Expand your knowledge of Precision Oncology: Genomics and Big Data through this program, where you will find the best teaching material with real practical cases. Learn here the latest advances in the specialty to be able to perform quality medical practice”

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 enormous 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. 

We have millions ofpublications and enormous amounts of data, but when analyzed by physicians or biologists, the conclusions are totally subjective and relative to the available publications or data which are prioritized arbitrarily. This generates partial knowledge, which is increasingly distanced from the genetic and biological knowledge available and supported by computing, so a giant step in the implementation of precision medicine is to reduce this distance through the massive analysis of available medical and pharmacological information.

Update your knowledge through the program in Precision Oncology: Genomics and Big Data”

This Professional master’s degree in Precision Oncology: Genomics and Big Data contains the most complete and up-to-date scientific program on the market. The most important features include:

• More than 75 practical cases presented by experts in Precision Oncology: Genomics and Big Data The graphic, schematic, and eminently practical contents with which they are created provide scientific and practical information on the disciplines that are essential for professional
• Novelties in precision oncology, genomics and big data
• Contains practical exercises where the self-evaluation process can be carried out to improve learning
• An algorithm-based interactive learning system for decision-making in the clinical situations presented throughout the course
• With special emphasis on evidence-based medicine and research methodologies in Precision Oncology: Genomics and Big Data
• 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 Professional master’s degree may be the best investment you can make in the selection of a refresher program for two reasons: in addition to updating your knowledge of Precision Oncology: Genomics and Big Data, you will obtain a qualification from TECH”

The teaching staff includes professionals from the field of precision oncology, who bring their experience to this specialization program, as well as renowned specialists from leading scientific societies.

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

This program is designed around Problem Based Learning, whereby the physician must try to solve the different professional practice situations that arise during the course. For this purpose, the physician will be assisted by an innovative interactive video system created by renowned and experienced experts in the field of precision oncology with extensive teaching experience.

This Professional master’s degree offers training in simulated environments, which provides an immersive learning experience designed to train for real-life situations"

It includes clinical cases to bring the program's degree as close as possible to the reality of medical care"

The structure of the contents has been designed by a team of professionals from the best hospitals and universities in the country, who are aware of the relevance of up-to-date training to be able to intervene in the diagnosis and treatment of oncological pathologies, and are committed to quality teaching through new educational technologies.

This Professional master’s degree in Precision Oncology: Genomics and Big Data 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. Tumoral Burden as a Predictive Biomarker
2.11.2.4.1.4. Tumoral 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

2.11.3. Immune Checkpoint Drug Development in Cancer
2.11.4. Available Drugs

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

3.1. Fluid Biopsies: Trend or the Future?

3.1.1. Introduction
3.1.2. Circulating Tumor Cells
3.1.3. ctDNA
3.1.4. Clinical Utilities
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 Related 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. Use of Unix and Linux in bioinformatics

4.1. Introduction to the Linux Operating System

4.1.1. What is an Operating System?
4.1.2. The Benefits of Using Linux

4.2. Linux Environment and Installation

4.2.1. Linux Distributions?
4.2.2. Linux Installation Using a USB Memory
4.2.3. Linux Installation Using a CD-ROM
4.2.4. Linux Installation Using a Virtual Machine

4.3. The Command Line

4.3.1. Introduction
4.3.2. What is a Command Line?
4.3.3. Working on the Terminal
4.3.4. Shell and Bash

4.4. Basic Browsing

4.4.1. Introduction
4.4.2. How to Learn the Current Location?
4.4.3. Absolute and Relative Routes
4.4.4. How to Navigate in the System?

