## Cancer Genomics Module

#### Genome 541 Course Website

### Schedule of lectures and homework

The Cancer Genomics Module is the third module in the course and consists of 4 lectures.

Homework #5 and #6 accompany this module.

**Dates:** April 28 - May 7

**Times:** Tuesday & Thursday @ 10:30am - 11:50am

Date | Lecture | Homework Assigned |
---|---|---|

April 28 | Introduction to Cancer Genome Analysis | |

April 30 | Probabilistic Methods for Mutation Detection | HW #5 (Due May 8 @ 11:59pm) |

May 5 | Probabilistic Methods for Copy Number Alteration Detection | |

May 7 | Additional Topics: Structural Variation, Signature Analysis | HW #6 (Due May 15 @ 11:59pm) |

### Module Outline

### Lecture 1: Introduction to cancer genome analysis

- Cancer biology: background & motivation
- Types of genomic alterations in cancer
- Cancer genomics concepts: Tumor heterogeneity, drivers vs passengers
- Tumor DNA sequencing: Technology, data & resources
- Cancer genome analysis: Computational strategies & methods for detection genomic alterations
- Primer on topics:
- Probability/uncertainty, statistical modeling (mixture modeling), graphical models
- Bayesian statistics and model inference

- Overview of tools/methods to be taught in next lectures
- References:

### Lecture 2: Probabilistic methods for mutation detection

- Introduction to single-nucleotide variants in cancer
- Drivers vs passengers, clonality
- Sequencing data: whole exome & genome, targeted panels

- Overview of SNV prediction strategy
- Read-level data from tumor and normal sequencing
- Problem formulation and probabilistic model
- Parametric vs non-parametric approaches

- Mutation detection tools: Mutect and SNVMix
- References:

#### Homework #5

### Lecture 3: Probabilistic methods for copy number alteration detection

- Introduction to copy number alterations (CNA) in cancer
- Gene dosage, somatic vs germline
- Data: SNP genotype arrays, whole genome & exome

- Overview of CNA prediction strategy
- Data normalization
- Tumor vs Reference (normal) signals
- Parametric vs non-parametric approaches

- Copy number segmentation algorithms (tools):
- Circular binary segmentation (CBS/DNAcopy)
- Hidden markov models (ichorCNA/HMMcopy)

- References: