This program will provide you with a sense of confidence in medical practice, which will help you grow personally and professionally”

The scale and complexity of genomic data dwarf the measurements traditionally used in laboratory testing. In recent years there has been an enormous development of informatics to analyze and interpret DNA sequencing, and it has created a gap between biological knowledge and its application to routine clinical practice. It is therefore necessary to educate, disseminate and incorporate these informatics techniques among the medical community in order to be able to interpret the massive analysis of data from publications, biological or medical databases and medical records, among others, and thus enrich the biological information available at the clinical level.

This machine learning will enable the development of precision oncology, in order to interpret genomic characteristics and find targeted therapies, or to identify risks to certain diseases and establish more individualized preventive measures. A fundamental objective of the program is to bring students closer to and disseminate computer knowledge, which is already applied in other fields of knowledge, but has minimal implementation in the medical world, despite the fact that for genomic medicine to become a reality, it is necessary to accurately interpret the huge volume of clinical information currently available and associate it with the biological data generated after a bioinformatic analysis. While this is a difficult challenge, it will allow the effects of genetic variation and potential therapies to be explored quickly, inexpensively and with greater precision than is currently possible.

Humans are not naturally equipped to perceive and interpret genomic sequences, to understand all the mechanisms, pathways and interactions that take place within a living cell, nor to make medical decisions with tens or hundreds of variables. To move forward, a system with superhuman analytical capabilities is required to simplify the work environment and show the relationships and proximities between variables. In genomics and biology, it is now recognized that it is better to spend resources on new computational techniques than on pure data collection, something that is possibly the same in medicine and, of course, oncology.

Update your knowledge with the Postgraduate diploma in Genomic and Precision Oncology"

This Postgraduate diploma in Genomic and Precision Oncology contains the most complete and up-to-date scientific program on the market. Its most notable features are:

• Case studies presented by experts in Genomic and Precision Oncology. Its graphic, schematic and practical contents provide scientific and practical information on those disciplines that are essential for professional practice
• News on genomic and precision oncology
• It contains practical exercises where the self-assessment process can be carried out to improve learning
• Special emphasis on innovative methodologies in Genomic and Precision Oncology
• All of this will be complemented by theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

This Postgraduate diploma may be the best investment you can make when selecting a refresher program, for two reasons: in addition to updating your knowledge in Genomic and Precision Oncology, you will obtain a Postgraduate diploma from TECH Global University"

Its teaching staff includes professionals belonging to the field of genomic and precision oncology, who bring to this program the experience of their work, as well as renowned specialists belonging to reference societies and prestigious universities.

Thanks to its multimedia content elaborated with the latest educational technology, this Postgraduate diploma will allow the professional a situated and contextual learning, that is to say, a simulated environment that will provide an immersive learning programmed to work in real situations.

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise during the course. To this end, the student will be assisted by a novel interactive video system developed by recognized experts in the field of genomic and precision oncology with extensive teaching experience.

Increase your decision-making confidence by updating your knowledge through this Postgraduate diploma"

Take the opportunity to learn about the latest advances in genomic and precision oncology and improve the care of your patients"

The structure of the contents has been designed by a team of professionals from the best educational centers, universities, and companies in the national territory, aware of the relevance of current specialization in order to intervene in the training and support of students, and committed to quality teaching through new educational technologies.

This Postgraduate diploma in Genomic and Precision Oncology contains the most complete and up-to-date scientific program on the market”

Module 1. Molecular Biology

1.1. Molecular Mechanisms of Cancer

1.1.1. Cellular Cycle
1.1.2. Detachment of Tumor Cells

1.2. Reprogramming of the Tumor Microenvironment

1.2.1. The Tumor Microenvironment: A Global Vision
1.2.2. TME as a Prognostic Factor in Lung Cancer
1.2.3. TME in the Progression and Metastasis of Lung Cancer

1.2.3.1. Cancer-Associated Fibroblasts (CAF)
1.2.3.2. Endothelial Cells
1.2.3.3. Hypoxia in Lung Cancer
1.2.3.4. Inflammation
1.2.3.5. Immune Cells

1.2.4. Contribution of TME to Therapeutic Resistance

1.2.4.1. Contribution of TME to Radiotherapy Resistance

1.2.5. TME as a Target Treatment in Lung Cancer

1.2.5.1. Future Directions

1.3. Tumor Immunology: The Bases of Immunotherapy in Cancer

1.3.1. Introduction to the Immune System
1.3.2. Tumor Immunology

1.3.2.1. Tumor-Associated Antigens
1.3.2.2. Identification of Tumor-Associated Antigens
1.3.2.3. Types of Tumor-Associated Antigens

