RNA biomarkers from proximal liquid biopsy for diagnosis of ovarian cancer.

BACKGROUND: Most ovarian cancer patients are diagnosed at an advanced stage and have a high mortality rate. Current screening strategies fail to improve prognosis because markers that are sensitive for early stage disease are lacking. This medical need justifies the search for novel approaches using utero-tubal lavage as a proximal liquid biopsy. METHODS: In this study, we explore the extracellular transcriptome of utero-tubal lavage fluid obtained from 26 ovarian cancer patients and 48 controls using messenger RNA (mRNA) capture and small RNA sequencing. RESULTS: We observed an enrichment of ovarian and fallopian tube specific messenger RNAs in utero-tubal lavage fluid compared to other human biofluids. Over 300 mRNAs and 41 miRNAs were upregulated in ovarian cancer samples compared with controls. Upregulated genes were enriched for genes involved in cell cycle activation and proliferation, hinting at a tumor-derived signal. CONCLUSION: This is a proof-of-principle that mRNA capture sequencing of utero-tubal lavage fluid is technically feasible, and that the extracellular transcriptome of utero-tubal lavage should be further explored in larger cohorts to assess the diagnostic value of the biomarkers identified in this study. IMPACT: Proximal liquid biopsy from the gynecologic tract is a promising source for mRNA and miRNA biomarkers for diagnosis of early-stage ovarian cancer.

Pooled variable scaling for cluster analysis.

MOTIVATION: Many popular clustering methods are not scale-invariant because they are based on Euclidean distances. Even methods using scale-invariant distances, such as the Mahalanobis distance, lose their scale invariance when combined with regularization and/or variable selection. Therefore, the results from these methods are very sensitive to the measurement units of the clustering variables. A simple way to achieve scale invariance is to scale the variables before clustering. However, scaling variables is a very delicate issue in cluster analysis: A bad choice of scaling can adversely affect the clustering results. On the other hand, reporting clustering results that depend on measurement units is not satisfactory. Hence, a safe and efficient scaling procedure is needed for applications in bioinformatics and medical sciences research. RESULTS: We propose a new approach for scaling prior to cluster analysis based on the concept of pooled variance. Unlike available scaling procedures, such as the SD and the range, our proposed scale avoids dampening the beneficial effect of informative clustering variables. We confirm through an extensive simulation study and applications to well-known real-data examples that the proposed scaling method is safe and generally useful. Finally, we use our approach to cluster a high-dimensional genomic dataset consisting of gene expression data for several specimens of breast cancer cells tissue obtained from human patients. AVAILABILITY AND IMPLEMENTATION: An R-implementation of the algorithms presented is available at https://wis.kuleuven.be/statdatascience/robust/software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploiting Multiple Descriptor Sets in QSAR Studies.

A quantitative structure-activity relationship (QSAR) is a model relating a specific biological response to the chemical structures of compounds. There are many descriptor sets available to characterize chemical structure, raising the question of how to choose among them or how to use all of them for training a QSAR model. Making efficient use of all sets of descriptors is particularly problematic when active compounds are rare among the assay response data. We consider various strategies to make use of the richness of multiple descriptor sets when assay data are poor in active compounds. Comparisons are made using data from four bioassays, each with five sets of molecular descriptors. The recommended method takes all available descriptors from all sets and uses an algorithm to partition them into groups called phalanxes. Distinct statistical models are trained, each based on only the descriptors in one phalanx, and the models are then averaged in an ensemble of models. By giving the descriptors a chance to contribute in different models, the recommended method uses more of the descriptors in model averaging. This results in better ranking of active compounds to identify a shortlist of drug candidates for development.

A natural robustification of the ordinary instrumental variables estimator.

Instrumental variables estimators are designed to provide consistent parameter estimates for linear regression models when some covariates are correlated with the error term. We propose a new robust instrumental variables estimator (RIV) which is a natural robustification of the ordinary instrumental variables estimator (OIV). Specifically, we construct RIV using a robust multivariate location and scatter S-estimator to robustify the solution of the estimating equations that define OIV. RIV is computationally inexpensive and readily available for applications through the R-library riv. It has attractive robustness and asymptotic properties, including high resilience to outliers, bounded influence function, consistency under weak distributional assumptions, asymptotic normality under mild regularity conditions, and equivariance. We further endow RIV with an iterative algorithm which allows for the estimation of models with endogenous continuous covariates and exogenous dummy covariates. We study the performance of RIV when the data contains outliers using an extensive Monte Carlo simulation study and by applying it to a limited-access dataset from the Framingham Heart Study-Cohort to estimate the effect of long-term systolic blood pressure on left atrial size.

Population density and feeding duration of cabbage looper larvae on tomato plants alter the levels of plant volatile emissions.

