CS 598MEB: Computational Cancer Genomics

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Semester	Spring 2024
Instructor	Prof. Mohammed El-Kebir
Time	TR 2:00-3:15 PM
Location	1302 in Siebel Center
Office hours	Tuesdays 3:30-4:30 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
01/16/2024	Mohammed El-Kebir	Introduction [slides]	Biology for Computer Scientists -- Lawrence Hunter [link]
01/18/2024	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
01/23/2024	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 Y. Qi, D. Pradhan and M. El-Kebir. Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors. Algorithms for Molecular Biology, 14:19, 2019
01/25/2024	Mohammed El-Kebir	ILP [slides]	Jupyter notebook for MaxClique [link] MaxClique instance C125.9 [link] Jupyter notebook for PPM [link]
01/30/2024	Mohammed El-Kebir	Cancer Phylogenetics III [slides I] [slides II]	Y. Qi, D. Pradhan and M. El-Kebir. Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors. Algorithms for Molecular Biology, 14:19, 2019 N. Aguse, Y. Qi and M. El-Kebir. Summarizing the Solution Space in Tumor Phylogeny Inference using Multiple Consensus Trees. Bioinformatics (ISMB/ECCB 2019), 35:i408-i146, 2019
02/06/2024	Mohammed El-Kebir	Cancer Phylogenetics IV [slides I] [slides II]	N. Aguse, Y. Qi and M. El-Kebir. Summarizing the Solution Space in Tumor Phylogeny Inference using Multiple Consensus Trees. Bioinformatics (ISMB/ECCB 2019), 35:i408-i146, 2019 M. El-Kebir. SPhyR: Tumor Phylogeny Estimation from Single-Cell Sequencing Data under Loss and Error. Bioinformatics (ECCB 2018), 34(17):i671-679, 2018
02/08/2024	Mohammed El-Kebir	Single-cell Phylogenetics I [slides]	M. El-Kebir. SPhyR: Tumor Phylogeny Estimation from Single-Cell Sequencing Data under Loss and Error. Bioinformatics (ECCB 2018), 34(17):i671-679, 2018
02/13/2024	Mohammed El-Kebir	Single-cell Phylogenetics II [slides]	L.L. Weber, C. Zhang, I. Ochoa and M. El-Kebir. Phertilizer: growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors. PLOS Computational Biology 19, no. 10: e1011544, 2023. Extended version of RECOMB-CCB 2023 paper.
02/15/2024	Mohammed El-Kebir	SCS Design [slides]	L. Weber, N. Aguse, N. Chia and M. El-Kebir. PhyDOSE: Design of Follow-up Single-cell Sequencing Experiments of Tumors. PLoS Computational Biology, 16(10):e1008240, 2020.
02/20/2024	Mohammed El-Kebir	Mutation Clustering [slides]	G. Satas*, S. Zaccaria*, M. El-Kebir*†, B.J. Raphael†. DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution. Cell Systems.
02/22/2024	Mohammed El-Kebir	CNA Calling from Bulk Data [recording]	S. Zaccaria S and B.J. Raphael. Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nat Commun. 2020;11(1):4301
02/27/2024	Mohammed El-Kebir	CNA Calling from Bulk Data [recording]	S. Zaccaria S and B.J. Raphael. Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nat Commun. 2020;11(1):4301
02/29/2024	Mohammed El-Kebir	Signatures [slides]	S. Christensen, M.D.M. Leiserson, M. El-Kebir. PhySigs: Phylogenetic Inference of Mutational Signature Dynamics. Pacific Symposium on Biocomputing, 25:226-237, 2021
03/05/2024	Mohammed El-Kebir	Clone Tree Integration + CNA Phylogeny [slides I] [slides II]	M. El-Kebir, B.J. Raphael, R. Shamir, R. Sharan, S. Zaccaria, M. Zehavi and R. Zeira. Complexity and algorithms for copy-number evolution problems. Algorithms for Molecular Biology, 12(1), 2017. P. Sashittal, S. Zaccaria and M. El-Kebir†. Parsimonious Clone Tree Reconciliation in Cancer. WABI 2021, Workshop on Algorithms in Bioinformatics, Chicago, IL (virtual), August 2-4, 2021.
03/07/2024	Mohammed El-Kebir	Metastasis [slides]	M. El-Kebir, G. Satas and B.J. Raphael. 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. M.S. Roddur, S. Snir and M. El-Kebir†. Inferring Temporally Consistent Migration Histories. WABI 2023, Workshop on Algorithms in Bioinformatics, Houston, TX, September 3-6, 2023.
03/19/2024	Stefan Ivanovic	CloMu + DeepCopy [slides]	S. Ivanovic and M. El-Kebir†. Modeling and Predicting Cancer Clonal Evolution with Reinforcement Learning. Genome Research 33: 1078-1088 (2023). S. Ivanovic and M. El-Kebir†. Evolution-Aware Deep Reinforcement Learning for Single-Cell DNA Copy Number Calling.
03/21/2024	Leah Weber	Cancer Immunology [slides]
03/26/2024	Students	Papers 1-2 [slides I] [slides II]	Abbosh et al. Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA. Nature, 616(7957), 553–562. Quinn et al. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science, 371(6532), eabc1944.
03/28/2024	Students	Papers 3-4 [slides I] [slides II]	Pan et al. LinRace: Cell division history reconstruction of single cells using paired lineage barcode and gene expression data. Nature Communications, 14(1), 8388. Yang et al. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell, 185(11), 1905-1923.e25.
04/02/2024	Students	Papers 5-6 [slides I] [slides II]	Sashittal et al. ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model. Genome Biology, 24(1), 272. Sashittal et al. Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing. Cell Systems, 14(12), 1113-1121.e9.
04/04/2024	Students	Papers 7-8 [slides I] [slides II]	Schmidt et al. A zero-agnostic model for copy number evolution in cancer. PLOS Computational Biology, 19(11), e1011590.. Melin et al. VICTree—A Variational Inference method for Clonal Tree reconstruction. bioRxiv.
04/09/2024	Students	Papers 9-10 [slides I] [slides II]	Gavish et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature, 618(7965), 598–606. Antonello et al. Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc. Genome Biology, 25(1), 38.
04/11/2024	Students	Papers 11-12 [slides I] [slides II]	Hie et al. Efficient evolution of human antibodies from general protein language models. Nature Biotechnology, 42(2), 275–283. Metkar et al. Tailor made: the art of therapeutic mRNA design. Nature Reviews Cancer.
04/16/2024	Students	Papers 13-15 [slides I] [slides III]	Archetti and Pienta. Cooperation among cancer cells: applying game theory to cancer. Nature Reviews Cancer. Shi et al. Allele-specific transcriptional effects of subclonal copy number alterations enable genotype-phenotype mapping in cancer cells. Nature Communications. Jones et al. Inference of single-cell phylogenies from lineage tracing data using Cassiopeia. Genome Biology, 21, 92 (2020).
04/18/2024	Students	Projects 1-5	TBD TBD TBD TBD TBD
04/23/2024	Students	Projects 6-10	TBD TBD TBD TBD TBD
04/25/2024	Students	Projects 11-15	TBD TBD TBD TBD TBD
04/30/2024		No class

