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E-raamat: Precision Medicine in Oncology [Wiley Online]

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  • Formaat: 288 pages
  • Ilmumisaeg: 22-Oct-2020
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 1119432480
  • ISBN-13: 9781119432487
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  • Wiley Online
  • Hind: 211,41 €*
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Precision Medicine in OncologySuurem pilt
  • Formaat: 288 pages
  • Ilmumisaeg: 22-Oct-2020
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 1119432480
  • ISBN-13: 9781119432487
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119432487

A FRESH EXAMINATION OF PRECISION MEDICINE'S INCREASINGLY PROMINENT ROLE IN THE FIELD OF ONCOLOGY

Precision medicine takes into account each patient's specific characteristics and requirements to arrive at treatment plans that are optimized towards the best possible outcome. As the field of oncology continues to advance, this tailored approach is becoming more and more prevalent, channelling data on genomics, proteomics, metabolomics and other areas into new and innovative methods of practice. Precision Medicine in Oncology draws together the essential research driving the field forward, providing oncology clinicians and trainees alike with an illuminating overview of the technology and thinking behind the breakthroughs currently being made.

Topics covered include:

  • Biologically-guided radiation therapy
  • Informatics for precision medicine
  • Molecular imaging
  • Biomarkers for treatment assessment
  • Big data
  • Nanoplatforms

Casting a spotlight on this emerging knowledge base and its impact upon the management of tumors, Precision Medicine in Oncology opens up new possibilities and ways of working – not only for oncologists, but also for molecular biologists, radiologists, medical geneticists, and others.

List of Contributors xiii
Preface xv
List of Abbreviations xvii
1 Genomic Strategies for Personalized Cancer Therapy 1(248)
Arkadiusz Z. Dudek
Kate Baxstrom
Sushma Bharadwaj
Anne Blaes
Amit Kulkarni
Emil Lou
Vijeyaluxmy Nehru
Emma Rabinovich
Ardaman Shergill
Maya Viner
1.1 Introduction
1(2)
1.1.1 Definition of Precision Medicine in Oncology
1(1)
1.1.2 DNA and RNA Sequencing Techniques
2(1)
1.2 Precision Medicine in Specific Tumors
3(25)
1.2.1 Lung Cancer
3(3)
1.2.1.1 Adenocarcinoma
4(1)
1.2.1.2 Squamous Cell Carcinoma
4(1)
1.2.1.3 Small-Cell Lung Carcinoma (SCLC)
4(1)
1.2.1.4 Epidermal Growth Factor Receptor (EGFR) Mutations
4(1)
1.2.1.5 Anaplastic Lymphoma Kinase (ALK)
5(1)
1.2.1.6 BRAF, ROS1, MET
5(1)
1.2.1.7 KRAS
6(1)
1.2.1.8 Other: RET, NTRK
6(1)
1.2.2 Head and Neck Cancers
6(3)
1.2.2.1 HPV-Positive Cancers
7(1)
1.2.2.2 HPV-Negative Cancers
8(1)
1.2.2.3 Targeting the Epidermal Growth Factor Receptor (EGFR) Pathway
8(1)
1.2.2.4 Thyroid Cancers
8(1)
1.2.2.5 Other Targets
8(1)
1.2.3 Hematological Malignancies
9(2)
1.2.3.1 Lymphoma
9(1)
