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Oncogenomics Workshop - EBI - UK
March 14th, 2013
Nuria Lopez-Bigas
University Pompeu Fabra
Barcelona
http://bg.upf.edu
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
across projects - across cancer sites
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
across projects - across cancer sites
http://beta.intogen.org
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
Expression
patterns
Somatic
mutations
Epigenomic
profiles
Structural
aberrations
Copy number
alterations
Patient cohort
Primary tumors
Cancer Genomics Data
Expression
patterns
Somatic
mutations
Epigenomic
profiles
Structural
aberrations
Copy number
alterations
Patient cohort
Primary tumors
Cancer Genomics Data
tumor sample
mached
normal sample
Exome/Whole
genome sequencing
Reads
Reads
Aligment
Aligned reads
FASTQ
Aligned reads
BAM
Mutation
calling
Tumor
somatic
mutations
VCFFile formats:
Analysis protocolLaboratory protocol
Cancer genome re-sequencing
Tumours are heterogeneous in nature (multiclonality)
Variant calling pipelines entail judgement calls
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
tumor sample
mached
normal sample
Exome/Whole
genome sequencing
Reads
Reads
Aligment
Aligned reads
FASTQ
Aligned reads
BAM
Mutation
calling
Tumor
somatic
mutations
VCFFile formats:
Analysis protocolLaboratory protocol
Cancer genome re-sequencing
Which mutations are
cancer drivers?
How to identify cancer drivers?
How to identify cancer drivers?
Find signs of positive selection across
tumour re-sequenced genomes
Frequency based approaches to identify drivers
Assume that cancer drivers are mutated more frequently than
background in a cohort of tumours
samples
Recurrence analysis
genes
genes
not mutatedmutated driver gene
MutSig (Broad Institute)
MuSiC-SMG (Washington University)
Frequency based approaches to identify drivers
Assume that cancer drivers are mutated more frequently than
background in a cohort of tumours
samples
Recurrence analysis
genes
genes
not mutatedmutated driver gene
MutSig (Broad Institute)
MuSiC-SMG (Washington University)
• Difficulty to correctly estimate the background mutation rates
• Cannot identify lowly recurrent mutated driver genes
• Need raw data (eg. BAM files) to assess sequencing coverage per region
• Computationally costly
Main Challenges of frequency based approaches
How to identify drivers across projects in a scalable way?
How to identify drivers across projects in a scalable way?
• Do not need large nor protected data (eg. list of tumour somatic mutations)
• Are not computationally expensive
• Are robust to differences in mutation calling
Ideally computational methods that:
How to identify drivers across projects in a scalable way?
• Do not need large nor protected data (eg. list of tumour somatic mutations)
• Are not computationally expensive
• Are robust to differences in mutation calling
Ideally computational methods that:
OncodriveFM OncodriveCLUST
We have developed 2 methods with these properties:
Finding drivers using functional impact bias (FM bias)
Gonzalez-Perez and Lopez-Bigas. NAR 2012
Abel Gonzalez-Perez
Gene A Gene B
Functional Impact metrics:
•SIFT
•Mutation Assessor
•Polyphen2
FI score
highlow
OncodriveFM
1. Compute FI scores for nsSNVs (combining MutationAssessor, SIFT, Polyphen2)
2. Compute FI scores of other variants (STOP, synonymous and frameshift) using a set of rules
SIFT Polyphen2 MutationAssessor
Synonymous 1 0 -2
STOP-gain 0 1 3.5
Frameshift 0 1 3.5
STEP 1: Assess the functional impact (FI) of all variants
FI score
not mutated
FI score
highlow
OncodriveFM method’s details
OncodriveFM method’s details
STEP 2: Compute FM bias per gene
samples
genes
genes
Functional Impact
HighLow
OncodriveFM
not mutated driver gene
OncodriveFM method’s details
Compute FM bias per module
not mutated
