In an era of rapidly advancing genomics and cancer research, understanding the evolutionary dynamics of tumors is critical for improving diagnostics, treatment strategies, and patient outcomes. The "Computational Tumor Evolution from Bulk DNA Sequencing" course is a comprehensive 5-day program, both theoretical and practical, designed to equip participants with the knowledge and tools necessary to analyze and interpret the complex genomic data generated from bulk DNA sequencing in the context of cancer.

The course combines theoretical lectures, hands-on practical sessions, group discussions, and case studies. Participants will have the opportunity to work with real sequencing data, apply computational tools, and engage in peer-to-peer learning. Throughout the five days, two experts in the field will provide guidance and answer questions.

This course is suitable for researchers, clinicians, bioinformaticians, and anyone interested in tumor genomics and computational analysis. Prior knowledge of basic genetics and genomics concepts is recommended, but not required. Basic R programming with packages from the tidyverse (e.g., dplyr, ggplot) is suggested as a pre-requisite.

By the end of the "Computational Tumor Evolution from Bulk DNA Sequencing" course, participants will have the skills and knowledge to unravel the intricate genomic landscape of tumors, 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 the field of 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

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 refresh your R coding skills in terms of data processing and visualisation.

= Day 2:  Quality Control for Mutation Calling and Copy Number Analysis

Dive into the essential steps of data preprocessing and quality control for bulk DNA sequencing data. Understand the challenges of noise 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