1.2.3.2 Leukemia
10(1)
1.2.3.3 Myelodysplastic Syndrome
11(1)
1.2.4 Gynecologic Malignancies
11(2)
1.2.4.1 Cervical
11(1)
1.2.4.2 Uterine
11(1)
1.2.4.3 Ovarian
12(1)
1.2.5 Melanoma
13(3)
1.2.6 Gastrointestinal Malignancies
16(3)
1.2.6.1 Gastroesophageal Cancers
17(1)
1.2.6.2 Colorectal Cancers
17(2)
1.2.7 Breast Cancer
19(2)
1.2.7.1 Basal-Like, or Triple Negative Breast Cancer
19(1)
1.2.7.2 Luminal A/B, or Hormone Positive
20(1)
1.2.7.3 HER2 Positive Breast Cancer
20(1)
1.2.7.4 Immunotherapy
20(1)
1.2.7.5 Germline Testing in Breast Cancer
21(1)
1.2.7.6 Conclusion
21(1)
1.2.8 Genitourinary Malignancies
21(3)
1.2.8.1 Prostate Cancer
21(2)
1.2.8.2 Renal Cell Cancer (RCC)
23(1)
1.2.8.3 Urothelial Cancers
23(1)
1.2.9 Pediatric Cancers
24(3)
1.2.9.1 Introduction
24(1)
1.2.9.2 Leukemia and Lymphoma
24(1)
1.2.9.3 Central and Peripheral Nervous System Tumors
25(1)
1.2.9.4 Bone and Soft Tissue Sarcomas
26(1)
1.2.9.5 Other Embryonal Tumors
26(1)
1.2.9.6 Conclusion
27(1)
1.2.10 Cancers of Unknown Primary Origin
27(1)
1.2.10.1 Diagnosis
27(1)
1.2.10.2 Gene Expression Profiling
28(1)
1.2.10.3 Mutational Testing with Next-Generation Sequencing (NGS)
28(1)
1.2.10.4 Treatment
28(1)
1.3 Biomarkers for Immunotherapy of Cancer
28(4)
1.3.1 PD-L1
29(1)
1.3.2 Soluble PD-L1 (sPD-L1)
29(1)
1.3.3 Combined Positive Score (CPS)
30(1)
1.3.4 Tumor Microenvironment
30(1)
1.3.5 Tumor Mutational Burden (TMB)
30(1)
1.3.6 Microsatellite Instability (MSI)
31(1)
1.3.7 MMR Deficiency
31(1)
1.3.8 Peripheral Blood Absolute Neutrophil Count/Absolute Lymphocyte Count
31(1)
1.3.9 Microbiome
31(1)
1.4 Clinical Trial Design in the Era of Precision Oncology
32(1)
1.5 Ethical, Legal, and Social Issues of Precision Oncology
33(3)
1.5.1 Ethical Issues
33(1)
1.5.2 Legal Issues
34(1)
1.5.3 Social Issues
35(1)
1.6 Databases, Data Sharing, and Challenges of Precision Oncology
36(1)
References
37(24)
2 Blood-Based Biomarkers for the Diagnosis and Prognosis of Cancer
61(22)
Shreetama Bandyopadhayaya
Chandi C. Mandal
2.1 Introduction
61(1)
2.2 Importance of Blood-Based Biomarkers
61(1)
2.3 Circulating Proteins as Biomarkers
62(2)
2.4 Circulating Long Non-coding RNAs as Biomarkers
64(1)
2.5 Circulating miRNAs as Biomarkers
65(2)
2.6 Circulating Autoantibodies as Biomarkers
67(2)
2.7 Circulating Tumor DNA as Biomarkers
69(1)
2.8 Metabolites as Biomarkers
70(2)
2.9 Lipids as Biomarkers
72(2)
2.10 Exosomes as Biomarkers
74(3)
2.11 Conclusion
77(1)
References
77(6)
3 Application of Circulating Cell-free DNA for Personalized Cancer Therapy
83(16)
Indranil Chattopadhyay
3.1 Introduction
83(1)
3.2 Drawbacks and Challenges of Invasive Tumor Tissue in Treatment and Diagnosis of Cancer
84(1)
3.3 Importance of Noninvasive Biomarkers in Treatment and Diagnosis of Cancer
84(1)
3.4 Liquid Biopsy: cfDNA and ctDNA
85(1)
3.4.1 Biogenesis of ctDNA: Mechanisms of Release, Characteristics, Quantity, and Quality
85(1)
3.4.2 Role of Preanalytical Factors that Affect cfDNA Measurements
86(1)
