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1 David Tamborero, PhD
2 Lopez-Bigas' lab
3 Study of Tumor Genomes
4 Study of Tumor Genomes Study sequencing data of tumors to understand the biological mechanisms shaping the mutational processes observed at different levels of resolution Marc Rosenthal
5 Genome scale: Mutation signatures reflecting 'dominant' biological processes acting through the genome Megabase scale: Mutation burden depends on region characteristics (chromatin state, transcription level, replication time,..) Nucleotide(s) scale: Mutation peak in TFBS of melanocytes due to physical competition with nucleotide excision repair DNAbinding elements Sabarinathan et al. Nature 2016
6 Mutagenic processes (intrinsic/extrinsic) Mechanisms of DNA repair The observation of the mutation pattern in sequenced tumor cohorts provides new insights on the interaction of the mutagenic processes and the ability of the cell to repair them
7 Study of Tumor Genomes Identify 'functional units' that confer the cancer hallmarks upon genomic alterations Hanahan et al.
8 How we identify driver genes? Mutations are fixed during clonal expansion if they confer a selective advantage to the tumor cells Genes whose alterations show signals of positive selection across a tumor cohort reveal the driver genes of that cancer Computational methods are aimed to detect mutations that do not follow a random behavior as estimated by a background mutational model
9 How we identify driver genes? Mutations are fixed during clonal expansion if they confer a selective advantage to the tumor cells Genes whose alterations show signals of positive selection across a tumor cohort reveal the driver genes of that cancer Computational methods are aimed to detect mutations that do not follow a random behavior as estimated by a background mutational model
10 Catalogs of driver genes per cancer Rubio-Perez, Tamborero et al., Cancer Cell 2015
11 Analysis of whole-genome sequencing data 2,500 tumors 37 cancer types
12 Context matters! Genomic/transcriptomic characteristics of tumors are associated with distinct immune infiltrate configurations Tamborero, R.Perez et al 2017
13 Study of Tumor Genomes How to interpret the data from a newly sequenced tumor?
14 How to (clinically) interpret sequencing data? Given a list of genomic alterations of a newly sequenced tumor sample: - which ones are more likely to drive that tumor? - which ones may be exploited therapeutically?
15 Note that CGI analyses mutations, CNAs and fusions Several formats accepted Cancer type specific analyses
16 Alteration analysis interactive report (mutation tab) All the annotations are included in the reports Variants are classified as: - already validated oncogenic/neutral - predicted driver/passenger for VUS
17 Alteration analysis interactive report (mutation tab) All the annotations are included in the reports Variants are classified as: - already validated oncogenic/neutral - predicted driver/passenger for VUS Computational prediction of the effect of VUS based on the knowledge retrieved from the sequencing data of large tumor cohorts
18 Drug prescription interactive report (biomarkers tab) Organized by distinct levels of clinical relevance Cancer Biomarkers database Rodrigo Dienstmann
19 Pan-cancer landscape of genomic biomarkers of drug response Proportion of tumors Tumors with exome-seq at diagnosis (e.g TCGA) n = 6,792 Proportion of tumors Advanced tumors with panel-seq (GENIE) n = 17,642 Tamborero et al. Bioarxiv 2017
20 CGI use cases CGI early adopters: research Improve predictive models to identify novel biomarkers of drug response Refine analyses of newly sequenced cohorts
21 CGI use cases CGI early adopters: translational Support of clinical decision-making Allocation of cancer patients to the most appropriate genomicguided clinical trial Exploration of drug re-purposing opportunities in pediatric cancers
22 Website statistics (oct 2016-today) >5,500 users >60,000 page views
23 SUMMARY
24 The study of sequencing data retrieves new insights of the biology of cancer and provides the rationale for molecular-guided therapeutic strategies
25 Tumor material gene panel / WES / WGS Clinical bioinformatics - digest the info to support clinical actions - generate data to drive novel findings Good et al. Genome Biology 2014
26 Rodrigo Dienstmann Aura Muntasell JM Piulats Carmen de Torres
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