1.3.3. The Bases of Immunotherapy in Cancer

1.3.3.1. Introduction to the Immunotherapeutic Approaches
1.3.3.2. Monoclonal Antibodies in Cancer Therapy

1.3.3.2.1. Production of Monoclonal Antibodies
1.3.3.2.2. Types of Therapeutic Antibodies
1.3.3.2.3. Mechanisms of Action of Antibodies
1.3.3.2.4. Modified Antibodies

1.3.4. Non-Specific Immune Modulators

1.3.4.1. Bacillus of Calmette-Guérin
1.3.4.2. Interferon-α
1.3.4.3. Interleucina-2
1.3.4.4.Imiquimod

1.3.5. Other Approaches for Immunotherapy

1.3.5.1. Dendritic Cell Vaccines
1.3.5.2. Sipuleucel- T
1.3.5.3. CTLA-4 Blocking
1.3.5.4. Adoptive T-cell Therapy

1.3.5.4.1. Adoptive Cell Therapy With T.-cell Clones
1.3.5.4.2. Adoptive Cell Therapy With Tumor-Infiltrating Lymphocytes

1.4. Molecular Mechanisms Involved in the Invasion and Metastasis Process

Module 2. Genomic or Precision Oncology

2.1. Usefulness of Gene Expression Profiling in Cancer
2.2. Molecular Subtypes of Breast Cancer
2.3. Prognostic-Predictive Genomic Platforms in Breast Cancer
2.4. Therapeutic Targets in Non-Small Cell Lung Cancer

2.4.1. Introduction
2.4.2. Molecular Detection Techniques
2.4.3. EGFR Mutation
2.4.4. ALK Translocation
2.4.5. ROS Translocation
2.4.6. BRAF Mutation
2.4.7. NRTK Rearrangements
2.4.8. HER2 Mutation
2.4.9. MET Mutation/Amplification
2.4.10. RET Rearrangements
2.4.11. Other Molecular Targets

2.5. Molecular Classification of Colon Cancer
2.6. Molecular Studies in Gastric Cancer

2.6.1. Treatment of Advanced Gastric Cancer
2.6.2. HER2 Overexpression in Advanced Gastric Cancer
2.6.3. Identification and Interpretation of HER2 Overexpression in Advanced Gastric Cancer
2.6.4. Drugs With Activity Against HER2
2.6.5. Trastuzumab in the First Line of Advanced Gastric Cancer
2.6.5.1. Treatment of HER2+ Advanced Gastric Cancer After Progression to Trastuzumab-Based Regimens
2.6.6. Activity of Other Anti-HER2 Drugs in Advanced Gastric Cancer

2.7. GIST as a Model of Translational Research: 15 Years of Experience

2.7.1. Introduction
2.7.2. Mutations of KIT and PDGFRA as Major Promoters in GIST
2.7.3. Genotype in GIST: Prognostic and Predictive Value
2.7.4. Genotype in GIST and Resistance to Imatinib
2.7.5. Conclusions

2.8. Molecular and Genomic Biomarkers in Melanoma
2.9. Molecular Classification of Brain Tumors
2.10. Molecular and Genomic Biomarkers in Melanoma
2.11. Immunotherapy and Biomarkers

2.11.1. Landscape of Immunological Therapies in Cancer Treatment and the Need to Define the Mutational Profile of a Tumor
2.11.2. Checkpoint Inhibitor Biomarkers: PD-L1 and Beyond

2.11.2.1. The Role of PD-L1 in Immune Regulation
2.11.2.2. Clinical Trial Data and PD-L1 Biomarker
2.11.2.3. Thresholds and Assays for PD-L1 Expression: a Complex Picture
2.11.2.4. Budding Biomarkers

2.11.2.4.1. Tumor Mutational Burden (TMB)

2.11.2.4.1.1. Quantification of the Tumor Mutational Burden
2.11.2.4.1.2. Evidence of the Tumor Mutational Burden
2.11.2.4.1.3. Tumor Burden as a Predictive Biomarker
2.11.2.4.1.4. Tumor Burden as a Prognosis Biomarker
2.11.2.4.1.5. The Future of the Mutational Burden

2.11.2.4.2. Microsatellite Instability
2.11.2.4.3. Immune Infiltrate Analysis
2.11.2.4.4. Toxicity Markers

2.11.2.5. Immune Checkpoint Drug Development in Cancer
2.11.2.6. Available Drugs

Module 3. Changes in Current Clinical Practice and New Applications With Genomic Oncology

