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CDAC 2018 Gonzales-Perez interpretation of cancer genomes

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

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CDAC 2018 Gonzales-Perez interpretation of cancer genomes

  1. 1. Interpretation of cancer genomes in the clinical setting Abel Gonzalez-Perez Ramon y Cajal Research Associate Institute for Research in Biomedicine Barcelona http://bbglab.irbbarcelona.org
  2. 2. Biomedical Genomics Lab Institute for Research in Biomedicine
  3. 3. Interpretation of cancer genomes in the clinical setting Understanding mutational processes Finding drivers of cancer Precision cancer medicine
  4. 4. In the clinical setting
  5. 5. The Cancer Genome Interpreter cancergenomeinterpreter.org Carlota Rubio David Tamborero Tamborero et al., Gen. Medicine. 2018
  6. 6. Mike Stratton. EMBO Molecular Medicine (2013)
  7. 7. Tumor development follows a Darwinian evolution selection variation Drivers confer selective advantage to the cell
  8. 8. The genetic drivers of cancer Signals of positive selection
  9. 9. Pre-compiled lists of driver genes across 48 cohorts of 28 tumor types (+manually curated biomarkers)
  10. 10. OncodriveFM OncodriveCLUST MuSiC-SMG F C RUsing complementary signals help obtaining a more comprehensive list of cancer drivers Tamborero et al., Scientifc eports 2013
  11. 11. Simulated mutations Compare low high Tumor mutations Loris Mularoni Mularoni et al. Genome Biology 2016 Building a background model: the case of OncodriveFML functional impact
  12. 12. OncodriveFML: Measuring functional impact Impact on NA structure Impact on TFBS Impact on micro NA targets Impact on protein function Combined Annotation Dependent Depletion (CADD) Fitness Consequence Scores (FitCons)
  13. 13. OncodriveFML: Simulates mutations locally following mutational processes Alexandrov et al. Nature 2013
  14. 14. OncodriveFML identifes genes with driver mutations
  15. 15. OncodriveFML identifes non-coding regions with driver mutations Low grade glioma (18 samples) - Promoter Bladder Urotelial (21 samples) - 5’UT
  16. 16. Using OncodriveFML
  17. 17. Using OncodriveFML
  18. 18. Using OncodriveFML
  19. 19. ● Identifies genes with a bias of ‘functional’ mutations across tumors ● Employs a ‘local’ null model ● Simulates mutations following their observed tri-nucleotide context OncodriveFML
  20. 20. http://www.intogen.org Rubio-Perez & Tamborero et al Cancer Cell (2015) Gonzalez-Perez et al Nature Methods (2013) The genetic drivers of cancer
  21. 21. The Cancer Genome Interpreter cancergenomeinterpreter.org Carlota Rubio David Tamborero Tamborero et al., Gen. Medicine. 2018
  22. 22. 6,792 tumors, 28 cancer types 850,082 mutations Somatic mutations in a cohort of 6792 tumors
  23. 23. 6,792 tumors, 28 cancer types 850,082 mutations Somatic mutations in a cohort of 6792 tumors 583,215 are protein-affecting
  24. 24. 6,792 tumors, 28 cancer types 850,082 mutations 37,845 are unique Somatic mutations in a cohort of 6792 tumors 583,215 are protein-affecting 44,648 are in cancer genes affect cancer genes?1
  25. 25. 6,792 tumors, 28 cancer types 850,082 mutations 37,845 are unique Somatic mutations in a cohort of 6792 tumors 583,215 are protein-affecting 44,648 are in cancer genes affect cancer genes?1 are driver mutations?2
  26. 26. 6,792 tumors, 28 cancer types 850,082 mutations 630 Known oncogenic 37,215 Uncertain significance 37,845 are unique Somatic mutations in a cohort of 6792 tumors 583,215 are protein-affecting 44,648 are in cancer genes affect cancer genes?1 are driver mutations?2
  27. 27. 20,226 Predicted drivers 16,989 Predicted passengers 6,792 tumors, 28 cancer types 850,082 mutations 630 Known oncogenic 37,215 Uncertain significance 37,845 are unique Somatic mutations in a cohort of 6792 tumors 583,215 are protein-affecting 44,648 are in cancer genes affect cancer genes?1 are driver mutations?2
  28. 28. CancerGenomeInterpreter.org Driver Actionable Interactive report mutations, CNAs and fusion events
  29. 29. Driver Actionable CancerGenomeInterpreter.org
  30. 30. Most recurrent driver mutations across the cohort
  31. 31. Driver versus passenger mutations across cancer genes
  32. 32. Driver Actionable Cancer bioMarkers-db Rodrigo Dientsmann David Tamborero
