Clinical proteomics in oncology : a passionate dance between science and clinic

Given the natural history of colorectal and breast cancer, early diagnosis appears to be the most appropriate tool to reduce disease-related mortality.[6;7] Currently, there is no early diagnostic test with high sensitivity, specificity and positive predictive value, which can be used as a routine screening tool. Therefore, there is a need for new biomarkers for both types of cancer that can improve early diagnosis, monitoring of disease progression and therapeutic response and detect disease recurrence. Proteomic expression profiles generated with mass spectrometry have been suggested as potential tools for the early diagnosis of cancer and other diseases. Because it is still in its infancy, many problems have to be overcome before clinical proteomics can be transferred form bench to bedside. Chapter 2 gives an insight in the different fields of translational research in colorectal cancer by our group. In chapter 3 reliability of human serum protein profiling using MALDI-TOF mass spectrometry is analysed. We present a pipeline for pre-processing, statistical data analysis and presentation of MALDI-TOF spectra. This novel analysis method was used to assess the effect of variable pre-analytical conditions on human serum protein profiles, and their effect on reproducibility. In line with the logistic conditions in a routine clinical setting, the effects of sample handling and storage, and also circadian rhythm factors on the serum protein profiles were analysed. In chapter 4 and 5 the feasibility of mass spectrometry based protein profiling for the discrimination of colorectal cancer patients from healthy individuals was assessed. In addition to standardizing technical factors and biological variations, we performed blinded tests and employed a randomised block design experimentation to minimize impact of potential confounding factors and to avoid bias. Especially, validation of our classifier, as a possible pitfall, was given much attention. Therefore, we performed a linear discriminant analysis with double cross-validation to separate cancer patients from healthy subjects. Chapter 6 reports on results from an identical designed protein profiling study for the detection of breast cancer. In chapter 7 a first validated study on the detection of breast cancer based on mass spectrometry generated protein profiles is described. In this study the same randomised blocked design and double cross validation is used, however the classifier was validated in an independent set of new patients and controls. Finally, the results and conclusions of all above mentioned studies and especially the current status of clinical proteomics in cancer are discussed in chapter 8. A Dutch summary of this thesis is written in chapter 9.LEI Universiteit LeidenChirurgische oncologi

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.</p> <p>Results</p> <p>In this paper, we describe the first systematic comparison of a wide range of normalisation methods, using two objectives that should be met by a good method. These objectives are minimisation of inter-spectra variance and maximisation of signal with respect to class separation. The former is assessed using an estimation of the coefficient of variation, the latter using the classification performance of three types of classifiers on real-world datasets representing two-class diagnostic problems. To obtain a maximally robust evaluation of a normalisation method, both objectives are evaluated over multiple datasets and multiple configurations of baseline correction and peak detection methods. Results are assessed for statistical significance and visualised to reveal the performance of each normalisation method, in particular with respect to using no normalisation. The normalisation methods described have been implemented in the freely available MASDA R-package.</p> <p>Conclusion</p> <p>In the general case, normalisation of mass spectra is beneficial to the quality of data. The majority of methods we compared performed significantly better than the case in which no normalisation was used. We have shown that normalisation methods that scale spectra by a factor based on the dispersion (e.g., standard deviation) of the data clearly outperform those where a factor based on the central location (e.g., mean) is used. Additional improvements in performance are obtained when these factors are estimated locally, using a sliding window within spectra, instead of globally, over full spectra. The underperforming category of methods using a globally estimated factor based on the central location of the data includes the method used by the majority of SELDI users.</p

On the combination of omics data for prediction of binary outcomes

Enrichment of predictive models with new biomolecular markers is an important task in high-dimensional omic applications. Increasingly, clinical studies include several sets of such omics markers available for each patient, measuring different levels of biological variation. As a result, one of the main challenges in predictive research is the integration of different sources of omic biomarkers for the prediction of health traits. We review several approaches for the combination of omic markers in the context of binary outcome prediction, all based on double cross-validation and regularized regression models. We evaluate their performance in terms of calibration and discrimination and we compare their performance with respect to single-omic source predictions. We illustrate the methods through the analysis of two real datasets. On the one hand, we consider the combination of two fractions of proteomic mass spectrometry for the calibration of a diagnostic rule for the detection of early-stage breast cancer. On the other hand, we consider transcriptomics and metabolomics as predictors of obesity using data from the Dietary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) study, a population-based cohort, from Finland

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

Hospital Teaching Status and Patients' Outcomes After Colon Cancer Surgery

Analysis and support of clinical decision makin

On the use of double cross-validation for the combination of proteomic mass spectral data for enhanced diagnosis and prediction

International audienceWe consider a proteomic mass spectrometry case-control study for the calibration of a diagnostic rule for the detection of early-stage breast cancer. For each patient, a pair of two distinct mass spectra is recorded, each of which derived from a different prior fractionation procedure on the available patient serum. We propose a procedure to combine the distinct spectral expressions from patients for the calibration of a diagnostic discriminant rule. This is achieved by first calibrating two distinct prediction rules separately, each of which on only one of the two available spectral data sources. A double cross-validatory approach is used to summarize the available spectral data using the two classifiers to posterior class probabilities, on which a combined predictor can be calibrated

Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study

Cervical cancer is still an important issue of public health since it is the fourth most frequent type of cancer in women worldwide. Much effort has been dedicated to combating this cancer, in particular by the early detection of cervical pre-cancerous lesions. For this purpose, this paper reports the use of mass spectrometry coupled with multivariate analysis as an untargeted lipidomic approach to classifying 76 blood plasma samples into negative for intraepithelial lesion or malignancy (NILM, n = 42) and squamous intraepithelial lesion (SIL, n = 34). The crude lipid extract was directly analyzed with mass spectrometry for untargeted lipidomics, followed by multivariate analysis based on the principal component analysis (PCA) and genetic algorithm (GA) with support vector machines (SVM), linear (LDA) and quadratic (QDA) discriminant analysis. PCA-SVM models outperformed LDA and QDA results, achieving sensitivity and specificity values of 80.0% and 83.3%, respectively. Five types of lipids contributing to the distinction between NILM and SIL classes were identified, including prostaglandins, phospholipids, and sphingolipids for the former condition and Tetranor-PGFM and hydroperoxide lipid for the latter. These findings highlight the potentiality of using mass spectrometry associated with chemometrics to discriminate between healthy women and those suffering from cervical pre-cancerous lesions

Genomic and oncoproteomic advances in detection and treatment of colorectal cancer

<p>Abstract</p> <p>Aims</p> <p>We will examine the latest advances in genomic and proteomic laboratory technology. Through an extensive literature review we aim to critically appraise those studies which have utilized these latest technologies and ascertain their potential to identify clinically useful biomarkers.</p> <p>Methods</p> <p>An extensive review of the literature was carried out in both online medical journals and through the Royal College of Surgeons in Ireland library.</p> <p>Results</p> <p>Laboratory technology has advanced in the fields of genomics and oncoproteomics. Gene expression profiling with DNA microarray technology has allowed us to begin genetic profiling of colorectal cancer tissue. The response to chemotherapy can differ amongst individual tumors. For the first time researchers have begun to isolate and identify the genes responsible. New laboratory techniques allow us to isolate proteins preferentially expressed in colorectal cancer tissue. This could potentially lead to identification of a clinically useful protein biomarker in colorectal cancer screening and treatment.</p> <p>Conclusion</p> <p>If a set of discriminating genes could be used for characterization and prediction of chemotherapeutic response, an individualized tailored therapeutic regime could become the standard of care for those undergoing systemic treatment for colorectal cancer. New laboratory techniques of protein identification may eventually allow identification of a clinically useful biomarker that could be used for screening and treatment. At present however, both expression of different gene signatures and isolation of various protein peaks has been limited by study size. Independent multi-centre correlation of results with larger sample sizes is needed to allow translation into clinical practice.</p

Expression of the phosphorylated MEK5 protein is associated with TNM staging of colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Activation of MEK5 in many cancers is associated with carcinogenesis through aberrant cell proliferation. In this study, we determined the level of phosphorylated MEK5 (pMEK5) expression in human colorectal cancer (CRC) tissues and correlated it with clinicopathologic data.</p> <p>Methods</p> <p>pMEK5 expression was examined by immunohistochemistry in a tissue microarray (TMA) containing 335 clinicopathologic characterized CRC cases and 80 cases of nontumor colorectal tissues. pMEK5 expression of 19 cases of primary CRC lesions and paired with normal mucosa was examined by Western blotting. The relationship between pMEK5 expression in CRC and clinicopathologic parameters, and the association of pMEK5 expression with CRC survival were analyzed respectively.</p> <p>Results</p> <p>pMEK5 expression was significantly higher in CRC tissues (185 out of 335, 55.2%) than in normal tissues (6 out of 80, 7.5%; <it>P </it>< 0.001). Western blotting demonstrated that pMEK5 expression was upregulated in 12 of 19 CRC tissues (62.1%) compared to the corresponding adjacent nontumor colorectal tissues. Overexpression of pMEK5 in CRC tissues was significantly correlated to the depth of invasion (<it>P </it>= 0.001), lymph node metastasis (<it>P </it>< 0.001), distant metastasis (<it>P </it>< 0.001) and high preoperative CEA level (<it>P </it>< 0.001). Consistently, the pMEK5 level in CRC tissues was increased following stage progression of the disease (<it>P </it>< 0.001). Analysis of the survival curves showed a significantly worse 5-year disease-free (<it>P </it>= 0.002) and 5-year overall survival rate (<it>P </it>< 0.001) for patients whose tumors overexpressed pMEK5. However, in multivariate analysis, pMEK5 was not an independent prognostic factor for CRC (DFS: <it>P </it>= 0.139; OS: <it>P </it>= 0.071).</p> <p>Conclusions</p> <p>pMEK5 expression is correlated with the staging of CRC and its expression might be helpful to the TNM staging system of CRC.</p