3.1. Liquid Biopsies: Fashion or Future?

3.1.1. Introduction
3.1.2. Circulating Tumor Cells
3.1.3. ctDNA
3.1.4. Clinical Applications
3.1.5. CtDNA Limitations
3.1.6. Conclusions and Future

3.2. Role of the Biobank in Clinical Research

3.2.1. Introduction
3.2.2. Is it Worth the Effort to Create a Biobank?
3.2.3. How to Begin Establishing a Biobank
3.2.4. Informed Consent for the Biobank
3.2.5. Collecting Samples for the Biobank
3.2.6. Quality Control
3.2.7. Access to Samples

3.3. Clinical trials: New Concepts Based on Precision Medicine

3.3.1. What Are Clinical Trials? What Sets Them Apart From Other Types of Research?

3.3.1.1. Types of Clinical Trials

3.3.1.1.1. By Their Objectives
3.3.1.1.2. By The Number of Partaking Centers
3.3.1.1.3. By Their Methodology
3.3.1.1.4. By Their Level of Masking

3.3.2. Results of Clinical Trials in Thoracic Oncology

3.3.2.1. Related to Survival Time
3.3.2.2. Results Related to the Tumor
3.3.2.3. Results Notified by the Patient

3.3.3. Clinical Trials in the New Age of Precision Medicine

3.3.3.1. Precision Medicine
3.3.3.2. Terminology Relate to the Design of Trials in the Era of Precision Medicine

3.4. Incorporation of Actionable Markers in Clinical Practice
3.5. Application of Genomics in Clinical Practice by Type of Tumor
3.6. Decision support Systems in Oncology Based on Artificial Intelligence

Module 4. New Techniques in the Age of Genomics

4.1. Understanding the New Technology: Next Generation Sequence (NGS) in Clinical Practice

4.1.1. Introduction
4.1.2. Background
4.1.3. Problems in the Application of Sanger Sequencing in Oncology
4.1.4. New Sequencing Techniques
4.1.5. Advantages of Using NGS in Clinical Practice
4.1.6. Limitations of Using NGS in Clinical Practice
4.1. 7. Terms and Definitions of Interest
4.1.8. Types of Studies Depending on Their Size and Depth

4.1.8.1. Genome
4.1.8.2. Exomes
4.1.8.3. Multigenic Panels

4.1.9. Stages of NGS Sequencing

4.1.9.1. Preparing Samples and Libraries
4.1.9.2. Preparing Templates and Sequencing
4.1.9.3. Bioinformatic Processing

4.1.10. Annotation and Classification of Variants

4.1.10.1. Population Databases
4.1.10.2. Locus-Specific Databases
4.1.10.3. Bioinformatic Predictors of Functionality

4.2. DNA Sequencing and Bioinformatic Analysis

4.2.1. Introduction
4.2.2. Software
4.2.3. Procedure

4.2.3.1. Extracting Raw Sequences
4.2.3.2. Aligning Sequences
4.2.3.3. Alignment Refinement
4.2.3.4. Variant Call
4.2.3.5. Variant Filtering

4.3.RNA Sequencing and Bioinformatic Analysis

4.3.1. Introduction
4.3.2. Software
4.3.3. Procedure

4.3.3.1. QC Evaluation of Raw Data
4.3.3.2. RNAr Filtering
4.3.3.3. Filtered Quality Control Data
4.3.3.4. Quality Trimming and Adapter Removal
4.3.3.5. Alignment of Reads to a Reference
4.3.3.6. Variant Call
4.3.3.7. Differential Gene Expression Analysis

4.4. ChIP-Seq Technology

4.4.1. Introduction
4.4.2. Software
4.4.3. Procedure

4.4.3.1. CHiP-Seq Data Set Description
4.4.3.2. Obtaining Information About the Experiment Using the GEO and SRA Website
4.4.3.3. Quality Control of the Sequencing Data
4.4.3.4. Trimming and Filtering Reads
4.4.3.5. Visualizing Results with the Integrated Genonme Browser (IGV)

4.5. Big Data Applied to Oncology Genomics

4.5.1. The Process of Analysis Data

4.6. Genomic Servers and Databases of Genetic Variants

4.6.1. Introduction
4.6.2. Online Genomic Servers
4.6.3. Genomic Server Architecture
4.6.4. Data Recovery and Analysis
4.6.5. Personalization

4.7. Annotation of Genetic Variants

4.7.1. Introduction.
4.7.2. What is Variant Calling?
4.7.3. Understanding the VCF Format
4.7.4. Variant Identification
4.7.5. Variant Analysis
4.7.6. Predicting the Effect of the Variation of a Protein’s Structure and Function

A unique, key, and decisive training experience to boost your professional development”