G.J. Postma

Triple-negative breast cancer: Present challenges and new perspectives

Triple‐negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined ‘omics’ approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi‐dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

Selecting a representative training set for the classification of demolition waste using remote NIR sensing

Abstract In the AUTOSORT project, the goal is the separation of demolition waste in three fractions: wood, plastics and stone. A remote near-infrared sensor measures reduced reflectance spectra (mini-spectra) of objects. Linear discriminant analysis (LDA) is used for the classification of these spectra. To obtain the LDA model, a representative training set is needed. New LDA-models will be regularly needed for recalibrations. Small training sets will save a lot of labour and additional costs. Two object selection methods are investigated: the Kennard–Stone algorithm and a statistical test procedure. Training sets are acquired from which the mini-spectra are used to obtain LDA models. In the training sets, the object amounts and their ratios are varied. Two object ratios are applied: the ratios as they occur in the complete data set and the equalised ratios. The Kennard–Stone selection algorithm is the preferred method. It gives a unique list of objects, mainly sampled at the cluster borders: partial cluster overlap is better defined. This is in contradiction with the sets of objects, accepted by the statistical test procedure: those objects tend to occur around the fraction means. This is a drawback for the classification performance: some accepted training sets are unacceptable. The ratios between the fraction amounts are not important, but equal fraction amounts are preferred. Selecting 25 objects for each fraction should be suitable.

Alignment of high resolution magic angle spinning magnetic resonance spectra using warping methods

The peaks of magnetic resonance (MR) spectra can be shifted due to variations in physiological and experimental conditions, and correcting for misaligned peaks is an important part of data processing prior to multivariate analysis. In this paper, five warping algorithms (icoshift, COW, fastpa, VPdtw and PTW) are compared for their feasibility in aligning spectral peaks in three sets of high resolution magic angle spinning (HR-MAS) MR spectra with different degrees of misalignments, and their merits are discussed. In addition, extraction of information that might be present in the shifts is examined, both for simulated data and the real MR spectra. The generic evaluation methodology employs a number of frequently used quality criteria for evaluation of the alignments, together with PLS-DA to assess the influence of alignment on the classification outcome. Peak alignment greatly improved the internal similarity of the data sets. Especially icoshift and COW seem suitable for aligning HR-MAS MR spectra, possibly because they perform alignment segment-wise. The choice of reference spectrum can influence the alignment result, and it is advisable to test several references. Information from the peak shifts was extracted, and in one case cancer samples were successfully discriminated from normal tissue based on shift information only. Based on these findings, general recommendations for alignment of HR-MAS MRS data are presented. Where possible, observations are generalized to other data types (e.g. chromatographic data).

Visualization and Recovery of the (Bio)chemical Interesting Variables in Data Analysis with Support Vector Machine Classification

Support vector machines (SVMs) have become a popular technique in the chemometrics and bioinformatics field, and other fields, for the classification of complex data sets. Especially because SVMs are able to model nonlinear relationships, the usage of this technique has increased substantially. This modeling is obtained by mapping the data in a higher-dimensional feature space. The disadvantage of such a transformation is, however, that information about the contribution of the original variables in the classification is lost. In this paper we introduce an innovative method which can retrieve the information about the variables of complex data sets. We apply the proposed method to several benchmark data sets and a metabolomics data set to illustrate that we can determine the contribution of the original variables in SVM classifications. The corresponding visualization of the contribution of the variables can assist in a better understanding of the underlying chemical or biological process.

Feasibility of MR Metabolomics for Immediate Analysis of Resection Margins during Breast Cancer Surgery

In this study, the feasibility of high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) of small tissue biopsies to distinguish between tumor and non-involved adjacent tissue was investigated. With the current methods, delineation of the tumor borders during breast cancer surgery is a challenging task for the surgeon, and a significant number of re-surgeries occur. We analyzed 328 tissue samples from 228 breast cancer patients using HR MAS MRS. Partial least squares discriminant analysis (PLS-DA) was applied to discriminate between tumor and non-involved adjacent tissue. Using proper double cross validation, high sensitivity and specificity of 91% and 93%, respectively was achieved. Analysis of the loading profiles from both principal component analysis (PCA) and PLS-DA showed the choline-containing metabolites as main biomarkers for tumor content, with phosphocholine being especially high in tumor tissue. Other indicative metabolites include glycine, taurine and glucose. We conclude that metabolic profiling by HR MAS MRS may be a potential method for on-line analysis of resection margins during breast cancer surgery to reduce the number of re-surgeries and risk of local recurrence.

Application of principal component analysis to detect outliers and spectral deviations in near-field surface-enhanced Raman spectra

Abstract A recently developed technique measures near-field surface-enhanced Raman spectra with 100-nm resolution, enabling a fast survey on the sample surface. This technique has two bottlenecks. One is a general problem: signal changes are attributed to either the sample composition or the substrate morphology. Therefore, it is mandatory to detect even small signal changes in order to distinguish between these two effects. Secondly, huge data amounts make the spectrum interpretation tedious. How to find the interesting and important information? To investigate these problems, a sample, containing dye-labeled DNA-fragments that are drop-coated onto a silver island substrate, is measured. The enhanced Raman spectra yield indirect information on the DNA-fragments. The goal of this investigation is to provide a tool that allows a fast and reliable spectral analysis. Is it possible to distinguish local differences in the sample composition and to correlate them with the sample morphology? A general explorative data analyses tool, principal component analysis (PCA), is used for a first investigation. PCA has a useful side-effect: spikes, well-known artifacts, are also detected. After removing these artifacts, PCA facilitated the detection of three neighboring spectra, clearly deviating from the others. Probably, the DNA double-strand unfolded and generated a direct Raman-signal. The automated PCA-procedure gives identical results. It is concluded that a general explorative tool can solve two major difficulties. Application of dedicated chemometrical tools could improve the results. The combination of chemometrics and this new technique is powerful and promising.