BACKGROUND: As part of their indirect defense, plants under herbivore attack release volatile chemicals that attract natural enemies of the herbivore. This is a very well-documented phenomenon. However, relatively few studies have investigated the response of plants to different population levels of herbivores and their feeding duration. RESULTS: Working with larvae of the cabbage looper, Trichoplusia ni (Hübner), and tomato plants, Lycopersicon esculentum Mill cv. clarence, and using an ultrafast gas chromatograph (the zNose™) for volatile analyses, the authors studied the effect of larval density and feeding duration on levels of plant volatile emissions. Intense herbivory caused higher emission levels of the herbivore-induced plant volatiles (HIPVs) (Z)-3-hexenyl acetate, (E)-ß-ocimene and ß-caryophyllene than those caused by moderate herbivory. When herbivory had ceased following 12-24 h of larval feeding, plants kept releasing HIPVs at a high level for a longer period of time than they did following only 6 h of larval feeding. The plants' slow adjustment in their volatile emissions following prolonged larval feeding might be strategic, as such feeding is more likely to have ceased just temporarily. CONCLUSION: This information may help in the development of a pest monitoring system that is based on herbivore-induced plant volatiles.

Herbivore-induced plant volatiles allow detection of Trichoplusia ni (Lepidoptera: Noctuidae) infestation on greenhouse tomato plants.

BACKGROUND: Monitoring of insect populations is an important component of integrated pest management and typically is based on the presence and number of insects in various development stages. Yet plants respond to insect herbivory and release herbivore-induced plant volatiles (HIPVs), which could be exploited in monitoring systems. The present objective was to investigate whether the information associated with HIPVs has potential to become part of advanced technologies for monitoring pest insect populations. RESULTS: In a laboratory experiment, it was determined that tomato plants, Lycopersicon esculentum Mill cv. clarence, each infested with 20 caterpillars of the cabbage looper, Trichoplusia ni (Hübner), emit HIPVs, of which (Z)-3-hexenyl acetate, (E)-beta-ocimene and beta-caryophyllene were selected as chemicals indicative of herbivory. Using an ultrafast portable gas chromatograph (zNose()) in a research greenhouse and in a commercial greenhouse, it was possible (i) to reveal differential emissions of these three indicator chemicals from plants with or without herbivory, (ii) to detect herbivory within 6 h of its onset, (iii) to track changes in indicator chemical emissions over time and (iv) to study the effect of environmental and crop-maintenance-related factors on the emission of indicator chemicals. CONCLUSION: HIPVs appear to be promising as reliable indicators of plant health, but further studies are needed to fully understand the potential of this concept.

MDQC: a new quality assessment method for microarrays based on quality control reports.

MOTIVATION: The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems. RESULTS: We propose a novel multivariate approach to evaluate the quality of an array that examines the 'Mahalanobis distance' of its quality attributes from those of other arrays. Thus, we call it Mahalanobis Distance Quality Control (MDQC) and examine different approaches of this method. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Using two case studies, we show that a multivariate analysis gives substantially richer information than analyzing each parameter of the QC report in isolation. Moreover, once the QC report is produced, our quality assessment method is computationally inexpensive and the results can be easily visualized and interpreted. Finally, we show that computing these distances on subsets of the quality measures in the report may increase the method's ability to detect unusual arrays and helps to identify possible reasons of the quality problems. AVAILABILITY: The library to implement MDQC will soon be available from Bioconductor.

Dynamic variable selection in SNP genotype autocalling from APEX microarray data.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are DNA sequence variations, occurring when a single nucleotide--adenine (A), thymine (T), cytosine (C) or guanine (G)--is altered. Arguably, SNPs account for more than 90% of human genetic variation. Our laboratory has developed a highly redundant SNP genotyping assay consisting of multiple probes with signals from multiple channels for a single SNP, based on arrayed primer extension (APEX). This mini-sequencing method is a powerful combination of a highly parallel microarray with distinctive Sanger-based dideoxy terminator sequencing chemistry. Using this microarray platform, our current genotype calling system (known as SNP Chart) is capable of calling single SNP genotypes by manual inspection of the APEX data, which is time-consuming and exposed to user subjectivity bias. RESULTS: Using a set of 32 Coriell DNA samples plus three negative PCR controls as a training data set, we have developed a fully-automated genotyping algorithm based on simple linear discriminant analysis (LDA) using dynamic variable selection. The algorithm combines separate analyses based on the multiple probe sets to give a final posterior probability for each candidate genotype. We have tested our algorithm on a completely independent data set of 270 DNA samples, with validated genotypes, from patients admitted to the intensive care unit (ICU) of St. Paul's Hospital (plus one negative PCR control sample). Our method achieves a concordance rate of 98.9% with a 99.6% call rate for a set of 96 SNPs. By adjusting the threshold value for the final posterior probability of the called genotype, the call rate reduces to 94.9% with a higher concordance rate of 99.6%. We also reversed the two independent data sets in their training and testing roles, achieving a concordance rate up to 99.8%. CONCLUSION: The strength of this APEX chemistry-based platform is its unique redundancy having multiple probes for a single SNP. Our model-based genotype calling algorithm captures the redundancy in the system considering all the underlying probe features of a particular SNP, automatically down-weighting any 'bad data' corresponding to image artifacts on the microarray slide or failure of a specific chemistry. In this regard, our method is able to automatically select the probes which work well and reduce the effect of other so-called bad performing probes in a sample-specific manner, for any number of SNPs.