New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

Chapter 4
(Bio-)Medical Applications
Contents
4.1 Data Used . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2 Statistical Remarks . . . . . . . . . . . . . . . . . . . 68
4.3 Study Results . . . . . . . . . . . . . . . . . . . . . . . 78
4.4 Identification of Proteomic Fingerprints in Blood
Serum by High-sensitive Bioinformatic Analysis of
SELDI-TOF MS Data for Detection of Testicular
Germ Cell Cancer . . . . . . . . . . . . . . . . . . . . 90
4.5 Identification of Proteomic Fingerprints in Blood
Serum by High-sensitive Bioinformatic Analysis of
MALDI-TOF MS Data for Detection of Thyroid
Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.6 Biological Applications . . . . . . . . . . . . . . . . . 101
This chapter describes the range of applications the algorithms introduced
in the previous chapter can be applied in and pinpoint some common pitfalls
when interpreting the results. Further, we described the dataset we used
throughout this thesis and for the development and benchmarking of our methods.
4.1 Data Used
This section describes the biological data we are using in this thesis to perform
our experiments and check our algorithms. Since the actual data we are using
is derived from biological samples (blood) we also give some background to
what must be taken care of when using such material.
4.1.1 Some Remarks on Blood
During the previous years in proteomics driven cancer research much emphasis
has been given to blood analysis. Usually, plasma or serum data from cancer
patients was compared against samples from matching healthy subjects to detect
differences in the proteome. Many interesting results have been obtained
by the evaluation of MS profiles, as the following examples show:
� (Zhang et al., 2004) identified and validated three early-stage ovarian
cancer biomarkers through MS analysis of the serum proteome. The
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