3.5 Practical Approach to Estimate ctDNA in Liquid Biopsy
86(2)
3.5.1 Isolation of cfDNA and ctDNA
86(1)
3.5.2 Analysis of ctDNA by Real-Time Quantitative PCR
86(1)
3.5.3 Analysis of ctDNA by Digital PCR (dPCR)
87(1)
3.5.4 Analysis of ctDNA by Beads, Emulsion, Amplification, and Magnetics (BEAMing)
87(1)
3.5.5 Analysis of ctDNA by Next-Generation Sequencing (NGS)
87(1)
3.6 Clinical Application of ctDNA Detection in Various Cancers
88(4)
3.6.1 Clinical Applications of ctDNA in Lung Cancer
88(1)
3.6.2 Clinical Application of ctDNA in Head and Neck Cancer
89(1)
3.6.3 Clinical Utility of Circulating Tumor DNA in Pancreatic Cancer
90(1)
3.6.4 Clinical Utility of Circulating Tumor DNA in Early and Metastatic Breast Cancer
90(1)
3.6.5 Clinical Utility of Circulating Tumor DNA in Colorectal Cancer
91(1)
3.6.6 Clinical Utility of Circulating Tumor DNA in Melanoma
91(1)
3.7 Clinical Utility of Methylation in ctDNA in Personalized Oncology
92(1)
3.8 Conclusion
92(1)
References
93(6)
4 Prognostic Implications of EGFR, p53, p16, Cyclin D1, and Bcl-2 in Head and Neck Squamous Cell Carcinoma (HNSCC)
99(34)
Zane Deliu
Ardaman Shergill
Anne Meier
Phyo Thazin Myint
Sarah Khan
Paramjeet Khosla
Lawrence Feldman
4.1 Introduction
99(1)
4.2 Epidermal Growth Factor Receptor (EGFR)
99(5)
4.2.1 EGFR Structure and Ligands
99(1)
4.2.2 Physiology
100(1)
4.2.3 EGFR Expression and Genetic Changes
100(1)
4.2.3.1 EGFR Expression in HNSCC
100(1)
4.2.3.2 Normal Adjacent Oral Mucosa and Pre-malignant Lesions
101(1)
4.2.4 EGFR Genetic Changes: Gene Copy Numbers, Amplifications, and Mutations in HNSCC
101(1)
4.2.4.1 Association of EGFR Expression or Genetic Changes with HPV Infection
101(1)
4.2.5 EGFR as a Prognostic and Predictive Marker
102(1)
4.2.5.1 EGFR as a Prognostic Marker
102(1)
4.2.5.2 EGFR as a Predictive Marker
102(1)
4.2.6 Future Perspectives
103(1)
4.2.6.1 EGFR in Immuno-SPECT or PET Imaging
103(1)
4.2.6.2 Molecular Profiling for Precision Medicine
104(1)
4.3 TP53 Mutations in Head and Neck Cancer
104(3)
4.3.1 Pathogenesis and Prevalence
104(1)
4.3.2 Risk Factors
104(1)
4.3.3 TP53 Structure and Physiology
105(1)
4.3.3.1 TP53 Structure
105(1)
4.3.3.2 TP53 as a Tumor Suppressor Gene
105(1)
4.3.4 TP53 Gain of Function Properties
105(1)
4.3.5 TP53 as a Prognostic and Predictive Marker
106(1)
4.3.6 Therapeutic Strategies Targeting TP53
106(1)
4.4 P16 and Cyclin Dl Mutations in Head and Neck Cancer
107(2)
4.4.1 Cyclin D1
107(1)
4.4.2 P16
108(1)
4.5 Bcl-2 Mutations in Head and Neck Cancer
109(4)
4.5.1 Bcl-2
109(1)
4.5.1.1 Physiological Role of Bcl-2
109(1)
4.5.2 Bcl-2 Family of Proteins
109(1)
4.5.3 Significance of Bcl-2 Overexpression
110(1)
4.5.4 Association with Chemoresistance and Radioresistance
111(1)
4.5.5 Role of Bcl-2 as a Marker of Prognosis
111(1)
4.5.6 Chemotherapeutics Targeting Bcl-2
112(1)
4.5.7 Bcl-2 Summary
113(1)
4.6 Conclusion
113(1)
References
114(19)
5 Immunotherapy and Cancer
133(24)
Maaly Bassiony
Adedoyin Victoria Aluko
James A. Radosevich
5.1 Introduction
133(1)
