Presentation at the CDAC 2018 Workshop and School on Cancer Development and Complexity http://cdac2018.lakecomoschool.org

1. Understanding cancer genomes:
from mutational processes to
tumor evolution
Abel Gonzalez-Perez
Ramon y Cajal Research Associate
Institute for Research in Biomedicine
Barcelona
http://bbglab.irbbarcelona.org

2. Understanding cancer genomes: from mutational
processes to tumor evolution
Biomedical Genomics Lab
Institute for Research in Biomedicine

3. Understanding
mutational
processes
Finding drivers
of cancer
Precision
cancer
medicine

4. Understanding
mutational
processes
Finding drivers
of cancer
Precision
cancer
medicine

5. Biomedical Genomics Lab
Institute for Research in Biomedicine
Joan
Sabari
Understanding mutational processes

6. Whole-genome sequenced cohorts of tumors to
understand mutational processes
Mike Stratton. EMBO Molecular Medicine (2013)

7. Understanding mutational processes
Mutagenic processes
(internal and external)
Mechanisms of DNA
repair

8. Mutational patterns inform about DNA repair
Schuster-Böckler & Lehner. Nature. 2012
Lawrence et al. Nature. 2013

9. Mutation rate variation at
megabase scale
Mutation rate variation at
nucleotide resolution
Do DNA-binding proteins
infuence mutation rateo
TF

10. Increased mutation rate in active TFBS in melanomas
proximal-TFBS
TF
38 Melanoma samples from TCGA

11. Increased mutation rate in most TFBS

12. Increased TFBS mutation rate in most melanomas

13. distal TFBS - located >5kb from transcription start sites
nucleosome signal from lymphoblastoid cell line
Increased mutation rate also in distal TFBS and nucleosome
sites
distal TFBS-DHS (n = 41,758)

14. Lans et al. Epigenetics & Chromatin 2012 5:4
Nucleotide Excision Repair (NER): Global and Transcription-
coupled repair

15. Lans et al. Epigenetics & Chromatin 2012 5:4
Genome-wide map of NER
activity
Hu et al., G & D, 2015 (Sancar
lab)
XR-seq (eXcision Repair
Sequencing)
● Skin fibroblast cell line (WT)
● CS-B mutant (deficient in TC-
NER)
● XP-C mutant (deficient in
GG-NER)
Nucleotide Excision Repair (NER): Global and Transcription-
coupled repair

16. CPD - cyclobutane pyrimidine dimers (UV induced photoproducts) | NHF1 - irradiated skin fibroblast
cell line
CS-B - mutant cell line which lack transcription-coupled repair
Decreased NER activity in active TFBS

17. Is mutation rate in TFBS
also increased in other
tumor typeso

18. Whole genome somatic mutation from TCGA (Fredriksson et al,. 2014)
Increased mutation rate at active TFBS of lung cancer

19. TFs bound to DNA interfere with DNA repair
machinery resulting in increased mutation rate
Radhakrishnan et al. Nature. 2016

20. Do exons and introns have diferent mutation rateo

21. Does exon-intron structure affect DNA repair?
Chapman et al. Nature. 2011

22. Does exon-intron structure affect DNA repair?
Schwartz et al. Nat. Struct. Mol. Biol. 2009

23. Does exon-intron structure affect DNA repair?

24. Does exon-intron structure affect DNA repair?
How to measure the decreased
exonic mutation burden?

25. Does exon-intron structure affect DNA repair?
How to measure the decreased exonic
mutation burden without biases?
At the gene level

26. Does exon-intron structure affect DNA repair?

27. Does exon-intron structure affect DNA repair?
Chapman et al. Nature. 2011

28. Does exon-intron structure affect DNA repair?

29. Does MMR repair exons more efficiently than introns?

30. Does MMR repair exons more efficiently than introns?

31. Does MMR repair exons more efficiently than introns?

32. Does exon-intron structure affect DNA repair?
Schwartz et al. Nat. Struct. Mol. Biol. 2009

33. Does H3K36me3 play a role in differential MMR?

34. Does H3K36me3 play a role in differential MMR?

35. Differential MMR results in decreased exon mutation
burden
Frigola, Radhakrishnan et al., Nature Genetics. 2017

36. Understanding
mutational
processes
Finding drivers
of cancer
Precision
cancer
medicine

37. How can we use the power of the cohort in drivers
identification?

38. How can we use the power of the cohort in drivers
identification?
Excess computed by NBR (Inigo Martincorena, Sanger)