Deadlines

All deadlines are 11:59pm CST.

• 03/19/2024: Pick paper to present
• 04/01/2024: Project proposal
• 04/08/2024: Proposal peer reviews
• 04/28/2024: Initial project report submission
• 05/03/2024: Initial project report peer reviews
• 05/10/2024: Final project report submission

Further instructions are on piazza.

Statement on Anti-racism and Inclusivity

The Grainger College of Engineering is committed to the creation of an anti-racist, inclusive community that welcomes diversity along a number of dimensions, including, but not limited to, race, ethnicity and national origins, gender and gender identity, sexuality, disability status, class, age, or religious beliefs. The College recognizes that we are learning together in the midst of the Black Lives Matter movement, that Black, Hispanic, and Indigenous voices and contributions have largely either been excluded from, or not recognized in, science and engineering, and that both overt racism and micro-aggressions threaten the well-being of our students and our university community.

The effectiveness of this course is dependent upon each of us to create a safe and encouraging learning environment that allows for the open exchange of ideas while also ensuring equitable opportunities and respect for all of us. Everyone is expected to help establish and maintain an environment where students, staff, and faculty can contribute without fear of personal ridicule, or intolerant or offensive language. If you witness or experience racism, discrimination, micro-aggressions, or other offensive behavior, you are encouraged to bring this to the attention of the course director if you feel comfortable. You can also report these behaviors to the Bias Assessment and Response Team (BART) (https://bart.illinois.edu/). Based on your report, BART members will follow up and reach out to students to make sure they have the support they need to be healthy and safe. If the reported behavior also violates university policy, staff in the Office for Student Conflict Resolution may respond as well and will take appropriate action.

CS CARES and CS Values and Code of Conduct

All members of the Illinois Computer Science department - faculty, staff, and students - are expected to adhere to the CS Values and Code of Conduct. The CS CARES Committee is available to serve as a resource to help people who are concerned about or experience a potential violation of the Code. If you experience such issues, please contact the CS CARES Committee. The Instructors of this course are also available for issues related to this class.

Statement on Mental Health

Diminished mental health, including significant stress, mood changes, excessive worry, substance/alcohol abuse, or problems with eating and/or sleeping can interfere with optimal academic performance, social development, and emotional wellbeing. The University of Illinois offers a variety of confidential services including individual and group counseling, crisis intervention, psychiatric services, and specialized screenings at no additional cost. If you or someone you know experiences any of the above mental health concerns, it is strongly encouraged to contact or visit any of the University’s resources provided below. Getting help is a smart and courageous thing to do -- for yourself and for those who care about you.

Counseling Center	217-333-3704	610 East John Street Champaign, IL 61820
McKinley Health Center	217-333-2700	1109 South Lincoln Avenue, Urbana, Illinois 61801