FI score
highlow 0.0010
FM qvalue
samplesmodule1module2
module 1
module 2
OncodriveFM
• It does not depend on background mutation rates
• Only needs list of somatic mutations
• It is computationally cheap
• Can identify lowly recurrent mutated driver genes
Main Advantages of FM bias approach
OncodriveFM main advantages
One example: TCGA Glioblastoma FMbias
qvalue
MutSig
qvalue
TP53
PTEN
EGRF
NF1
RB1
FKBP9
ERBB2
PIK3R1
PIK3CA
PIK3C2G
IDH1
ZNF708
FGFR3
CDKN2A
ALDH1A3
PDGFRA
FGFR1
MAPK9
DCN
PIK3C2A
CHEK2
PSMD13
GSTM5
8.5E-11
8.5E-11
8.5E-11
8.5E-11
2.5E-9
8.5E-11
1.2E-8
1.2E-8
2.3E-4
0.002
8.5E-11
7.4E-10
3.2E-9
2.5E-8
5.2E-5
1.5E-6
2.0E-6
2.2E-5
1.5E-6
6.2E-5
1
1
1
<1.0E-8
<1.0E-8
<1.0E-8
<1.0E-8
<1.0E-8
2.7E-8
1.0E-8
1.0E-8
1.0E-8
6.1E-5
NA
NS
0.82
NS
NS
0.21
0.65
NS
NS
NS
0.002
0.01
0.009
not mutated
MA score
5-2 0
0.05 10
FM / MutSig qvalue
Gonzalez-Perez and Lopez-Bigas. NAR 2012
OncodriveFM Results
OncodriveFM Results
PIK3R1PTEN
EGFR
TP53
IDH1
RB1NF1
BRAF
PIK3CA
SPTA1
KRTAP4-11
GABRA6
KEL
CDH18
RPL5
STAG2
OR8K3
OR5AR1
LZTR1
MYH8
RPL5
OncodriveFMQvalue
MutSig Qvalue
TCGA Glioblastoma (2013)
TP53
KDM6A
FBXW7
NFE2L2
EP300
RB1
ERCC2
CDKN1A
ARID1A
OncodriveFMQvalue
MutSig Qvalue
TCGA BLCA (2013)
OncodriveFM Results
PIK3CA is recurrently mutated in the
same residue in breast tumours
Lowly scored by
functional impact metrics
H1047L
PIK3CA
Protein position
0 1047
Proteinaffectingmutations
80
0
Finding drivers using regional clustering of mutations
Tamborero et al., Under review
Proteinaffectingmutations
Protein position
KRAS
0
500
0 175
OncodriveCLUST
12
David Tamborero
OncodriveCLUST method’s details
Th
Gene A Gene B
(I)
(II)
(III)
(IV)
(V)
Th
SgeneA
= Sc1
SgeneB
= Sc1
+ SC2
(VI)
0
ZA
ZB
mutations
Amino acid
C1
C1 C2
Amino acid
mutationsmutationsmutations
S
geneA
SgeneB
Tamborero et al., Under review
background model obtained by
calculating the clustering score per
gene of the coding-silent mutations
• It does not depend on background mutation rates
• It is computationally cheap
• Only needs list of somatic mutations
• It is complementary to OncodriveFM
Main Advantages of FM bias approach
OncodriveCLUST main advantages
OncodriveCLUST Results
CGC
qOncoFMqOncoCLUST
qMutSig
138
9
4
9
12
21
10
7
6
5
5
8
186
3
5
7
3
4
3
4
8
7
4
4
4
8
4
TP53
CDH1
GATA3
SF3B1
AKT1
MLL3
MAP2K4
RUNX1
PTEN
RB1
MYB
NF1
PIK3CA
GNAS
CBFB
PIK3R1
KRAS
FGFR2
EP300
HLF
ARID1A
MLLT4
JAK2
BRCA1
ARID2
ERBB2
NIN
BRCA LUSC
CGC
qOncoFMqOncoCLUST
qMutSig
TP53
CDKN2A
NFE2L2
FBXW7
PIK3CA
PTEN
NF1
EP300
MLL2
JUN
CDH11
EGFR
NOTCH1
MLL3
RB1
PPP2R1A
GPC3
ABL2
SMARCA4
MYH9
NSD1
TSC1
EBF1
NCOA2
ARID1A
APC
BRCA1
DICER1
89
10
20
10
20
11
18
6
28
3
4
5
8
18
2
4
5
4
5
11
7
4
6
9
7
9
6
7
Gene significance is obtained by:
3 methods
2 methods
1 method
only by OncodriveCLUST
Cancer gene census phenotype:
dominant
recessive
Corrected p values scale:
0
0.05
1
Not assessable
Combining methods with
complementary principles helps to
obtain a more comprehensive and
reliable list of cancer drivers
✓ Functional Impact Bias
✓ Mutation Clustering
✓ Mutation Frequency
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
Catalogs of
tumor somatic
mutations
✓ Identify consequences of mutations (Ensembl VEP)
✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC)
✓ Compute frequency of mutations per gene and pathway
✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST)
Input data Analysis Pipeline (powered by Wok) Browser
IntOGen SM-Analysis pipeline
To interpret catalogs of cancer somatic mutations
Christian Perez-Llamas
Workflow Management Sytem
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Catalogs of
tumor somatic
mutations
✓ Identify consequences of mutations (Ensembl VEP)
✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC)
✓ Compute frequency of mutations per gene and pathway
✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST)
