|Title||The human serum glycan cancer biomarker analysis pipeline|
Biomarkers are biological molecules which indicate a disease phenotype. They are immensely valuable for detection, prognosis and treatment of diseases. Biomarkers can also be used for the evaluation of treatments leading to new forms of therapeutics. Through the analysis of biomarkers, diseases can be properly diagnosed and new cures discovered. Glycans are promising targets for biomarkers because glycans are often found on cell surfaces and in extracellular matrices making them the first point of contact for cellular interactions. The sources of biomarkers and the tools required to isolate and analyze them present many scientific challenges. The challenges can be grouped into three groups dealing with natural samples, technology limitations, and multidisciplinary requirements. Challenge 1: Natural Biological Samples. Biological diversity is widespread in natural samples making it difficult to isolate effects. In addition, natural samples contain numerous compounds that interfere with the detection of specific molecules or types of molecules. As a result, large, well defined sample sets are required to explore any hypothesis. Studying human samples makes the process even more challenging because human biology is much more complex than lower organisms. The social and political implications of human research make sample collection and in vivo testing challenging. For humans, the best biomarkers are collected in a non-invasive or minimally-invasive manner. Serum, the portion of blood remaining after cells and clotting factors are removed, provides a good medium for biomarker discovery because it contains chemical signatures from all parts of the body and can be collected in a minimally invasive manner. Despite its advantages, material analysis is still complex because a wide range of proteins, metabolites, and other biological molecules secreted into the blood stream. Challenge 2: Technology Limitations. Mass spectrometry is rapidly becoming the tool of choice for biomarker discovery because of its sensitivity, throughput, and amount of relevant information it can provide about the composition, and structure of biological compounds. New developments in ionization sources, ion transmission optics, and detectors in modern mass spectrometers allow for the analysis of a greater diversity of biological molecules. The continually improving accuracy of the instruments provide greater certainty in assignments and quantitation. However, imprecision of the measurements, although very small, can still be large enough to generate significant amounts of false assignments. Addition of tandem mass spectrometry to the analysis can often eliminate false assignments but requires highly trained operators to manually analyze the data. Challenge 3: Multidisciplinary Requirements. Many disciplines are involved in sample collection, biomarker discovery and validation. Briefly, medical doctors, laboratory technicians, analytical chemists, molecular biologists, mass spectrometrists, electrical engineers, mechanical engineers, machinists, ion physicists, computer programmers, and statisticians are involved. The number of scientists and diversity of expertise required for success is immense and is a major barrier to success. Since specialist from each field have a different educational background, approach to problem solving, and a relatively limited scope of focus, inter-field communication is often challenging. Furthermore, vast quantities of financing, laboratory space, and administrative support are required to hold the team together. This dissertation attempts to cover much of the glycan biomarker discovery process pipeline. Chapter I is a background section describing glycan biomarkers and contains a review of how other research groups study glycans using mass spectrometry. Chapter II covers the chemical methods used for glycan enrichment and analysis. It also includes methods for evaluating the performance of an analytical method. Chapter III discusses the development of theoretical retrosynthetic glycan library models that could be used to annotate mass spectra. These models are applied to 46 normal serum samples to establish baseline serum glycan profiles. Chapter IV covers the “Glycolyzer” software used to analyze data and elucidate biomarkers. The Glycolyzer was used to find 25 ovarian cancer biomarkers from a 96 sample data set. Chapter V examines results from a breast cancer data set. Several candidate breast cancer biomarkers were found that could distinguish cancerous patients from healthy control individuals in a small blinded sample set. The conclusion section compares includes comments on future directions for glycan biomarker discovery.
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