Discrimination between Metastasis and Glioblastoma Multiforme Based on Morphometric Analysis of MR Images

BACKGROUND AND PURPOSE: Solitary MET and GBM are difficult to distinguish by using MR imaging. Differentiation is useful before any metastatic work-up or biopsy. Our hypothesis was that MET and GBM tumors differ in morphology. Shape analysis was proposed as an indicator for discriminating these 2 types of brain pathologies. The purpose of this study was to evaluate the accuracy of this approach in the discrimination of GBMs and brain METs. MATERIALS AND METHODS: The dataset consisted of 33 brain MR imaging sets of untreated patients, of which 18 patients were diagnosed as having a GBM and 15 patients, as having solitary metastatic brain tumor. The MR imaging was segmented by using the K-means algorithm. The resulting set of classes (also called “clusters”) represented the variety of tissues observed. A morphology-based approach allowed discrimination of the 2 types of tumors. This approach was validated by a leave-1-patient-out procedure. RESULTS: A method was developed for the discrimination of GBMs and solitary METs. Two masses out of 33 were wrongly classified; the overall results were accurate in 93.9% of the observed cases. CONCLUSIONS: A semiautomated method based on a morphologic analysis was developed. Its application was found to be useful in the discrimination of GBM from solitary MET.

Lactate and glycine-potential mr biomarkers of prognosis in estrogen receptor-positive breast cancers

Breast cancer is a heterogeneous disease with a variable prognosis. Clinical factors provide some information about the prognosis of patients with breast cancer; however, there is a need for additional information to stratify patients for improved and more individualized treatment. The aim of this study was to examine the relationship between the metabolite profiles of breast cancer tissue and 5‐year survival. Biopsies from breast cancer patients (n = 98) were excised during surgery and analyzed by high‐resolution magic angle spinning MRS. The data were analyzed by multivariate principal component analysis and partial least‐squares discriminant analysis, and the findings of important metabolites were confirmed by spectral integration of the metabolite peaks. Predictions of 5‐year survival using metabolite profiles were compared with predictions using clinical parameters. Based on the metabolite profiles, patients with estrogen receptor (ER)‐positive breast cancer (n = 71) were separated into two groups with significantly different survival rates (p = 0.024). Higher levels of glycine and lactate were found to be associated with lower survival rates by both multivariate analyses and spectral integration, and are suggested as biomarkers for breast cancer prognosis. Similar metabolic differences were not observed for ER‐negative patients, where survivors could not be separated from nonsurvivors. Predictions of 5‐year survival of ER‐positive patients using metabolite profiles gave better and more robust results than those using traditional clinical parameters. The results imply that the metabolic state of a tumor may provide additional information concerning breast cancer prognosis. Further studies should be conducted in order to evaluate the role of MR metabolomics as an additional clinical tool for determining the prognosis of patients with breast cancer. Copyright

Opening the kernel of kernel partial least squares and support vector machines

Kernel partial least squares (KPLS) and support vector regression (SVR) have become popular techniques for regression of complex non-linear data sets. The modeling is performed by mapping the data in a higher dimensional feature space through the kernel transformation. The disadvantage of such a transformation is, however, that information about the contribution of the original variables in the regression is lost. In this paper we introduce a method which can retrieve and visualize the contribution of the variables to the regression model and the way the variables contribute to the regression of complex data sets. The method is based on the visualization of trajectories using so-called pseudo samples representing the original variables in the data. We test and illustrate the proposed method to several synthetic and real benchmark data sets. The results show that for linear and non-linear regression models the important variables were identified with corresponding linear or non-linear trajectories. The results were verified by comparing with ordinary PLS regression and by selecting those variables which were indicated as important and rebuilding a model with only those variables.

Statistical methods for improving verification of claims of origin for Italian wines based on stable isotope ratios.

Wine derives its economic value to a large extent from geographical origin, which has a significant impact on the quality of the wine. According to the food legislation, wines can be without geographical origin (table wine) and wines with origin. Wines with origin must have characteristics which are essential due to its region of production and must be produced, processed and prepared, exclusively within that region. The development of fast and reliable analytical methods for the assessment of claims of origin is very important. The current official method is based on the measurement of stable isotope ratios of water and alcohol in wine, which are influenced by climatic factors. The results in this paper are based on 5220 Italian wine samples collected in the period 2000-2010. We evaluate the univariate approach underlying the official method to assess claims of origin and propose several new methods to get better geographical discrimination between samples. It is shown that multivariate methods are superior to univariate approaches in that they show increased sensitivity and specificity. In cases where data are non-normally distributed, an approach based on mixture modelling provides additional improvements.