  33. 33. Driver Actionable CancerGenomeInterpreter.org
  34. 34. Rational design of gene panels to interrogate cancer genomes
  35. 35. ● Supports clinicians and researchers in the interpretation of tumor alterations ● Interprets tumor genomes ‘one at a time’ ● Flags known driver alterations ● Annotates alterations of unknown significance and classifies them ● Matches tumor alterations to biomarkers of anti-cancer drug response
  36. 36. Gene/regions panels in (clinically-oriented) cancer genomics ● Cost-effective with respect to whole-exome/whole-genome sequencing ● Lower detection limit for variants: better suited for the detection of mutations in FFPS ● More accurate assessment of clonality of mutations One-size-fits-many solution
  37. 37. Hurdles to design cancer gene panels ● Laborious selection of genes/regions relevant for tumorigenesis in the cancer type and aim; interpreting results ● Assessment of the cost-effectiveness of the designed panel/fine- tunning
  38. 38. OncoPaD: a flexible tool to design NGS cancer panels Carlota Rubio
  39. 39. Solving hurdles to design cancer gene panels http://bg.upf.edu/oncopad
  40. 40. Solving hurdles to design cancer gene panels ● Pre-compiled lists of driver genes across 48 cohorts of 28 tumor types (+manually curated biomarkers) ● Reports that support interpreting results ● In silico assessment of the cost-effectiveness of the designed panel vs real-life cohorts of tumors; dynamic fine-tunning
  41. 41. Cost-effectiveness of a gene panel: In silico assessment Balance between: a) the fraction of samples in a real-life cohort of tumors with mutations in at least one of the genes in the panel and b) the length of DNA of all regions included in the panel
  42. 42. Panel name Genes in panel Cohort fraction DNA Kbps Fraction of drivers Fraction of biomarkers Onco-GeneSG (SG Kits) 80 0.49 188.07 0.34 0.23 Cancer Genomics Resource List (Zutter et al.,2014) 290 0.54 669.63 0.19 0.05 Martinez et al. 2013 25 0.55 53.17 0.48 0.40 OncoGxOne (GENEWIZ) 65 0.62 167.18 0.69 0.72 Comprehensive Cancer Gene Set v2 (Washington University) 43 0.63 114.11 0.91 0.81 TruSeq Pan-Cancer (Illumina, Inc.) 48 0.64 124.35 0.90 0.81 Cancer Gene Mutational Panel v2 (Baylor University) 50 0.64 125.71 0.88 0.80 IntelliGEN Oncology Therapeutic Panel (LabCorp) 51 0.64 127.97 0.88 0.80 ICG100 (Intermountain healthcare) 97 0.68 237.48 0.67 0.64 Pan-Cancer Panel (xGen®) 127 0.75 353.94 0.76 0.39 Gene Read DNAseq Targeted Panels v2 (QIAGEN) 160 0.76 439.07 0.76 0.46 OncoPlex (Washington University) 234 0.77 577.83 0.47 0.38 Pan-cancer (FoundationOne®) 237 0.80 634.24 0.57 0.45 Comprehensive Cancer Panel (Ion AmpliSeq™) 409 0.84 1130.73 0.39 0.26 Cost-effectiveness of gene panels for solid tumors: in silico assessment
  43. 43. Fine-tunning the panel based on in silico cost-effectiveness Maximum fraction of samples identified with minimum number of bps sequenced
  44. 44. Fine-tunning the panel based on in silico cost-effectiveness Maximum fraction of samples identified with minimum number of bps sequenced
  45. 45. Panel name Genes Cohort fraction DNA Kbps Proportion of cancer drivers Proportion of biomarkers FusionPlex Solid Tumor Panel (Archer)1 53 0.41 138.40 0.32 0.42 GeneTrails Solid Tumor (Knight labs)10 37 0.67 90.26 0.86 0.89 OncoVantage Solid Tumor Mutation Analysis (Quests diagnostics)16 34 0.68 82.55 0.88 0.97 Solid Tumor Mutation Panel (Arup Laboratories)12 47 0.70 118.10 0.89 0.79 Solid Tumor Targeted Cancer Gene Panel (Mayo Clinic)13 50 0.71 127.97 0.90 0.82 SureSeq Solid Tumour Panel (Oxford gene technology)14 60 0.74 196.03 0.87 0.70 Solid tumor panel (Centrogene)3 62 0.76 222.27 0.87 0.79 OncoPaD regions* - drug profiling** (Tier1&2) 10 genes + 584 regions 0.73 75.85 1 1 OncoPaD whole exome - drug profiling** (Tier1&2) 51 0.75 186.32 1 1 OncoPaD whole exome - drug profiling** (Tier1) 8 0.60 13.32 1 1 OncoPaD whole exome (Tier1) 54 0.80 343.37 1 0.52 OncoPaD regions* (Tier1) 91 genes + 434 regions 0.84 286.97 1 0.27 Cost-effectiveness of commercial and OncoPaD panels
  46. 46. Advantages of OncoPaD designed panels ● Adjusted to cancer type (drivers) ● Versatile (early detection/stratification) ● In silico cost-effectiveness (fine tuning) ● Reports (oncogenic + biomarker mutations)
  47. 47. Thank you!

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