5.2 What Is Cancer Immunotherapy?
134(1)
5.3 How Does Immunotherapy Work?
135(1)
5.4 Timing of Immunotherapy
135(1)
5.5 Combination Immunotherapy
136(1)
5.6 Side Effects of Immunotherapy
137(1)
5.7 Types of Cancer Immunotherapy Treatments
137(2)
5.7.1 Immune Checkpoint Inhibitors
137(1)
5.7.2 Monoclonal Antibodies and Tumor-Agnostic Therapies
138(1)
5.7.3 Adoptive T Cell Therapy
138(1)
5.8 Cancer Vaccines
139(1)
5.9 Oncolytic Viral Immunotherapy (OVIs)
140(1)
5.10 Non-specific Immunotherapies
141(1)
5.11 Immunotherapy by Cancer Type
141(9)
5.11.1 Skin Cancer
141(1)
5.11.2 Lung Cancer
142(1)
5.11.3 Breast Cancer
142(1)
5.11.4 Kidney and Prostate Cancers
143(2)
5.11.5 Brain Cancer
145(1)
5.11.6 Colorectal Cancer
146(1)
5.11.7 Bladder Cancer
147(1)
5.11.8 Cervical Cancer
147(1)
5.11.9 Leukemia
147(1)
5.11.10 Liver Cancer
148(2)
5.12 Proven Studies
150(1)
5.13 Cancer Immunity Pathway
150(1)
5.14 Recent Developments in Immunotherapy
150(1)
5.15 Neoantigens for Cancer Immunotherapy
151(1)
5.16 Discussion
152(1)
References
153(4)
6 Predictive and Prognostic Markers for Cancer Medicine
157(46)
Elif Zeynep Yilmaz
Ebru Esin Yoruker
6.1 Introduction
157(1)
6.2 Historical Development of Cancer Markers
157(1)
6.3 Characteristics of the Ideal Cancer Markers
158(5)
6.3.1 Ideal Source of Cancer Markers
159(3)
6.3.2 Kinetics of Cancer Markers
162(1)
6.3.3 Sensitivity and Specificity for Evaluation of Cancer Markers
163(1)
6.4 Utilization of Cancer Markers in Most Common Cancers
163(13)
6.4.1 Colorectal Cancer
166(1)
6.4.1.1 CEA
166(1)
6.4.1.2 KRAS/NRAS
167(1)
6.4.1.3 MSI
168(1)
6.4.1.4 PD-1/PD - L1
168(1)
6.4.1.5 BRAF
169(1)
6.4.1.6 Oncotype DX Colon Cancer Test
169(1)
6.4.1.7 ColoPrint
169(1)
6.4.1.8 CTC
169(1)
6.4.2 Breast Cancer
169(1)
6.4.2.1 ER/PR
170(1)
6.4.2.2 HER2
171(1)
6.4.2.3 Oncotype DX
171(1)
6.4.2.4 MammaPrint
171(1)
6.4.2.5 uPA/PAI-1
172(1)
6.4.3 Ovarian Cancer
172(1)
6.4.4 Lung Cancer
172(1)
6.4.4.1 EGFR
173(1)
6.4.4.2 ALK Rearrangements
173(1)
6.4.4.3 ROS1 Rearragements
174(1)
6.4.5 Urological Cancers
174(1)
6.4.5.1 Prostate Cancer
174(1)
6.4.5.2 Renal Cancer
175(1)
6.5 Classification and Techniques for Studying of Cancer Markers
176(9)
6.5.1 Circulating Tumor Cells as Cancer Markers
176(1)
6.5.2 DNA-Based Cancer Markers
177(1)
6.5.2.1 Microsatellite Alterations
177(1)
6.5.2.2 cfDNA Integrity
177(1)
6.5.2.3 DNA Methylation
178(1)
6.5.2.4 Mutations and Single Nucleotide Polymorphisms (SNPs)
178(2)
6.5.3 RNA-Based Tumor Markers
180(1)
6.5.3.1 mRNAs
180(1)
6.5.3.2 Noncoding RNAs
180(1)
6.5.4 Protein-Based Tumor Markers
181(4)
6.6 Clinical Validation of Cancer Markers
185(1)
6.7 Conclusions and Future Perspectives
186(1)
References
