Handbook of cancer models with applications

CONTENTS......Page 6
Contributors......Page 18
Preface......Page 24
1. Introduction......Page 26
2.1. Description of the Data......Page 27
2.2. The Oncogenetic Tree Model......Page 28
3. Reconstruction......Page 30
4. Sample Size Estimation......Page 36
5. Parameter Estimation......Page 38
6. Example: Renal Carcinoma Development......Page 39
7. Properties of the Oncogenetic Tree Estimator: A Simulation Study......Page 40
7.2. Probability of Correct Reconstruction......Page 41
7.3. Sample Size for High Probability of Reconstruction......Page 42
8.2. Analysis of the Stable Portions......Page 44
9. Discussion......Page 47
References......Page 48
1. Introduction......Page 50
2. Basic Definitions and Notation......Page 51
3. Mathematical Details......Page 57
4. Concluding Remarks......Page 66
References......Page 67
1. Introduction......Page 70
2. Some Recent Cancer Biology for Modeling Carcinogenesis......Page 72
2.1. The Multi-Staging Nature of Carcinogenesis......Page 74
2.2. The Sequential Nature......Page 75
2.3. The Genetic Changes and Cancer Genes......Page 78
2.4. Cell Cycle and Carcinogenesis......Page 80
2.5. Epigenetic and Cancer......Page 82
2.6. Telomere, Immortalization and Cancer......Page 85
2.7. Single Pathway versus Multiple Pathways of Carcinogenesis......Page 86
3.1. The Extended Multi-Event Model of Carcinogenesis......Page 89
3.2. The Mixed Models of Carcinogenesis......Page 90
4. Some New Approaches for Analyzing Stochastic Models of Carcinogenesis......Page 91
4.1. Stochastic Differential Equations......Page 92
4.3. Probability Distribution of the State Variables......Page 94
5. A State Space Model for the Extended Multi-Event Model of Carcinogenesis......Page 95
5.1. The Stochastic System Model, theAugmented State Variables and Probability Distribution......Page 96
5.2. The Observation Model and the Probability Distribution of Cancer Incidence......Page 97
5.3. The Posterior Distribution of the Unknown Parameters and State Variables......Page 99
5.4. The Generalized Bayesian Method for Estimating Unknown Parameters and State Variables......Page 100
6. Analysis of British Physician Data of Lung Cancer and Smoking......Page 101
Acknowledgments......Page 108
References......Page 109
4. Modeling the Effects of Radiation on Cell Cycle Regulation and Carcinogenesis William D. Hazelton......Page 116
1.1. Multistage Carcinogenesis Models......Page 117
1.2. Analyses of Environmentally Exposed Cohorts......Page 118
2.1. A Combined Cell Cycle and Multistage Clonal Expansion Model......Page 120
3. Summary......Page 127
References......Page 128
Appendix......Page 129
1. Introduction......Page 134
2. Armitage-Doll Multi-Stage Model......Page 138
3. Two-Mutation Model......Page 144
4. Generalized MVK and Multi-Stage Models......Page 148
5. Multiple Pathway Models......Page 153
5.1. Multiple Pathway Models Incorporating Genomic Instability......Page 154
6. Discussion and Conclusions......Page 158
References......Page 163
1. Introduction......Page 174
2.3. Metastasis Formation......Page 180
2.4. Timeline of the Natural History of Metastatic Cancer and Observables......Page 181
2.6. Metastasis Growth......Page 182
3. Distribution of the Sizes of Detectable Metastases......Page 183
4.1. Model Specification and Results......Page 187
4.2. Model Identification......Page 190
Acknowledgments......Page 192
References......Page 193
1. Introduction......Page 198
2. Cancer Models......Page 201
2.1. Models of Leukemia......Page 202
2.2. Cell Kinetics......Page 220
3. Cancer Treatment Models......Page 225
3.1. Optimal Control Models......Page 226
4. Parameter Estimation......Page 235
5. Concluding Remarks......Page 236
References......Page 238
8. Major Epigenetic Hypotheses of Carcinogenesis Revisited King-Thom Chung......Page 250
1. Introduction......Page 251
2. Why are Epigenetic Factors Important?......Page 255
3. TheWarburg’s Hypothesis......Page 257
4. The Linus Pauling Hypothesis: Vitamin C and Cancer......Page 258
5. Szent-Györgyi (Bioelectronic) Hypothesis......Page 261
6. Micronutrients and Cancer......Page 263
7. NAD Deficiency as a Factor in Carcinogenesis......Page 267
8. GAP Junction Intercellular Communication (GJIC) and Cancer......Page 276
9. Viral Infections and Cancer......Page 279
10. Other Epigenetic Hypotheses......Page 284
11. Concluding Remarks and Perspectives......Page 287
Acknowledgments......Page 290
References......Page 291
1.1. Absorbed Dose......Page 316
1.2. Linear Energy Transfer (LET)......Page 317
1.3. Microdosimetry......Page 319
2.2. Mechanisms......Page 322
2.3. InitialYield and Characteristics......Page 325
3.1. Mechanisms......Page 328
3.2. Excision Repair Outcomes and Kinetics......Page 329
3.3. Point Mutations Arising from Base Damage and Single-Strand Breaks......Page 332
4.1. Mechanisms......Page 333
4.2. Repair Kinetics, Chromosome Aberrations and Small-Scale Mutations......Page 335
References......Page 339
Appendix......Page 344
1.1. Introduction......Page 348
1.2. Definition of Bystander Effects......Page 350
1.3. History of Bystander Effects......Page 351
1.4. Bystander Endpoints......Page 353
1.5. Transmission of Signal......Page 355
1.6. Identification of Signal......Page 356
1.7. Status of Sending and Receiving Cells......Page 358
1.8. Dependence on Radiation Type......Page 360
