Autoantibodies to aberrantly glycosylated MUC1 in early stage breast cancer are associated with a better prognosis.

INTRODUCTION: Detection of serum biomarkers for early diagnosis of breast cancer remains an important goal. Changes in the structure of O-linked glycans occur in all breast cancers resulting in the expression of glycoproteins that are antigenically distinct. Indeed, the serum assay widely used for monitoring disease progression in breast cancer (CA15.3), detects a glycoprotein (MUC1), but elevated levels of the antigen cannot be detected in early stage patients. However, since the immune system acts to amplify the antigenic signal, antibodies can be detected in sera long before the antigen. We have exploited the change in O-glycosylation to measure autoantibody responses to cancer-associated glycoforms of MUC1 in sera from early stage breast cancer patients. METHODS: We used a microarray platform of 60mer MUC1 glycopeptides, to confirm the presence of autoantibodies to cancer associated glycoforms of MUC1 in a proportion of early breast cancer patients (54/198). Five positive sera were selected for detailed definition of the reactive epitopes using on chip glycosylation technology and a panel of glycopeptides based on a single MUC1 tandem repeat carrying specific glycans at specific sites. Based on these results, larger amounts of an extended repertoire of defined MUC1 glycopeptides were synthesised, printed on microarrays, and screened with sera from a large cohort of breast cancer patients (n = 395), patients with benign breast disease (n = 108) and healthy controls (n = 99). All sera were collected in the 1970s and 1980s and complete clinical follow-up of breast cancer patients is available. RESULTS: The presence and level of autoantibodies was significantly higher in the sera from cancer patients compared with the controls, and a highly significant correlation with age was observed. High levels of a subset of autoantibodies to the core3MUC1 (GlcNAcß1-3GalNAc-MUC1) and STnMUC1 (NeuAcα2,6GalNAc-MUC1) glycoforms were significantly associated with reduced incidence and increased time to metastasis. CONCLUSIONS: Autoantibodies to specific cancer associated glycoforms of MUC1 are found more frequently and at higher levels in early stage breast cancer patients than in women with benign breast disease or healthy women. Association of strong antibody response with reduced rate and delay in metastases suggests that autoantibodies can affect disease progression.

Peptides generated ex vivo from serum proteins by tumor-specific exopeptidases are not useful biomarkers in ovarian cancer.

BACKGROUND: The serum peptidome may be a valuable source of diagnostic cancer biomarkers. Previous mass spectrometry (MS) studies have suggested that groups of related peptides discriminatory for different cancer types are generated ex vivo from abundant serum proteins by tumor-specific exopeptidases. We tested 2 complementary serum profiling strategies to see if similar peptides could be found that discriminate ovarian cancer from benign cases and healthy controls. METHODS: We subjected identically collected and processed serum samples from healthy volunteers and patients to automated polypeptide extraction on octadecylsilane-coated magnetic beads and separately on ZipTips before MALDI-TOF MS profiling at 2 centers. The 2 platforms were compared and case control profiling data analyzed to find altered MS peak intensities. We tested models built from training datasets for both methods for their ability to classify a blinded test set. RESULTS: Both profiling platforms had CVs of approximately 15% and could be applied for high-throughput analysis of clinical samples. The 2 methods generated overlapping peptide profiles, with some differences in peak intensity in different mass regions. In cross-validation, models from training data gave diagnostic accuracies up to 87% for discriminating malignant ovarian cancer from healthy controls and up to 81% for discriminating malignant from benign samples. Diagnostic accuracies up to 71% (malignant vs healthy) and up to 65% (malignant vs benign) were obtained when the models were validated on the blinded test set. CONCLUSIONS: For ovarian cancer, altered MALDI-TOF MS peptide profiles alone cannot be used for accurate diagnoses.

Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling.