Input data Analysis Pipeline (powered by Wok) Browser
IntOGen SM-Analysis pipeline
To interpret catalogs of cancer somatic mutations
Christian Perez-Llamas
Workflow Management Sytem
Catalogs of
tumor somatic
mutations
✓ Identify consequences of mutations (Ensembl VEP)
✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC)
✓ Compute frequency of mutations per gene and pathway
✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST)
Input data Analysis Pipeline (powered by Wok) Browser
IntOGen SM-Analysis pipeline
To interpret catalogs of cancer somatic mutations
Currently:
27 Projects
12 Cancer sites
3229 tumours
.org
http://beta.intogen.org
Christian Perez-Llamas
Workflow Management Sytem
27 cancer sequencing datasets analysed so far
Total = 3329
CANCER SITE AUTHORS SOURCE
Number of
Samples
brain TCGA TCGA DATA PORTAL 248
brain DKFZ ICGC DCC 114
brain Johns Hopkins University ICGC DCC 88
breast TCGA TCGA DATA PORTAL 510
breast Broad Institute PubMed 102
breast WTSI ICGC DCC 100
breast Washington University School of Medicine PubMed 75
breast University of British Columbia PubMed 65
breast Johns Hopkins University ICGC DCC 41
colon TCGA TCGA DATA PORTAL 105
colon Johns Hopkins University ICGC DCC 34
corpus uteri TCGA TCGA DATA PORTAL 247
hematopoietic CLL-ICGC ICGC DCC 109
hematopoietic Dana-Farber Cancer Institute PubMed 90
Kidney TCGA TCGA DATA PORTAL 298
liver and bile ducts IACR ICGC DCC 24
lung and bronchus TCGA TCGA DATA PORTAL 177
lung and bronchus Washington University School of Medicine ICGC DCC 156
lung and bronchus Johns Hopkins University PubMed 43
lung and bronchus Medical College of Wisconsin PubMed 31
lung and bronchus University of Cologne PubMed 26
oropharynx Broad Institute PubMed 74
ovary TCGA TCGA DATA PORTAL 337
pancreas Johns Hopkins University ICGC DCC 113
pancreas Queensland Centre for Medical Genomics ICGC DCC 67
pancreas Ontario Institute for Cancer Research ICGC DCC 33
stomach Pfizer Worldwide Research and Development PubMed 22
Combining results across projects
0.05 1
p-value
0
project1
samples
genes
Functional Impact
project 1
HighLow
No mutation
OncodriveFM
genes
Combining results across projects
0.05 1
p-value
0
project1
samples
genes
Functional Impact
project 1
HighLow
No mutation
OncodriveFM
genes
+
project2
project3
project4
CancersiteA
...
combine
Cancer site A
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
Jordi Deu-Pons
Powered by
Onexus creates IntOGen web discovery tool
Web discovery toolTabulated Files
www.onexus.org
http://beta.intogen.org
http://beta.intogen.org
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
KRASTP53SMAD4CDKN2A
SMARCA4
Frequency
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
http://beta.intogen.org/analysis
Tumor Somatic Mutations in one tumor
Users’s Data User’s private browser
SM
pipeline
Tumor Somatic Mutations per sample
Users’s Data User’s private browser
SM
pipeline
Use case 1: Cohort analysis
Use case 2: Single sample analysis
View matrix of mutated genes per sample
See predicted impact of mutations
Find cancer driver genes
Find FMbiased pathways
Explore the results in the context of accummulated knownledge in IntOGen
See predicted impact of mutations
Find recurrent mutations found in IntOGen
Find mutations in candidate driver genes found in IntOGen
Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
.org
PanCancer project
The Mechanisms of tumorigenesis
Data
Computational
methods
Analysis
Results
PanCancer project
Visualization and analysis of genomic
data using Interactive Heatmaps
http://www.gitools.org Perez-Llamas and Lopez-Bigas. PLoS ONE 2011
Christian Perez-Llamas
Muldimesional heatmaps
Michael P. Schroeder
Sort by mutually exclusive alterations
Schroeder MP, Gonzalez-Perez A and Lopez-Bigas N. Visualizing multidimensional cancer genomics data.
