Zhiyuan Luo

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.

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.

Gene Selection Using Wilcoxon Rank Sum Test and Support Vector Machine for Cancer Classification

Gene selection is an important problem in microarray data processing. A new gene selection method based on Wilcoxon rank sum test and Support Vector Machine (SVM) is proposed in this paper. First, Wilcoxon rank sum test is used to select a subset. Then each selected gene is trained and tested using SVM classifier with linear kernel separately, and genes with high testing accuracy rates are chosen to form the final reduced gene subset. Leave-one-out cross validation (LOOCV) classification results on two datasets: Breast Cancer and ALL/AML leukemia, demonstrate the proposed method can get 100% success rate with the final reduced subset. The selected genes are listed and their expression levels are sketched, which show that the selected genes can make clear separation between two classes.

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.

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 patients 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.

Study of a QCM Dimethyl Methylphosphonate Sensor Based on a ZnO-Modified Nanowire-Structured Manganese Dioxide Film

Sensitive, selective and fast detection of chemical warfare agents is necessary for anti-terrorism purposes. In our search for functional materials sensitive to dimethyl methylphosphonate (DMMP), a simulant of sarin and other toxic organophosphorus compounds, we found that zinc oxide (ZnO) modification potentially enhances the absorption of DMMP on a manganese dioxide (MnO2) surface. The adsorption behavior of DMMP was evaluated through the detection of tiny organophosphonate compounds with quartz crystal microbalance (QCM) sensors coated with ZnO-modified MnO2 nanofibers and pure MnO2 nanofibers. Experimental results indicated that the QCM sensor coated with ZnO-modified nanostructured MnO2 film exhibited much higher sensitivity and better selectivity in comparison with the one coated with pure MnO2 nanofiber film. Therefore, the DMMP sensor developed with this composite nanostructured material should possess excellent selectivity and reasonable sensitivity towards the tiny gaseous DMMP species.

Synthesis of flowerlike nano-SnO2 and a study of its gas sensing response

Flowerlike nanometer scaled tin oxide (nano-SnO2) was synthesized by a novel and green method without annealing. Accordingly, a dimethyl methylphosphonate (DMMP) gas sensor based on a quartz crystal microbalance was fabricated and studied. According to the experimental results, the sensor with the flowerlike nano-SnO2 sensing film working at room temperature exhibited good sensitivity and selectivity as well as a rapid response to DMMP. An environmentally friendly idea to synthesize material in a green way and fabricate sensors to detect harmful or toxic gases was realized.

Reliable Probabilistic Classification and Its Application to Internet Traffic

Many machine learning algorithms have been used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.

A Comparison of Venn Machine with Platt’s Method in Probabilistic Outputs

The main aim of this paper is to compare the results of several methods of prediction with confidence. In particular we compare the results of Venn Machine with Platt’s Method of estimating confidence. The results are presented and discussed.

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.