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CDAC 2018 Gonzales-Perez understanding cancer genomes

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

Published in: Health & Medicine
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CDAC 2018 Gonzales-Perez understanding cancer genomes

  1. 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. 2. Understanding cancer genomes: from mutational processes to tumor evolution Biomedical Genomics Lab Institute for Research in Biomedicine
  3. 3. Understanding mutational processes Finding drivers of cancer Precision cancer medicine
  4. 4. Understanding mutational processes Finding drivers of cancer Precision cancer medicine
  5. 5. Biomedical Genomics Lab Institute for Research in Biomedicine Joan Sabari Understanding mutational processes
  6. 6. Whole-genome sequenced cohorts of tumors to understand mutational processes Mike Stratton. EMBO Molecular Medicine (2013)
  7. 7. Understanding mutational processes Mutagenic processes (internal and external) Mechanisms of DNA repair
  8. 8. Mutational patterns inform about DNA repair Schuster-Böckler & Lehner. Nature. 2012 Lawrence et al. Nature. 2013
  9. 9. Mutation rate variation at megabase scale Mutation rate variation at nucleotide resolution Do DNA-binding proteins infuence mutation rateo TF
  10. 10. Increased mutation rate in active TFBS in melanomas proximal-TFBS TF 38 Melanoma samples from TCGA
  11. 11. Increased mutation rate in most TFBS
  12. 12. Increased TFBS mutation rate in most melanomas
  13. 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. 14. Lans et al. Epigenetics & Chromatin 2012 5:4 Nucleotide Excision Repair (NER): Global and Transcription- coupled repair
  15. 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. 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. 17. Is mutation rate in TFBS also increased in other tumor typeso
  18. 18. Whole genome somatic mutation from TCGA (Fredriksson et al,. 2014) Increased mutation rate at active TFBS of lung cancer
  19. 19. TFs bound to DNA interfere with DNA repair machinery resulting in increased mutation rate Radhakrishnan et al. Nature. 2016
  20. 20. Do exons and introns have diferent mutation rateo
  21. 21. Does exon-intron structure affect DNA repair? Chapman et al. Nature. 2011
  22. 22. Does exon-intron structure affect DNA repair? Schwartz et al. Nat. Struct. Mol. Biol. 2009
  23. 23. Does exon-intron structure affect DNA repair?
  24. 24. Does exon-intron structure affect DNA repair? How to measure the decreased exonic mutation burden?
  25. 25. Does exon-intron structure affect DNA repair? How to measure the decreased exonic mutation burden without biases? At the gene level
  26. 26. Does exon-intron structure affect DNA repair?
  27. 27. Does exon-intron structure affect DNA repair? Chapman et al. Nature. 2011
  28. 28. Does exon-intron structure affect DNA repair?
  29. 29. Does MMR repair exons more efficiently than introns?
  30. 30. Does MMR repair exons more efficiently than introns?
  31. 31. Does MMR repair exons more efficiently than introns?
  32. 32. Does exon-intron structure affect DNA repair? Schwartz et al. Nat. Struct. Mol. Biol. 2009
  33. 33. Does H3K36me3 play a role in differential MMR?
  34. 34. Does H3K36me3 play a role in differential MMR?
  35. 35. Differential MMR results in decreased exon mutation burden Frigola, Radhakrishnan et al., Nature Genetics. 2017
  36. 36. Understanding mutational processes Finding drivers of cancer Precision cancer medicine
  37. 37. How can we use the power of the cohort in drivers identification?
  38. 38. How can we use the power of the cohort in drivers identification? Excess computed by NBR (Inigo Martincorena, Sanger)
  39. 39. Are all mutations affecting driver elements drivers? Excess computed by NBR (Inigo Martincorena, Sanger)
  40. 40. How about non-coding driver elements?
  41. 41. Elements mutated below the power of the cohort Strict rule-based approach
  42. 42. The panorama of driver alterations Collaboration with Rameen Berroukhim’s and Jan Korbel’s labs
  43. 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. 44. Are all tumors of genomic origin? Collaboration with Rameen Berroukhim’s and Jan Korbel’s labs Driver alterations in 90% of tumors
  45. 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. 46. How many driver mutations in a tumor? 4.6 driver alterations on average
  47. 47. How many driver mutations in a tumor?
  48. 48. What is the contribution of mutations and structural variants to tumorigenesis?
  49. 49. Half of all driver genes affected by more than one type of alteration
  50. 50. Half of all driver genes affected by more than one type of alteration
  51. 51. How frequent are double-hit biallelic inactivations of tumor suppressors? Collaboration with Nikos Sidiropoulos and Joachim Weischenfeldt, BRIC Copenhagen
  52. 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. 53. Cancer from the bottom up: mutational processes, origin and ecosystem Understanding mutational processes Finding drivers of cancer Precision cancer medicine
  54. 54. Cancer from the bottom up: mutational processes, origin and ecosystem Biomedical Genomics Lab Institute for Research in Biomedicine DavidCarlota
  55. 55. Three scenarios of tumor immune infiltration and mechanisms of evasion
  56. 56. Cancer (somatic) development is an evolutionary process It operates on variation and selection
  57. 57. Tumors develop mechanisms to avoid immune destruction Taken from Hanahan and Weinberg, 2011
  58. 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. 59. Systematically identify pan-cancer (~10,000 tumors of 29 cancer types) mechanisms of immune evasion
  60. 60. Systematically identify pan-cancer mechanisms of immune evasion Discrete phenotypes of immune infiltration
  61. 61. Immune-phenotypes group tumors with similar infiltration pattern 934 non-TN breast tumors
  62. 62. Immune-phenotypes group tumors with similar infiltration pattern Across cancer types, independent of overall infiltration
  63. 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. 64. Immune-phenotypes correlate with clinical variables
  65. 65. Global genomic features explain immunogenicity
  66. 66. Global genomic features evidence immune edition
  67. 67. What do transcriptional programs across immune-phenotypes look like?
  68. 68. How about specific mechanisms of immune evasion? Detect driver events (with signals of positive selection)
  69. 69. What about specific mechanisms of immune evasion?
  70. 70. Three scenarios of immune infiltration and evasion
  71. 71. Tamborero, Rubio-Perez et al., Clinical Cancer Research, 2018
  72. 72. Thank you! CarlotaFerran Sabari David

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