BIOINFORMATICS FOR HEALTH SCIENCESbbglab.irbbarcelona.org/courses/bco/documents/BCO11.pdf · BIOINFORMATICS FOR HEALTH SCIENCES Introduction to Cancer Genomics Nuria Lopez-Bigas [email protected]

BIOINFORMATICS FOR HEALTH SCIENCES

Introduction to Cancer GenomicsNuria Lopez-Bigas

[email protected]

mailto:[email protected]

mailto:[email protected]

Moving towards personalized cancer medicine

Marc Rosenthal

Cancer Genomics

Cancer Genomics

ACTCAGCCCCAGCGGAGGTGAAGGACGTCCTTCCCCAGGAGCCGGTGAGAAGCGCAGTCGGGGGCACGGGGATGAGCTCAGGGGCCTCTAGAAAGATGTAGCTGGGACCTCGGGAAGCCCTGGCCTCCAGGTAGTCTCAGGAGAGCTACTCAGGGTCGGGCTTGGGGAGAGGAGGAGCGGGGGTGAGGCCAGCAGCAGGGGACTGGACCTGGGAAGGGCTGGGCAGCAGAGACGACCCGACCCGCTAGAAGGTGGGGTGGGGAGAGCATGTGGACTAGGAGCTAAGCCACAGCAGGACCCCCACGAGTTGTCACTGTCATTTATCGAGCACCTACTGGGTGTCCCCAGTGTCCTCAGATCTCCATAACTGGGAAGCCAGGGGCAGCGACACGGTAGCTAGCCGTCGATTGGAGAACTTTAAAATGAGGACTGAATTAGCTCATAAATGGAAAACGGCGCTTAAATGTGAGGTTAGAGCTTAGAATGTGAAGGGAGAATGAGGAATGCGAGACTGGGACTGAGATGGAACCGGCGGTGGGGAGGGGGAGGGGGTGTGGAATTTGAACCCCGGGAGAGAAAGATGGAATTTTGGCTATGGAGGCCGACCTGGGGATGGGGAAATAAGAGAAGACCAGGAGGGAGTTAAATAGGGAATGGGTTGGGGGCGGCTTGGTAACTGTTTGTGCTGGGATTAGGCTGTTGCAGATAATGGAGCAAGGCTTGGAAGGCTAACCTGGGGTGGGGCCGGGTTGGGGTCGGGCTGGGGGCGGGAGGAGTCCTCACTGGCGGTTGATTGACAGTTTCTCCTTCCCCAGACTGGCCAATCACAGGCAGGAAGATGAAGGTTCTGTGGGCTGCCCCGACCCGCTAGAAGGTGGGGTGGGGAGAGCATGTGGACTAGGAGCTAAGCCACAGCAGGACCCCCACGAGTTGTCACTGTCATTTATCGAGCACCTACTGGGTGTCCCCAGTGTCCTCAGATCTCCATAACTGGGAAGCCAGGGGCAGCGAC

Arrays Parallel Sequencing

Cancer Genomics Projects

Expression patterns

Somatic mutations

Epigenomic profiles

Structural aberrations

Copy number alterations

Patient cohortPrimary tumors


Expression patterns

Somatic mutations

Epigenomic profiles

Structural aberrations

Copy number alterations

Patient cohortPrimary tumors


Cancer Genomic Projects

OBJECTIVE:Obtain full catalog of genetic alterations in

500 tumors from 50 tumor types

• Somatic mutations• Copy Number Alterations • Abnormal expression of genes• Translocations• Epigenetic modifications• etc.

Cancer Genomics: What for?

•Finding new cancer genes (cancer drivers)•Finding new therapeutic targets• Identify molecular signatures to stratify tumors•Move towards personalized cancer treatment





1985

BCR-ABL fusion cause Chronic Myelogenous Leukemia (CML)

Weisberg et al., Nature Reviews Cancer 2007

BCR-ABL: constitutive active ABL kinase activity

Imatinib

Imatinib inhibits tyrosine-kinase activity of ABL

Kantarjian et al., Blood 2012

Dramatically improved long term survival rates (95.2%) since the introduction of Gleevec in 2001

BRAF is frequently mutated in melanoma

VemurafenibPLX432

BRAF is frequently mutated in melanoma

2 setmanesVemurafenib



Personalized medicine / Precision medicine

Vemurafenib

Targeted Cancer Therapy

Cancer-causing mutations with drug treatment available

Mutation with no drug available

Radiation and chemotherapyCancer drug

Schema adapted from NY times



Identify molecular signatures to stratify tumors

Good prognosisFavorable response

Bad prognosisUnfavorable response Increased toxicity



Move towards personalized cancer treatment

YESTERDAY TODAY TOMORROW

Find the right treatment for the right patient

at the right time



Finding Cancer Drivers

Cancer genome sequencing

Which mutations are cancer drivers?

