Autoantibodies to MUC1 glycopeptides cannot be used as a screening assay for early detection of breast, ovarian, lung or pancreatic cancer.

BACKGROUND: Autoantibodies have been detected in sera before diagnosis of cancer leading to interest in their potential as screening/early detection biomarkers. As we have found autoantibodies to MUC1 glycopeptides to be elevated in early-stage breast cancer patients, in this study we analysed these autoantibodies in large population cohorts of sera taken before cancer diagnosis. METHODS: Serum samples from women who subsequently developed breast cancer, and aged-matched controls, were identified from UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) and Guernsey serum banks to formed discovery and validation sets. These were screened on a microarray platform of 60mer MUC1 glycopeptides and recombinant MUC1 containing 16 tandem repeats. Additional case-control sets comprised of women who subsequently developed ovarian, pancreatic and lung cancer were also screened on the arrays. RESULTS: In the discovery (273 cases, 273 controls) and the two validation sets (UKCTOCS 426 cases, 426 controls; Guernsey 303 cases and 606 controls), no differences were found in autoantibody reactivity to MUC1 tandem repeat peptide or glycoforms between cases and controls. Furthermore, no differences were observed between ovarian, pancreatic and lung cancer cases and controls. CONCLUSION: This robust, validated study shows autoantibodies to MUC1 peptide or glycopeptides cannot be used for breast, ovarian, lung or pancreatic cancer screening. This has significant implications for research on the use of MUC1 in cancer detection.

Plant promoter prediction with confidence estimation.

Accurate prediction of promoters is fundamental to understanding gene expression patterns, where confidence estimation is one of the main requirements. Using recently developed transductive confidence machine (TCM) techniques, we developed a new program TSSP-TCM for the prediction of plant promoters that also provides confidence of the prediction. The program was trained on 132 and 104 sequences and tested on 40 and 25 sequences (containing TATA and TATA-less promoters, respectively) with known transcription start sites (TSSs). As negative training samples for TCM learning we used coding and intron sequences of plant genes annotated in the GenBank. In the test set of TATA promoters, the program correctly predicted TSS for 35 out of 40 (87.5%) genes with a median deviation of several base pairs from the true site location. For 25 TATA-less promoters, TSSs were predicted for 21 out of 25 (84%) genes, including 14 cases of 5 bp distance between annotated and predicted TSSs. Using TSSP-TCM program we annotated promoters in the whole Arabidopsis genome. The predicted promoters were in good agreement with the start position of known Arabidopsis mRNAs. Thus, TCM technique has produced a plant-oriented promoter prediction tool of high accuracy. TSSP-TCM program and annotated promoters are available at http://mendel.cs.rhul.ac.uk/mendel.php?topic=fgen.

Statistical modelling in specific case analysis.

In spite of the problems associated with the development of a statistical approach in specific case analysis, the potential of two statistical methods, logistic regression and Bayesian belief networks, has been investigated and found encouraging in the context of a database relating to child murders with a sexual connotation. Continual collaboration between statisticians and detectives would be needed in the collection of the data, the choice and construction of the models, and the interpretation of the results.

Bayesian diagnostic probabilities without assuming independence of symptoms.

The paper describes an application of Bayes' Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the "Simple Bayes" model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the "Simple Bayes" method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

A mathematical model of phytochrome - the receptor of photomorphogenetic processes in plants.

A mathematical model of the phytochrome pigment system is considered, the parameters of which are determined by solving the problem of identification on the basis of the dynamic characteristics of the photoreceptor obtained experimentally. The model is based on two photochemical and four dark reactions occurring in the phytochrome system. A study of the model permitted establishing the dynamics of processes in the photoreceptor and determining the relation between the behavior of the active form of phytochrome and the intensity and spectral distribution of light. The results of the investigation allows explaining of the high-energy reaction of photomorphogenesis as a consequence of the dynamics of phytochrome processes.