## Cancer Genomics Module

#### Genome 541 Sp 2020 Course Website

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

Homework #5 and #6 accompany this module.

### Schedule of lectures and homework

**Dates:** April 28 - May 7

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

**TA:** Anna-Lisa Doebley (adoebley@uw.edu)

Date | Lecture Title | Lecture Slides | Homework Assigned |
---|---|---|---|

April 28 | Introduction to Cancer Genome Analysis | Lecture 1 | Homework 5 |

April 30 | Probabilistic Methods for Mutation Detection | Lecture 2 | |

May 5 | Probabilistic Methods for Copy Number Alteration Detection | Lecture 3 | Homework 6 |

May 7 | Additional Topics: Tumor heterogeneity, Mutation power analysis, Structural Variants in cancer | Lecture 4 |

#### Homework

Homework | Files | Due Date |
---|---|---|

Homework #5: Single nucleotide variant genotyping |
1. Assignment 2. R Markdown template 3. Python Jupyter notebook template 4. Homework5_alleleCounts.txt |
May 8, 11:59pm |

Homework #6: Profiling copy number alterations |
1. Assignment 2. R Markdown template 3. Python Jupyter notebook template 4. Homework6_log2ratios_chr1.txt |
May 15, 11:59pm |

### Module Outline

### Lecture 1: Introduction to cancer genome analysis

- Background on Cancer Genome Alterations
- Genomic alterations in cancer: drivers vs passengers, somatic vs germline
- Tumor evolution and heterogeneity

- Overview of Cancer Genome Analysis
- Computational strategy and workflow
- Tumor DNA Sequencing
- Types of genomic alterations predicted from tumor sequencing
- Methods/tools/algorithms in following lectures

- Primer on statistical modeling
- Probability distribution, Bayesian statistics, inference

### Lecture 2: Probabilistic methods for mutation detection

- Primer on statistical modeling (contâ€™d)
- Mixture models and inference using the EM algorithm

- Detecting Mutations in Cancer Genomes
- Visualizing somatic vs germline SNVs
- Sequencing read count data
- SNV genotyping strategy

- Mixture Models for SNV Detection
- SNVMix probabilistic model and EM inference
- Predicting somatic SNVs in cancer

- References:

SNVMix: Goya et al. Bioinformatics (2010)

JointSNVMix: Roth et al. Bioinformatics (2012)

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

- Detecting Copy Number Alterations in Cancer Genomes
- Predicting copy number features from sequence data
- Copy number analysis workflow
- Data normalization

- Continuous Hidden Markov Model (HMM)
- Graphical model representation
- Components of a continuous HMM
- Inference & parameter estimation using expectation-maximization (EM)

- Copy Number Profiling using a Hidden Markov Model
- Probabilistic model for copy number analysis
- Predicting copy number segments using the Viterbi algorithm

### Lecture 4: Additional topics

- Additional Copy Number Analysis Features
- Allelic copy number analysis

- Estimating tumor heterogeneity
- Modeling tumor-normal admixture
- Modeling tumor clonality and heterogeneity

- Assessing Statistical Power for Variant Discovery
- Power analysis
- Calibrating sequencing depth for variant discovery

- Structural Rearrangement Analysis in Cancer Genomes
- Structural variant types predicted from sequencing analysis
- Complex genomic structural rearrangements