Normal cell Cancer cell

Sequencing machines

Somatic mutations

Finding Cancer Drivers

1. Predict consequences of mutations2. Assess the functional impact of nsSNVs3. Identify cancer drivers based on recurrence4. Identify cancer drivers based on FMbias

1. Predict consequences of mutationsACTGCCTACGTCTCACCGTCGACTTCAAATCGCTTAACCCGTACTCCCATGCTACTGCATCTCGGGTTAACTCGACGTTTTTCATGCATGTGTGCACCCCAATATATATGCAACTTTTGTGCACCTCTGTCACGCGCGAGTTGGCACTGTCGCCCCTGTGTGCATGTGCACTGTCTCTCGCTGCACTGCCTACGTCTCACCGTCGACTTCAAATCGCTTAACCCGTACTCCCATGCTACTGCATCTCGGGTTAACTCGACGTTTTGCATGCATGTGTGCACCCCAATATATATGCAACTTTTGTGCACCTCTGTCACGCGCGAGTTGGCACTGTCGCCCCTGTGTGCATGTGCACTGTCTCTCGA

Map mutations into genome annotations to predict its possible effect

Tools to annotate consequences of mutations

ANNOVAR

snpEff

VAGrENT

annTools

ASOoVIREnsembl VEP

2. Assess the functional impact of nsSNVs

ATC GAA GCA CGTMet Glu Ala Gly

nsSNVs = non-synonymos Single Nucleotide Variant (missense)

ATC GAC GCA CGTMet Asp Ala Gly

Computational methods to assess the functional impact of nsSNVs

MutationTaster

SIFTPolyPhen2

CondelCHASM

PMut

SNPs&GO

SNPeffect

MutPred

MutationAssessor

CanPredict

LogRe

transFIC

Which mutations are cancer drivers?

Normal cell Cancer cell

Sequencing machines

Somatic mutations

Patient cohort

3. Identify cancer drivers from somatic mutations

Find signals of selection across tumors

Cancer is an evolutionary process

Yates and Campbell et al, Nat Rev Genet 2012

How to differentiate drivers from passengers?

ACTGCCTACGTCTCACCGTCGACTTCAAATCGCTTAACCCGTACTCCCATGCTACTGCATCTCGGGTTAACTCGACGTTTTTCATGCATGTGTGCACCCCAATATATATGCAACTTTTGTGCACCTCTGTCACGCGCGAGTTGGCACTGTCGCCCCTGTGTGCATGTGCACTGTCTCTCGCTGCACTGCCTACGTCTCACCGTCGACTTCAAATCGCTTAACCCGTACTCCCATGCTACTGCATCTCGGGTTAACTCGACGTTTTGCATGCATGTGTGCACCCCAATATATATGCAACTTTTGTGCACCTCTGTCACGCGCGAGTTGGCACTGTCGCCCCTGTGTGCATGTGCACTGTCTCTCGAGTTTTGCATGCATGTGTGCACTGTGCACCTCTGTTACGTCT

How to differentiate drivers from passengers?

