Introduction to Cancer Bioinformatics II: Tumor Phylogeny

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" summary

Describes reconstructing tumor trees under the infinite sites assumption using perfect phylogeny, factoring sample variant allele frequencies (VAFs) via U×B (NP‑hard, ILP and heuristics used) and Dirichlet‑process clustering; presents a Bayesian MCMC method for noisy single‑cell data with explicit false positive/negative rates, applied to 58 cells and 18 mutations with 45% missing data, FP≈6e‑5 and FN≈0.43, yielding a mostly linear mutation tree with ambiguous sample attachments; population models quantify fixation (1−e^(−N u ρ t)), two‑hit dynamics and therapy resistance under mono vs combo treatments.

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" chapters (24)

1. 0:00Intro
2. 0:23Cancer is an evolutionary process
3. 3:25Phylogenetic Tree Reconstruction
4. 4:22Character-based tree reconstruction
5. 6:30A simple model
6. 7:28Binary characters
7. 8:21Rooted Binary Perfect Phylogeny
8. 9:11Perfect Phylogeny Theorem (Rooted, Binary O ancestor) Theorem: (Gusfield 1991)
9. 12:34Complications in Tumor Phylogeny
10. 14:47Sequencing mixtures
11. 18:08Single nucleotide variants and VAF
12. 20:19Clonal evolution and sequencing
13. 22:45Problem statement
14. 24:36Multi-Sample Sequencing and Approaches
15. 28:00Taxa tree vs. mutation tree
16. 28:51Likelihood
17. 32:11Attachment of samples
18. 35:21Single-cell sequencing of a myeloproliferative neoplasm (Hou et al., Cell 2012)
19. 48:33Towards the rearrangement tumor phylogeny
20. 51:12Dynamics of oncogene activation
21. 53:09Tumor suppressor genes (TSGS)
22. 54:05Dynamics of TSG inactivation
23. 55:40Traveling waves of driver clones
24. 57:34What's special about cancer evolution?

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