CS 598MEB: Computational Cancer Genomics

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Semester	Spring 2019
Instructor	Prof. Mohammed El-Kebir
Time	TR 2:00-3:15 PM
Location	1103 Siebel Center
Office hours	Tuesdays 3:15-4:15 PM in 3216 Siebel Center

Course description

This course introduces fundamental problems and algorithmic approaches in cancer genomics. Covered topics include:

1. tumor phylogeny inference,
2. cancer immunotherapy,
3. driver mutation identification,
4. somatic variant calling.

This course will not teach you how to run popular bioinformatics tools. Rather, we will focus on the underlying algorithmic ideas and the issues that arise when translating a biological problem into a computational problem and ultimately an accurate tool for biologists to use. In addition, this course will teach you how to read scientific papers and how to propose and conduct independent research.

Prerequisites

This course is appropriate for graduate students in computer science, bioengineering, mathematics and statistics. Familiarity with basic statistics, probability and algorithms is expected.

Grading

• Class participation: 20%
• Asking critical questions
• Peer review
• Paper presentation: 30%
• Project: 50%
• Proposal
• Report/paper
• Presentation

Course schedule

Date	Presenter	Slides	Reading
01/15/2019	Mohammed El-Kebir	Introduction [slides]	Biology for Computer Scientists -- Lawrence Hunter [link]
01/17/2019	Mohammed El-Kebir	Cancer Phylogenetics I [slides]	Lecture notes [link] M. El-Kebir, L. Oesper, H. Acheson-Field and B. J. Raphael. Reconstruction of clonal trees and tumor composition from multi-sample sequencing data. Bioinformatics (Special Issue: Proceedings of ISMB), 31(12):i62-i70, 2015
01/22/2019	Mohammed El-Kebir	Cancer Phylogenetics II [slides]	M. El-Kebir, L. Oesper, H. Acheson-Field and B. J. Raphael. Reconstruction of clonal trees and tumor composition from multi-sample sequencing data. Bioinformatics (Special Issue: Proceedings of ISMB), 31(12):i62-i70, 2015 D. Pradhan and M. El-Kebir. On the Non-uniqueness of Solutions to the Perfect Phylogeny Mixture Problem. In: Blanchette M., Ouangraoua A. (eds) Comparative Genomics. RECOMB-CG 2018. Lecture Notes in Computer Science, vol 11183. Springer, Cham.
01/24/2019	Mohammed El-Kebir	Cancer Phylogenetics III [slides]	D. Pradhan and M. El-Kebir. On the Non-uniqueness of Solutions to the Perfect Phylogeny Mixture Problem. In: Blanchette M., Ouangraoua A. (eds) Comparative Genomics. RECOMB-CG 2018. Lecture Notes in Computer Science, vol 11183. Springer, Cham.
01/29/2019	Mohammed El-Kebir	Consensus Trees	K. Govek, C. Sikes and L. Oesper A Consensus Approach to Infer Tumor Evolutionary Histories. In: ACM-BCB’18.
01/31/2019	Mohammed El-Kebir	Single-cell Phylogeny Inference [slides]	Lecture notes: [link] M. El-Kebir†. SPhyR: Tumor Phylogeny Estimation from Single-Cell Sequencing Data under Loss and Error. Bioinformatics (ECCB 2018), 34(17):i671-679, 2018.
02/05/2019	Mohammed El-Kebir	Phylogeny Inference with CNAs [slides]	M. El-Kebir*, G. Satas* , L. Oesper and B.J. Raphael. Inferring the Mutational History of a Tumor using Multi-State Perfect Phylogeny Mixtures. Cell Systems, 3(1):43-53, 2016.
02/07/2019	Mohammed El-Kebir	Mutation Clustering I [slides]	DCF preprint [Google drive, see piazza]
02/12/2019	Mohammed El-Kebir	Mutation Clustering II [slides]	DCF preprint [Google drive, see piazza]
02/14/2019	Mohammed El-Kebir	Mutation Clustering III [slides]	DCF preprint [Google drive, see piazza]
02/19/2019	Mohammed El-Kebir	ILP [slides]	Jupyter notebook [link] MaxClique instance C125.9 [link]
02/21/2019	Mohammed El-Kebir	CNA Phylogenies [slides]	M. El-Kebir et al. Complexity and algorithms for copy-number evolution problems. Algorithms for Molecular Biology, 12(1), 2017
