1. Big data challenges in personalized cancer medicine Bioinformatics activities in the Norwegian Cancer Genomics Consortium (NCGC)
Sigve Nakken
Postdoctoral fellow, Eivind Hovigs group
Norwegian Cancer Genomics Consortium (NCGC)
Department of Tumor Biology, ICR, OUS

2. Norwegian Cancer 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

3. NCGC sample cohorts Cancer type REK approvals Sequencing Samples
Analysis
Melanoma
Approved
Done
115
On-going
Colon cancer
100
Multiple myeloma
Lymphoma 76
Leukemia 41
Sarcoma -
Prostate 75
Breast cancer
Ovarian cancer
Submitted

4. NCGC cancer genome sequencing
Exome sequencing
Goal: identify & characterize the acquired genetic changes in the tumor sample by massively parallel deep sequencing
SNVs & Insertions/deletions
Copy number aberrations
Structural rearrangements

5. Cancer genome sequencing (II)
Variant calling pipeline

6. 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

7. 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

8. 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

9. SNVs – how well do we agree?

10. InDels – how well do we agree?

11. 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

12. Accuracy – SNV/InDels

13. Impact of aligner-caller combination

14. Benchmark manuscript

15. Improved accuracy – SNVs/InDels
EH_rev
EH_rev

16. 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

17. 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

18. Variants – clinical associations?
Recent promising resources/data on clinically associated variants

19. 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)

20. NCGC – data trends

21. Mutational heterogeneity – across cancer types

22. Mutational heterogeneity – within cancer types
CRC
Melanoma

23. Functional heterogeneity

24. 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

25. 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

26. Other activities

27. Acknowledgements NCGC ICGC Technical Validation group
Principal investigators
Department of Tumor Biology
Leonardo Meza-Zepeda, Susanne Lorenz, Ola Myklebost
Daniel Vodak, Ghislain Fournous, Lars Birger Aasheim, Eivind Hovig
ICGC Technical Validation group