Teacher

Rosalba Giugno

Referent

Rosalba Giugno

Credits

3

Language

English

Class attendance

Free Choice

Location

VERONA

Moodle Seminars 0

Learning objectives

Acquisition of advanced techniques for the analysis of biomedical data.

Acquire essential knowledge of tumor genomics.

Understand the architecture of precision oncology systems.

Learn methods and application software for processing, managing, and analyzing clinical and genomic data.

Practically apply sequencing data analysis using the R language.

Prerequisites and basic notions

Knowledge of omics analysis and programming skills are required.

Program

Introduction to Cancer:
Hallmarks of cancer
Case study: Multiple myeloma
Overview of cancer therapy
Introduction to Precision Oncology:
Rationale and objectives
Progress and milestones
Clinical trials and regulatory considerations
Limitations and future prospects
High-Throughput Sequencing Technologies:
DNA sequencing
RNA sequencing
Architecture of a Precision Oncology Platform:
Designing and implementing a precision oncology workflow
From raw data to actionable insights
Pre-Processing of Sequencing Data and Quality Control:
Quality control measures
Data preprocessing steps
DNA Sequencing Analysis:
Read alignment
Mutation calling: single-nucleotide variations (SNVs) and short indels
Copy number alterations
Genomic metrics and interpretation
RNA Sequencing Analysis:
Read alignment: reference-based vs. pseudo-alignment
Gene expression quantification
Gene expression profiling
Data normalization techniques
Differential expression analysis and biomarker identification
Functional analysis (gene set and pathway analysis)
Gene fusion detection
Survival Analysis:
Kaplan-Meier estimation
Cox proportional hazards model
Stratified analysis and interpretation
Intra-Tumor Heterogeneity: Modeling and Tumor Evolution:
Clonal evolution
Computational modeling techniques
Drug Repurposing: Methods and Applications:
Computational drug repurposing methods
Case studies and success stories
Data Annotation, Interpretation, and Prioritization:
Integrating clinical data
Annotation tools and databases
Prioritization algorithms
Precision Medicine Reports:
Structure and components of a precision medicine report
Communicating findings to clinicians
Case Study: Precision Medicine in Multiple Myeloma:
Detailed case study
Integrative analysis
Clinical impact

Didactic methods

Theory and practice

Learning assessment procedures

Presentation of a report

Assessment

The correctness of the methodology and analyzes conducted, the clarity of exposition and the student's autonomy will be evaluated.

Criteria for the composition of the final grade

The average of each rating point.

Scheduled Lessons