MIT CompBio Lecture 22 - Cancer Genomics (Fall 2019)

MIT Computational Biology: Genomes, Networks, Evolution, Health
http://compbio.mit.edu/6.047/
Prof. Manolis Kellis

Full playlist with all videos in order is here:    • Machine Learning in Genomics - Fall 2019  

All slides from Fall 2019 are here: https://stellar.mit.edu/S/course/6/fa...

Outline for this lecture:
0. Introduction: oncogenes, tumor suppressors, hallmarks
- Hallmarks of cancer, tumor-suppressors, proto-oncogenes/oncogenes, mutator phenotype, oncoviruses, and fusion oncogenes.
- Exome sequencing lessons: recurrence and heterogeneity
1. Recurrence: common signatures across types/patients/tumors/clones
- Evolutionary dynamics: clonal heterogeneity, computational models
- WGS lessons: non-coding drivers and convergence
2. Background mutation rate: regions, chromatin states, patients, plexi
- Convergence: mutations  enhancers  genes  pathways
3. Beyond mutations: epigenomics, functional heterogeneity
- Epigenomic alterations: reprogramming
- Single-cell sequencing: functional heterogeneity
4. Tumor immunology, microenvironment, immunotherapy
- Tumor-microenvironment interactions, cancer immunoediting.
- Neo-antigens, immune suppression, immunotherapy.