Genome Medicine. 2013, 5:9
Summary
• OncodriveFM and OncodriveCLUST are complementary methods
to identify cancer drivers
• Oncodrive methods are scalable and robust
• IntOGen contains results of analysing more than 3000 tumours to
identify cancer drivers across sites
• IntOGenSM pipeline is available to run your own projects
• TCGA PanCancer analysis on the way
• Gitools - interactive heatmaps - useful to explore multidimesional
cancer genomics data
Biomedical Genomics Lab
@bbglab
@nlbigas
Gunes Gundem
Christian Perez-Llamas
Jordi Deu-Pons
Michael Schroeder
Alba Jené-Sanz
Nuria Lopez-Bigas David Tamborero Abel Gonzalez-Perez
Alberto Santos
http://bg.upf.edu/blog

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Lopez-Bigas talk at the EBI/EMBL Cancer Genomics Workshop

  • 1. Oncogenomics Workshop - EBI - UK March 14th, 2013 Nuria Lopez-Bigas University Pompeu Fabra Barcelona http://bg.upf.edu
  • 2. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org across projects - across cancer sites
  • 3. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org across projects - across cancer sites http://beta.intogen.org
  • 4. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org
  • 7. tumor sample mached normal sample Exome/Whole genome sequencing Reads Reads Aligment Aligned reads FASTQ Aligned reads BAM Mutation calling Tumor somatic mutations VCFFile formats: Analysis protocolLaboratory protocol Cancer genome re-sequencing Tumours are heterogeneous in nature (multiclonality) Variant calling pipelines entail judgement calls
  • 8. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org
  • 9. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org
  • 10. tumor sample mached normal sample Exome/Whole genome sequencing Reads Reads Aligment Aligned reads FASTQ Aligned reads BAM Mutation calling Tumor somatic mutations VCFFile formats: Analysis protocolLaboratory protocol Cancer genome re-sequencing Which mutations are cancer drivers?
  • 11. How to identify cancer drivers?
  • 12. How to identify cancer drivers? Find signs of positive selection across tumour re-sequenced genomes
  • 13. Frequency based approaches to identify drivers Assume that cancer drivers are mutated more frequently than background in a cohort of tumours samples Recurrence analysis genes genes not mutatedmutated driver gene MutSig (Broad Institute) MuSiC-SMG (Washington University)
  • 14. Frequency based approaches to identify drivers Assume that cancer drivers are mutated more frequently than background in a cohort of tumours samples Recurrence analysis genes genes not mutatedmutated driver gene MutSig (Broad Institute) MuSiC-SMG (Washington University) • Difficulty to correctly estimate the background mutation rates • Cannot identify lowly recurrent mutated driver genes • Need raw data (eg. BAM files) to assess sequencing coverage per region • Computationally costly Main Challenges of frequency based approaches
  • 15. How to identify drivers across projects in a scalable way?
