Cancer troublemakers: a tale of usual suspects and novel villains

Transcription

1 Cancer troublemakers: a tale of usual suspects and novel villains Abel González-Pérez and Núria López-Bigas Biomedical Genomics Group Lab web:

2 Driver genes/mutations: the troublemakers MR Stratton et al. Nature 458, (2009) doi: /nature07943

3 Recurrence of mutations to detect driver genes Hypothesis: Genes mutated more frequently than the background are drivers Challenges: The background model is not homogeneous The twilight zone: low recurrent drivers

4 Accumulated of functional impact of mutations to detect driver genes: Oncodrive-fm Hypothesis: Due to positive selection, driver genes will accumulate functional mutations FM bias: bias towards the accumulation of functional mutations

5 Oncodrive-fm: the method 1. Compute FI scores of mutations detected in all genes. (SIFT, Polyphen2, MutationAssessor) 2. Extend FI scores to ssnvs, stsnvs and fsindels SIFT PPH2 MA ssnvs stsnvs fsindels

6 Oncodrive-fm: the method 1. Compute average FI scores across genes with more than 1% samples mutated. (SIFT, Polyphen2, MutationAssessor) 2. Sample equally-sized groups of mutations from a null distribution 3. Compute a Zscore and a pvalue Two null distributions: a) naturally occurring SNVs in similar genes from 1000genomes (external) b) pooled somatic mutations from nine datasets of tumors' whole-exome-ish sequences (internal)

7 Oncodrive-fm: the method 1. Compute average FI scores across gene modules with at least 10 samples mutated. (SIFT, Polyphen2, MutationAssessor) 2. Sample equally-sized groups of mutations from a null distribution 3. Compute a Zscore and a pvalue External null distribution

8 Oncodrive-fm on cancer datasets: test Cancer Projects Breast cancer (JHU, US) CLL (ICGC Spain) Colorectal cancer (JHU, US) Glioblastoma multiforme (TCGA, US) Glioblastoma multiforme (JHU, US) Lung adenocarcinoma (TSP, US) Ovarian serous carcinoma (TCGA, US) Pancreatic cancer (JHU, US) Pancreatic cancer (ICGC, Canada) ALL Genes SNVs Samples with (unique) mutations Seq Exome Exome Exome 601 genes Exome 623 genes Exome Exome Exome

9 Oncodrive-fm on glioblastoma (TCGA, US)-135 samples

10 Oncodrive-fm on ovarian serous carcinoma (TCGA, US)-316 samples

11 Oncodrive-fm on CLL (ICGC, Spain)-109 samples

12 Oncodrive-fm on glioblastoma (TCGA, US)-135 samples and glioblastoma (JHU, US)-78 samples

13 Oncodrive-fm on pancreatic cancer (JHU, US)-114 samples and pancreatic cancer (OICR, Canada)-33 samples

14 Oncodrive-fm: pathway analysis

15 Mountains and hills/tumor-specific and multi-tumor Mountains Hills

16 Mountains and hills/tumor-specific and multi-tumor CLL BreastJHU ColorectalJHU Glioblastoma GlioblastomaJHU LungTSP Ovary PancreasJHU Multi-tumor

17 Similar genes targeted in different tumors CLL BreastJHU ColorectalJHU Glioblastoma GlioblastomaJHU LungTSP Ovary PancreasJHU Multi-tumor

18 Functionally related genes targeted in different tumors CLL BreastJHU ColorectalJHU Glioblastoma GlioblastomaJHU LungTSP Ovary PancreasJHU Multi-tumor

19 Targeting cancer pathways in different tumors

20 Targeting MAPK pathway in different tumors

21 Targeting MAPK pathway in different tumors

22 Oncodrive-fm on glioblastoma (TCGA, US)-135 samples and glioblastoma (JHU, US)-78 samples

23 Improving the assessment of functional impact of individual mutations Rationale: Existing methods score nssnvs on all genes equally, but genes with different functions are expected to have different basal tolerance to aminoacid changes Approach: Compare the score of nssnvs with the distribution of scores of those that occur naturally in genes with similar function

24 Improving the assessment of functional impact of individual mutations Step 1 MA score

25 Improving the assessment of functional impact of individual mutations Step 2

26 Improving the assessment of functional impact of individual mutations Cosmic database Cosmic database Recurrent mutations (2 or more samples) Highly recurrent mutations (5 or more samples) Non-recurrent mutations (1 sample) Non-recurrent mutations (1 sample) MA, ZMA SIFT, ZSIFT PPH2, ZPPH2 ROC curves, MCC ROC curves, MCC

27 Improving the assessment of functional impact of individual mutations Zcondel: validation Cosmic2+/Cosmic1(CP)

28 Improving the assessment of functional impact of individual mutations Zcondel: validation Cosmic5+/Cosmic1(CP)

29 Oncodrive-fm on cancer datasets: workflow Download cancer datasets from ICGC, TCGA, etc. Somatic mutations (genomic coordinates) VEP, Filter, MA Mutations-Samples (genomic coordinates) Merge, Extend Fiscores, Filter redundancy Somatic mutations (FI scores) Mutations-Samples (FI scores) Oncodrive-fm

30 Driver mutations, genes and pathways: where we are going next 1. Use transformed scores to measure the functional impact of nssnvs 2. Null models 3. Create automatic pipeline to run Oncodrive-fm on multiple cancer datasets 4. Set up IntOGen-fm 5. Combine Oncodrive-fm with Oncodrive-CNV to improve the assessment of driver genes

31 Biomedical Genomics Group Web: bg.upf.edu BGBlog: bg.upf.edu/blog/