ACTGCCTACGTCTCACCGTCGACTTCAAATCGCTTAACCCGTACTCCCATGCTACTGCATCTCGGGTTAACTCGACGTTTTTCATGCATGTGTGCACCCCAATATATATGCAACTTTTGTGCACCTCTGTCACGCGCGAGTTGGCACTGTCGCCCCTGTGTGCATGTGCACTGTCTCTCGCTGCACTGCCTACGTCTCACCGTCGACTTCAAATCGCTTAACCCGTACTCCCATGCTACTGCATCTCGGGTTAACTCGACGTTTTGCATGCATGTGTGCACCCCAATATATATGCAACTTTTGTGCACCTCTGTCACGCGCGAGTTGGCACTGTCGCCCCTGTGTGCATGTGCACTGTCTCTCGAGTTTTGCATGCATGTGTGCACTGTGCACCTCTGTTACGTCT

Find signals of positive selection across tumour re-sequenced genomes

Recurrence

Identify genes mutated more frequently than background mutation rate

MuSiC-SMG / MutSigCV

Mutation

Signals of positive selection

Recurrence

Identify genes mutated more frequently than background mutation rate

MuSiC-SMG / MutSigCV

Mutation


Challenge: Background mutation rate varies across patients and genomic regions

Replication time

Stamatoyannoppoulos et al., Nature Genetics 2009 Schuster-Böckler and Lehner, Nature 2011

Chromatin organization


Functional impact bias (FMbias)

Mutation

OncodriveFM

Gonzalez-Perez and Lopez-Bigas. NAR 2012

Functional Impact


• Based on consequences of mutations (eg. synonymous is

lowest and STOPgain, frameshift indel highest)

• And SIFT, PPH2 and MA for missense

How to measure functional impact of mutations?


Mutation

OncodriveFM


Functional Impact



Mutation

• It does not depend on background mutation rates

• Only needs list of somatic mutations

• It is computationally cheap

Main Advantages of FM bias approach


Functional Impact

OncodriveFM



Mutation

One example: TCGA Glioblastoma FMbiasqvalue

TP53PTENEGRFNF1RB1FKBP9ERBB2PIK3R1PIK3CAPIK3C2GIDH1ZNF708FGFR3CDKN2AALDH1A3PDGFRAFGFR1MAPK9DCNPIK3C2ACHEK2PSMD13GSTM5

8.5E-118.5E-118.5E-118.5E-112.5E-98.5E-111.2E-81.2E-82.3E-40.0028.5E-117.4E-103.2E-92.5E-85.2E-51.5E-62.0E-62.2E-51.5E-66.2E-5111

not mutatedMA score

5-2 0 0.05 10

FM bias qvalue

OncodriveFM

Functional Impact

PIK3CA is recurrently mutated in the same residue in breast tumours

H1047L

PIK3CA

Protein position0 1047

Prot

ein

affe

ctin

g m

utat

ions

80

0


Mutation clustering

Mutation

OncodriveCLUST

Tamborero et al., Bioinformatics 2013

Th

Gene A Gene B(I)

(II)

(III)

(IV)

(V)

Th

SgeneA

= Sc1 S

geneB = Sc1

+ SC2

(VI)

0

ZA

ZB

mut

atio

ns

Amino acid

C1

C1 C2

Amino acid

mut

atio

ns

mut

atio

ns

mut

atio

ns

SgeneA

SgeneB

Background model obtained by calculating the clustering score per gene of the coding-silent mutations

Signals of positive selection: OncodriveCLUST

Tamborero et al., Bioinformatics 2013

List of tumor somatic

mutations

Input data

IntOGen mutations pipeline To interpret catalogs of cancer 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)✓ Identify pathways with FM bias (OncodriveFM)

Gonzalez-Perez et al, Nature Methods 2013

Analysis Pipeline Browser

✓ 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)✓ Identify pathways with FM bias (OncodriveFM)

Input data

Working version:49 Projects 28 Cancer types6792 tumours

.org

http://www.intogen.org/mutations

IntOGen mutations pipeline To interpret catalogs of cancer somatic mutations


List of tumor somatic

mutations

Current version:31 Projects 13 Cancer sites4623 tumours

Analysis Pipeline Browser

http://www.intogen.org/mutations


http://www.intogen.org/mutations/analysis




IntOGen-mutations pipelineTo interpret catalogs of cancer somatic mutations



Stratify tumors based on molecular patterns

Good prognosisFavorable response

Bad prognosisUnfavorable response Increased toxicity



One example: Breast Cancer Intrinsic Subtypes

TCGA Pan-Cancer project

Documents

BIOINFORMATICS FOR HEALTH SCIENCESbbglab.irbbarcelona.org/courses/bco/documents/BCO11.pdf · BIOINFORMATICS FOR HEALTH SCIENCES Introduction to Cancer Genomics Nuria Lopez-Bigas [email protected]