a sham treatment followed by 4 weeks of full spine "doctor's choice" SM. Sensorimotor function tests are performed before and immediately after treatment at baseline, week 2 and week 6. Self-report outcome assessments are also collected. The primary aims of this study are to 1) determine immediate pre to post changes in sensorimotor function as measured by postural sway following delivery of a single HVLA-SM or LVVA-SM treatment when compared to a sham treatment and 2) to determine changes from baseline to 2 weeks (4 treatments) of HVLA-SM or LVVA-SM compared to a sham treatment. Secondary aims include changes in response to sudden loads and lumbar repositioning accuracy at these endpoints, estimating sensorimotor function in the SM groups after 6 weeks of treatment, and exploring if changes in sensorimotor function are associated with changes in self-report outcome assessments.</p> <p>Discussion</p> <p>This study may provide clues to the sensorimotor mechanisms that explain observed functional deficits associated with LBP, as well as the mechanism of action of SM.</p> <p>Trial registration</p> <p>This trial is registered in ClinicalTrials.gov, with the ID number of <a href="http://www.clinicaltrials.gov/ct2/show/NCT00830596">NCT00830596</a>, registered on January 27, 2009. The first participant was allocated on 30 January 2009 and the final participant was allocated on 17 March 2011.</p

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice

Clinical development of new drug-radiotherapy combinations.

In countries with the best cancer outcomes, approximately 60% of patients receive radiotherapy as part of their treatment, which is one of the most cost-effective cancer treatments. Notably, around 40% of cancer cures include the use of radiotherapy, either as a single modality or combined with other treatments. Radiotherapy can provide enormous benefit to patients with cancer. In the past decade, significant technical advances, such as image-guided radiotherapy, intensity-modulated radiotherapy, stereotactic radiotherapy, and proton therapy enable higher doses of radiotherapy to be delivered to the tumour with significantly lower doses to normal surrounding tissues. However, apart from the combination of traditional cytotoxic chemotherapy with radiotherapy, little progress has been made in identifying and defining optimal targeted therapy and radiotherapy combinations to improve the efficacy of cancer treatment. The National Cancer Research Institute Clinical and Translational Radiotherapy Research Working Group (CTRad) formed a Joint Working Group with representatives from academia, industry, patient groups and regulatory bodies to address this lack of progress and to publish recommendations for future clinical research. Herein, we highlight the Working Group's consensus recommendations to increase the number of novel drugs being successfully registered in combination with radiotherapy to improve clinical outcomes for patients with cancer.National Institute for Health ResearchThis is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/nrclinonc.2016.7