4.5. File Manipulation

4.5.1. Introduction
4.5.2. How to Build a Directory?
4.5.3. How to Move to a Directory?
4.5.4. How to Create an Empty File?
4.5.5. Copying a File and Directory
4.5.6. Deleting a File and Directory

4.6. VI Text Editor

4.6.1. Introduction
4.6.2. How to Save and Exit?    
4.6.3. How to Browse a File in the VI Text Editor?
4.6.4. Deleting Contents
4.6.5. The Undo Command

4.7. Wildcards

4.7.1. Introduction
4.7.2. What are Wildcards?
4.7.3. Examples of Wildcards

4.8. Licences

4.8.1. Introduction
4.8.2. How to See the Licences of a File?
4.8.3. How to Change the Licences?
4.8.4. Licence Configuration
4.8.5. Licences for Directories
4.8.6. The “Root” User

4.9. Filters

4.9.1. Introduction
4.9.2. Head
4.9.3. Tail
4.9.4. Sort
4.9.5. nl
4.9.6. wc
4.9.7. cut
4.9.8. sed
4.9.9. uniq
4.9.10. tac
4.9.11. Other Filters

4.10. Grep and Common Expressions

4.10.1. Introduction
4.10.2. eGrep
4.10.3. Common Expressions
4.10.4. Some Examples

4.11. Pipelines and Redirection

4.11.1. Introduction
4.11.2. Redirect to a File
4.11.3. Save to a File
4.11.4. Redirect From a File
4.11.5. STDERR Redirection
4.11.6. Pipelines

4.12. Managing Processes

4.12.1. Introduction
4.12.2. Active Processes
4.12.3. Closing a Corrupt Program
4.12.4. Foreground and Background Work

4.13. Bash

4.13.1. Introduction
4.13.2. Important Points
4.13.3. Why the ./ ?
4.13.4. Variables
4.13.5. The Declarations

Module 5. Data analysis in big data projects: R programming language

5.1. Introduction to R programming language

5.1.1. What is R?
5.1.2. R Installation and the Graphic Interface of R
5.1.3. Packages

5.1.3.1. Standard Packages
5.1.3.2. Contributed Packages and CRAN

5.2. Basic Features of R

5.2.1. The Environment of R
5.2.2. Software and Related Documentation
5.2.3. R and Statistics
5.2.4. R and the Window System
5.2.5. Using R Interactively
5.2.6. An Introductory Session
5.2.7. Obtaining Help with Functions and Features
5.2.8. R Commands, Cap Sensitivity, Etc
5.2.9. Recovery and Correction of Previous Commands
5.2.10. Execute Commands or Diverting the Output to a File
5.2.11. Data Storage and Object Deletion

5.3. Types of Objects in R

5.3.1. Simple Manipulations; Numbers and Vectors

5.3.1.1. Vectors and Their Assignment
5.3.1.2. Vector Arithmetic
5.3.1.3. Generating Regular Sequences
5.3.1.4. Logical Vectors
5.3.1.5. Lost Values
5.3.1.6. Character Vectors
5.3.1.7. Index Vectors

5.3.1.7.1. Selecting and Modifying Subsets of a Dataset

5.3.1.8. Other Types of Objects

5.3.2. Objects, Their Modes and Attributes

5.3.2.1. Intrinsic Attributes: Mode and Length
5.3.2.2. Changing the Length of an Object
5.3.2.3. Obtaining and Configuring Attributes
5.3.2.4. The Class of an Object

5.3.3. Sorted and Unsorted Factors

5.3.3.1. A Specific Example
5.3.3.2. The Tapply () Function and Unequal Matrices
5.3.3.3. Sorted Factors

5.3.4. Matrices

5.3.4.1. Matrices
5.3.4.2. Matrix Indexation. The Subsections of a Matrix
5.3.4.3. Index Matrices
5.3.4.4. The Array () Function
5.3.4.5. Mixed Arithmetic of Vectors and Matrices. The Recycling Rule
5.3.4.6. The Outer Product of Two Matrices
5.3.4.7. The General Transposition of a Matrix
5.3.4.8. Matrix Multiplication
5.3.4.9. Eigenvalues and Eigenvectors
5.3.4.10. Decomposition of Singular Values and Determinants
5.3.4.11. Forming Partitioned Matrices, Cbind () and Rbind ()
5.3.4.12. The Concatenation Function, c (), With Matrices

