cancergenomics.no

Big data challenges in personalized cancer medicine

Bioinformatics activities in the Norwegian Cancer Genomics Consortium (NCGC)

Sigve NakkenPostdoctoral fellow, Eivind Hovigs group

Norwegian Cancer Genomics Consortium (NCGC)Department of Tumor Biology, ICR, OUS

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N o r w e g i a n C a n cer genomics Consortium (NCGC)

• Founded by oncologists and cancer scientists across the country (Tromsø, Trondheim, Bergen, Oslo)

• Contributing to and following the national priorization of ”Individualized cancer treatment based on the gene profile of the tumour” as the most important topic in cancer research

• Has obtained grants of 75 Mkr (≈ 10 MUSD) from the Research Council

• Industrial partners: OCC, PubGene, BergenBio

• Project divided into work packages WP4: Data handling and establishment of national infrastructure

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cancergenomics.no

NCGC sample cohorts

Cancer type REK approvals Sequencing Samples Analysis

Melanoma Approved Done 115 On-going

Colon cancer Approved Done 100 On-going

Multiple myeloma Approved On-going On-going

Lymphoma Approved Done 76 On-going

Leukemia Approved On-going 41 On-going

Sarcoma Approved On-going -

Prostate Approved On-going 75 -

Breast cancer Approved On-going -

Ovarian cancer Submitted

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NCGC cancer genome sequencing

• Exome sequencing• Goal: identify & characterize the acquired

genetic changes in the tumor sample by massively parallel deep sequencingSNVs & Insertions/deletionsCopy number aberrationsStructural rearrangements

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Cancer genome sequencing (II)

Variant calling pipeline

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Cancer genome sequencing (III)• How deep should I sequence my

tumor sample? (to detect a mutant subpopulation at X percent?)

• Biological complexity Tumor purity Ploidy Local CNAs

• Technical biases Uneven coverage (GC) PCR artefacts Sequencing quality/errors Oxidation (DNA extraction + library

prep)

• Other Tumor-control mismatch

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Somatic variant calling

• Two key components Read alignment – mapping each

read to its proper position in the genome

Mutation calling – quantify the likelihood of a true somatic mutation

• Best-practice workflows defined Still many different

algorithms to choose from

• Need for benchmark

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ICGC mutation benchmark• Purpose: Assess concordance & accuracy of somatic SNV/indel calling

among variant calling pipelines used in different research groups• Evaluate impact of different algorithms (aligner, caller etc.)

• NCGC: optimize and verify running pipeline (“ICGC stamp”)

• Participants were given raw sequence reads from a medulloblastoma (MB99) genome (tumor + normal), ~40X coverage task: submit somatic indels + snvs

• Coordinated by CNAG, Barcelona (Ivo Gut’s lab)

• Weekly global telephone conferences

• BM1.2

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SNVs – how well do we agree?

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InDels – how well do we agree?

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Verification of calls – GOLD set 300X sequencing of the same genome Six different pipelines called somatic SNVs and InDels SNVs with concordance of > 3 accepted SNVs with concordance < 3 and all indels reviewed manually

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Accuracy – SNV/InDels

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Impact of aligner-caller combination

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Benchmark manuscript

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Improved accuracy – SNVs/InDels

• EH_rev

• EH_rev

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Interpretation of variants• Which variants/genes are of

functional relevance? Is my variant a frequent mutation?

Which cancer types? Is my variant likely to alter the

activity of the encoding protein? Is my variant known as a drug

sensitivity marker? Which mutant genes are known

drug targets?• Annotation pipeline

Variant calling

Functional annotation

Prioritization

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cancergenomics.no

Variants – phenotypic effect?• Computational prediction of

damaging variants• Machine learning• Numerous algorithms

SIFT, PolyPhen2, MutationTaster, MutationAssessor, Provean, FATHMM, etc..

• Challenge: many have been trained with Mendelian disease mutations Gain-of-function mutations hard to

predict

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Variants – clinical associations?

• Recent promising resources/data on clinically associated variants

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Which genes are key drivers?• Which genes show significantly

more mutations than random expectation? Requires sophisticated modeling

of the background mutation rates MutSigCV

• Which genes are enriched with functionally biased variants? IntoGen

Lawrence at al., Nature (2013)

Gonzalez-Perez at al., Nature Methods (2013)

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NCGC – data trends

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Mutational heterogeneity – across cancer types

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Mutational heterogeneity – within cancer types

CRC Melanoma

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Functional heterogeneity

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Mutational signatures• Distinct mutational patterns

(mutation types & sequence context) that reflect underlying mutational processes

• Mathematical framework to infer the k mutational signatures contributing to a cohort

• What is the relative contribution of each process in each sample?

S1 – Alkylating agents (?) S2 – UV damage S3 - Aging

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In progress/future plans

• Evaluation of more read aligners/variant callers• Integration of improved calling of copy number

aberrations• Inference of clonal population structure• Report pr. tumor case – QC, mutated cancer genes,

actionable targets etc.• Improved tools for visualization of results

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Other activities

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Acknowledgements

• NCGC Principal investigatorsDepartment of Tumor Biology

• Leonardo Meza-Zepeda, Susanne Lorenz, Ola Myklebost

• Daniel Vodak, Ghislain Fournous, Lars Birger Aasheim, Eivind Hovig

• ICGC Technical Validation group