  • 16. How to identify drivers across projects in a scalable way? • Do not need large nor protected data (eg. list of tumour somatic mutations) • Are not computationally expensive • Are robust to differences in mutation calling Ideally computational methods that:
  • 17. How to identify drivers across projects in a scalable way? • Do not need large nor protected data (eg. list of tumour somatic mutations) • Are not computationally expensive • Are robust to differences in mutation calling Ideally computational methods that: OncodriveFM OncodriveCLUST We have developed 2 methods with these properties:
  • 18. Finding drivers using functional impact bias (FM bias) Gonzalez-Perez and Lopez-Bigas. NAR 2012 Abel Gonzalez-Perez Gene A Gene B Functional Impact metrics: •SIFT •Mutation Assessor •Polyphen2 FI score highlow OncodriveFM
  • 19. 1. Compute FI scores for nsSNVs (combining MutationAssessor, SIFT, Polyphen2) 2. Compute FI scores of other variants (STOP, synonymous and frameshift) using a set of rules SIFT Polyphen2 MutationAssessor Synonymous 1 0 -2 STOP-gain 0 1 3.5 Frameshift 0 1 3.5 STEP 1: Assess the functional impact (FI) of all variants FI score not mutated FI score highlow OncodriveFM method’s details
  • 20. OncodriveFM method’s details STEP 2: Compute FM bias per gene samples genes genes Functional Impact HighLow OncodriveFM not mutated driver gene
  • 21. OncodriveFM method’s details Compute FM bias per module not mutated FI score highlow 0.0010 FM qvalue samplesmodule1module2 module 1 module 2 OncodriveFM
  • 22. • It does not depend on background mutation rates • Only needs list of somatic mutations • It is computationally cheap • Can identify lowly recurrent mutated driver genes Main Advantages of FM bias approach OncodriveFM main advantages
  • 23. One example: TCGA Glioblastoma FMbias qvalue MutSig qvalue TP53 PTEN EGRF NF1 RB1 FKBP9 ERBB2 PIK3R1 PIK3CA PIK3C2G IDH1 ZNF708 FGFR3 CDKN2A ALDH1A3 PDGFRA FGFR1 MAPK9 DCN PIK3C2A CHEK2 PSMD13 GSTM5 8.5E-11 8.5E-11 8.5E-11 8.5E-11 2.5E-9 8.5E-11 1.2E-8 1.2E-8 2.3E-4 0.002 8.5E-11 7.4E-10 3.2E-9 2.5E-8 5.2E-5 1.5E-6 2.0E-6 2.2E-5 1.5E-6 6.2E-5 1 1 1 <1.0E-8 <1.0E-8 <1.0E-8 <1.0E-8 <1.0E-8 2.7E-8 1.0E-8 1.0E-8 1.0E-8 6.1E-5 NA NS 0.82 NS NS 0.21 0.65 NS NS NS 0.002 0.01 0.009 not mutated MA score 5-2 0 0.05 10 FM / MutSig qvalue Gonzalez-Perez and Lopez-Bigas. NAR 2012 OncodriveFM Results
  • 26. PIK3CA is recurrently mutated in the same residue in breast tumours Lowly scored by functional impact metrics H1047L PIK3CA Protein position 0 1047 Proteinaffectingmutations 80 0
  • 27. Finding drivers using regional clustering of mutations Tamborero et al., Under review Proteinaffectingmutations Protein position KRAS 0 500 0 175 OncodriveCLUST 12 David Tamborero
  • 28. OncodriveCLUST method’s details Th Gene A Gene B (I) (II) (III) (IV) (V) Th SgeneA = Sc1 SgeneB = Sc1 + SC2 (VI) 0 ZA ZB mutations Amino acid C1 C1 C2 Amino acid mutationsmutationsmutations S geneA SgeneB Tamborero et al., Under review background model obtained by calculating the clustering score per gene of the coding-silent mutations
  • 29. • It does not depend on background mutation rates • It is computationally cheap • Only needs list of somatic mutations • It is complementary to OncodriveFM Main Advantages of FM bias approach OncodriveCLUST main advantages
  • 31. Combining methods with complementary principles helps to obtain a more comprehensive and reliable list of cancer drivers ✓ Functional Impact Bias ✓ Mutation Clustering ✓ Mutation Frequency
  • 32. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org
  • 33. Catalogs of tumor somatic mutations ✓ Identify consequences of mutations (Ensembl VEP) ✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC) ✓ Compute frequency of mutations per gene and pathway ✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST) Input data Analysis Pipeline (powered by Wok) Browser IntOGen SM-Analysis pipeline To interpret catalogs of cancer somatic mutations Christian Perez-Llamas Workflow Management Sytem
  • 35. Catalogs of tumor somatic mutations ✓ Identify consequences of mutations (Ensembl VEP) ✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC) ✓ Compute frequency of mutations per gene and pathway ✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST) Input data Analysis Pipeline (powered by Wok) Browser IntOGen SM-Analysis pipeline To interpret catalogs of cancer somatic mutations Christian Perez-Llamas Workflow Management Sytem
