Introduction to Cancer Bioinformatics III: Cancer Progression Networks

on YouTube ↗56m

" summary

Defines and fits explicit progression models: a restricted Bayesian network (oncogenetic tree) and its generalization (conjunctive Bayesian network) with one parameter per event (θ) and exponential waiting times (λ), handles censoring and noisy calls via a nested EM and an MCMC sampler, and shows it recovers structure on simulated data. Applications: 251 renal CGH cases (12 events) highlight early 3p loss and unexpected 4q loss, pathway-level models are more stable and improve survival prediction.

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" chapters (26)

1. 0:00Cancer progression models
2. 1:44Vogelgram: Linear genetic progression
3. 3:48Linear progression
4. 4:14Phylogenetic vs. oncogenetic models
5. 5:56Outline
6. 6:52Modeling oncogenesis
7. 14:22Tree reconstruction
8. 17:16Posets and order ideals
9. 21:29Exit sets
10. 22:35Conjunctive Bayesian networks (CBN)
11. 27:58Individual progression
12. 29:45Waiting time distribution
13. 33:43Censoring
14. 35:36Posets define the geometry of genotype space
15. 37:41Hidden conjunctive Bayesian network (H-CBN)
16. 41:57Bayesian inference - MCMC sampler
17. 42:47Uncertainty: edge-wise posterior
18. 43:09Comparative genome hybridization (CGH)
19. 43:30Example: Renal cell carcinoma
20. 44:41Renal cell carcinoma: survival analysis
21. 46:48The genetic progression score (GPS) is a prognostic biomarker
22. 47:56Fitness advantage (colorectal cancer)
23. 48:06Progression on the level of pathways
24. 49:08Example: glioblastoma multiforme
25. 49:51Global model of pathway progression
26. 50:42Learning pathways and their dependencies simultaneously

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