OBJECTIVES: Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/ionization (MALDI) time-of-flight profiling could improve diagnostic performance. MATERIALS AND METHODS: Serum samples from women with ovarian neoplasms and healthy volunteers were subjected to CA125 assay and MALDI time-of-flight mass spectrometry (MS) profiling. Models were built from training data sets using discriminatory MALDI MS peaks in combination with CA125 values and tested their ability to classify blinded test samples. These were compared with models using CA125 threshold levels from 193 patients with ovarian cancer, 290 with benign neoplasm, and 2236 postmenopausal healthy controls. RESULTS: Using a CA125 cutoff of 30 U/mL, an overall sensitivity of 94.8% (96.6% specificity) was obtained when comparing malignancies versus healthy postmenopausal controls, whereas a cutoff of 65 U/mL provided a sensitivity of 83.9% (99.6% specificity). High classification accuracies were obtained for early-stage cancers (93.5% sensitivity). Reasons for high accuracies include recruitment bias, restriction to postmenopausal women, and inclusion of only primary invasive epithelial ovarian cancer cases. The combination of MS profiling information with CA125 did not significantly improve the specificity/accuracy compared with classifications on the basis of CA125 alone. CONCLUSIONS: We report unexpectedly good performance of serum CA125 using threshold classification in discriminating healthy controls and women with benign masses from those with invasive ovarian cancer. This highlights the dependence of diagnostic tests on the characteristics of the study population and the crucial need for authors to provide sufficient relevant details to allow comparison. Our study also shows that MS profiling information adds little to diagnostic accuracy. This finding is in contrast with other reports and shows the limitations of serum MS profiling for biomarker discovery and as a diagnostic tool.

Clinical mass spectrometry proteomic diagnosis by conformal predictors.

The paper describes an application of conformal predictors to diagnose breast cancer using proteomic mass spectrometry data provided by Leiden University Medical Center. Unlike many conventional classification systems, this approach allows us not just to classify samples, but add valid measures of confidence in our predictions for individual patients.

Predicting clinical outcome in patients diagnosed with synchronous ovarian and endometrial cancer.

PURPOSE: Patients with synchronous ovarian and endometrial cancers may represent cases of a single primary tumor with metastasis (SPM) or dual primary tumors (DP). The diagnosis given will influence the patient's treatment and prognosis. Currently, a diagnosis of SPM or DP is made using histologic criteria, which are frequently unable to make a definitive diagnosis. EXPERIMENTAL DESIGN: In this study, we used genetic profiling to make a genetic diagnosis of SPM or DP in 90 patients with synchronous ovarian/endometrial cancers. We compared genetic diagnoses in these patients with the original histologic diagnoses and evaluated the clinical outcome in this series of patients based on their diagnoses. RESULTS: Combining genetic and histologic approaches, we were able make a diagnosis in 88 of 90 cases, whereas histology alone was able to make a diagnosis in only 64 cases. Patients diagnosed with SPM had a significantly worse survival than patients with DP (P = 0.002). Patients in which both tumors were of endometrioid histology survived longer than patients of other histologic subtypes (P = 0.025), and patients diagnosed with SPM had a worse survival if the mode of spread was from ovary to endometrium rather than from endometrium to ovary (P = 0.019). CONCLUSIONS: Genetic analysis may represent a powerful tool for use in clinical practice for distinguishing between SPM and DP in patients with synchronous ovarian/endometrial cancer and predicting disease outcome. The data also suggest a hitherto uncharacterized level of heterogeneity in these cases, which, if accurately defined, could lead to improved treatment and survival.

PlantProm: a database of plant promoter sequences.

