In an era of rapidly advancing genomics and cancer research, understanding the evolutionary dynamics of tumours is critical for improving diagnostics, treatment strategies, and patient outcomes. The Cancer Genomics course is a 5-day theoretical and practical program designed to equip participants with the knowledge and tools necessary to analyse and interpret the complex genomic data generated from bulk DNA sequencing in the context of cancer, including learning state-of-the-art pipelines for bioinformatics data analysis. The course combines theoretical lectures, hands-on practical sessions (bioinformatics and data analysis), group discussions, and case studies. Participants will work with real sequencing data and simulated tumours that mimic the disease course of actual patients. Moreover, during hands-on sessions, participants can work with their own data, if available. All participants will apply computational tools and engage in peer-to-peer learning throughout the five days, with two experts in the field providing guidance and answering questions.

This course suits researchers, clinicians, bioinformaticians, and anyone interested in tumour genomics and computational analysis. Prior knowledge of basic genetics and genomics concepts is recommended but optional. Fluency with R programming using packages from the tidyverse (e.g., dplyr, ggplot) is highly suggested as a pre-requisite to benefit from the hands- on session. Nonetheless, all the source codes will be released to the participants, allowing continuous learning even after the course.

Configuration materials and training topics will be provided to participants several weeks before the course, ensuring ample time for preparation and enabling everyone to engage effectively in the lectures. On the first day of the course, participants with their own data will be asked to present briefly the context of the analyses of their study. On the last day of the project, they will be able to work with their own data. For this reason, they might wish to work alone to avoid sharing data that is yet unpublished. Participants who do not own any dataset will be given one to work with from the instructors.

By the end of this course, participants will have the skills and knowledge to unravel the intricate genomic landscape of tumours using computational approaches. This course will set the ground to gain more advanced skills independently, enabling participants to contribute to the ongoing efforts to improve cancer diagnosis and treatment strategies. This program is an invaluable resource for anyone seeking to make a meaningful impact in cancer research.

Sessions from 14:00 to 20:00 (Monday to Friday, after every 50min will be a 10min break).
Sessions will follow a learn-by-practice mode. After every topic will be discussion, Q&A, and practice.

= Day 1: A primer on tumor evolution and mutation calling.

Gain a fundamental understanding of cancer biology, genetic mutations, and their implications under the light of tumor heterogeneity and clonal evolution. Learn about the significance of bulk DNA sequencing in capturing genomic alterations, and how to detect using advances Nextflow pipelines like Sarek.

= Day 2: Copy Number Analysis and Quality Control.

Dive into the essential data preprocessing steps and quality control for bulk DNA sequencing data. Understand noise challenges and learn to identify "good quality" data. Learn to use R QC packages to integrate mutation and copy number data from whole-genome/ whole-exome sequencing.

= Day 3: Evolutionary Analysis

Discover the principles of clone tree construction for tumor evolution from a single sample, discuss the interpretation of clonal and subclonal mutations and work on case studies to analyze tumor evolution patterns using advanced R packages.

= Day 4: Follow-up analyses: multi-sample and mutational signatures

Explore more advanced designs with multiple samples from the same tumour (primary/metastasis or diagnosis/relapse), and learn to detect mutational signatures to profile endogenous and exogenous mutagenic processes in cancer using advanced R packages.

= Day 5: Project day

Gather in groups and design a "tumour evolution" analysis, trying to get to some results starting from real DNA bulk sequencing data provided by the instructors, or by yourself.

Should you have any further questions, please send an email to info@physalia-courses.org