02/26/2019	Mohammed El-Kebir	Metastasis I [slides]	M. El-Kebir et al. Inferring parsimonious migration histories for metastatic cancers. Nature Genetics, 50:718-726, 2018. M. El-Kebir. Parsimonious Migration History Problem: Complexity and Algorithms. WABI 2018.
02/28/2019	Mohammed El-Kebir	Metastasis II [slides]	M. El-Kebir et al. Inferring parsimonious migration histories for metastatic cancers. Nature Genetics, 50:718-726, 2018. M. El-Kebir. Parsimonious Migration History Problem: Complexity and Algorithms. WABI 2018.
03/05/2019	Mohammed El-Kebir	Paper: CNA calling [slides]	Zaccaria, S. & Raphael, B. J. (2018). Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. bioRxiv.
03/07/2019	Chuanyi Zhang	Paper: SNV calling [slides]	Cooke, D. P., Wedge, D. C., & Lunter, G. (2018). A unified haplotype-based method for accurate and comprehensive variant calling. bioRxiv.
03/12/2019	Bryce Kille Yuanyuan Qi	Paper: CNA phylogeny [slides] Paper: SV calling [slides]	Letu, Q., et al. (2018). On the Minimum Copy Number Generation Problem in Cancer Genomics. ACM BCB, 260–269. Deshpande, V., et al. (2019). Exploring the landscape of focal amplifications in cancer using AmpliconArchitect. Nature Communications, 10(1).
03/14/2019	Ashwin Ramesh Ram Gupta	Paper: Evo. models I [slides] Paper: Evo. models II [slides]	Sun, R., et al. (2017). Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nature Genetics, 49(7), 1015–1024. Williams, M. J., et al. (2018). Quantification of subclonal selection in cancer from bulk sequencing data. Nature Genetics, 50(6), 895–903.
03/26/2019	Nuraini Aguse Wei Qian	Paper: Cancer immunology I [slides] Paper: Cancer immunology II [slides]	Łuksza, M., et al. (2017). A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature, 551(7681), 517. Bulik-Sullivan, B., et al. (2019). Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nature Biotechnology, 37(1), 55–63.
03/28/2019	Lan Qin Sarah Christensen	Paper: Mutual exclusivity [slides] Paper: MWCS [slides]	Kim, J. W., et al. (2016). Characterizing genomic alterations in cancer by complementary functional associations. Nature Biotechnology, 34(5), 539–546. Eduardo Alvarez-Miranda, et al. (2013). The Maximum Weight Connected Subgraph Problem. Facets of Combinatorial Optimization, 245-270.
04/02/2019	Sarah Christensen Vaishnavi Subramanian	Paper: MWCS [slides] Paper: Liquid biopsies [slides]	Eduardo Alvarez-Miranda, et al. (2013). The Maximum Weight Connected Subgraph Problem. Facets of Combinatorial Optimization, 245-270. Abbosh, C., et al. (2017). Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature, 545(7655), 446–451.
04/04/2019	Shayan Tabe Bordbar Mohammed El-Kebir	Paper: Single-cell I [slides] Paper: Single-cell II	Bian, S. et al., et al. (2018). Single-cell multiomics sequencing and analyses of human colorectal cancer. Science, 362(6418), 1060–1063. Malikic, S., et al. (2017). Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. bioRxiv, 234914.
04/09/2019	Juho Kim Vladimir Smirnov	Paper: Trajectories I [slides] Paper: PPM [slides]	Caravagna, G., et al. (2018). Detecting repeated cancer evolution from multiregion tumor sequencing data. Nature Methods, 15(9), 707–.. Jia, B., et al. (2018). Efficient Projection onto the Perfect Phylogeny Model. In Advances in Neural Information Processing Systems (pp. 4108-4118). NIPS 2018.
04/11/2019	Mohammed El-Kebir	Paper: Temporal phylogenies [slides]	Myers, M. A., et al. (2019). Inferring tumor evolution from longitudinal samples. bioRxiv, 526814.

Papers

See schedule.

Project

Example projects:

• Analyze cancer progression using SEER data
• Non-uniqueness and consensus tree clustering of 1-Dollo phylogenies