PlantProm DB, a plant promoter database, is an annotated, non-redundant collection of proximal promoter sequences for RNA polymerase II with experimentally determined transcription start site(s), TSS, from various plant species. The first release (2002.01) of PlantProm DB contains 305 entries including 71, 220 and 14 promoters from monocot, dicot and other plants, respectively. It provides DNA sequence of the promoter regions (-200 : +51) with TSS on the fixed position +201, taxonomic/promoter type classification of promoters and Nucleotide Frequency Matrices (NFM) for promoter elements: TATA-box, CCAAT-box and TSS-motif (Inr). Analysis of TSS-motifs revealed that their composition is different in dicots and monocots, as well as for TATA and TATA-less promoters. The database serves as learning set in developing plant promoter prediction programs. One such program (TSSP) based on discriminant analysis has been created by Softberry Inc. and the application of a support ftp: vector machine approach for promoter identification is under development. PlantProm DB is available at http://mendel.cs.rhul.ac.uk/ and http://www.softberry.com/.

Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines.

We focus on the problem of prediction with confidence and describe a recently developed learning algorithm called transductive confidence machine for making qualified region predictions. Its main advantage, in comparison with other classifiers, is that it is well-calibrated, with number of prediction errors strictly controlled by a given predefined confidence level. We apply the transductive confidence machine to the problems of acute leukaemia and ovarian cancer prediction using microarray and proteomics pattern diagnostics, respectively. We demonstrate that the algorithm performs well, yielding well-calibrated and informative predictions whilst maintaining a high level of accuracy.

Determining confidence of predicted interactions between HIV-1 and human proteins using conformal method.

Identifying protein-protein interactions (PPI's) is critical for understanding virtually all cellular molecular mechanisms. Previously, predicting PPI's was treated as a binary classification task and has commonly been solved in a supervised setting which requires a positive labeled set of known PPI's and a negative labeled set of non-interacting protein pairs. In those methods, the learner provides the likelihood of the predicted interaction, but without a confidence level associated with each prediction. Here, we apply a conformal prediction framework to make predictions and estimate confidence of the predictions. The conformal predictor uses a function measuring relative 'strangeness' interacting pairs to check whether prediction of a new example added to the sequence of already known PPI's would conform to the 'exchangeability' assumption: distribution of interacting pairs is invariant with any permutations of the pairs. In fact, this is the only assumption we make about the data. Another advantage is that the user can control a number of errors by providing a desirable confidence level. This feature of CP is very useful for a ranking list of possible interactive pairs. In this paper, the conformal method has been developed to deal with just one class - class interactive proteins - while there is not clearly defined of 'non-interactive'pairs. The confidence level helps the biologist in the interpretation of the results, and better assists the choices of pairs for experimental validation. We apply the proposed conformal framework to improve the identification of interacting pairs between HIV-1 and human proteins.

Reliable confidence measures for medical diagnosis with evolutionary algorithms.

Conformal Predictors (CPs) are machine learning algorithms that can provide predictions complemented with valid confidence measures. In medical diagnosis, such measures are highly desirable, as medical experts can gain additional information for each machine diagnosis. A risk assessment in each prediction can play an important role for medical decision making, in which the outcome can be critical for the patients. Several classical machine learning methods can be incorporated into the CP framework. In this paper, we propose a CP that makes use of evolved rule sets generated by a genetic algorithm (GA). The rule-based GA has the advantage of being human readable. We apply our method on two real-world datasets for medical diagnosis, one dataset for breast cancer diagnosis, which contains data gathered from fine needle aspirate of breast mass; and one dataset for ovarian cancer diagnosis, which contains proteomic patterns identified in serum. Our results on both datasets show that the proposed method is as accurate as the classical techniques, while it provides reliable and useful confidence measures.

Early detection of ovarian cancer in samples pre-diagnosis using CA125 and MALDI-MS peaks.

AIM: A nested case-control discovery study was undertaken to test whether information within the serum peptidome can improve on the utility of CA125 for early ovarian cancer detection. MATERIALS AND METHODS: High-throughput matrix-assisted laser desorption ionisation mass spectrometry (MALDI-MS) was used to profile 295 serum samples from women pre-dating their ovarian cancer diagnosis and from 585 matched control samples. Classification rules incorporating CA125 and MS peak intensities were tested for discriminating ability. RESULTS: Two peaks were found which in combination with CA125 discriminated cases from controls up to 15 and 11 months before diagnosis, respectively, and earlier than using CA125 alone. One peak was identified as connective tissue-activating peptide III (CTAPIII), whilst the other was putatively identified as platelet factor 4 (PF4). ELISA data supported the down-regulation of PF4 in early cancer cases. CONCLUSION: Serum peptide information with CA125 improves lead time for early detection of ovarian cancer. The candidate markers are platelet-derived chemokines, suggesting a link between platelet function and tumour development.

Preanalytic influence of sample handling on SELDI-TOF serum protein profiles.

BACKGROUND: High-throughput proteomic methods for disease biomarker discovery in human serum are promising, but concerns exist regarding reproducibility of results and variability introduced by sample handling. This study investigated the influence of different preanalytic handling methods on surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) protein profiles of prefractionated serum. We investigated whether older collections with longer sample transit times yield useful protein profiles, and sought to establish the most feasible collection methods for future clinical proteomic studies. METHODS: To examine the effect of tube type, clotting time, transport/incubation time, temperature, and storage method on protein profiles, we used 6 different handling methods to collect sera from 25 healthy volunteers. We used a high-throughput, prefractionation strategy to generate anion-exchange fractions and examined their protein profiles on CM10, IMAC30-Cu, and H50 arrays by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. RESULTS: Prolonged transport and incubation at room temperature generated low mass peaks, resulting in distinctions among the protocols. The most and least stringent methods gave the lowest overall peak variances, indicating that proteolysis in the latter may have been nearly complete. For samples transported on ice there was little effect of clotting time, storage method, or transit time. Certain proteins (TTR, ApoCI, and transferrin) were unaffected by handling, but others (ITIH4 and hemoglobin beta) displayed significant variability. CONCLUSIONS: Changes in preanalytical handling variables affect profiles of serum proteins, including proposed disease biomarkers. Proteomic analysis of samples from serum banks collected using less stringent protocols is applicable if all samples are handled identically.

Prospective gene expression analysis accurately subtypes acute leukaemia in children and establishes a commonality between hyperdiploidy and t(12;21) in acute lymphoblastic leukaemia.

We have prospectively analysed and correlated the gene expression profiles of children presenting with acute leukaemia to the Royal London and Great Ormond Street Hospitals with morphological diagnosis, immunophenotype and karyotype. Total RNA extracted from freshly sorted blast cells was obtained from 84 lymphoblastic [acute lymphoblastic leukaemia (ALL)], 20 myeloid [acute myeloid leukaemia (AML)] and three unclassified acute leukaemias and hybridised to the high density Affymetrix U133A oligonucleotide array. Analysis of variance and significance analysis of microarrays was used to identify discriminatory genes. A novel 50-gene set accurately identified all patients with ALL and AML and predicted for a diagnosis of AML in three patients with unclassified acute leukaemia. A unique gene set was derived for each of eight subtypes of acute leukaemia within our data set. A common profile for children with ALL with an ETV6-RUNX1 fusion, amplification or deletion of ETV6, amplification of RUNX1 or hyperdiploidy with an additional chromosome 21 was identified. This suggests that these rearrangements share a commonality in biological pathways that maintains the leukaemic state. The gene TERF2 was most highly expressed in this group of patients. Our analyses demonstrate that not only is microarray analysis the single most effective tool for the diagnosis of acute leukaemias of childhood but it has the ability to identify unique biological pathways. To further evaluate its prognostic value it needs to be incorporated into the routine diagnostic analysis for large-scale clinical trials in childhood acute leukaemias.

Sequence alignment kernel for recognition of promoter regions.

UNLABELLED: In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 sigma70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data. AVAILABILITY: The demo version of our method is accessible from our website http://mendel.cs.rhul.ac.uk/