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

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. Biomedical Genomics Lab
Institute for Research in Biomedicine

3. Interpretation of cancer genomes in the clinical
setting
Understanding
mutational
processes
Finding drivers
of cancer
Precision
cancer
medicine

4. In the clinical setting

7. The Cancer Genome Interpreter
cancergenomeinterpreter.org
Carlota Rubio
David Tamborero
Tamborero et al., Gen. Medicine. 2018

10. Mike Stratton. EMBO Molecular Medicine (2013)

11. Tumor development follows a Darwinian evolution
selection
variation
Drivers confer selective advantage to the cell

12. The genetic drivers of cancer
Signals of positive selection

13. Pre-compiled lists of driver genes across 48 cohorts of
28 tumor types (+manually curated biomarkers)

14. OncodriveFM
OncodriveCLUST
MuSiC-SMG
F
C
RUsing complementary signals help obtaining a more
comprehensive list of cancer drivers
Tamborero et al., Scientifc eports 2013

15. 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

16. 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)

17. OncodriveFML: Simulates mutations locally following
mutational processes
Alexandrov et al. Nature 2013

18. OncodriveFML identifes genes with driver mutations

19. OncodriveFML identifes non-coding regions with driver mutations
Low grade glioma
(18 samples) - Promoter
Bladder Urotelial
(21 samples) - 5’UT

20. Using OncodriveFML

21. Using OncodriveFML

22. Using OncodriveFML

23. ●
Identifies genes with a bias of ‘functional’ mutations across tumors
●
Employs a ‘local’ null model
●
Simulates mutations following their observed tri-nucleotide context
OncodriveFML

24. http://www.intogen.org
Rubio-Perez & Tamborero et al Cancer Cell (2015) Gonzalez-Perez et al Nature Methods (2013)
The genetic drivers of
cancer

26. The Cancer Genome Interpreter
cancergenomeinterpreter.org
Carlota Rubio
David Tamborero
Tamborero et al., Gen. Medicine. 2018

27. 6,792 tumors, 28 cancer types
850,082 mutations
Somatic mutations in a cohort of 6792 tumors

28. 6,792 tumors, 28 cancer types
850,082 mutations
Somatic mutations in a cohort of 6792 tumors
583,215 are protein-affecting

30. 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

31. 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

33. 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

35. 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

40. CancerGenomeInterpreter.org
Driver
Actionable
Interactive
report
mutations,
CNAs and
fusion events

41. Driver
Actionable
CancerGenomeInterpreter.org

42. Most recurrent driver mutations across the cohort

43. Driver versus passenger mutations across cancer genes

44. Driver
Actionable
Cancer bioMarkers-db
Rodrigo Dientsmann
David Tamborero

46. Driver
Actionable
CancerGenomeInterpreter.org

48. Rational design of gene panels to interrogate cancer
genomes

49. ●
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

50. 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

51. 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

52. OncoPaD: a flexible tool to design NGS cancer panels
Carlota Rubio

53. Solving hurdles to design cancer gene panels
http://bg.upf.edu/oncopad

54. 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

56. 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

57. 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

58. Fine-tunning the panel based on in silico cost-effectiveness
Maximum fraction of samples identified with minimum number of bps sequenced

59. Fine-tunning the panel based on in silico cost-effectiveness
Maximum fraction of samples identified with minimum number of bps sequenced

60. 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

65. Advantages of OncoPaD designed panels
●
Adjusted to cancer type (drivers)
●
Versatile (early detection/stratification)
●
In silico cost-effectiveness (fine tuning)
●
Reports (oncogenic + biomarker mutations)

66. Thank you!