Prothymosin alpha: a ubiquitous polypeptide with potential use in cancer diagnosis and therapy

The thymus is a central lymphoid organ with crucial role in generating T cells and maintaining homeostasis of the immune system. More than 30 peptides, initially referred to as “thymic hormones,” are produced by this gland. Although the majority of them have not been proven to be thymus-speciWc, thymic peptides comprise an eVective group of regulators, mediating important immune functions. Thymosin fraction Wve (TFV) was the Wrst thymic extract shown to stimulate lymphocyte proliferation and diVerentiation. Subsequent fractionation of TFV led to the isolation and characterization of a series of immunoactive peptides/polypeptides, members of the thymosin family. Extensive research on prothymosin (proT) and thymosin 1 (T1) showed that they are of clinical signiWcance and potential medical use. They may serve as molecular markers for cancer prognosis and/or as therapeutic agents for treating immunodeWciencies, autoimmune diseases and malignancies. Although the molecular mechanisms underlying their eVect are yet not fully elucidated proT and T1 could be considered as candidates for cancer immunotherapy. In this review, we will focus in principle on the eventual clinical utility of proT, both as a tumor biomarker and in triggering anticancer immune responses. Considering the experience acquired via the use of T1 to treat cancer patients, we will also discuss potential approaches for the future introduction of proT into the clinical setting

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Anastomotic leakage: experience from a colorectal unit

Background: Whilst the incidence of anastomotic dehiscence is decreasing, it remains a significant setback to the patients and their surgeons. In most centres, minor leaks are treated conservatively but surgery remains an options. Major leaks are best treated aggressively by surgical means, as the mortality among this group of patients remains unacceptably high.Methods: We reviewed all case-notes, radiological records and histology reports of all patients who underwent major colonic restorative resection between July 1997 and September 199 in order to determine the leak rate and their outcomes. Seven Surgeons (3 Consultant Colorectal surgeons and 4 Senior Colorectal Registrars) were involved in these resections.Results: Of the 348 restorative resections performed during the study period, 6% leaked. In 52%, the leak was classified as major and all of these patients underwent further surgery. Most leaks followed anterior resection and in most patients the anastomoses were below the peritoneal reflection. Among the minor leaks, four of the patients were defunctioned primarily. Mortality rate among patients with major leaks remain significantly high.Conclusion: Anastomotic dehiscence remains a significant problem. Although blood supply, nutritional factors, the level of anastomosis and the experience of the surgeon are perhaps the two most important factors that determine the outcome of anastomosi

Very long term height and weight recovery after childhood liver transplantation

Aims: Artificial neural networks (ANN) are computer programs used to identify complexrelations within data that cannot be detected with conventional linear-statistical analysis.The routine clinical predictions of need for lower gastrointestinal endoscopy have beenbased on population statistics with little meaning for individual patient. This results in largenumber of unnecessary colonoscopies. We aimed to develop a neural network algorithmwhich can accurately predict presence of pathology in patients attending routine outpatientclinics. Methods: 300 patients undergoing lower gastrointestinal endoscopy prospectivelycompleted a specifically developed questionnaire which included 40 variables based onclinical symptoms, signs, past and family history. Complete data sets of 50 percent of serieswere used to train the artificial neural network; the remaining 50 percent were used forinternal validation. The primary output used was a positive finding on the colonoscopy,including polyps, cancer, diverticular disease or colitis. Results: The outcome and pathologyreports of all the patients were obtained and assessed. Clear correlation between actual datavalue and artificial neural network value were found (r = 0.931; P = 0.0001). The predictiveaccuracy of neural network was 95% in the training group and was 89% (95% CI 84-96)in the validation set. This accuracy was significantly higher than the clinical accuracy (69%).Conclusions: We have shown that ANN is more accurate than standard statistics whenapplied to prediction in individual patients of need for lower gastrointestinal endoscopy.These results have obvious implications, with at least 20% resultant decrease in need forunnecessary lower gastrointestinal endoscopy. The logistic and economic impact with thisdevelopment is tremendous

Neural network analysis of anal sphincter repair

PURPOSE: Prediction of success after anterior sphincter repair for incontinence is difficult. Standard multivariate analysis techniques have only 75 to 80 percent accuracy. Artificial intelligence, including artificial neural networks, has been used in the analysis of complex clinical data and has proved to be successful in predicting the outcome of other surgical procedures. Using a neural network algorithm, we have assessed the probability of success after anterior sphincter repair. METHODS: Prospective anorectal physiology data of 72 patients undergoing anterior sphincter repair was collected between 1995 and 1999. Complete data sets of 75 percent of the series were used to train an artificial neural network; the remaining 25 percent were used for data validation. The output was continence grading, ranging from 0 to 4 (worse to continent). RESULTS: The outcome at 3, 6, and 12 months postoperatively was obtained and assessed. The best correlation between actual data value and artificial neural network value was found at 12 months (r = 0.931; P = 0.0001). Clear correlations also were found at three months (r = 0.898; P = 0.0001) and six months (r = 0.742; P = 0.002). Results of applying a net to details excluding pudendal nerve latency were poor. CONCLUSIONS: Artificial neural networks are more accurate (93 percent correlation) than standard statistics (75 percent) when applied to the prediction of outcome after anterior sphincter repair. This assessment also confirms the usefulness of pudendal latency in the prediction of anterior sphincter repair outcome. The results obtained highlight the obvious usefulness of artificial neural networks, which could now be used in a prospective evaluation for application of the technique