5.3.5. Factor Frequency Tables
5.3.6. Lists

5.3.6.1. Creating and Modifying Lists
5.3.6.2. Concatenation Lists

5.3.7. Dataframes

5.3.7.1. How to Create Dataframes?
5.3.7.2. Attach () and Separate ()
5.3.7.3. Working With Dataframes

5.4. Reading and Writing Data

5.4.1. The Read.Table () Function
5.4.2. The Scan () Function
5.4.3. Access to the Sets of Incorporated Data
5.4.4. Loading Data From Other R Packages
5.4.5. Editing Data

5.5. Grouping, Loops and Conditional Execution

5.5.1. Grouped Expressions
5.5.2. Control Statements

5.5.2.1. Conditional Execution: If Sentences
5.5.2.2. Repetitive Execution: For Loops, Repetition and Time

5.6. Writing Your Own Functions

5.6.1. Simple Examples
5.6.2. Defining New Binary Operators
5.6.3. Arguments With Name and Default Value
5.6.4. Argument “...”
5.6.5. Assignments Within Functions

Module 6. The graphical environment in R

6.1. Graphical Procedures

6.1.1. High-Level Plotting Commands

6.1.1.1. The Plot () Function
6.1.1.2. Multivariate Data Visualization
6.1.1.3. Screen Graphics
6.1.1.4. High-Level Plotting Arguments

6.1.2. Low-Level Plotting Commands

6.1.2.1. Mathematical Annotation
6.1.2.2. Hershey Vectorial Sources

6.1.3. Interacting With Graphics
6.1.4. The Use of Graphic Parameters

6.1.4.1. Permanent Changes: the Par () Function
6.1.4.2. Temporary Changes: Arguments to Graphic Functions

6.1.5. List of Graphic Parameters

6.1.5.1. Graphical Elements
6.1.5.2. Axles and Markings
6.1.5.3. Figure Margins
6.1.5.4. Multi-Figure Environment

6.1.6. Descriptive Statistics: Graphic Representations

Module 7. Statistical analysis in R

7.1. Discrete Probability Distributions
7.2. Continuous Probability Distributions
7.3. Introduction to Inference and Sampling (Point Estimate)
7.4. Confidence Intervals
7.5. Hypothesis Testing
7.6. ANOVA of a Factor
7.7. Adjustment Bondat (Chi-Square Test)
7.8. QFitdist Package
7.9. Introduction to Multivariant Statistics

Module 8. Machine learning for analysing big data

8.1. Introduction to Machine Learning
8.2. Presentation of the Problem, Loading Data and Libraries
8.3. Data Cleaning (Nas, Categories, Dummy Variables)
8.4. Exploratory Data Analysis (Ggplot) + Crossed Validation
8.5. Prediction Algorithms: Multiple Linear Regression, Support Vector Machine, Regression Trees, Random Forest...
8.6. Classification Algorithms: Logistic Regression, Support Vector Regression, Classification Trees, Random Forest...
8.7. Adjustment of the Hyper Parameters of the Algorithm
8.8. Predicting Data with Different Models
8.9. ROC Curves and Confusion Matrices for Assessing Model Quality

Module 9. Data mining applied to genomics

9.1. Introduction
9.2. Initiation to Variables
9.3. Text Cleaning and Conditioning
9.4. Generating the Word Matrix

9.4.1. Creating the TDM Word Matrix
9.4.2. Visualizations on the TDM Word Matrix

9.5. Description of the Word Matrix

9.5.1. Graphic Representation of the Frequencies
9.5.2. Creating a Word Cloud

9.6. Creating a Valid Data.frame for K-NN
9.7. Creating a Classification Model
9.8. Validating a Classification Model
9.9. Guided Practical Exercise on Data Mining in Cancer Genomics