39. Are all mutations affecting driver elements drivers?
Excess computed by NBR (Inigo Martincorena, Sanger)

40. How about non-coding driver elements?

41. Elements mutated below the power of the cohort
Strict rule-based approach

42. The panorama of driver alterations
Collaboration with Rameen Berroukhim’s and Jan Korbel’s labs

43. The whole-genome panorama of driver events
●
Do all tumors have genomic origin?
●
How many driver events are required to initiate a
tumors?
●
What is the contribution of non-coding mutations to
tumorigenesis?
●
And what is the contribution of different types of
alterations?
Radhakrishnan, Pich et al. Manuscript in preparation

44. Are all tumors of genomic origin?
Collaboration with Rameen Berroukhim’s and Jan Korbel’s labs
Driver alterations in 90% of tumors

45. What is the contribution of non-coding mutations?
10% of driver mutations affect non-coding elements
21% of the tumors bear at least one non-coding driver mutation

46. How many driver mutations in a tumor?
4.6 driver alterations on average

47. How many driver mutations in a tumor?

48. What is the contribution of mutations and structural
variants to tumorigenesis?

49. Half of all driver genes affected by more than one type
of alteration

50. Half of all driver genes affected by more than one type
of alteration

51. How frequent are double-hit biallelic inactivations of
tumor suppressors?
Collaboration with Nikos Sidiropoulos and Joachim Weischenfeldt, BRIC Copenhagen

52. The whole-genome panorama of driver events
●
Virtually all tumors are rooted in genomic alterations
●
The number of drivers is remarkably stable despite
huge fluctuations of mutational burden
●
21% of tumors contain non-coding driver mutations
●
Half of all cancer genes suffer multiple types of
alterations
Radhakrishnan, Pich et al. Manuscript in preparation

53. Cancer from the bottom up: mutational processes,
origin and ecosystem
Understanding
mutational
processes
Finding drivers
of cancer
Precision
cancer
medicine

54. Cancer from the bottom up: mutational processes,
origin and ecosystem
Biomedical Genomics Lab
Institute for Research in Biomedicine
DavidCarlota

55. Three scenarios of tumor immune
infiltration and mechanisms of evasion

56. Cancer (somatic) development is an evolutionary process
It operates on variation and selection

57. Tumors develop mechanisms to avoid immune destruction
Taken from Hanahan and Weinberg, 2011

58. Tumors are immunogenic
●
HLA suppression
●
De novo somatic antigens expression
●
Oncogenic viruses antigens expression
Avoid or escape immune surveillance
Suppression of immune action
●
PDL1 expression
●
Secretion of TGF-beta

59. Systematically identify pan-cancer (~10,000 tumors of 29 cancer types)
mechanisms of immune evasion

60. Systematically identify pan-cancer mechanisms of immune evasion
Discrete phenotypes of immune infiltration

61. Immune-phenotypes group tumors with similar infiltration pattern
934 non-TN breast tumors

62. Immune-phenotypes group tumors with similar infiltration pattern
Across cancer types, independent of overall infiltration

63. Systematically identify pan-cancer mechanisms of immune evasion
Discrete phenotypes of immune infiltration
Associations between immune-phenotypes and tumor features
●
Features that explain tumor immunogenicity
●
Evidences of immune edition
●
Events that are positively selected for favoring immune evasion

64. Immune-phenotypes correlate with clinical variables

65. Global genomic features explain immunogenicity

66. Global genomic features evidence immune edition

67. What do transcriptional programs across immune-phenotypes look like?

68. How about specific mechanisms of immune evasion?
Detect driver events (with signals of positive selection)

69. What about specific mechanisms of immune evasion?

70. Three scenarios of immune infiltration and evasion

71. Tamborero, Rubio-Perez et al., Clinical Cancer Research, 2018

72. Thank you!
CarlotaFerran Sabari
David