1.10. Evolutionary Considerations of Bystander Effects......Page 361
References......Page 363
1. Introduction......Page 370
2.1. The LOH Pathway of Human Colon Cancer (The APC-ß – Catenin – Tcf – myc Pathway)......Page 372
2.2. The MSI (Micro-Satellite Instability) Pathway of Human Colon Cancer......Page 374
3. The Stochastic Multi-Stage Model of Carcinogenesis......Page 377
3.1. Stochastic Equations of State Variables......Page 378
3.2. The Expected Number of Ij(t)......Page 379
3.3. The Probability Distribution of the Number of Detectable Tumors......Page 380
4. A Statistical Model and the Probability Distribution of Cancer Incidence Data......Page 381
4.1. Data Augmentation and the Expanded Model......Page 383
4.2. The Genetic Parameters......Page 384
5.1. The Prior Distribution of the Parameters......Page 386
5.2. The Posterior Distribution of the Parameters Given {Y, Z}......Page 387
5.3. The Multi-Level Gibbs Sampling Procedure for Estimating Parameters......Page 388
6. Application and Results......Page 389
7. Conclusions and Discussion......Page 395
References......Page 396
1. Introduction......Page 400
2. A General Stochastic Model of Carcinogenesis......Page 402
2.1. The Stochastic Difference Equations for State Variables......Page 405
2.3. Probability Distribution of the State Variables......Page 406
3. The Data for Risk Assessment of Environmental Agents......Page 407
4. State Space Models of Carcinogenesis and the Prediction of State Variables......Page 408
5.1. The Stochastic System Model and Probability Distributions......Page 409
5.2. The Observation Model......Page 410
6. The Genetic Algorithm and the Predicted Inference Procedures......Page 411
6.2. The Predictive Inference Procedures......Page 412
8. Developing Dose-Response Curves of Environmental Agents by Genetic Algorithm......Page 413
9. An Application and Illustration......Page 414
References......Page 419
1. Introduction......Page 422
2.1. The Multistage Model with Clonal Expansion of Intermediate Cells......Page 426
2.2. A Geometric Model for Colonies of Intermediate Cells......Page 430
2.3. Comparison of Multistage and Color-Shift Model......Page 431
3.1. Mouse Skin Carcinogenesis: Testing Biological Hypotheses about Papilloma and Carcinoma Formation......Page 433
3.2. Liver Focal Lesion Data: Testing Hypotheses about FAH Formation and Phenotype Change......Page 436
3.3. Liver Focal Lesion Data: Dose-Response Analyses......Page 437
4. Discussion......Page 441
References......Page 443
Appendix A: Basic Ideas of the Color-Shift Model......Page 447
Appendix B(1): Likelihood Function for Skin Papilloma and Carcinoma Data......Page 448
Appendix B(2): Likelihood Function for Liver Focal Lesion Data......Page 449
1. Introduction......Page 450
2. Biological Background......Page 451
3. Preliminaries for Mathematical Models......Page 454
4.1. A Simple, Two-Compartmental Model......Page 456
4.2. Evolution of Drug Resistance Stemming from Gene Amplification......Page 459
4.3. Partial Sensitivity of the Resistant Subpopulation......Page 464
4.4. Phase-Specific Chemotherapy......Page 465
4.5. General Compartmental Model......Page 467
5. Multidrug Therapy and Drug Resistance......Page 472
5.2. A Four-Compartmental Model......Page 473
6. Concluding Remarks......Page 474
Acknowledgments......Page 476
References......Page 477
15. Bladder Cancer Screening by Magnetic Resonance Imaging Lihong Li, Zigang Wang and Zhengrong Liang......Page 482
1. Introduction......Page 483
2.1. MR Image Protocols......Page 484
2.2. Image Segmentation......Page 486
2.3. Interactive Visualization System......Page 487
2.4. Detection of Bladder Lesions......Page 488
3. Results......Page 490
4. Discussion and Conclusions......Page 491
Acknowledgments......Page 492
References......Page 493
1. Introduction......Page 496
2. Mathematical Representation and Preprocessing of Maldi MS Data......Page 500
2.1. Mathematical Model for MALDI-TOF MS Data......Page 501
2.1.1. Baseline correction and normalization......Page 504
2.1.2. Spectra registration and peak alignment......Page 505
3. Multiscale Tools......Page 507
3.1. Wavelets and WaveSpec Software......Page 508
3.2. Diffusion Maps......Page 513
4. Clustering and Cancer Data Classifications......Page 514
References......Page 521
17. Advanced Statistical Methods for the Design and Analysis of Tumor Xenograft Experiments Ming Tan and Hong-Bin Fang......Page 526
1. Introduction......Page 527
2. Design of Experiments for Combination Studies......Page 529
2.2. Abdelbasit-Plackett Optimal Experimental Design......Page 531
2.3. Uniform Experimental Design......Page 532
3. Statistical Analysis for Tumor Growth......Page 534
3.1. Statistical Models......Page 535
3.2. Parameter Estimation via the ECM Algorithm......Page 537
4. Comparison of Treatment Effects......Page 539
4.1. Quasi t-Test Based on the EM Algorithm......Page 540
5. Summary and Discussion......Page 541
References......Page 543
1. Introduction......Page 546
2. A Review of the Literature......Page 548
3. Preliminary Considerations......Page 552
3.1. Constructing the Likelihood Function......Page 555
3.2. Non-Parametric Settings......Page 556
3.3. Fitting the Semi-Parametric Model......Page 559
4. Interval Estimation......Page 564
5. Testing Tumor Lethality and Carcinogenic Effect......Page 568
6. Two Examples......Page 571
7. Discussion......Page 581
References......Page 585
Index......Page 588