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92 CHAPTER 4. (BIO-)MEDICAL APPLICATIONS The acquired data was then analyzed and processed by our “proteomics.net” pipeline as described in chapter 3 resulting in different sets of fingerprints for the two groups under scrutiny (“cancer” vs. “healthy”). During the analyses 138 peaks in a mass range of 3800-10000 Da where detected that could discriminate between tumor and nontumor serum samples. There was no single peak that could separate the two groups (seminoma vs. control subjects). Five peaks (6.48 kDa, 6.84 kDa, 8.14 kDa, 8.17 kDa, 8.92 kDa) were combined to construct the fingerprint to train classifiers. 4.4.3 Study Results As the classification results show (see next section) this study clearly demonstrates that is is possible to find highly significant differences in protein profiles of cancer vs. control generated by SELDI-TOF mass spectrometry in testicular germ cell cancer patients. The potential value of the SELDI-TOF mass spectrometric pattern is particularly evident in the low molecular weight range. This feature closes the gap between SELDI-TOF mass spectrometry and valuable conventional methods like 2D gel electrophoresis that are not capable of detecting low molecular weight areas, especially in low-abundant proteins and peptides thanks to the newly developed algorithms in this thesis. In our patient cohort, the analysis of MS-generated protein patterns not only yielded a clear assignment to either the cancer or non-cancer cohort but also enabled even beta-human chorionic gonadotropin-negative patients to be correctly distinguished from normal controls. The protein pattern on a CM10 ProteinChip � achieved 80% sensitivity and 70% specificity when classified by decision trees (95% confidence interval of 60.7%-87.1%). Four different peaks with molecular masses of 6.94 kDa, 7.76 kDa, 8.63 kDa, and 8.69 kDa were utilized to build the fingerprint and train the classifiers. In seminoma patients, beta-HCG is currently the only available tumor marker in clinical use, but according to several authors, the overall incidence of beta-HCG secretion is low and dependent on the stage of disease (Butcher et al., 1985; Huyghe, Muller, Mieusset, Bujan, Bachaud, Chevreau, Plante and Thonneau, 2007). Patterns generated by SELDI-TOF MS would significantly facilitate the detection of marker-negative seminomas. In addition, this new proteomic approach would be most beneficial in the therapy monitoring, surveillance and follow up of these patients. 4.4.4 Classification Results Using the fingerprint described above classifier were trained and evaluated by the 10-fold cross-validation schema. These classifier correctly predicted the spectra in 90.4% (decision tree analysis, 95% confidence interval of 82.6%- 95.5%), 80.8% (support vector machines, 95% confidence interval of 71.4%- 88.2%) and 89.3% of the samples (neural networks, 95% confidence interval of 81.3%-94.7%). Decision tree analysis discriminated between seminoma and healthy subjects with a sensitivity of 91.5% and a specificity of 89.4%. Figure 4.4.8 shows the specificity and sensitivity achieved in the tested cohorts by using support vector machines and neural network analysis (CM10 ProteinChip � ). Figure 4.4.9 depicts a representative spectrum of a seminoma sample in a mass range of 4000-10000 Daltons. Figure 4.4.10 shows a decision tree example generated from a CM10 ProteinChip � .
4.4. IDENTIFICATION OF PROTEOMIC FINGERPRINTS IN BLOOD SERUM BY HIGH-SENSITIVE B Figure 4.4.8: Classification of the training set and test sets in 10-fold-stratified cross-validation Figure 4.4.9: Representative spectra of a seminoma sample in a mass range from 4000-10000 Dalton (CM10 ProteinChip � ). Figure 4.4.10: Decision trees generated from a CM10 ProteinChip � . Circles represent splitting nodes containing array type and moleular mass in Dalton. Peak intensity cut off is defined as < x µA (micro ampere) or >/= x µA because of different intensities (ranging between 35,1 µA-39,58 µA). Squares represent decision nodes with class assigned by algorithm (cancer and control).
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New Statistical Algorithms for the
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Contents Acknowledgments . . . . .
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New Statistical Algorithms for the
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Extended Abstract English Version M
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German Version Das Gebiet der Prote
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Chapter 1 Introduction and Survey 1
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1.2. GOALS, OBJECTIVES AND TASKS 7
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1.2. GOALS, OBJECTIVES AND TASKS 9
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Chapter 2 Preliminaries 2.1 Topic O
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2.1. TOPIC OVERVIEW 13 Figure 2.1.1
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2.1. TOPIC OVERVIEW 15 completeness
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2.2. AN EXAMPLE 17 (a) Opera A (b)
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2.2. AN EXAMPLE 19 Figure 2.2.6: Tw
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2.2. AN EXAMPLE 21 successes. We ca
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Chapter 3 Mathematical Modeling and
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3.2. INTRODUCTION TO MALDI TOF MS 2
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3.3. PREPROCESSING 31 mix (external
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3.3. PREPROCESSING 33 Figure 3.3.5:
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3.3. PREPROCESSING 35 Figure 3.3.7:
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3.4. HIGHLY SENSITIVE PEAK DETECTIO
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3.4. HIGHLY SENSITIVE PEAK DETECTIO
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Page 63 and 64: 3.8. EXTRACTING FINGERPRINTS 57 Fig
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Page 69 and 70: 3.9. COMPLEXITY ANALYSIS 63 3.9 Com
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Page 73 and 74: 4.1. DATA USED 67 4.1.2 Serum Data
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Page 79 and 80: 4.2. STATISTICAL REMARKS 73 � Fir
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Page 85 and 86: 4.3. STUDY RESULTS 79 Figure 4.3.3:
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Page 89 and 90: 4.3. STUDY RESULTS 83 � kNN (gen.
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5.6. QAD GRID WORKFLOWS 143 Service
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5.6. QAD GRID WORKFLOWS 145 Figure
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5.7. RELATED WORK 147 to set-up sys
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5.7. RELATED WORK 149 Table 5.7.1 -
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Chapter 6 proteomics.net - Product-
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6.2. CASE STUDIES 153 6.2 Case Stud
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6.2. CASE STUDIES 155 Figure 6.2.2:
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6.2. CASE STUDIES 157 MASCOT and SE
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6.2. CASE STUDIES 159 The peak pick
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6.2. CASE STUDIES 161 first entry i
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6.2. CASE STUDIES 163 Approach Base
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6.2. CASE STUDIES 165 Figure 6.2.5:
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Chapter 7 Related Work In this chap
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Chapter 8 Conclusion and Future Dir
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8.3. FROM BIOMARKERS TO BIOPRINTS 1
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Appendix A Implementation Details T
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Appendix B Curriculum Vitae Name Ti
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References Aebersold, R. and Mann,
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REFERENCES 179 Breiman, L. (2001).
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REFERENCES 181 Downard, K. M. and M
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REFERENCES 183 Gillette, M. A., Man
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REFERENCES 185 Huyghe, E., Muller,
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REFERENCES 187 Kuijpens, J. L. P.,
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REFERENCES 189 McLachlan, S. M. and
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REFERENCES 191 Platt, J. C. (1999).
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REFERENCES 193 Stone, M. (1974). Cr
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REFERENCES 195 Washburn, M. P., Wol
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