Module 10. Techniques for extracting genomic data

10.1. Introduction to “Scraping Data”
10.2. Importing Spreadsheet Data Files Stored Online
10.3. Scraping HTML Text
10.4. Scraping Data from an HTML Table
10.5. Using APIs for Data Scraping
10.6. Extracting Relevant Information
10.7. Using the Rvest Package of R
10.8. Obtaining Data Distributed Over Multiple Pages
10.9. Extracting Genomic Data from the “My Cancer Genome” Platform
10.10. Extracting Information on Genes from the “HGNC Hugo Gene Nomenclature Committee” Database
10.11. Extracting Pharmacological Data from the “ONCOKG” (Precision Oncology Knowledge Base) Database

Module 11. New techniques in the age of genomics

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

11.1.1. Introduction
11.1.2. Medical history
11.1.3. Problems in the Application of Sanger Sequencing in Oncology
11.1.4. New Sequencing Techniques
11.1.5. Advantages of Using NGS in Clinical Practice
11.1.6. Limitations of Using NGS in Clinical Practice
11.1.7. Terms and Definitions of Interest
11.1.8. Types of Studies Depending on Their Size and Depth

11.1.8.1. Genome
11.1.8.2. Exomes
11.1.8.3. Multigenic Panels

11.1.9. Stages of NGS Sequencing

11.1.9.1. Preparing Samples and Libraries
11.1.9.2. Preparing Templates and Sequencing
11.1.9.3. Bioinformatic Processing

11.1.10. Annotation and Classification of Variants

11.1.10.1. Population Databases
11.1.10.2. Locus-Specific Databases
11.1.10.3. Bioinformatic Predictors of Functionality

11.2. DNA Sequencing and Bioinformatic Analysis

11.2.1. Introduction
11.2.2. Software
11.2.3. Procedure

11.2.3.1. Extracting Raw Sequences
11.2.3.2. Aligning Sequences
11.2.3.3. Alignment Refinement
11.2.3.4. Variant Call
11.2.3.5. Variant Filtering

11.3. RNA Sequencing and Bioinformatic Analysis

11.3.1. Introduction
11.3.2. Software
11.3.3. Procedure

11.3.3.1. QC Evaluation of Raw Data
11.3.3.2. RNAr Filtering
11.3.3.3. Filtered Quality Control Data
11.3.3.4. Quality Trimming and Adapter Removal
11.3.3.5. Alignment of Reads to a Reference
11.3.3.6. Variant Call
11.3.3.7. Differential Gene Expression Analysis

11.4. ChIP-Seq Technology

11.4.1. Introduction
11.4.2. Software
11.4.3. Procedure

11.4.3.1. CHiP-Seq Data Set Description
11.4.3.2. Obtaining Information About the Experiment Using the GEO and SRA Websites
11.4.3.3. Quality Control of the Sequencing Data
11.4.3.4. Trimming and Filtering Reads
11.4.3.5. Visualizing Results with the Integrated Genome Browser (IGV)

11.5. Big Data Applied to Oncology Genomics

11.5.1. The Process of Analysis Data

11.6. Genomic Servers and Databases of Genetic Variants

11.6.1. Introduction
11.6.2. Online Genomic Servers
11.6.3. Genomic Server Architecture
11.6.4. Data Recovery and Analysis
11.6.5.  Personalization

11.7. Annotation of Genetic Variants

11.7.1. Introduction
11.7.2. What is Variant Calling?
11.7.3. Understanding the VCF Format
11.7.4. Variant Identification
11.7.5. Variant Analysis
11.7.6. Predicting the Effect of the Variation of a Protein’s Structure and Function

Module 12. Application of bioinformatics in genomic oncology

12.1. Clinical and Pharmacological Enrichment of Gene Variants
12.2. Mass Search in PubMed for Genomic Information
12.3. Mass Search in DGIdb for Genomic Information
12.4. Mass Search in Clinical Trials for Clinical Trials on Genomic Data
12.5. Gene Similarity Search for the Interpretation of a Gene Panel or Exome
12.6. Mass Search for Genes Connected to a Disease
12.7. Enrich-Gen: Platform for the Clinical and Pharmacological Enrichment of Genes
12.8. Procedure to Produce a Genomic Report in the Age of Precision Oncology

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