  • 36. Catalogs of tumor somatic mutations ✓ Identify consequences of mutations (Ensembl VEP) ✓ Assess functional impact of nsSNVs (SIFT, PPH2, MA and TransFIC) ✓ Compute frequency of mutations per gene and pathway ✓ Identify candidate driver genes (OncodriveFM and OncodriveCLUST) Input data Analysis Pipeline (powered by Wok) Browser IntOGen SM-Analysis pipeline To interpret catalogs of cancer somatic mutations Currently: 27 Projects 12 Cancer sites 3229 tumours .org http://beta.intogen.org Christian Perez-Llamas Workflow Management Sytem
  • 37. 27 cancer sequencing datasets analysed so far Total = 3329 CANCER SITE AUTHORS SOURCE Number of Samples brain TCGA TCGA DATA PORTAL 248 brain DKFZ ICGC DCC 114 brain Johns Hopkins University ICGC DCC 88 breast TCGA TCGA DATA PORTAL 510 breast Broad Institute PubMed 102 breast WTSI ICGC DCC 100 breast Washington University School of Medicine PubMed 75 breast University of British Columbia PubMed 65 breast Johns Hopkins University ICGC DCC 41 colon TCGA TCGA DATA PORTAL 105 colon Johns Hopkins University ICGC DCC 34 corpus uteri TCGA TCGA DATA PORTAL 247 hematopoietic CLL-ICGC ICGC DCC 109 hematopoietic Dana-Farber Cancer Institute PubMed 90 Kidney TCGA TCGA DATA PORTAL 298 liver and bile ducts IACR ICGC DCC 24 lung and bronchus TCGA TCGA DATA PORTAL 177 lung and bronchus Washington University School of Medicine ICGC DCC 156 lung and bronchus Johns Hopkins University PubMed 43 lung and bronchus Medical College of Wisconsin PubMed 31 lung and bronchus University of Cologne PubMed 26 oropharynx Broad Institute PubMed 74 ovary TCGA TCGA DATA PORTAL 337 pancreas Johns Hopkins University ICGC DCC 113 pancreas Queensland Centre for Medical Genomics ICGC DCC 67 pancreas Ontario Institute for Cancer Research ICGC DCC 33 stomach Pfizer Worldwide Research and Development PubMed 22
  • 38. Combining results across projects 0.05 1 p-value 0 project1 samples genes Functional Impact project 1 HighLow No mutation OncodriveFM genes
  • 39. Combining results across projects 0.05 1 p-value 0 project1 samples genes Functional Impact project 1 HighLow No mutation OncodriveFM genes + project2 project3 project4 CancersiteA ... combine Cancer site A
  • 40. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org
  • 41. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org
  • 42. Jordi Deu-Pons Powered by Onexus creates IntOGen web discovery tool Web discovery toolTabulated Files www.onexus.org
  • 57. Tumor Somatic Mutations in one tumor Users’s Data User’s private browser SM pipeline Tumor Somatic Mutations per sample Users’s Data User’s private browser SM pipeline Use case 1: Cohort analysis Use case 2: Single sample analysis View matrix of mutated genes per sample See predicted impact of mutations Find cancer driver genes Find FMbiased pathways Explore the results in the context of accummulated knownledge in IntOGen See predicted impact of mutations Find recurrent mutations found in IntOGen Find mutations in candidate driver genes found in IntOGen
  • 59. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org
  • 60. The Mechanisms of tumorigenesis Data Computational methods Analysis Results .org PanCancer project
  • 61. The Mechanisms of tumorigenesis Data Computational methods Analysis Results PanCancer project
  • 62. Visualization and analysis of genomic data using Interactive Heatmaps http://www.gitools.org Perez-Llamas and Lopez-Bigas. PLoS ONE 2011 Christian Perez-Llamas
  • 63. Muldimesional heatmaps Michael P. Schroeder Sort by mutually exclusive alterations Schroeder MP, Gonzalez-Perez A and Lopez-Bigas N. Visualizing multidimensional cancer genomics data. Genome Medicine. 2013, 5:9
  • 64. Summary • OncodriveFM and OncodriveCLUST are complementary methods to identify cancer drivers • Oncodrive methods are scalable and robust • IntOGen contains results of analysing more than 3000 tumours to identify cancer drivers across sites • IntOGenSM pipeline is available to run your own projects • TCGA PanCancer analysis on the way • Gitools - interactive heatmaps - useful to explore multidimesional cancer genomics data
  • 65. Biomedical Genomics Lab @bbglab @nlbigas Gunes Gundem Christian Perez-Llamas Jordi Deu-Pons Michael Schroeder Alba Jené-Sanz Nuria Lopez-Bigas David Tamborero Abel Gonzalez-Perez Alberto Santos http://bg.upf.edu/blog