187(16)
7 Dual Energy Imaging in Precision Radiation Therapy
203(26)
John C. Roeske
Maksat Haytmyradov
Roberto Cassetta
Murat Surucu
7.1 Introduction and Overview
203(1)
7.2 Historical Perspective
203(5)
7.2.1 X-Ray Production
204(1)
7.2.2 X-Ray Interactions in Matter
205(1)
7.2.3 Planar Image Formation
205(1)
7.2.4 Computed Tomography
206(1)
7.2.5 X-Ray Imaging in Radiation Oncology
207(1)
7.2.6 Dual Energy Imaging in Radiation Therapy
207(1)
7.3 Planar Dual Energy Imaging
208(6)
7.3.1 Theory
209(1)
7.3.2 Planar Dual Energy Imaging Methods
210(1)
7.3.3 Applications in Radiation Therapy
210(1)
7.3.3.1 Image-Guided Radiation Therapy
211(1)
7.3.3.2 Markerless Tumor Tracking
211(1)
7.3.3.3 Megavoltage Dual Energy Imaging
214(1)
7.4 Dual Energy Computed Tomography
214(8)
7.4.1 Theory
215(1)
7.4.2 Dual Energy Scanning Methods
216(2)
7.4.3 Applications in Radiation Therapy
218(1)
7.4.3.1 Brachytherapy Planning
218(1)
7.4.3.2 Proton Planning
218(1)
7.4.3.3 Normal Tissue Segmentation
220(1)
7.4.3.4 Assessment of Therapy Response
221(1)
7.4.3.5 Dual Energy Cone Beam Computed Tomography
222(1)
7.5 Summary and Future Directions
222(1)
Acknowledgement
223(1)
References
224(5)
8 The Role of Big Data in Personalized Medicine
229(20)
Jean-Emmanuel Bibault
Lei Xing
8.1 Introduction
229(1)
8.2 The Concept of Big Data and the Specificities of Healthcare
230(3)
8.2.1 Volume: How Big Is Big Data?
230(1)
8.2.2 Variety: Where Does Big Data Come from?
231(1)
8.2.3 Velocity: How Fast Is Big Data Generated and Interpreted?
232(1)
8.2.4 Variability: How Does Big Data Change?
232(1)
8.2.5 Veracity: How Accurate Is Big Data?
232(1)
8.2.6 Value: Why Is Big Data Important?
232(1)
8.3 Sources of Data
233(3)
8.3.1 Genomics, Epigenomics, and Transcriptomics
233(1)
8.3.2 Proteomics and Metabolomics
234(1)
8.3.3 Medical Imaging and Radiomics
235(1)
8.3.4 Clinical Informatics
236(1)
8.4 Big Data Analytical Techniques
236(3)
8.4.1 Machine Learning
236(1)
8.4.2 Deep Learning
237(1)
8.4.3 Natural Language Processing
238(1)
8.5 Challenges in Big Data Analytics
239(2)
8.5.1 Implementing a Big Data Approach
239(1)
8.5.2 Developing an Information-Sharing Culture
239(1)
8.5.3 Security Measures
240(1)
8.5.4 Ethics in Big Data Analysis
240(1)
8.5.4.1 Consent in the Era of Big Data
240(1)
8.5.4.2 Privacy
240(1)
8.5.4.3 Anonymization
241(1)
8.5.4.4 Ownership
241(1)
References
241(8)
Index 249
Bulent Aydogan, PhD, Associate Professor, Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL, USA.

James Radosevich, PhD, Professor, Department of Oral Medicine and Diagnostic Sciences, University of Illinois at Chicago, Chicago, IL, USA.
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