1. Lecture 11. Topics in Omic Studies (Cancer Genomics, Transcriptomics and Epignomics) The Chinese University of Hong Kong CSCI5050 Bioinformatics and Computational Biology

2. Lecture outline 1.Special considerations in cancer omics Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 20152

3. SPECIAL CONSIDERATIONS IN CANCER OMICS Part 1

4. Some considerations Large number of mutations – Structural variations – Driver vs. passenger mutations – Tumor heterogeneity Mixture of tumor and non-tumor cells Emphasis of somatic changes – Choice of control samples Presence of cancer sub-types Search for druggable targets Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 20154

5. Large number of mutations Causes: – Carcinogens (polycyclic aromatic hydrocarbons (PAH) in cigarette smoke, UV, etc.) – Defect of DNA repair – Disrupted apoptosis pathway Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 20155 Image credit: Brown and Attardi, Nature Reviews Cancer 5(3):231-237, (2005)

6. Structural variations In many types of omic studies, SVs are considered rare. In cancer omic studies, the detection of SVs is considered an indispensible step. Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 20156

7. Driver vs. passenger mutations Driver mutation: Causal mutation in oncogenesis – Growth advantage – Positively selected Passenger mutation: Not contributing to cancer development Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 20157 Image credit: Stratton et al., Nature 458(7239):719-724, (2009)

8. Detection of driver mutations Mutations that affect known cancer genes Unexpected high frequency of recurrence – Same mutation in different cells in the same sample Detected by allele ratio Implication of early event and positive selection – Mutations that affect the same genes/pathways in different samples – Statistical significance needs to carefully evaluated according to the non-uniform background [discussion paper] Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 20158

9. Tumor heterogeneity Due to the high mutation rate, different tumor cells in a tumor can have different genomes – And potentially transcriptomes and epigenomes Standard sequencing of a tumor sample results in data that reflect the mixed population of cells rather than individual cells – Sequencing different parts of a tumor – Single-cell sequencing Potential biases caused by whole-genome amplification Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 20159

10. Tumor heterogeneity Single-cell sequencing from different sectors of a breast cancer sample: Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201510 Image credit: Navin et al., Nature 472(7341):90-94, (2011)

11. Mixture of tumor and non-tumor cells Presence of infiltrating stromal and immune cells – Micro-dissection – Estimation of tumor content – Computational removal of “contaminating” data from non-tumor cells Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201511

12. Consequence of non-tumor cells Suppose the sample contains c% of non-tumor cells – If G= AA in normal cells and there are no sequencing errors, expect (100-c)% reads supporting alternative allele if G= aa in tumor cells (100-c)/2% reads supporting alternative allele if G= Aa in tumor cells Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201512

13. Consequence of non-tumor cells Assuming all bases have a phred score of 30 (base error=0.001) and 60x coverage: Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201513

14. Consequence of non-tumor cells Assuming all bases have a phred score of 30 (base error=0.001) and 60x coverage: Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201514

15. Emphasis on somatic changes Comparing tumor and non-tumor samples Choice of non-tumor control: – Normal tissue How to obtain? Transplant? – Tumor-adjacent from same patient Can be considered as normal? – Blood from same patient Useful given different tissue types? Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201515

16. Special analysis pipelines Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201516 Figure credit: Saunders et al., Bioinformatics 28(14):1811-1817, (2012); Wang et al., Genome Medicine 5(10):91, (2013)

17. Cancer sub-types Patients diagnosed to have the same type of cancer could have very different prognosis and drug response Cancer sub-types can be identified by molecular signatures Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201517

18. Cancer sub-types Sub-types of gastric cancer: Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201518 Figure credit: The Cancer Genome Atlas Research Network, Nature 513(7517):202-209, (2014)

19. Druggable targets Cancer omics do not only aim at understanding the molecular mechanisms, but also identifying druggable targets Druggable targets: Proteins of mutated/aberrantly activated genes with known inhibitors – Other members of the same families (e.g., kinases) Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201519

20. Identification of druggable targets Computational docking siRNA screens CRISPR-Cas9 knock-out Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201520

21. Summary Factors of abnormal allele ratios – Copy number variation – Tumor heterogeneity – Contamination of non-tumor cells Some major research directions – Identification of driver (somatic) mutations – Discovery of cancer sub-types – Search for druggable targets Last update: 13-Nov-2015CSCI5050 Bioinformatics and Computational Biology | Kevin Yip-cse-cuhk | Fall 201521